Add model to changelog

Add mdlp as library in lib/
Fix tests to reach 99.1% of coverage

Reviewed-on: #31

.devcontainer/Dockerfile

@@ -1,6 +1,6 @@
 FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
 
-ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.22.2"
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.29.3"
 
 # Optionally install the cmake for vcpkg
 COPY ./reinstall-cmake.sh /tmp/
@@ -23,7 +23,7 @@ RUN add-apt-repository ppa:ubuntu-toolchain-r/test
 RUN apt-get update
 
 # Install GCC 13.1
-RUN apt-get install -y gcc-13 g++-13
+RUN apt-get install -y gcc-13 g++-13 doxygen
 
 # Install lcov 2.1
 RUN wget --quiet https://github.com/linux-test-project/lcov/releases/download/v2.1/lcov-2.1.tar.gz && \

.gitignore

@@ -40,4 +40,8 @@ puml/**
 .vscode/settings.json
 sample/build
 **/.DS_Store
+docs/manual
+docs/man3
+docs/man
+docs/Doxyfile
 

.gitmodules

@@ -1,8 +1,3 @@
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-	main = main
-	update = merge
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
@@ -21,3 +16,6 @@
 [submodule "tests/lib/Files"]
 	path = tests/lib/Files
 	url = https://github.com/rmontanana/ArffFiles
+[submodule "lib/mdlp"]
+	path = lib/mdlp
+	url = https://github.com/rmontanana/mdlp

.vscode/launch.json

@@ -14,11 +14,11 @@
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                "[Node]"
+                "No features selected"
             ],
-            "cwd": "${workspaceFolder}/build_debug/tests"
+            "cwd": "${workspaceFolder}/build_Debug/tests"
         },
         {
             "name": "(gdb) Launch",

CHANGELOG.md

@@ -7,6 +7,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.0.6] 2024-11-23
+
+### Fixed
+
+- Prevent existing edges to be added to the network in the `add_edge` method.
+- Don't allow to add nodes or edges on already fiited networks.
+- Number of threads spawned
+- Network class tests
+
 ### Added
 
 - Library logo generated with <https://openart.ai> to README.md
@@ -14,12 +23,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
 - SPnDE model.
 - A2DE model.
+- BoostA2DE model.
 - A2DE & SPnDE tests.
 - Add tests to reach 99% of coverage.
 - Add tests to check the correct version of the mdlp, folding and json libraries.
+- Library documentation generated with Doxygen.
+- Link to documentation in the README.md.
+- Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.
 
 ### Internal
 
+- Fixed doxygen optional dependency
+- Add env parallel variable to Makefile
+- Add CountingSemaphore class to manage the number of threads spawned.
+- Ignore CUDA language in CMake CodeCoverage module.
+- Update mdlp library as a git submodule.
 - Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
 - Refactor catch2 library location to test/lib
 - Refactor loadDataset function in tests.
@@ -27,6 +45,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Refactor Coverage Report generation.
 - Add devcontainer to work on apple silicon.
 - Change build cmake folder names to Debug & Release.
+- Add a Makefile target (doc) to generate the documentation.
+- Add a Makefile target (doc-install) to install the documentation.
+
+### Libraries versions
+
+- mdlp: 2.0.1
+- Folding: 1.1.0
+- json: 3.11
+- ArffFiles: 1.1.0
 
 ## [1.0.5] 2024-04-20
 

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.5.1
+  VERSION 1.0.6
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -26,7 +26,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
 if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
     set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
 endif()
@@ -49,11 +49,12 @@ if (CMAKE_BUILD_TYPE STREQUAL "Debug")
   set(CODE_COVERAGE ON)
 endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
-
+get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
+message(STATUS "Languages=${LANGUAGES}")
 if (CODE_COVERAGE)
   enable_testing()
   include(CodeCoverage)
-  MESSAGE("Code coverage enabled")
+  MESSAGE(STATUS "Code coverage enabled")
   SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -63,6 +64,7 @@ endif (ENABLE_CLANG_TIDY)
 
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
+
 # include(FetchContent)
 add_git_submodule("lib/json")
 add_git_submodule("lib/mdlp")
@@ -75,7 +77,7 @@ add_subdirectory(bayesnet)
 # Testing
 # -------
 if (ENABLE_TESTING)
-MESSAGE("Testing enabled")
+MESSAGE(STATUS "Testing enabled")
   add_subdirectory(tests/lib/catch2)
   include(CTest)
   add_subdirectory(tests)
@@ -88,4 +90,19 @@ install(TARGETS BayesNet
         LIBRARY DESTINATION lib
         CONFIGURATIONS Release)
 install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
-install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)
+install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)
+
+# Documentation
+# -------------
+find_package(Doxygen)
+if (Doxygen_FOUND)
+  set(DOC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/docs)
+  set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
+  set(doxyfile ${DOC_DIR}/Doxyfile)
+  configure_file(${doxyfile_in} ${doxyfile} @ONLY)
+  doxygen_add_docs(doxygen
+      WORKING_DIRECTORY ${DOC_DIR}
+    CONFIG_FILE ${doxyfile})
+else (Doxygen_FOUND) 
+  MESSAGE("* Doxygen not found")
+endif (Doxygen_FOUND)

Makefile

@@ -1,6 +1,6 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge doc doc-install
 
 f_release = build_Release
 f_debug = build_Debug
@@ -12,7 +12,11 @@ plantuml = plantuml
 lcov = lcov
 genhtml = genhtml
 dot = dot
-n_procs = -j 16
+docsrcdir = docs/manual
+mansrcdir = docs/man3
+mandestdir = /usr/local/share/man
+sed_command_link = 's/e">LCOV -/e"><a href="https:\/\/rmontanana.github.io\/bayesnet">Back to manual<\/a> LCOV -/g'
+sed_command_diagram = 's/Diagram"/Diagram" width="100%" height="100%" /g'
 
 define ClearTests
 	@for t in $(test_targets); do \
@@ -39,7 +43,7 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@echo "* You should install plantuml & graphviz for the diagrams"
 
-diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/BayesNet.png)
+diagrams: ## Create an UML class diagram & dependency of the project (diagrams/BayesNet.png)
 	@which $(plantuml) || (echo ">>> Please install plantuml"; exit 1)
 	@which $(dot) || (echo ">>> Please install graphviz"; exit 1)
 	@which $(clang-uml) || (echo ">>> Please install clang-uml"; exit 1)
@@ -54,10 +58,10 @@ diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/
 	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug BayesNet tests...";
@@ -101,7 +105,7 @@ opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running BayesNet tests...";
 	@$(MAKE) clean
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 	@for t in $(test_targets); do \
 		echo ">>> Running $$t...";\
 		if [ -f $(f_debug)/tests/$$t ]; then \
@@ -114,7 +118,7 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 
 coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Building tests with coverage..."
-	@which $(lcov) || (echo ">>> Please install lcov"; exit 1)
+	@which $(lcov) || (echo ">>ease install lcov"; exit 1)
 	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
 	@echo ">>> Building report..."
 	@cd $(f_debug)/tests; \
@@ -130,9 +134,14 @@ coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Done";	
 
 viewcoverage: ## View the html coverage report
-	@which $(genhtml) || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
+	@which $(genhtml) >/dev/null || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
-	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory html --title "BayesNet Coverage Report" -s -k -f --legend >/dev/null 2>&1;
+	@if [ ! -d $(docsrcdir)/coverage ]; then mkdir -p $(docsrcdir)/coverage; fi
-	@xdg-open html/index.html || open html/index.html 2>/dev/null
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
+	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory $(docsrcdir)/coverage --title "BayesNet Coverage Report" -s -k -f --legend >/dev/null 2>&1;
+	@xdg-open $(docsrcdir)/coverage/index.html || open $(docsrcdir)/coverage/index.html 2>/dev/null
 	@echo ">>> Done";
 
 updatebadge: ## Update the coverage badge in README.md
@@ -145,6 +154,34 @@ updatebadge: ## Update the coverage badge in README.md
 	@env python update_coverage.py $(f_debug)/tests
 	@echo ">>> Done";
 
+doc: ## Generate documentation
+	@echo ">>> Generating documentation..."
+	@cmake --build $(f_release) -t doxygen
+	@cp -rp diagrams $(docsrcdir)
+	@
+	@if [ "$(shell uname)" = "Darwin" ]; then \
+		sed -i "" $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i "" $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	else \
+		sed -i $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	fi
+	@echo ">>> Done";
+
+docdir = ""
+doc-install: ## Install documentation
+	@echo ">>> Installing documentation..."
+	@if [ "$(docdir)" = "" ]; then \
+		echo "docdir parameter has to be set when calling doc-install"; \
+		exit 1; \
+	fi
+	@if [ ! -d $(docdir) ]; then \
+		@$(MAKE) doc; \
+	fi
+	@cp -rp $(docsrcdir)/* $(docdir)
+	@sudo cp -rp $(mansrcdir) $(mandestdir)
+	@echo ">>> Done";
+
 help: ## Show help message
 	@IFS=$$'\n' ; \
 	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \

README.md

@@ -7,9 +7,9 @@
 [![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
 [![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
 ![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
-[![Coverage Badge](https://img.shields.io/badge/Coverage-99,0%25-green)](html/index.html)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](html/index.html)
 
-Bayesian Network Classifiers using libtorch from scratch
+Bayesian Network Classifiers library
 
 ## Dependencies
 
@@ -67,10 +67,16 @@ make sample fname=tests/data/glass.arff
 
 #### - SPODE
 
+#### - SPnDE
+
 #### - AODE
 
+#### - A2DE
+
 #### - [BoostAODE](docs/BoostAODE.md)
 
+#### - BoostA2DE
+
 ### With Local Discretization
 
 #### - TANLd
@@ -81,6 +87,12 @@ make sample fname=tests/data/glass.arff
 
 #### - AODELd
 
+## Documentation
+
+### [Manual](https://rmontanana.github.io/bayesnet/)
+
+### [Coverage report](https://rmontanana.github.io/bayesnet/coverage/index.html)
+
 ## Diagrams
 
 ### UML Class Diagram
@@ -90,7 +102,3 @@ make sample fname=tests/data/glass.arff
 ### Dependency Diagram
 
 ![BayesNet Dependency Diagram](diagrams/dependency.svg)
-
-## Coverage report
-
-### [Coverage report](docs/coverage.pdf)

bayesnet/BaseClassifier.h

@@ -8,16 +8,18 @@
 #include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
+#include "bayesnet/network/Network.h"
+
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     public:
         // X is nxm std::vector, y is nx1 std::vector
-        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
         // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
@@ -39,7 +41,7 @@ namespace bayesnet {
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
     protected:
-        virtual void trainModel(const torch::Tensor& weights) = 0;
+        virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         std::vector<std::string> validHyperparameters;
     };
 }

bayesnet/CMakeLists.txt

@@ -1,5 +1,5 @@
 include_directories(
-    ${BayesNet_SOURCE_DIR}/lib/mdlp
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}
@@ -9,4 +9,4 @@ include_directories(
 file(GLOB_RECURSE Sources "*.cc")
 
 add_library(BayesNet ${Sources})
-target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(BayesNet fimdlp "${TORCH_LIBRARIES}")

bayesnet/classifiers/Classifier.cc

@@ -11,7 +11,7 @@
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
     const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
-    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->features = features;
         this->className = className;
@@ -23,7 +23,7 @@ namespace bayesnet {
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
         buildModel(weights);
-        trainModel(weights);
+        trainModel(weights, smoothing);
         fitted = true;
         return *this;
     }
@@ -41,20 +41,20 @@ namespace bayesnet {
             throw std::runtime_error(oss.str());
         }
     }
-    void Classifier::trainModel(const torch::Tensor& weights)
+    void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
     {
-        model.fit(dataset, weights, features, className, states);
+        model.fit(dataset, weights, features, className, states, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = X;
         buildDataset(y);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
         for (int i = 0; i < X.size(); ++i) {
@@ -63,18 +63,18 @@ namespace bayesnet {
         auto ytmp = torch::tensor(y, torch::kInt32);
         buildDataset(ytmp);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     void Classifier::checkFitParameters()
     {

bayesnet/classifiers/Classifier.h

@@ -8,7 +8,6 @@
 #define CLASSIFIER_H
 #include <torch/torch.h>
 #include "bayesnet/utils/BayesMetrics.h"
-#include "bayesnet/network/Network.h"
 #include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
@@ -16,10 +15,10 @@ namespace bayesnet {
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
@@ -51,10 +50,10 @@ namespace bayesnet {
         std::vector<std::string> notes; // Used to store messages occurred during the fit process
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildDataset(torch::Tensor& y);
     private:
-        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/classifiers/KDBLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
-        KDB::fit(dataset, features, className, states);
+        KDB::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/KDBLd.h

@@ -15,7 +15,7 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };

bayesnet/classifiers/Proposal.cc

@@ -70,7 +70,7 @@ namespace bayesnet {
                 states[pFeatures[index]] = xStates;
             }
             const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
-            model.fit(pDataset, weights, pFeatures, pClassName, states);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
         }
         return states;
     }

bayesnet/classifiers/SPODELd.cc

@@ -8,25 +8,25 @@
 
 namespace bayesnet {
     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         Xf = X_;
         y = y_;
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         if (!torch::is_floating_point(dataset)) {
             throw std::runtime_error("Dataset must be a floating point tensor");
         }
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
         y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         features = features_;
         className = className_;
@@ -34,7 +34,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
-        SPODE::fit(dataset, features, className, states);
+        SPODE::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/SPODELd.h

@@ -14,10 +14,10 @@ namespace bayesnet {
     public:
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
-        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+        std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };
     };

bayesnet/classifiers/TANLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        TAN::fit(dataset, features, className, states);
+        TAN::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
 

bayesnet/classifiers/TANLd.h

@@ -15,10 +15,9 @@ namespace bayesnet {
     public:
         TANLd();
         virtual ~TANLd() = default;
-        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        std::vector<std::string> graph(const std::string& name = "TAN") const override;
+        std::vector<std::string> graph(const std::string& name = "TANLd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
-        static inline std::string version() { return "0.0.1"; };
     };
 }
 #endif // !TANLD_H

bayesnet/ensembles/AODELd.cc

@@ -10,7 +10,7 @@ namespace bayesnet {
     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
     {
     }
-    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -21,7 +21,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        Ensemble::fit(dataset, features, className, states);
+        Ensemble::fit(dataset, features, className, states, smoothing);
         return *this;
 
     }
@@ -34,10 +34,10 @@ namespace bayesnet {
         n_models = models.size();
         significanceModels = std::vector<double>(n_models, 1.0);
     }
-    void AODELd::trainModel(const torch::Tensor& weights)
+    void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         for (const auto& model : models) {
-            model->fit(Xf, y, features, className, states);
+            model->fit(Xf, y, features, className, states, smoothing);
         }
     }
     std::vector<std::string> AODELd::graph(const std::string& name) const

bayesnet/ensembles/AODELd.h

@@ -15,10 +15,10 @@ namespace bayesnet {
     public:
         AODELd(bool predict_voting = true);
         virtual ~AODELd() = default;
-        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_) override;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildModel(const torch::Tensor& weights) override;
     };
 }

bayesnet/ensembles/BoostA2DE.cc

@@ -19,7 +19,7 @@ namespace bayesnet {
     BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
     {
     }
-    std::vector<int> BoostA2DE::initializeModels()
+    std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
     {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         std::vector<int> featuresSelected = featureSelection(weights_);
@@ -32,7 +32,7 @@ namespace bayesnet {
             for (int j = i + 1; j < featuresSelected.size(); j++) {
                 auto parents = { featuresSelected[i], featuresSelected[j] };
                 std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 models.push_back(std::move(model));
                 significanceModels.push_back(1.0); // They will be updated later in trainModel
                 n_models++;
@@ -41,7 +41,7 @@ namespace bayesnet {
         notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
         return featuresSelected;
     }
-    void BoostA2DE::trainModel(const torch::Tensor& weights)
+    void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         //
         // Logging setup
@@ -58,7 +58,10 @@ namespace bayesnet {
         bool finished = false;
         std::vector<int> featuresUsed;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
+            if (featuresUsed.size() == 0) {
+                return;
+            }
             auto ypred = predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
@@ -96,7 +99,7 @@ namespace bayesnet {
                 pairSelection.erase(pairSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
                     auto ypred = model->predict(X_train);

bayesnet/ensembles/BoostA2DE.h

@@ -17,9 +17,9 @@ namespace bayesnet {
         virtual ~BoostA2DE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::vector<int> initializeModels();
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/ensembles/BoostAODE.cc

@@ -16,13 +16,13 @@ namespace bayesnet {
     BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
     }
-    std::vector<int> BoostAODE::initializeModels()
+    std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
     {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         std::vector<int> featuresSelected = featureSelection(weights_);
         for (const int& feature : featuresSelected) {
             std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
-            model->fit(dataset, features, className, states, weights_);
+            model->fit(dataset, features, className, states, weights_, smoothing);
             models.push_back(std::move(model));
             significanceModels.push_back(1.0); // They will be updated later in trainModel
             n_models++;
@@ -30,7 +30,7 @@ namespace bayesnet {
         notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
         return featuresSelected;
     }
-    void BoostAODE::trainModel(const torch::Tensor& weights)
+    void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         //
         // Logging setup
@@ -47,7 +47,7 @@ namespace bayesnet {
         bool finished = false;
         std::vector<int> featuresUsed;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
             auto ypred = predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
@@ -89,7 +89,7 @@ namespace bayesnet {
                 featureSelection.erase(featureSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPODE>(feature);
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
                     auto ypred = model->predict(X_train);

bayesnet/ensembles/BoostAODE.h

@@ -18,9 +18,9 @@ namespace bayesnet {
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::vector<int> initializeModels();
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/ensembles/Ensemble.cc

@@ -3,22 +3,21 @@
 // SPDX-FileType: SOURCE
 // SPDX-License-Identifier: MIT
 // ***************************************************************
-
 #include "Ensemble.h"
+#include "bayesnet/utils/CountingSemaphore.h"
 
 namespace bayesnet {
 
     Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
     {
-
     };
     const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
-    void Ensemble::trainModel(const torch::Tensor& weights)
+    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
             // fit with std::vectors
-            models[i]->fit(dataset, features, className, states);
+            models[i]->fit(dataset, features, className, states, smoothing);
         }
     }
     std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
@@ -85,17 +84,9 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict_proba(X);
-                auto ypredict = models[i]->predict_proba(X);
+            y_pred += ypredict * significanceModels[i];
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred += ypredict * significanceModels[i];
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         y_pred /= sum;
@@ -105,23 +96,15 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict_proba(X);
-                auto ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.size());
-                assert(ypredict.size() == y_pred.size());
+            assert(ypredict[0].size() == y_pred[0].size());
-                assert(ypredict[0].size() == y_pred[0].size());
+            // Multiply each prediction by the significance of the model and then add it to the final prediction
-                std::lock_guard<std::mutex> lock(mtx);
+            for (auto j = 0; j < ypredict.size(); ++j) {
-                // Multiply each prediction by the significance of the model and then add it to the final prediction
+                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
-                for (auto j = 0; j < ypredict.size(); ++j) {
+                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                    std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
+            }
-                        [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                }
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         //Divide each element of the prediction by the sum of the significances
@@ -141,17 +124,9 @@ namespace bayesnet {
     {
         // Build a m x n_models tensor with the predictions of each model
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict(X);
-                auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         return voting(y_pred);
     }

bayesnet/ensembles/Ensemble.h

@@ -46,7 +46,7 @@ namespace bayesnet {
         unsigned n_models;
         std::vector<std::unique_ptr<Classifier>> models;
         std::vector<double> significanceModels;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         bool predict_voting;
     };
 }

bayesnet/network/Network.cc

@@ -5,20 +5,20 @@
 // ***************************************************************
 
 #include <thread>
-#include <mutex>
 #include <sstream>
+#include <numeric>
+#include <algorithm>
 #include "Network.h"
 #include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <pthread.h>
+#include <fstream>
 namespace bayesnet {
-    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    Network::Network() : fitted{ false }, classNumStates{ 0 }
     {
     }
-    Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    Network::Network(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
-    {
+        fitted(other.fitted), samples(other.samples)
-
-    }
-    Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
-        maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
     {
         if (samples.defined())
             samples = samples.clone();
@@ -35,16 +35,15 @@ namespace bayesnet {
         nodes.clear();
         samples = torch::Tensor();
     }
-    float Network::getMaxThreads() const
-    {
-        return maxThreads;
-    }
     torch::Tensor& Network::getSamples()
     {
         return samples;
     }
     void Network::addNode(const std::string& name)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add node to a fitted network. Initialize first.");
+        }
         if (name == "") {
             throw std::invalid_argument("Node name cannot be empty");
         }
@@ -94,12 +93,21 @@ namespace bayesnet {
     }
     void Network::addEdge(const std::string& parent, const std::string& child)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add edge to a fitted network. Initialize first.");
+        }
         if (nodes.find(parent) == nodes.end()) {
             throw std::invalid_argument("Parent node " + parent + " does not exist");
         }
         if (nodes.find(child) == nodes.end()) {
             throw std::invalid_argument("Child node " + child + " does not exist");
         }
+        // Check if the edge is already in the graph
+        for (auto& node : nodes[parent]->getChildren()) {
+            if (node->getName() == child) {
+                throw std::invalid_argument("Edge " + parent + " -> " + child + " already exists");
+            }
+        }
         // Temporarily add edge to check for cycles
         nodes[parent]->addChild(nodes[child].get());
         nodes[child]->addParent(nodes[parent].get());
@@ -155,7 +163,7 @@ namespace bayesnet {
         classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
@@ -164,17 +172,17 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
         this->className = className;
         this->samples = samples;
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
         checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
@@ -185,17 +193,43 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         setStates(states);
-        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
         std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        const double n_samples = static_cast<double>(samples.size(1));
+        auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
+            std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            double numStates = static_cast<double>(node.second->getNumStates());
+            double smoothing_factor;
+            switch (smoothing) {
+                case Smoothing_t::ORIGINAL:
+                    smoothing_factor = 1.0 / n_samples;
+                    break;
+                case Smoothing_t::LAPLACE:
+                    smoothing_factor = 1.0;
+                    break;
+                case Smoothing_t::CESTNIK:
+                    smoothing_factor = 1 / numStates;
+                    break;
+                default:
+                    smoothing_factor = 0.0; // No smoothing 
+            }
+            node.second->computeCPT(samples, features, smoothing_factor, weights);
+            semaphore.release();
+            };
+        int i = 0;
         for (auto& node : nodes) {
-            threads.emplace_back([this, &node, &weights]() {
+            semaphore.acquire();
-                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
+            threads.emplace_back(worker, std::ref(node), i++);
-                });
         }
         for (auto& thread : threads) {
             thread.join();
@@ -207,14 +241,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
+        // Ensure the sample size is equal to the number of features
+        if (samples.size(0) != features.size() - 1) {
+            throw std::invalid_argument("(T) Sample size (" + std::to_string(samples.size(0)) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
         torch::Tensor result;
+        std::vector<std::thread> threads;
+        std::mutex mtx;
+        auto& semaphore = CountingSemaphore::getInstance();
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
-        for (int i = 0; i < samples.size(1); ++i) {
+        auto worker = [&](const torch::Tensor& sample, int i) {
-            const torch::Tensor sample = samples.index({ "...", i });
+            std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
-            //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
+            {
-            result.index_put_({ i, "..." }, temp);
+                std::lock_guard<std::mutex> lock(mtx);
+                result.index_put_({ i, "..." }, temp);
+            }
+            semaphore.release();
+            };
+        for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
+            const torch::Tensor sample = samples.index({ "...", i });
+            threads.emplace_back(worker, sample, i);
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         if (proba)
             return result;
@@ -239,18 +297,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
-        std::vector<int> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<int> predictions(tsamples[0].size(), 0);
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto classProbabilities = predict_sample(sample);
+            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+            int predictedClass = distance(classProbabilities.begin(), maxElem);
+            prediction = predictedClass;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            std::vector<double> classProbabilities = predict_sample(sample);
+            semaphore.acquire();
-            // Find the class with the maximum posterior probability
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
-            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+        }
-            int predictedClass = distance(classProbabilities.begin(), maxElem);
+        for (auto& thread : threads) {
-            predictions.push_back(predictedClass);
+            thread.join();
         }
         return predictions;
     }
@@ -261,14 +339,36 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict_proba()");
         }
-        std::vector<std::vector<double>> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            predictions.push_back(predict_sample(sample));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
@@ -286,11 +386,6 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const std::vector<int>& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size() != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size()) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
@@ -300,44 +395,26 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const torch::Tensor& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size(0) != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size(0)) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(0); ++i) {
             evidence[features[i]] = sample[i].item<int>();
         }
         return exactInference(evidence);
     }
-    double Network::computeFactor(std::map<std::string, int>& completeEvidence)
-    {
-        double result = 1.0;
-        for (auto& node : getNodes()) {
-            result *= node.second->getFactorValue(completeEvidence);
-        }
-        return result;
-    }
     std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
     {
         std::vector<double> result(classNumStates, 0.0);
-        std::vector<std::thread> threads;
+        auto completeEvidence = std::map<std::string, int>(evidence);
-        std::mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
-            threads.emplace_back([this, &result, &evidence, i, &mtx]() {
+            completeEvidence[getClassName()] = i;
-                auto completeEvidence = std::map<std::string, int>(evidence);
+            double partial = 1.0;
-                completeEvidence[getClassName()] = i;
+            for (auto& node : getNodes()) {
-                double factor = computeFactor(completeEvidence);
+                partial *= node.second->getFactorValue(completeEvidence);
-                std::lock_guard<std::mutex> lock(mtx);
+            }
-                result[i] = factor;
+            result[i] = partial;
-                });
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         // Normalize result
-        double sum = accumulate(result.begin(), result.end(), 0.0);
+        double sum = std::accumulate(result.begin(), result.end(), 0.0);
         transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }

bayesnet/network/Network.h

@@ -12,14 +12,18 @@
 #include "Node.h"
 
 namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
     class Network {
     public:
         Network();
-        explicit Network(float);
         explicit Network(const Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        float getMaxThreads() const;
         void addNode(const std::string&);
         void addEdge(const std::string&, const std::string&);
         std::map<std::string, std::unique_ptr<Node>>& getNodes();
@@ -32,9 +36,9 @@ namespace bayesnet {
         /*
         Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
         */
-        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
         std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
@@ -50,19 +54,16 @@ namespace bayesnet {
     private:
         std::map<std::string, std::unique_ptr<Node>> nodes;
         bool fitted;
-        float maxThreads = 0.95;
         int classNumStates;
         std::vector<std::string> features; // Including classname
         std::string className;
-        double laplaceSmoothing;
         torch::Tensor samples; // n+1xm tensor used to fit the model
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         std::vector<double> predict_sample(const std::vector<int>&);
         std::vector<double> predict_sample(const torch::Tensor&);
         std::vector<double> exactInference(std::map<std::string, int>&);
-        double computeFactor(std::map<std::string, int>&);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
         void setStates(const std::map<std::string, std::vector<int>>&);
     };
 }

bayesnet/network/Node.cc

@@ -90,51 +90,54 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
         transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
         // Create a tensor of zeros with the dimensions of the CPT
-        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
+        cpTable = torch::zeros(dimensions, torch::kDouble) + smoothing;
         // Fill table with counts
         auto pos = find(features.begin(), features.end(), name);
         if (pos == features.end()) {
             throw std::logic_error("Feature " + name + " not found in dataset");
         }
         int name_index = pos - features.begin();
+        c10::List<c10::optional<at::Tensor>> coordinates;
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            c10::List<c10::optional<at::Tensor>> coordinates;
+            coordinates.clear();
-            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            auto sample = dataset.index({ "...", n_sample });
+            coordinates.push_back(sample[name_index]);
             for (auto parent : parents) {
                 pos = find(features.begin(), features.end(), parent->getName());
                 if (pos == features.end()) {
                     throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
                 }
                 int parent_index = pos - features.begin();
-                coordinates.push_back(dataset.index({ parent_index, n_sample }));
+                coordinates.push_back(sample[parent_index]);
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
+            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
         // Normalize the counts
+        // Divide each row by the sum of the row
         cpTable = cpTable / cpTable.sum(0);
     }
-    float Node::getFactorValue(std::map<std::string, int>& evidence)
+    double Node::getFactorValue(std::map<std::string, int>& evidence)
     {
         c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(at::tensor(evidence[name]));
         transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-        return cpTable.index({ coordinates }).item<float>();
+        return cpTable.index({ coordinates }).item<double>();
     }
     std::vector<std::string> Node::graph(const std::string& className)
     {
         auto output = std::vector<std::string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
-        output.push_back(name + " [shape=circle" + suffix + "] \n");
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
-        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return "\"" + name + "\" -> \"" + child->getName() + "\""; });
         return output;
     }
 }

bayesnet/network/Node.h

@@ -23,12 +23,12 @@ namespace bayesnet {
         std::vector<Node*>& getParents();
         std::vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
+        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
         std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
-        float getFactorValue(std::map<std::string, int>&);
+        double getFactorValue(std::map<std::string, int>&);
     private:
         std::string name;
         std::vector<Node*> parents;

bayesnet/utils/CountingSemaphore.h

@@ -0,0 +1,46 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include <thread>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

bayesnet/utils/Mst.cc

@@ -53,14 +53,14 @@ namespace bayesnet {
         }
     }
 
-    void insertElement(std::list<int>& variables, int variable)
+    void MST::insertElement(std::list<int>& variables, int variable)
     {
         if (std::find(variables.begin(), variables.end(), variable) == variables.end()) {
             variables.push_front(variable);
         }
     }
 
-    std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
+    std::vector<std::pair<int, int>> MST::reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
     {
         // Create the edges of a DAG from the MST
         // replacing unordered_set with list because unordered_set cannot guarantee the order of the elements inserted

bayesnet/utils/Mst.h

@@ -14,6 +14,8 @@ namespace bayesnet {
     public:
         MST() = default;
         MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        void insertElement(std::list<int>& variables, int variable);
+        std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original);
         std::vector<std::pair<int, int>> maximumSpanningTree();
     private:
         torch::Tensor weights;

cmake/modules/CodeCoverage.cmake

@@ -137,7 +137,7 @@
 
 include(CMakeParseArguments)
 
-option(CODE_COVERAGE_VERBOSE "Verbose information" FALSE)
+option(CODE_COVERAGE_VERBOSE "Verbose information" TRUE)
 
 # Check prereqs
 find_program( GCOV_PATH gcov )
@@ -160,7 +160,11 @@ foreach(LANG ${LANGUAGES})
     endif()
   elseif(NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "GNU"
          AND NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(LLVM)?[Ff]lang")
-    message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+         if ("${LANG}" MATCHES "CUDA")
+             message(STATUS "Ignoring CUDA")
+        else()
+            message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+        endif()
   endif()
 endforeach()
 

docs/Doxyfile.in

@@ -42,26 +42,26 @@ DOXYFILE_ENCODING      = UTF-8
 # title of most generated pages and in a few other places.
 # The default value is: My Project.
 
-PROJECT_NAME           = BayesNet
+PROJECT_NAME           = "@CMAKE_PROJECT_NAME@"
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number. This
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = 1.0.5
+PROJECT_NUMBER         = @PROJECT_VERSION_MAJOR@.@PROJECT_VERSION_MINOR@.@PROJECT_VERSION_PATCH@.@PROJECT_VERSION_TWEAK@
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
 # quick idea about the purpose of the project. Keep the description short.
 
-PROJECT_BRIEF          = "Bayesian Network Classifiers using libtorch from scratch"
+PROJECT_BRIEF          = "@CMAKE_PROJECT_DESCRIPTION@"
 
 # With the PROJECT_LOGO tag one can specify a logo or an icon that is included
 # in the documentation. The maximum height of the logo should not exceed 55
 # pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
 # the logo to the output directory.
 
-PROJECT_LOGO           = /Users/rmontanana/Code/BayesNet/docs/logo_small.png
+PROJECT_LOGO           = logo_small.png
 
 # With the PROJECT_ICON tag one can specify an icon that is included in the tabs
 # when the HTML document is shown. Doxygen will copy the logo to the output
@@ -74,7 +74,7 @@ PROJECT_ICON           =
 # entered, it will be relative to the location where doxygen was started. If
 # left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       = /Users/rmontanana/Code/BayesNet/docs
+OUTPUT_DIRECTORY       = 
 
 # If the CREATE_SUBDIRS tag is set to YES then doxygen will create up to 4096
 # sub-directories (in 2 levels) under the output directory of each output format
@@ -949,7 +949,8 @@ WARN_LOGFILE           =
 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = /Users/rmontanana/Code/BayesNet/bayesnet
+INPUT                  = ../bayesnet
+INPUT                  += ../README.md
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -1174,7 +1175,7 @@ FILTER_SOURCE_PATTERNS =
 # (index.html). This can be useful if you have a project on for instance GitHub
 # and want to reuse the introduction page also for the doxygen output.
 
-USE_MDFILE_AS_MAINPAGE =
+USE_MDFILE_AS_MAINPAGE = README.md
 
 # The Fortran standard specifies that for fixed formatted Fortran code all
 # characters from position 72 are to be considered as comment. A common
@@ -1424,7 +1425,7 @@ HTML_EXTRA_STYLESHEET  =
 # files will be copied as-is; there are no commands or markers available.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_EXTRA_FILES       =
+HTML_EXTRA_FILES       = ../logo.png
 
 # The HTML_COLORSTYLE tag can be used to specify if the generated HTML output
 # should be rendered with a dark or light theme.
@@ -2262,7 +2263,7 @@ GENERATE_MAN           = YES
 # The default directory is: man.
 # This tag requires that the tag GENERATE_MAN is set to YES.
 
-MAN_OUTPUT             = /Users/rmontanana/Code/BayesNet/docs
+MAN_OUTPUT             = ./
 
 # The MAN_EXTENSION tag determines the extension that is added to the generated
 # man pages. In case the manual section does not start with a number, the number

docs/man3/bayesnet_A2DE.3

@@ -1,257 +0,0 @@
-.TH "bayesnet::A2DE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::A2DE
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::Ensemble\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBA2DE\fP (bool predict_voting=false)"
-.br
-.ti -1c
-.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='A2DE') const override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "\fBEnsemble\fP (bool predict_voting=true)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfNodes\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfEdges\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfStates\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBshow\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBtopological_order\fP () override"
-.br
-.ti -1c
-.RI "std::string \fBdump_cpt\fP () const override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "\fBClassifier\fP (\fBNetwork\fP model)"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override"
-.br
-.ti -1c
-.RI "void \fBaddNodes\fP ()"
-.br
-.ti -1c
-.RI "int \fBgetClassNumStates\fP () const override"
-.br
-.ti -1c
-.RI "status_t \fBgetStatus\fP () const override"
-.br
-.ti -1c
-.RI "std::string \fBgetVersion\fP () override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgetNotes\fP () const override"
-.br
-.ti -1c
-.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()"
-.br
-.in -1c
-.SS "Protected Member Functions"
-
-.in +1c
-.ti -1c
-.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override"
-.br
-.in -1c
-
-Protected Member Functions inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)"
-.br
-.ti -1c
-.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override"
-.br
-.in -1c
-
-Protected Member Functions inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "void \fBcheckFitParameters\fP ()"
-.br
-.ti -1c
-.RI "void \fBbuildDataset\fP (torch::Tensor &y)"
-.br
-.in -1c
-.SS "Additional Inherited Members"
-
-
-Protected Attributes inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "unsigned \fBn_models\fP"
-.br
-.ti -1c
-.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP"
-.br
-.ti -1c
-.RI "std::vector< double > \fBsignificanceModels\fP"
-.br
-.ti -1c
-.RI "bool \fBpredict_voting\fP"
-.br
-.in -1c
-
-Protected Attributes inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "bool \fBfitted\fP"
-.br
-.ti -1c
-.RI "unsigned int \fBm\fP"
-.br
-.ti -1c
-.RI "unsigned int \fBn\fP"
-.br
-.ti -1c
-.RI "\fBNetwork\fP \fBmodel\fP"
-.br
-.ti -1c
-.RI "Metrics \fBmetrics\fP"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBfeatures\fP"
-.br
-.ti -1c
-.RI "std::string \fBclassName\fP"
-.br
-.ti -1c
-.RI "std::map< std::string, std::vector< int > > \fBstates\fP"
-.br
-.ti -1c
-.RI "torch::Tensor \fBdataset\fP"
-.br
-.ti -1c
-.RI "status_t \fBstatus\fP = NORMAL"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBnotes\fP"
-.br
-.in -1c
-
-Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "std::vector< std::string > \fBvalidHyperparameters\fP"
-.br
-.in -1c
-.SH "Detailed Description"
-.PP 
-Definition at line \fB12\fP of file \fBA2DE\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::A2DE::A2DE (bool predict_voting = \fRfalse\fP)"
-
-.PP
-Definition at line \fB10\fP of file \fBA2DE\&.cc\fP\&.
-.SS "virtual bayesnet::A2DE::~A2DE ()\fR [inline]\fP, \fR [virtual]\fP"
-
-.PP
-Definition at line \fB15\fP of file \fBA2DE\&.h\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::A2DE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::Classifier\fP\&.
-.PP
-Definition at line \fB23\fP of file \fBA2DE\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::A2DE::graph (const std::string & title = \fR'A2DE'\fP) const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB36\fP of file \fBA2DE\&.cc\fP\&.
-.SS "void bayesnet::A2DE::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB14\fP of file \fBA2DE\&.cc\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_AODE.3

@@ -1,257 +0,0 @@
-.TH "bayesnet::AODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::AODE
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::Ensemble\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBAODE\fP (bool predict_voting=false)"
-.br
-.ti -1c
-.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='AODE') const override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "\fBEnsemble\fP (bool predict_voting=true)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfNodes\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfEdges\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfStates\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBshow\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBtopological_order\fP () override"
-.br
-.ti -1c
-.RI "std::string \fBdump_cpt\fP () const override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "\fBClassifier\fP (\fBNetwork\fP model)"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override"
-.br
-.ti -1c
-.RI "void \fBaddNodes\fP ()"
-.br
-.ti -1c
-.RI "int \fBgetClassNumStates\fP () const override"
-.br
-.ti -1c
-.RI "status_t \fBgetStatus\fP () const override"
-.br
-.ti -1c
-.RI "std::string \fBgetVersion\fP () override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgetNotes\fP () const override"
-.br
-.ti -1c
-.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()"
-.br
-.in -1c
-.SS "Protected Member Functions"
-
-.in +1c
-.ti -1c
-.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override"
-.br
-.in -1c
-
-Protected Member Functions inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)"
-.br
-.ti -1c
-.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override"
-.br
-.in -1c
-
-Protected Member Functions inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "void \fBcheckFitParameters\fP ()"
-.br
-.ti -1c
-.RI "void \fBbuildDataset\fP (torch::Tensor &y)"
-.br
-.in -1c
-.SS "Additional Inherited Members"
-
-
-Protected Attributes inherited from \fBbayesnet::Ensemble\fP
-.in +1c
-.ti -1c
-.RI "unsigned \fBn_models\fP"
-.br
-.ti -1c
-.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP"
-.br
-.ti -1c
-.RI "std::vector< double > \fBsignificanceModels\fP"
-.br
-.ti -1c
-.RI "bool \fBpredict_voting\fP"
-.br
-.in -1c
-
-Protected Attributes inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "bool \fBfitted\fP"
-.br
-.ti -1c
-.RI "unsigned int \fBm\fP"
-.br
-.ti -1c
-.RI "unsigned int \fBn\fP"
-.br
-.ti -1c
-.RI "\fBNetwork\fP \fBmodel\fP"
-.br
-.ti -1c
-.RI "Metrics \fBmetrics\fP"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBfeatures\fP"
-.br
-.ti -1c
-.RI "std::string \fBclassName\fP"
-.br
-.ti -1c
-.RI "std::map< std::string, std::vector< int > > \fBstates\fP"
-.br
-.ti -1c
-.RI "torch::Tensor \fBdataset\fP"
-.br
-.ti -1c
-.RI "status_t \fBstatus\fP = NORMAL"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBnotes\fP"
-.br
-.in -1c
-
-Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "std::vector< std::string > \fBvalidHyperparameters\fP"
-.br
-.in -1c
-.SH "Detailed Description"
-.PP 
-Definition at line \fB12\fP of file \fBAODE\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::AODE::AODE (bool predict_voting = \fRfalse\fP)"
-
-.PP
-Definition at line \fB10\fP of file \fBAODE\&.cc\fP\&.
-.SS "virtual bayesnet::AODE::~AODE ()\fR [inline]\fP, \fR [virtual]\fP"
-
-.PP
-Definition at line \fB15\fP of file \fBAODE\&.h\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::AODE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::Classifier\fP\&.
-.PP
-Definition at line \fB24\fP of file \fBAODE\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::AODE::graph (const std::string & title = \fR'AODE'\fP) const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB34\fP of file \fBAODE\&.cc\fP\&.
-.SS "void bayesnet::AODE::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB15\fP of file \fBAODE\&.cc\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_AODELd.3

@@ -1,296 +0,0 @@
-.TH "bayesnet::AODELd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::AODELd
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::Ensemble\fP, and \fBbayesnet::Proposal\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBAODELd\fP (bool predict_voting=true)"
-.br
-.ti -1c
-.RI "\fBAODELd\fP & \fBfit\fP (torch::Tensor &X_, torch::Tensor &y_, const std::vector< std::string > &features_, const std::string &className_, map< std::string, std::vector< int > > &states_) override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='AODELd') const override"
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-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_BaseClassifier.3

@@ -1,116 +0,0 @@
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docs/man3/bayesnet_Boost.3

@@ -1,369 +0,0 @@
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-Definition at line \fB37\fP of file \fBBoost\&.h\fP\&.
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-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_BoostA2DE.3

@@ -1,316 +0,0 @@
-.TH "bayesnet::BoostA2DE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
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-Protected Attributes inherited from \fBbayesnet::Ensemble\fP
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-.RI "unsigned \fBn_models\fP"
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-.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP"
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-.RI "std::vector< double > \fBsignificanceModels\fP"
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-.RI "bool \fBpredict_voting\fP"
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-Protected Attributes inherited from \fBbayesnet::Classifier\fP
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-.RI "\fBNetwork\fP \fBmodel\fP"
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-Definition at line \fB14\fP of file \fBBoostA2DE\&.h\fP\&.
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-Definition at line \fB19\fP of file \fBBoostA2DE\&.cc\fP\&.
-.SH "Member Function Documentation"
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-Implements \fBbayesnet::BaseClassifier\fP\&.
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-Definition at line \fB163\fP of file \fBBoostA2DE\&.cc\fP\&.
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-Implements \fBbayesnet::BaseClassifier\fP\&.
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-Definition at line \fB44\fP of file \fBBoostA2DE\&.cc\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_BoostAODE.3

@@ -1,316 +0,0 @@
-.TH "bayesnet::BoostAODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::BoostAODE
-.SH SYNOPSIS
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-.PP
-.PP
-Inherits \fBbayesnet::Boost\fP\&.
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-
-.in +1c
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-.RI "\fBBoostAODE\fP (bool predict_voting=false)"
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-Public Member Functions inherited from \fBbayesnet::Boost\fP
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-.RI "\fBBoost\fP (bool predict_voting=false)"
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-Public Member Functions inherited from \fBbayesnet::Ensemble\fP
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-.RI "\fBEnsemble\fP (bool predict_voting=true)"
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-Protected Attributes inherited from \fBbayesnet::Classifier\fP
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-.SH "Detailed Description"
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-Definition at line \fB15\fP of file \fBBoostAODE\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
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-.SS "bayesnet::BoostAODE::BoostAODE (bool predict_voting = \fRfalse\fP)\fR [explicit]\fP"
-
-.PP
-Definition at line \fB16\fP of file \fBBoostAODE\&.cc\fP\&.
-.SH "Member Function Documentation"
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-.SS "std::vector< std::string > bayesnet::BoostAODE::graph (const std::string & title = \fR'BoostAODE'\fP) const\fR [override]\fP, \fR [virtual]\fP"
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-Implements \fBbayesnet::BaseClassifier\fP\&.
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-Definition at line \fB157\fP of file \fBBoostAODE\&.cc\fP\&.
-.SS "void bayesnet::BoostAODE::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP"
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-Implements \fBbayesnet::BaseClassifier\fP\&.
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-Definition at line \fB33\fP of file \fBBoostAODE\&.cc\fP\&.
-
-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_Classifier.3

@@ -1,360 +0,0 @@
-.TH "bayesnet::Classifier" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::Classifier
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Inherited by \fBbayesnet::Ensemble\fP, \fBbayesnet::KDB\fP, \fBbayesnet::SPODE\fP, \fBbayesnet::SPnDE\fP, and \fBbayesnet::TAN\fP\&.
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-.RI "\fBClassifier\fP (\fBNetwork\fP model)"
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-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
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-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override"
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-.RI "void \fBaddNodes\fP ()"
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-.RI "int \fBgetNumberOfEdges\fP () const override"
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-.RI "int \fBgetNumberOfStates\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetClassNumStates\fP () const override"
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-.ti -1c
-.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override"
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-.ti -1c
-.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "status_t \fBgetStatus\fP () const override"
-.br
-.ti -1c
-.RI "std::string \fBgetVersion\fP () override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBshow\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBtopological_order\fP () override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgetNotes\fP () const override"
-.br
-.ti -1c
-.RI "std::string \fBdump_cpt\fP () const override"
-.br
-.ti -1c
-.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "virtual std::vector< std::string > \fBgraph\fP (const std::string &title='') const =0"
-.br
-.ti -1c
-.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()"
-.br
-.in -1c
-.SS "Protected Member Functions"
-
-.in +1c
-.ti -1c
-.RI "void \fBcheckFitParameters\fP ()"
-.br
-.ti -1c
-.RI "virtual void \fBbuildModel\fP (const torch::Tensor &weights)=0"
-.br
-.ti -1c
-.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override"
-.br
-.ti -1c
-.RI "void \fBbuildDataset\fP (torch::Tensor &y)"
-.br
-.in -1c
-.SS "Protected Attributes"
-
-.in +1c
-.ti -1c
-.RI "bool \fBfitted\fP"
-.br
-.ti -1c
-.RI "unsigned int \fBm\fP"
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-.ti -1c
-.RI "unsigned int \fBn\fP"
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-.ti -1c
-.RI "\fBNetwork\fP \fBmodel\fP"
-.br
-.ti -1c
-.RI "Metrics \fBmetrics\fP"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBfeatures\fP"
-.br
-.ti -1c
-.RI "std::string \fBclassName\fP"
-.br
-.ti -1c
-.RI "std::map< std::string, std::vector< int > > \fBstates\fP"
-.br
-.ti -1c
-.RI "torch::Tensor \fBdataset\fP"
-.br
-.ti -1c
-.RI "status_t \fBstatus\fP = NORMAL"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBnotes\fP"
-.br
-.in -1c
-
-Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP
-.in +1c
-.ti -1c
-.RI "std::vector< std::string > \fBvalidHyperparameters\fP"
-.br
-.in -1c
-.SH "Detailed Description"
-.PP 
-Definition at line \fB15\fP of file \fBClassifier\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::Classifier::Classifier (\fBNetwork\fP model)"
-
-.PP
-Definition at line \fB12\fP of file \fBClassifier\&.cc\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::Classifier::addNodes ()"
-
-.PP
-Definition at line \fB155\fP of file \fBClassifier\&.cc\fP\&.
-.SS "void bayesnet::Classifier::buildDataset (torch::Tensor & y)\fR [protected]\fP"
-
-.PP
-Definition at line \fB30\fP of file \fBClassifier\&.cc\fP\&.
-.SS "void bayesnet::Classifier::checkFitParameters ()\fR [protected]\fP"
-
-.PP
-Definition at line \fB79\fP of file \fBClassifier\&.cc\fP\&.
-.SS "std::string bayesnet::Classifier::dump_cpt () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB184\fP of file \fBClassifier\&.cc\fP\&.
-.SS "\fBClassifier\fP & bayesnet::Classifier::fit (std::vector< std::vector< int > > & X, std::vector< int > & y, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB57\fP of file \fBClassifier\&.cc\fP\&.
-.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & dataset, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB68\fP of file \fBClassifier\&.cc\fP\&.
-.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & dataset, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states, const torch::Tensor & weights)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB74\fP of file \fBClassifier\&.cc\fP\&.
-.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & X, torch::Tensor & y, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB49\fP of file \fBClassifier\&.cc\fP\&.
-.SS "int bayesnet::Classifier::getClassNumStates () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB176\fP of file \fBClassifier\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Classifier::getNotes () const\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB38\fP of file \fBClassifier\&.h\fP\&.
-.SS "int bayesnet::Classifier::getNumberOfEdges () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB168\fP of file \fBClassifier\&.cc\fP\&.
-.SS "int bayesnet::Classifier::getNumberOfNodes () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB163\fP of file \fBClassifier\&.cc\fP\&.
-.SS "int bayesnet::Classifier::getNumberOfStates () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB172\fP of file \fBClassifier\&.cc\fP\&.
-.SS "status_t bayesnet::Classifier::getStatus () const\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB32\fP of file \fBClassifier\&.h\fP\&.
-.SS "std::string bayesnet::Classifier::getVersion ()\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB33\fP of file \fBClassifier\&.h\fP\&.
-.SS "std::vector< int > bayesnet::Classifier::predict (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB103\fP of file \fBClassifier\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Classifier::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB96\fP of file \fBClassifier\&.cc\fP\&.
-.SS "std::vector< std::vector< double > > bayesnet::Classifier::predict_proba (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB124\fP of file \fBClassifier\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Classifier::predict_proba (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB117\fP of file \fBClassifier\&.cc\fP\&.
-.SS "float bayesnet::Classifier::score (std::vector< std::vector< int > > & X, std::vector< int > & y)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB144\fP of file \fBClassifier\&.cc\fP\&.
-.SS "float bayesnet::Classifier::score (torch::Tensor & X, torch::Tensor & y)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB139\fP of file \fBClassifier\&.cc\fP\&.
-.SS "void bayesnet::Classifier::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB188\fP of file \fBClassifier\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Classifier::show () const\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB151\fP of file \fBClassifier\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Classifier::topological_order ()\fR [override]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB180\fP of file \fBClassifier\&.cc\fP\&.
-.SS "void bayesnet::Classifier::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP"
-
-.PP
-Implements \fBbayesnet::BaseClassifier\fP\&.
-.PP
-Definition at line \fB44\fP of file \fBClassifier\&.cc\fP\&.
-.SH "Member Data Documentation"
-.PP 
-.SS "std::string bayesnet::Classifier::className\fR [protected]\fP"
-
-.PP
-Definition at line \fB47\fP of file \fBClassifier\&.h\fP\&.
-.SS "torch::Tensor bayesnet::Classifier::dataset\fR [protected]\fP"
-
-.PP
-Definition at line \fB49\fP of file \fBClassifier\&.h\fP\&.
-.SS "std::vector<std::string> bayesnet::Classifier::features\fR [protected]\fP"
-
-.PP
-Definition at line \fB46\fP of file \fBClassifier\&.h\fP\&.
-.SS "bool bayesnet::Classifier::fitted\fR [protected]\fP"
-
-.PP
-Definition at line \fB42\fP of file \fBClassifier\&.h\fP\&.
-.SS "unsigned int bayesnet::Classifier::m\fR [protected]\fP"
-
-.PP
-Definition at line \fB43\fP of file \fBClassifier\&.h\fP\&.
-.SS "Metrics bayesnet::Classifier::metrics\fR [protected]\fP"
-
-.PP
-Definition at line \fB45\fP of file \fBClassifier\&.h\fP\&.
-.SS "\fBNetwork\fP bayesnet::Classifier::model\fR [protected]\fP"
-
-.PP
-Definition at line \fB44\fP of file \fBClassifier\&.h\fP\&.
-.SS "unsigned int bayesnet::Classifier::n\fR [protected]\fP"
-
-.PP
-Definition at line \fB43\fP of file \fBClassifier\&.h\fP\&.
-.SS "std::vector<std::string> bayesnet::Classifier::notes\fR [protected]\fP"
-
-.PP
-Definition at line \fB51\fP of file \fBClassifier\&.h\fP\&.
-.SS "std::map<std::string, std::vector<int> > bayesnet::Classifier::states\fR [protected]\fP"
-
-.PP
-Definition at line \fB48\fP of file \fBClassifier\&.h\fP\&.
-.SS "status_t bayesnet::Classifier::status = NORMAL\fR [protected]\fP"
-
-.PP
-Definition at line \fB50\fP of file \fBClassifier\&.h\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_Ensemble.3

@@ -1,348 +0,0 @@
-.TH "bayesnet::Ensemble" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::Ensemble
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::Classifier\fP\&.
-.PP
-Inherited by \fBbayesnet::A2DE\fP, \fBbayesnet::AODE\fP, \fBbayesnet::AODELd\fP, and \fBbayesnet::Boost\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBEnsemble\fP (bool predict_voting=true)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override"
-.br
-.ti -1c
-.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override"
-.br
-.ti -1c
-.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfNodes\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfEdges\fP () const override"
-.br
-.ti -1c
-.RI "int \fBgetNumberOfStates\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBshow\fP () const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBtopological_order\fP () override"
-.br
-.ti -1c
-.RI "std::string \fBdump_cpt\fP () const override"
-.br
-.in -1c
-
-Public Member Functions inherited from \fBbayesnet::Classifier\fP
-.in +1c
-.ti -1c
-.RI "\fBClassifier\fP (\fBNetwork\fP model)"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override"
-.br
-.ti -1c
-.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override"
-.br
-.ti -1c
-.RI "void \fBaddNodes\fP ()"
-.br
-.ti -1c
-.RI "int \fBgetClassNumStates\fP () const override"
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-Definition at line \fB47\fP of file \fBEnsemble\&.h\fP\&.
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-Definition at line \fB48\fP of file \fBEnsemble\&.h\fP\&.
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-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_KDB.3

@@ -1,201 +0,0 @@
-.TH "bayesnet::KDB" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
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-.SH NAME
-bayesnet::KDB
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-Definition at line \fB15\fP of file \fBKDB\&.cc\fP\&.
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-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_KDBLd.3

@@ -1,254 +0,0 @@
-.TH "bayesnet::KDBLd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
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-.SH NAME
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-.in +1c
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-Definition at line \fB11\fP of file \fBKDBLd\&.cc\fP\&.
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-Definition at line \fB31\fP of file \fBKDBLd\&.cc\fP\&.
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-Definition at line \fB26\fP of file \fBKDBLd\&.cc\fP\&.
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-Definition at line \fB21\fP of file \fBKDBLd\&.h\fP\&.
-
-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_Network.3

@@ -1,223 +0,0 @@
-.TH "bayesnet::Network" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
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-.SH SYNOPSIS
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-.br
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-.RI "void \fBfit\fP (const torch::Tensor &X, const torch::Tensor &y, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)"
-.br
-.ti -1c
-.RI "void \fBfit\fP (const torch::Tensor &samples, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)"
-.br
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-.RI "torch::Tensor \fBpredict_proba\fP (const torch::Tensor &)"
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-.RI "double \fBscore\fP (const std::vector< std::vector< int > > &, const std::vector< int > &)"
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-.RI "std::vector< std::string > \fBtopological_sort\fP ()"
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-.RI "std::vector< std::string > \fBshow\fP () const"
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-.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const"
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-.RI "void \fBinitialize\fP ()"
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-.RI "std::string \fBdump_cpt\fP () const"
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-.RI "std::string \fBversion\fP ()"
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-.SH "Detailed Description"
-.PP 
-Definition at line \fB15\fP of file \fBNetwork\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::Network::Network ()"
-
-.PP
-Definition at line \fB13\fP of file \fBNetwork\&.cc\fP\&.
-.SS "bayesnet::Network::Network (float maxT)\fR [explicit]\fP"
-
-.PP
-Definition at line \fB16\fP of file \fBNetwork\&.cc\fP\&.
-.SS "bayesnet::Network::Network (const \fBNetwork\fP & other)\fR [explicit]\fP"
-
-.PP
-Definition at line \fB20\fP of file \fBNetwork\&.cc\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::Network::addEdge (const std::string & parent, const std::string & child)"
-
-.PP
-Definition at line \fB95\fP of file \fBNetwork\&.cc\fP\&.
-.SS "void bayesnet::Network::addNode (const std::string & name)"
-
-.PP
-Definition at line \fB46\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::string bayesnet::Network::dump_cpt () const"
-
-.PP
-Definition at line \fB420\fP of file \fBNetwork\&.cc\fP\&.
-.SS "void bayesnet::Network::fit (const std::vector< std::vector< int > > & input_data, const std::vector< int > & labels, const std::vector< double > & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)"
-
-.PP
-Definition at line \fB177\fP of file \fBNetwork\&.cc\fP\&.
-.SS "void bayesnet::Network::fit (const torch::Tensor & samples, const torch::Tensor & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)"
-
-.PP
-Definition at line \fB169\fP of file \fBNetwork\&.cc\fP\&.
-.SS "void bayesnet::Network::fit (const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)"
-
-.PP
-Definition at line \fB158\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::string bayesnet::Network::getClassName () const"
-
-.PP
-Definition at line \fB75\fP of file \fBNetwork\&.cc\fP\&.
-.SS "int bayesnet::Network::getClassNumStates () const"
-
-.PP
-Definition at line \fB63\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::pair< std::string, std::string > > bayesnet::Network::getEdges () const"
-
-.PP
-Definition at line \fB371\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Network::getFeatures () const"
-
-.PP
-Definition at line \fB59\fP of file \fBNetwork\&.cc\fP\&.
-.SS "float bayesnet::Network::getMaxThreads () const"
-
-.PP
-Definition at line \fB38\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::map< std::string, std::unique_ptr< \fBNode\fP > > & bayesnet::Network::getNodes ()"
-
-.PP
-Definition at line \fB116\fP of file \fBNetwork\&.cc\fP\&.
-.SS "int bayesnet::Network::getNumEdges () const"
-
-.PP
-Definition at line \fB383\fP of file \fBNetwork\&.cc\fP\&.
-.SS "torch::Tensor & bayesnet::Network::getSamples ()"
-
-.PP
-Definition at line \fB42\fP of file \fBNetwork\&.cc\fP\&.
-.SS "int bayesnet::Network::getStates () const"
-
-.PP
-Definition at line \fB67\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Network::graph (const std::string & title) const"
-
-.PP
-Definition at line \fB357\fP of file \fBNetwork\&.cc\fP\&.
-.SS "void bayesnet::Network::initialize ()"
-
-.PP
-Definition at line \fB29\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< int > bayesnet::Network::predict (const std::vector< std::vector< int > > & tsamples)"
-
-.PP
-Definition at line \fB237\fP of file \fBNetwork\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Network::predict (const torch::Tensor & samples)"
-
-.PP
-Definition at line \fB230\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::vector< double > > bayesnet::Network::predict_proba (const std::vector< std::vector< int > > & tsamples)"
-
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-Definition at line \fB259\fP of file \fBNetwork\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Network::predict_proba (const torch::Tensor & samples)"
-
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-Definition at line \fB224\fP of file \fBNetwork\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Network::predict_tensor (const torch::Tensor & samples, const bool proba)"
-
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-Definition at line \fB205\fP of file \fBNetwork\&.cc\fP\&.
-.SS "double bayesnet::Network::score (const std::vector< std::vector< int > > & tsamples, const std::vector< int > & labels)"
-
-.PP
-Definition at line \fB275\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Network::show () const"
-
-.PP
-Definition at line \fB344\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Network::topological_sort ()"
-
-.PP
-Definition at line \fB387\fP of file \fBNetwork\&.cc\fP\&.
-.SS "std::string bayesnet::Network::version ()\fR [inline]\fP"
-
-.PP
-Definition at line \fB49\fP of file \fBNetwork\&.h\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_Node.3

@@ -1,135 +0,0 @@
-.TH "bayesnet::Node" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::Node
-.SH SYNOPSIS
-.br
-.PP
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBNode\fP (const std::string &)"
-.br
-.ti -1c
-.RI "void \fBclear\fP ()"
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-.ti -1c
-.RI "void \fBaddParent\fP (\fBNode\fP *)"
-.br
-.ti -1c
-.RI "void \fBaddChild\fP (\fBNode\fP *)"
-.br
-.ti -1c
-.RI "void \fBremoveParent\fP (\fBNode\fP *)"
-.br
-.ti -1c
-.RI "void \fBremoveChild\fP (\fBNode\fP *)"
-.br
-.ti -1c
-.RI "std::string \fBgetName\fP () const"
-.br
-.ti -1c
-.RI "std::vector< \fBNode\fP * > & \fBgetParents\fP ()"
-.br
-.ti -1c
-.RI "std::vector< \fBNode\fP * > & \fBgetChildren\fP ()"
-.br
-.ti -1c
-.RI "torch::Tensor & \fBgetCPT\fP ()"
-.br
-.ti -1c
-.RI "void \fBcomputeCPT\fP (const torch::Tensor &dataset, const std::vector< std::string > &features, const double laplaceSmoothing, const torch::Tensor &weights)"
-.br
-.ti -1c
-.RI "int \fBgetNumStates\fP () const"
-.br
-.ti -1c
-.RI "void \fBsetNumStates\fP (int)"
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-.ti -1c
-.RI "unsigned \fBminFill\fP ()"
-.br
-.ti -1c
-.RI "std::vector< std::string > \fBgraph\fP (const std::string &clasName)"
-.br
-.ti -1c
-.RI "float \fBgetFactorValue\fP (std::map< std::string, int > &)"
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-.in -1c
-.SH "Detailed Description"
-.PP 
-Definition at line \fB14\fP of file \fBNode\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::Node::Node (const std::string & name)\fR [explicit]\fP"
-
-.PP
-Definition at line \fB11\fP of file \fBNode\&.cc\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::Node::addChild (\fBNode\fP * child)"
-
-.PP
-Definition at line \fB39\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::addParent (\fBNode\fP * parent)"
-
-.PP
-Definition at line \fB27\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::clear ()"
-
-.PP
-Definition at line \fB15\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::computeCPT (const torch::Tensor & dataset, const std::vector< std::string > & features, const double laplaceSmoothing, const torch::Tensor & weights)"
-
-.PP
-Definition at line \fB93\fP of file \fBNode\&.cc\fP\&.
-.SS "std::vector< \fBNode\fP * > & bayesnet::Node::getChildren ()"
-
-.PP
-Definition at line \fB47\fP of file \fBNode\&.cc\fP\&.
-.SS "torch::Tensor & bayesnet::Node::getCPT ()"
-
-.PP
-Definition at line \fB59\fP of file \fBNode\&.cc\fP\&.
-.SS "float bayesnet::Node::getFactorValue (std::map< std::string, int > & evidence)"
-
-.PP
-Definition at line \fB124\fP of file \fBNode\&.cc\fP\&.
-.SS "std::string bayesnet::Node::getName () const"
-
-.PP
-Definition at line \fB23\fP of file \fBNode\&.cc\fP\&.
-.SS "int bayesnet::Node::getNumStates () const"
-
-.PP
-Definition at line \fB51\fP of file \fBNode\&.cc\fP\&.
-.SS "std::vector< \fBNode\fP * > & bayesnet::Node::getParents ()"
-
-.PP
-Definition at line \fB43\fP of file \fBNode\&.cc\fP\&.
-.SS "std::vector< std::string > bayesnet::Node::graph (const std::string & clasName)"
-
-.PP
-Definition at line \fB132\fP of file \fBNode\&.cc\fP\&.
-.SS "unsigned bayesnet::Node::minFill ()"
-
-.PP
-Definition at line \fB70\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::removeChild (\fBNode\fP * child)"
-
-.PP
-Definition at line \fB35\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::removeParent (\fBNode\fP * parent)"
-
-.PP
-Definition at line \fB31\fP of file \fBNode\&.cc\fP\&.
-.SS "void bayesnet::Node::setNumStates (int numStates)"
-
-.PP
-Definition at line \fB55\fP of file \fBNode\&.cc\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_Proposal.3

@@ -1,95 +0,0 @@
-.TH "bayesnet::Proposal" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::Proposal
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherited by \fBbayesnet::AODELd\fP, \fBbayesnet::KDBLd\fP, \fBbayesnet::SPODELd\fP, and \fBbayesnet::TANLd\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)"
-.br
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-.SS "Protected Member Functions"
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-.in +1c
-.ti -1c
-.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)"
-.br
-.ti -1c
-.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)"
-.br
-.ti -1c
-.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)"
-.br
-.ti -1c
-.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)"
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-.in +1c
-.ti -1c
-.RI "torch::Tensor \fBXf\fP"
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-.ti -1c
-.RI "torch::Tensor \fBy\fP"
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-.SH "Detailed Description"
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-Definition at line \fB17\fP of file \fBProposal\&.h\fP\&.
-.SH "Constructor & Destructor Documentation"
-.PP 
-.SS "bayesnet::Proposal::Proposal (torch::Tensor & pDataset, std::vector< std::string > & features_, std::string & className_)"
-
-.PP
-Definition at line \fB10\fP of file \fBProposal\&.cc\fP\&.
-.SS "bayesnet::Proposal::~Proposal ()\fR [virtual]\fP"
-
-.PP
-Definition at line \fB11\fP of file \fBProposal\&.cc\fP\&.
-.SH "Member Function Documentation"
-.PP 
-.SS "void bayesnet::Proposal::checkInput (const torch::Tensor & X, const torch::Tensor & y)\fR [protected]\fP"
-
-.PP
-Definition at line \fB17\fP of file \fBProposal\&.cc\fP\&.
-.SS "map< std::string, std::vector< int > > bayesnet::Proposal::fit_local_discretization (const torch::Tensor & y)\fR [protected]\fP"
-
-.PP
-Definition at line \fB77\fP of file \fBProposal\&.cc\fP\&.
-.SS "map< std::string, std::vector< int > > bayesnet::Proposal::localDiscretizationProposal (const map< std::string, std::vector< int > > & states, \fBNetwork\fP & model)\fR [protected]\fP"
-
-.PP
-Definition at line \fB26\fP of file \fBProposal\&.cc\fP\&.
-.SS "torch::Tensor bayesnet::Proposal::prepareX (torch::Tensor & X)\fR [protected]\fP"
-
-.PP
-Definition at line \fB104\fP of file \fBProposal\&.cc\fP\&.
-.SH "Member Data Documentation"
-.PP 
-.SS "map<std::string, mdlp::CPPFImdlp*> bayesnet::Proposal::discretizers\fR [protected]\fP"
-
-.PP
-Definition at line \fB28\fP of file \fBProposal\&.h\fP\&.
-.SS "torch::Tensor bayesnet::Proposal::Xf\fR [protected]\fP"
-
-.PP
-Definition at line \fB26\fP of file \fBProposal\&.h\fP\&.
-.SS "torch::Tensor bayesnet::Proposal::y\fR [protected]\fP"
-
-.PP
-Definition at line \fB27\fP of file \fBProposal\&.h\fP\&.
-
-.SH "Author"
-.PP 
-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_SPODE.3

@@ -1,195 +0,0 @@
-.TH "bayesnet::SPODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
-.nh
-.SH NAME
-bayesnet::SPODE
-.SH SYNOPSIS
-.br
-.PP
-.PP
-Inherits \fBbayesnet::Classifier\fP\&.
-.PP
-Inherited by \fBbayesnet::SPODELd\fP\&.
-.SS "Public Member Functions"
-
-.in +1c
-.ti -1c
-.RI "\fBSPODE\fP (int root)"
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docs/man3/bayesnet_SPODELd.3

@@ -1,268 +0,0 @@
-.TH "bayesnet::SPODELd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
-.ad l
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-.SH NAME
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-Definition at line \fB29\fP of file \fBSPODELd\&.cc\fP\&.
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-Definition at line \fB22\fP of file \fBSPODELd\&.h\fP\&.
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-.SH "Author"
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-Generated automatically by Doxygen for BayesNet from the source code\&.

docs/man3/bayesnet_SPnDE.3

@@ -1,193 +0,0 @@
-.TH "bayesnet::SPnDE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*-
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-.nh
-.SH NAME
-bayesnet::SPnDE
-.SH SYNOPSIS
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docs/man3/bayesnet_TAN.3

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docs/man3/bayesnet_TANLd.3

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-Definition at line \fB32\fP of file \fBTANLd\&.cc\fP\&.
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-Definition at line \fB27\fP of file \fBTANLd\&.cc\fP\&.
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-Definition at line \fB21\fP of file \fBTANLd\&.h\fP\&.
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-Generated automatically by Doxygen for BayesNet from the source code\&.

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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;A2DE.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span>    A2DE::A2DE(<span class="keywordtype">bool</span> predict_voting) : Ensemble(predict_voting)</div>
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-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>        validHyperparameters = { <span class="stringliteral">&quot;predict_voting&quot;</span> };</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    }</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    <span class="keywordtype">void</span> A2DE::setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters_)</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    {</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keyword">auto</span> hyperparameters = hyperparameters_;</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;predict_voting&quot;</span>)) {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>            predict_voting = hyperparameters[<span class="stringliteral">&quot;predict_voting&quot;</span>];</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>            hyperparameters.erase(<span class="stringliteral">&quot;predict_voting&quot;</span>);</div>
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-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        Classifier::setHyperparameters(hyperparameters);</div>
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-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    <span class="keywordtype">void</span> A2DE::buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>    {</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        models.clear();</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        significanceModels.clear();</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; features.size() - 1; ++i) {</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = i + 1; j &lt; features.size(); ++j) {</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>                <span class="keyword">auto</span> model = std::make_unique&lt;SPnDE&gt;(std::vector&lt;int&gt;({ i, j }));</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>                models.push_back(std::move(model));</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>            }</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        }</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        n_models = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span><span class="keyword">&gt;</span>(models.size());</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>    }</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>    std::vector&lt;std::string&gt; A2DE::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        <span class="keywordflow">return</span> Ensemble::graph(title);</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    }</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>}</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_a2_d_e.html">   12</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_a2_d_e.html">A2DE</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_ensemble.html">Ensemble</a> {</div>
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-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters) <span class="keyword">override</span>;</div>
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-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    <span class="keyword">protected</span>:</div>
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-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
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-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="keyword">auto</span> hyperparameters = hyperparameters_;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;predict_voting&quot;</span>)) {</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>            predict_voting = hyperparameters[<span class="stringliteral">&quot;predict_voting&quot;</span>];</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>            hyperparameters.erase(<span class="stringliteral">&quot;predict_voting&quot;</span>);</div>
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-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        Classifier::setHyperparameters(hyperparameters);</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    }</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>    <span class="keywordtype">void</span> AODE::buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    {</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        models.clear();</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        significanceModels.clear();</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; features.size(); ++i) {</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>            models.push_back(std::make_unique&lt;SPODE&gt;(i));</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        }</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        n_models = models.size();</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    }</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    std::vector&lt;std::string&gt; AODE::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordflow">return</span> Ensemble::graph(title);</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    }</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>}</div>
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-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef AODE_H</span></div>
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-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;Ensemble.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="foldopen" id="foldopen00012" data-start="{" data-end="};">
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_a_o_d_e.html">   12</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_a_o_d_e.html">AODE</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_ensemble.html">Ensemble</a> {</div>
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-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; title = <span class="stringliteral">&quot;AODE&quot;</span>) <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <span class="keywordtype">void</span> buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights) <span class="keyword">override</span>;</div>
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-<div class="ttc" id="aclassbayesnet_1_1_a_o_d_e_html"><div class="ttname"><a href="classbayesnet_1_1_a_o_d_e.html">bayesnet::AODE</a></div><div class="ttdef"><b>Definition</b> <a href="#l00012">AODE.h:12</a></div></div>
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-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;AODELd.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span>    AODELd::AODELd(<span class="keywordtype">bool</span> predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)</div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    {</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    }</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    AODELd&amp; AODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features_, <span class="keyword">const</span> std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    {</div>
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-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        className = className_;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        Xf = X_;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        y = y_;</div>
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-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        states = fit_local_discretization(y);</div>
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-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        <span class="comment">// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network</span></div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        Ensemble::fit(dataset, features, className, states);</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span> </div>
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-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    {</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        models.clear();</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; features.size(); ++i) {</div>
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-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        n_models = models.size();</div>
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-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    <span class="keywordtype">void</span> AODELd::trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    {</div>
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-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>            model-&gt;fit(Xf, y, features, className, states);</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        }</div>
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-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    std::vector&lt;std::string&gt; AODELd::graph(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keywordflow">return</span> Ensemble::graph(name);</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    }</div>
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-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#pragma once</span></div>
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-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    <span class="keyword">enum</span> status_t { NORMAL, WARNING, ERROR };</div>
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-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>        <span class="comment">// X is nxm std::vector, y is nx1 std::vector</span></div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">BaseClassifier</a>&amp; fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="comment">// X is nxm tensor, y is nx1 tensor</span></div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">BaseClassifier</a>&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">BaseClassifier</a>&amp; fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) = 0;</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">BaseClassifier</a>&amp; fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights) = 0;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">~BaseClassifier</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        torch::Tensor <span class="keyword">virtual</span> predict(torch::Tensor&amp; X) = 0;</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        std::vector&lt;int&gt; <span class="keyword">virtual</span> predict(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        torch::Tensor <span class="keyword">virtual</span> predict_proba(torch::Tensor&amp; X) = 0;</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        std::vector&lt;std::vector&lt;double&gt;&gt; <span class="keyword">virtual</span> predict_proba(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        status_t <span class="keyword">virtual</span> getStatus() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        <span class="keywordtype">float</span> <span class="keyword">virtual</span> score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) = 0;</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        <span class="keywordtype">float</span> <span class="keyword">virtual</span> score(torch::Tensor&amp; X, torch::Tensor&amp; y) = 0;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        <span class="keywordtype">int</span> <span class="keyword">virtual</span> getNumberOfNodes()<span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        <span class="keywordtype">int</span> <span class="keyword">virtual</span> getNumberOfEdges()<span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordtype">int</span> <span class="keyword">virtual</span> getNumberOfStates() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        <span class="keywordtype">int</span> <span class="keyword">virtual</span> getClassNumStates() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::vector&lt;std::string&gt; <span class="keyword">virtual</span> show() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        std::vector&lt;std::string&gt; <span class="keyword">virtual</span> graph(<span class="keyword">const</span> std::string&amp; title = <span class="stringliteral">&quot;&quot;</span>) <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="keyword">virtual</span> std::string getVersion() = 0;</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        std::vector&lt;std::string&gt; <span class="keyword">virtual</span> topological_order() = 0;</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        std::vector&lt;std::string&gt; <span class="keyword">virtual</span> getNotes() <span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        std::string <span class="keyword">virtual</span> dump_cpt()<span class="keyword">const</span> = 0;</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        <span class="keyword">virtual</span> <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters) = 0;</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        std::vector&lt;std::string&gt;&amp; getValidHyperparameters() { <span class="keywordflow">return</span> validHyperparameters; }</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="keyword">virtual</span> <span class="keywordtype">void</span> trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights) = 0;</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        std::vector&lt;std::string&gt; validHyperparameters;</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    };</div>
-</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>}</div>
-<div class="ttc" id="aclassbayesnet_1_1_base_classifier_html"><div class="ttname"><a href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a></div><div class="ttdef"><b>Definition</b> <a href="#l00013">BaseClassifier.h:13</a></div></div>
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-  <div class="headertitle"><div class="title">Boost.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;folding.hpp&gt;</span></div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/CFS.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/FCBF.h&quot;</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/IWSS.h&quot;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;Boost.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    Boost::Boost(<span class="keywordtype">bool</span> predict_voting) : Ensemble(predict_voting)</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    {</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>        validHyperparameters = { <span class="stringliteral">&quot;order&quot;</span>, <span class="stringliteral">&quot;convergence&quot;</span>, <span class="stringliteral">&quot;convergence_best&quot;</span>, <span class="stringliteral">&quot;bisection&quot;</span>, <span class="stringliteral">&quot;threshold&quot;</span>, <span class="stringliteral">&quot;maxTolerance&quot;</span>,</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="stringliteral">&quot;predict_voting&quot;</span>, <span class="stringliteral">&quot;select_features&quot;</span>, <span class="stringliteral">&quot;block_update&quot;</span> };</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    }</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    <span class="keywordtype">void</span> Boost::setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters_)</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    {</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <span class="keyword">auto</span> hyperparameters = hyperparameters_;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;order&quot;</span>)) {</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>            std::vector&lt;std::string&gt; algos = { Orders.ASC, Orders.DESC, Orders.RAND };</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>            order_algorithm = hyperparameters[<span class="stringliteral">&quot;order&quot;</span>];</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>            <span class="keywordflow">if</span> (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid order algorithm, valid values [&quot;</span> + Orders.ASC + <span class="stringliteral">&quot;, &quot;</span> + Orders.DESC + <span class="stringliteral">&quot;, &quot;</span> + Orders.RAND + <span class="stringliteral">&quot;]&quot;</span>);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>            }</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>            hyperparameters.erase(<span class="stringliteral">&quot;order&quot;</span>);</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        }</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;convergence&quot;</span>)) {</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>            convergence = hyperparameters[<span class="stringliteral">&quot;convergence&quot;</span>];</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>            hyperparameters.erase(<span class="stringliteral">&quot;convergence&quot;</span>);</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        }</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;convergence_best&quot;</span>)) {</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>            convergence_best = hyperparameters[<span class="stringliteral">&quot;convergence_best&quot;</span>];</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>            hyperparameters.erase(<span class="stringliteral">&quot;convergence_best&quot;</span>);</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        }</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;bisection&quot;</span>)) {</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>            bisection = hyperparameters[<span class="stringliteral">&quot;bisection&quot;</span>];</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>            hyperparameters.erase(<span class="stringliteral">&quot;bisection&quot;</span>);</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        }</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;threshold&quot;</span>)) {</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>            threshold = hyperparameters[<span class="stringliteral">&quot;threshold&quot;</span>];</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>            hyperparameters.erase(<span class="stringliteral">&quot;threshold&quot;</span>);</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        }</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;maxTolerance&quot;</span>)) {</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>            maxTolerance = hyperparameters[<span class="stringliteral">&quot;maxTolerance&quot;</span>];</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>            <span class="keywordflow">if</span> (maxTolerance &lt; 1 || maxTolerance &gt; 4)</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid maxTolerance value, must be greater in [1, 4]&quot;</span>);</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>            hyperparameters.erase(<span class="stringliteral">&quot;maxTolerance&quot;</span>);</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        }</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;predict_voting&quot;</span>)) {</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>            predict_voting = hyperparameters[<span class="stringliteral">&quot;predict_voting&quot;</span>];</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>            hyperparameters.erase(<span class="stringliteral">&quot;predict_voting&quot;</span>);</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        }</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;select_features&quot;</span>)) {</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>            <span class="keyword">auto</span> selectedAlgorithm = hyperparameters[<span class="stringliteral">&quot;select_features&quot;</span>];</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>            std::vector&lt;std::string&gt; algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>            selectFeatures = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>            select_features_algorithm = selectedAlgorithm;</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>            <span class="keywordflow">if</span> (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid selectFeatures value, valid values [&quot;</span> + SelectFeatures.IWSS + <span class="stringliteral">&quot;, &quot;</span> + SelectFeatures.CFS + <span class="stringliteral">&quot;, &quot;</span> + SelectFeatures.FCBF + <span class="stringliteral">&quot;]&quot;</span>);</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>            }</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>            hyperparameters.erase(<span class="stringliteral">&quot;select_features&quot;</span>);</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        }</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;block_update&quot;</span>)) {</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>            block_update = hyperparameters[<span class="stringliteral">&quot;block_update&quot;</span>];</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>            hyperparameters.erase(<span class="stringliteral">&quot;block_update&quot;</span>);</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        }</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        Classifier::setHyperparameters(hyperparameters);</div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    }</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="keywordtype">void</span> Boost::buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    {</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="comment">// Models shall be built in trainModel</span></div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        models.clear();</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        significanceModels.clear();</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        n_models = 0;</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="comment">// Prepare the validation dataset</span></div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="keyword">auto</span> y_ = dataset.index({ -1, <span class="stringliteral">&quot;...&quot;</span> });</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        <span class="keywordflow">if</span> (convergence) {</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>            <span class="comment">// Prepare train &amp; validation sets from train data</span></div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>            <span class="keyword">auto</span> fold = folding::StratifiedKFold(5, y_, 271);</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>            <span class="keyword">auto</span> [train, test] = fold.getFold(0);</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            <span class="keyword">auto</span> train_t = torch::tensor(train);</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            <span class="keyword">auto</span> test_t = torch::tensor(test);</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            <span class="comment">// Get train and validation sets</span></div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            y_train = dataset.index({ -1, train_t });</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            y_test = dataset.index({ -1, test_t });</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            dataset = X_train;</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            m = X_train.size(1);</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            <span class="keyword">auto</span> n_classes = states.at(className).size();</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            <span class="comment">// Build dataset with train data</span></div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>            buildDataset(y_train);</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>            metrics = Metrics(dataset, features, className, n_classes);</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            <span class="comment">// Use all data to train</span></div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), <span class="stringliteral">&quot;...&quot;</span> });</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            y_train = y_;</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        }</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    }</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    std::vector&lt;int&gt; Boost::featureSelection(torch::Tensor&amp; weights_)</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    {</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <span class="keywordtype">int</span> maxFeatures = 0;</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        <span class="keywordflow">if</span> (select_features_algorithm == SelectFeatures.CFS) {</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            featureSelector = <span class="keyword">new</span> CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (select_features_algorithm == SelectFeatures.IWSS) {</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            <span class="keywordflow">if</span> (threshold &lt; 0 || threshold &gt;0.5) {</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid threshold value for &quot;</span> + SelectFeatures.IWSS + <span class="stringliteral">&quot; [0, 0.5]&quot;</span>);</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            }</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            featureSelector = <span class="keyword">new</span> IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (select_features_algorithm == SelectFeatures.FCBF) {</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <span class="keywordflow">if</span> (threshold &lt; 1e-7 || threshold &gt; 1) {</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid threshold value for &quot;</span> + SelectFeatures.FCBF + <span class="stringliteral">&quot; [1e-7, 1]&quot;</span>);</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            }</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            featureSelector = <span class="keyword">new</span> FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        }</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        featureSelector-&gt;fit();</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        <span class="keyword">auto</span> featuresUsed = featureSelector-&gt;getFeatures();</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="keyword">delete</span> featureSelector;</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="keywordflow">return</span> featuresUsed;</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    }</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    std::tuple&lt;torch::Tensor&amp;, double, bool&gt; Boost::update_weights(torch::Tensor&amp; ytrain, torch::Tensor&amp; ypred, torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    {</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <span class="keywordtype">bool</span> terminate = <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <span class="keywordtype">double</span> alpha_t = 0;</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="keyword">auto</span> mask_wrong = ypred != ytrain;</div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keyword">auto</span> mask_right = ypred == ytrain;</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        <span class="keyword">auto</span> masked_weights = weights * mask_wrong.to(weights.dtype());</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keywordtype">double</span> epsilon_t = masked_weights.sum().item&lt;<span class="keywordtype">double</span>&gt;();</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <span class="keywordflow">if</span> (epsilon_t &gt; 0.5) {</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>            <span class="comment">// Inverse the weights policy (plot ln(wt))</span></div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>            <span class="comment">// &quot;In each round of AdaBoost, there is a sanity check to ensure that the current base </span></div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>            <span class="comment">// learner is better than random guess&quot; (Zhi-Hua Zhou, 2012)</span></div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>            terminate = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            <span class="keywordtype">double</span> wt = (1 - epsilon_t) / epsilon_t;</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <span class="comment">// Step 3.2: Update weights for next classifier</span></div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            <span class="comment">// Step 3.2.1: Update weights of wrong samples</span></div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            <span class="comment">// Step 3.2.2: Update weights of right samples</span></div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <span class="comment">// Step 3.3: Normalise the weights</span></div>
-<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <span class="keywordtype">double</span> totalWeights = torch::sum(weights).item&lt;<span class="keywordtype">double</span>&gt;();</div>
-<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            weights = weights / totalWeights;</div>
-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        }</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <span class="keywordflow">return</span> { weights, alpha_t, terminate };</div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    }</div>
-<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    std::tuple&lt;torch::Tensor&amp;, double, bool&gt; Boost::update_weights_block(<span class="keywordtype">int</span> k, torch::Tensor&amp; ytrain, torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    {</div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        <span class="comment">/* Update Block algorithm</span></div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span><span class="comment">            k = # of models in block</span></div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span><span class="comment">            n_models = # of models in ensemble to make predictions</span></div>
-<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment">            n_models_bak = # models saved</span></div>
-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">            models = vector of models to make predictions</span></div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">            models_bak = models not used to make predictions</span></div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="comment">            significances_bak = backup of significances vector</span></div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment"></span> </div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment">            Case list</span></div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment">            A) k = 1, n_models = 1      =&gt; n = 0 , n_models = n + k</span></div>
-<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="comment">            B) k = 1, n_models = n + 1  =&gt; n_models = n + k</span></div>
-<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span><span class="comment">            C) k &gt; 1, n_models = k + 1  =&gt; n= 1, n_models = n + k</span></div>
-<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="comment">            D) k &gt; 1, n_models = k      =&gt; n = 0, n_models = n + k</span></div>
-<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment">            E) k &gt; 1, n_models = k + n  =&gt; n_models = n + k</span></div>
-<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment"></span> </div>
-<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment">            A, D) n=0, k &gt; 0, n_models == k</span></div>
-<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">            1. n_models_bak &lt;- n_models</span></div>
-<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment">            2. significances_bak &lt;- significances</span></div>
-<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment">            3. significances = vector(k, 1)</span></div>
-<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="comment">            4. Don’t move any classifiers out of models</span></div>
-<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment">            5. n_models &lt;- k</span></div>
-<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment">            6. Make prediction, compute alpha, update weights</span></div>
-<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">            7. Don’t restore any classifiers to models</span></div>
-<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">            8. significances &lt;- significances_bak</span></div>
-<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">            9. Update last k significances</span></div>
-<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">            10. n_models &lt;- n_models_bak</span></div>
-<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment"></span> </div>
-<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">            B, C, E) n &gt; 0, k &gt; 0, n_models == n + k</span></div>
-<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="comment">            1. n_models_bak &lt;- n_models</span></div>
-<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment">            2. significances_bak &lt;- significances</span></div>
-<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">            3. significances = vector(k, 1)</span></div>
-<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">            4. Move first n classifiers to models_bak</span></div>
-<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">            5. n_models &lt;- k</span></div>
-<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment">            6. Make prediction, compute alpha, update weights</span></div>
-<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">            7. Insert classifiers in models_bak to be the first n models</span></div>
-<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">            8. significances &lt;- significances_bak</span></div>
-<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment">            9. Update last k significances</span></div>
-<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment">            10. n_models &lt;- n_models_bak</span></div>
-<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment">        */</span></div>
-<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        <span class="comment">// Make predict with only the last k models</span></div>
-<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        std::unique_ptr&lt;Classifier&gt; model;</div>
-<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        std::vector&lt;std::unique_ptr&lt;Classifier&gt;&gt; models_bak;</div>
-<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <span class="comment">// 1. n_models_bak &lt;- n_models 2. significances_bak &lt;- significances</span></div>
-<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>        <span class="keyword">auto</span> significance_bak = significanceModels;</div>
-<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        <span class="keyword">auto</span> n_models_bak = n_models;</div>
-<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        <span class="comment">// 3. significances = vector(k, 1)</span></div>
-<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        significanceModels = std::vector&lt;double&gt;(k, 1.0);</div>
-<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        <span class="comment">// 4. Move first n classifiers to models_bak</span></div>
-<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        <span class="comment">// backup the first n_models - k models (if n_models == k, don&#39;t backup any)</span></div>
-<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; n_models - k; ++i) {</div>
-<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>            model = std::move(models[0]);</div>
-<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            models.erase(models.begin());</div>
-<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>            models_bak.push_back(std::move(model));</div>
-<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        }</div>
-<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        assert(models.size() == k);</div>
-<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        <span class="comment">// 5. n_models &lt;- k</span></div>
-<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        n_models = k;</div>
-<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        <span class="comment">// 6. Make prediction, compute alpha, update weights</span></div>
-<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        <span class="keyword">auto</span> ypred = predict(X_train);</div>
-<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="comment">// Update weights</span></div>
-<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="keywordtype">double</span> alpha_t;</div>
-<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <span class="keywordtype">bool</span> terminate;</div>
-<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);</div>
-<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        <span class="comment">// Restore the models if needed</span></div>
-<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        <span class="comment">// 7. Insert classifiers in models_bak to be the first n models</span></div>
-<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        <span class="comment">// if n_models_bak == k, don&#39;t restore any, because none of them were moved</span></div>
-<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        <span class="keywordflow">if</span> (k != n_models_bak) {</div>
-<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>            <span class="comment">// Insert in the same order as they were extracted</span></div>
-<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>            <span class="keywordtype">int</span> bak_size = models_bak.size();</div>
-<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; bak_size; ++i) {</div>
-<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>                model = std::move(models_bak[bak_size - 1 - i]);</div>
-<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>                models_bak.erase(models_bak.end() - 1);</div>
-<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>                models.insert(models.begin(), std::move(model));</div>
-<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>            }</div>
-<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        }</div>
-<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        <span class="comment">// 8. significances &lt;- significances_bak</span></div>
-<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>        significanceModels = significance_bak;</div>
-<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        <span class="comment">// Update the significance of the last k models</span></div>
-<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        <span class="comment">// 9. Update last k significances</span></div>
-<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; k; ++i) {</div>
-<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            significanceModels[n_models_bak - k + i] = alpha_t;</div>
-<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        }</div>
-<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <span class="comment">// 10. n_models &lt;- n_models_bak</span></div>
-<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        n_models = n_models_bak;</div>
-<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="keywordflow">return</span> { weights, alpha_t, terminate };</div>
-<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>    }</div>
-<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>}</div>
-</div><!-- fragment --></div><!-- contents -->
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-   <div id="projectbrief">Bayesian Network Classifiers using libtorch from scratch</div>
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-  <div class="headertitle"><div class="title">Boost.h</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef BOOST_H</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#define BOOST_H</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;string&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;tuple&gt;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &lt;nlohmann/json.hpp&gt;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="preprocessor">#include &lt;torch/torch.h&gt;</span></div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="preprocessor">#include &quot;Ensemble.h&quot;</span></div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/FeatureSelect.h&quot;</span></div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    <span class="keyword">const</span> <span class="keyword">struct </span>{</div>
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-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        std::string FCBF = <span class="stringliteral">&quot;FCBF&quot;</span>;</div>
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-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        std::string DESC = <span class="stringliteral">&quot;desc&quot;</span>;</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        std::string RAND = <span class="stringliteral">&quot;rand&quot;</span>;</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>    }Orders;</div>
-<div class="foldopen" id="foldopen00027" data-start="{" data-end="};">
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_boost.html">   27</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_boost.html">Boost</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_ensemble.html">Ensemble</a> {</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    <span class="keyword">public</span>:</div>
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-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_boost.html">~Boost</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters_) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::vector&lt;int&gt; featureSelection(torch::Tensor&amp; weights_);</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        <span class="keywordtype">void</span> buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        std::tuple&lt;torch::Tensor&amp;, double, bool&gt; update_weights(torch::Tensor&amp; ytrain, torch::Tensor&amp; ypred, torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        std::tuple&lt;torch::Tensor&amp;, double, bool&gt; update_weights_block(<span class="keywordtype">int</span> k, torch::Tensor&amp; ytrain, torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        torch::Tensor X_train, y_train, X_test, y_test;</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        <span class="comment">// Hyperparameters</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        <span class="keywordtype">bool</span> bisection = <span class="keyword">true</span>; <span class="comment">// if true, use bisection stratety to add k models at once to the ensemble</span></div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="keywordtype">int</span> maxTolerance = 3;</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        std::string order_algorithm; <span class="comment">// order to process the KBest features asc, desc, rand</span></div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="keywordtype">bool</span> convergence = <span class="keyword">true</span>; <span class="comment">//if true, stop when the model does not improve</span></div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keywordtype">bool</span> convergence_best = <span class="keyword">false</span>; <span class="comment">// wether to keep the best accuracy to the moment or the last accuracy as prior accuracy</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        <span class="keywordtype">bool</span> selectFeatures = <span class="keyword">false</span>; <span class="comment">// if true, use feature selection</span></div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        std::string select_features_algorithm = Orders.DESC; <span class="comment">// Selected feature selection algorithm</span></div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        FeatureSelect* featureSelector = <span class="keyword">nullptr</span>;</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="keywordtype">double</span> threshold = -1;</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        <span class="keywordtype">bool</span> block_update = <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span> </div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    };</div>
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-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="preprocessor">#endif</span></div>
-<div class="ttc" id="aclassbayesnet_1_1_boost_html"><div class="ttname"><a href="classbayesnet_1_1_boost.html">bayesnet::Boost</a></div><div class="ttdef"><b>Definition</b> <a href="#l00027">Boost.h:27</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_ensemble_html"><div class="ttname"><a href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></div><div class="ttdef"><b>Definition</b> <a href="_ensemble_8h_source.html#l00015">Ensemble.h:15</a></div></div>
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-  <div class="headertitle"><div class="title">BoostA2DE.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;set&gt;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;functional&gt;</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;limits.h&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;tuple&gt;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &lt;folding.hpp&gt;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/CFS.h&quot;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/FCBF.h&quot;</span></div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="preprocessor">#include &quot;bayesnet/feature_selection/IWSS.h&quot;</span></div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="preprocessor">#include &quot;BoostA2DE.h&quot;</span></div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span> </div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span> </div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    BoostA2DE::BoostA2DE(<span class="keywordtype">bool</span> predict_voting) : Boost(predict_voting)</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    {</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    }</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    std::vector&lt;int&gt; BoostA2DE::initializeModels()</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    {</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        std::vector&lt;int&gt; featuresSelected = featureSelection(weights_);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        <span class="keywordflow">if</span> (featuresSelected.size() &lt; 2) {</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>            notes.push_back(<span class="stringliteral">&quot;No features selected in initialization&quot;</span>);</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>            status = ERROR;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>            <span class="keywordflow">return</span> std::vector&lt;int&gt;();</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        }</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; featuresSelected.size() - 1; i++) {</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = i + 1; j &lt; featuresSelected.size(); j++) {</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>                <span class="keyword">auto</span> parents = { featuresSelected[i], featuresSelected[j] };</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>                std::unique_ptr&lt;Classifier&gt; model = std::make_unique&lt;SPnDE&gt;(parents);</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>                model-&gt;fit(dataset, features, className, states, weights_);</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>                models.push_back(std::move(model));</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>                significanceModels.push_back(1.0); <span class="comment">// They will be updated later in trainModel</span></div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>                n_models++;</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>            }</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        }</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        notes.push_back(<span class="stringliteral">&quot;Used features in initialization: &quot;</span> + std::to_string(featuresSelected.size()) + <span class="stringliteral">&quot; of &quot;</span> + std::to_string(features.size()) + <span class="stringliteral">&quot; with &quot;</span> + select_features_algorithm);</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="keywordflow">return</span> featuresSelected;</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    }</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    <span class="keywordtype">void</span> BoostA2DE::trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    {</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="comment">// Logging setup</span></div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <span class="comment">// loguru::set_thread_name(&quot;BoostA2DE&quot;);</span></div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        <span class="comment">// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;</span></div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        <span class="comment">// loguru::add_file(&quot;boostA2DE.log&quot;, loguru::Truncate, loguru::Verbosity_MAX);</span></div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span> </div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <span class="comment">// Algorithm based on the adaboost algorithm for classification</span></div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="comment">// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)</span></div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        fitted = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        <span class="keywordtype">double</span> alpha_t = 0;</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <span class="keywordtype">bool</span> finished = <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        std::vector&lt;int&gt; featuresUsed;</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="keywordflow">if</span> (selectFeatures) {</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>            featuresUsed = initializeModels();</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>            <span class="keyword">auto</span> ypred = predict(X_train);</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>            <span class="comment">// Update significance of the models</span></div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>                significanceModels[i] = alpha_t;</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>            }</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>            <span class="keywordflow">if</span> (finished) {</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>                <span class="keywordflow">return</span>;</div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>            }</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        }</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <span class="keywordtype">int</span> numItemsPack = 0; <span class="comment">// The counter of the models inserted in the current pack</span></div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="comment">// Variables to control the accuracy finish condition</span></div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        <span class="keywordtype">double</span> priorAccuracy = 0.0;</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        <span class="keywordtype">double</span> improvement = 1.0;</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <span class="keywordtype">double</span> convergence_threshold = 1e-4;</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordtype">int</span> tolerance = 0; <span class="comment">// number of times the accuracy is lower than the convergence_threshold</span></div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="comment">// Step 0: Set the finish condition</span></div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        <span class="comment">// epsilon sub t &gt; 0.5 =&gt; inverse the weights policy</span></div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="comment">// validation error is not decreasing</span></div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="comment">// run out of features</span></div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <span class="keywordtype">bool</span> ascending = order_algorithm == Orders.ASC;</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        std::mt19937 g{ 173 };</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        std::vector&lt;std::pair&lt;int, int&gt;&gt; pairSelection;</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="keywordflow">while</span> (!finished) {</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            <span class="comment">// Step 1: Build ranking with mutual information</span></div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); <span class="comment">// Get all the pairs sorted</span></div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <span class="keywordflow">if</span> (order_algorithm == Orders.RAND) {</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>                std::shuffle(pairSelection.begin(), pairSelection.end(), g);</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            }</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            <span class="keywordtype">int</span> k = bisection ? pow(2, tolerance) : 1;</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            <span class="keywordtype">int</span> counter = 0; <span class="comment">// The model counter of the current pack</span></div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            <span class="comment">// VLOG_SCOPE_F(1, &quot;counter=%d k=%d featureSelection.size: %zu&quot;, counter, k, featureSelection.size());</span></div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>            <span class="keywordflow">while</span> (counter++ &lt; k &amp;&amp; pairSelection.size() &gt; 0) {</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>                <span class="keyword">auto</span> feature_pair = pairSelection[0];</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>                pairSelection.erase(pairSelection.begin());</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>                std::unique_ptr&lt;Classifier&gt; model;</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>                model = std::make_unique&lt;SPnDE&gt;(std::vector&lt;int&gt;({ feature_pair.first, feature_pair.second }));</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>                model-&gt;fit(dataset, features, className, states, weights_);</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>                alpha_t = 0.0;</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>                <span class="keywordflow">if</span> (!block_update) {</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>                    <span class="keyword">auto</span> ypred = model-&gt;predict(X_train);</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>                    <span class="comment">// Step 3.1: Compute the classifier amout of say</span></div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>                }</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>                <span class="comment">// Step 3.4: Store classifier and its accuracy to weigh its future vote</span></div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>                numItemsPack++;</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                models.push_back(std::move(model));</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>                significanceModels.push_back(alpha_t);</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>                n_models++;</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>                <span class="comment">// VLOG_SCOPE_F(2, &quot;numItemsPack: %d n_models: %d featuresUsed: %zu&quot;, numItemsPack, n_models, featuresUsed.size());</span></div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            }</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <span class="keywordflow">if</span> (block_update) {</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            }</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            <span class="keywordflow">if</span> (convergence &amp;&amp; !finished) {</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                <span class="keyword">auto</span> y_val_predict = predict(X_test);</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>                <span class="keywordtype">double</span> accuracy = (y_val_predict == y_test).sum().item&lt;<span class="keywordtype">double</span>&gt;() / (<span class="keywordtype">double</span>)y_test.size(0);</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                <span class="keywordflow">if</span> (priorAccuracy == 0) {</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                    priorAccuracy = accuracy;</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>                    improvement = accuracy - priorAccuracy;</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>                }</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>                <span class="keywordflow">if</span> (improvement &lt; convergence_threshold) {</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>                    <span class="comment">// VLOG_SCOPE_F(3, &quot;  (improvement&lt;threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>                    tolerance++;</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                    <span class="comment">// VLOG_SCOPE_F(3, &quot;* (improvement&gt;=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>                    tolerance = 0; <span class="comment">// Reset the counter if the model performs better</span></div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>                    numItemsPack = 0;</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>                }</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>                <span class="keywordflow">if</span> (convergence_best) {</div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>                    <span class="comment">// Keep the best accuracy until now as the prior accuracy</span></div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>                    priorAccuracy = std::max(accuracy, priorAccuracy);</div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>                    <span class="comment">// Keep the last accuray obtained as the prior accuracy</span></div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>                    priorAccuracy = accuracy;</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>                }</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            }</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            <span class="comment">// VLOG_SCOPE_F(1, &quot;tolerance: %d featuresUsed.size: %zu features.size: %zu&quot;, tolerance, featuresUsed.size(), features.size());</span></div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            finished = finished || tolerance &gt; maxTolerance || pairSelection.size() == 0;</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        }</div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keywordflow">if</span> (tolerance &gt; maxTolerance) {</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <span class="keywordflow">if</span> (numItemsPack &lt; n_models) {</div>
-<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>                notes.push_back(<span class="stringliteral">&quot;Convergence threshold reached &amp; &quot;</span> + std::to_string(numItemsPack) + <span class="stringliteral">&quot; models eliminated&quot;</span>);</div>
-<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>                <span class="comment">// VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; %d models eliminated of %d&quot;, numItemsPack, n_models);</span></div>
-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; numItemsPack; ++i) {</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>                    significanceModels.pop_back();</div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>                    models.pop_back();</div>
-<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                    n_models--;</div>
-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>                }</div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>                notes.push_back(<span class="stringliteral">&quot;Convergence threshold reached &amp; 0 models eliminated&quot;</span>);</div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                <span class="comment">// VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; 0 models eliminated n_models=%d numItemsPack=%d&quot;, n_models, numItemsPack);</span></div>
-<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            }</div>
-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        }</div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="keywordflow">if</span> (pairSelection.size() &gt; 0) {</div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            notes.push_back(<span class="stringliteral">&quot;Pairs not used in train: &quot;</span> + std::to_string(pairSelection.size()));</div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            status = WARNING;</div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        }</div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        notes.push_back(<span class="stringliteral">&quot;Number of models: &quot;</span> + std::to_string(n_models));</div>
-<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    }</div>
-<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    std::vector&lt;std::string&gt; BoostA2DE::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        <span class="keywordflow">return</span> Ensemble::graph(title);</div>
-<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    }</div>
-<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>}</div>
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-  <div class="headertitle"><div class="title">BoostA2DE.h</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef BOOSTA2DE_H</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#define BOOSTA2DE_H</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;string&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &quot;bayesnet/classifiers/SPnDE.h&quot;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &quot;Boost.h&quot;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="foldopen" id="foldopen00014" data-start="{" data-end="};">
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_boost_a2_d_e.html">   14</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_boost_a2_d_e.html">BoostA2DE</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_boost.html">Boost</a> {</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keyword">explicit</span> <a class="code hl_class" href="classbayesnet_1_1_boost_a2_d_e.html">BoostA2DE</a>(<span class="keywordtype">bool</span> predict_voting = <span class="keyword">false</span>);</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_boost_a2_d_e.html">~BoostA2DE</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; title = <span class="stringliteral">&quot;BoostA2DE&quot;</span>) <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <span class="keywordtype">void</span> trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        std::vector&lt;int&gt; initializeModels();</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    };</div>
-</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>}</div>
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-  <div class="headertitle"><div class="title">BoostAODE.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;random&gt;</span> </div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;set&gt;</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;functional&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;limits.h&gt;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &lt;tuple&gt;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &quot;BoostAODE.h&quot;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span> </div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    BoostAODE::BoostAODE(<span class="keywordtype">bool</span> predict_voting) : Boost(predict_voting)</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    }</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    std::vector&lt;int&gt; BoostAODE::initializeModels()</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    {</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        std::vector&lt;int&gt; featuresSelected = featureSelection(weights_);</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keywordtype">int</span>&amp; feature : featuresSelected) {</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>            std::unique_ptr&lt;Classifier&gt; model = std::make_unique&lt;SPODE&gt;(feature);</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>            model-&gt;fit(dataset, features, className, states, weights_);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>            models.push_back(std::move(model));</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>            significanceModels.push_back(1.0); <span class="comment">// They will be updated later in trainModel</span></div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>            n_models++;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        }</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        notes.push_back(<span class="stringliteral">&quot;Used features in initialization: &quot;</span> + std::to_string(featuresSelected.size()) + <span class="stringliteral">&quot; of &quot;</span> + std::to_string(features.size()) + <span class="stringliteral">&quot; with &quot;</span> + select_features_algorithm);</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordflow">return</span> featuresSelected;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    }</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    <span class="keywordtype">void</span> BoostAODE::trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    {</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="comment">// Logging setup</span></div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        <span class="comment">//</span></div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        <span class="comment">// loguru::set_thread_name(&quot;BoostAODE&quot;);</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        <span class="comment">// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;</span></div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="comment">// loguru::add_file(&quot;boostAODE.log&quot;, loguru::Truncate, loguru::Verbosity_MAX);</span></div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span> </div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="comment">// Algorithm based on the adaboost algorithm for classification</span></div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="comment">// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        fitted = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keywordtype">double</span> alpha_t = 0;</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="keywordtype">bool</span> finished = <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        std::vector&lt;int&gt; featuresUsed;</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <span class="keywordflow">if</span> (selectFeatures) {</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>            featuresUsed = initializeModels();</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>            <span class="keyword">auto</span> ypred = predict(X_train);</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>            <span class="comment">// Update significance of the models</span></div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>                significanceModels[i] = alpha_t;</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>            }</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>            <span class="keywordflow">if</span> (finished) {</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>                <span class="keywordflow">return</span>;</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>            }</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        }</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        <span class="keywordtype">int</span> numItemsPack = 0; <span class="comment">// The counter of the models inserted in the current pack</span></div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <span class="comment">// Variables to control the accuracy finish condition</span></div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="keywordtype">double</span> priorAccuracy = 0.0;</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        <span class="keywordtype">double</span> improvement = 1.0;</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordtype">double</span> convergence_threshold = 1e-4;</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordtype">int</span> tolerance = 0; <span class="comment">// number of times the accuracy is lower than the convergence_threshold</span></div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        <span class="comment">// Step 0: Set the finish condition</span></div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        <span class="comment">// epsilon sub t &gt; 0.5 =&gt; inverse the weights policy</span></div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        <span class="comment">// validation error is not decreasing</span></div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        <span class="comment">// run out of features</span></div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <span class="keywordtype">bool</span> ascending = order_algorithm == Orders.ASC;</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        std::mt19937 g{ 173 };</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="keywordflow">while</span> (!finished) {</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            <span class="comment">// Step 1: Build ranking with mutual information</span></div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>            <span class="keyword">auto</span> featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); <span class="comment">// Get all the features sorted</span></div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>            <span class="keywordflow">if</span> (order_algorithm == Orders.RAND) {</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>                std::shuffle(featureSelection.begin(), featureSelection.end(), g);</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>            }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>            <span class="comment">// Remove used features</span></div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&amp;](<span class="keyword">auto</span> x)</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>                { <span class="keywordflow">return</span> std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>                end(featureSelection)</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            );</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            <span class="keywordtype">int</span> k = bisection ? pow(2, tolerance) : 1;</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            <span class="keywordtype">int</span> counter = 0; <span class="comment">// The model counter of the current pack</span></div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            <span class="comment">// VLOG_SCOPE_F(1, &quot;counter=%d k=%d featureSelection.size: %zu&quot;, counter, k, featureSelection.size());</span></div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            <span class="keywordflow">while</span> (counter++ &lt; k &amp;&amp; featureSelection.size() &gt; 0) {</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>                <span class="keyword">auto</span> feature = featureSelection[0];</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>                featureSelection.erase(featureSelection.begin());</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>                std::unique_ptr&lt;Classifier&gt; model;</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>                model = std::make_unique&lt;SPODE&gt;(feature);</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>                model-&gt;fit(dataset, features, className, states, weights_);</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>                alpha_t = 0.0;</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>                <span class="keywordflow">if</span> (!block_update) {</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>                    <span class="keyword">auto</span> ypred = model-&gt;predict(X_train);</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>                    <span class="comment">// Step 3.1: Compute the classifier amout of say</span></div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>                }</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>                <span class="comment">// Step 3.4: Store classifier and its accuracy to weigh its future vote</span></div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>                numItemsPack++;</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>                featuresUsed.push_back(feature);</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>                models.push_back(std::move(model));</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>                significanceModels.push_back(alpha_t);</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>                n_models++;</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>                <span class="comment">// VLOG_SCOPE_F(2, &quot;numItemsPack: %d n_models: %d featuresUsed: %zu&quot;, numItemsPack, n_models, featuresUsed.size());</span></div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            }</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>            <span class="keywordflow">if</span> (block_update) {</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            }</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="keywordflow">if</span> (convergence &amp;&amp; !finished) {</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>                <span class="keyword">auto</span> y_val_predict = predict(X_test);</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                <span class="keywordtype">double</span> accuracy = (y_val_predict == y_test).sum().item&lt;<span class="keywordtype">double</span>&gt;() / (<span class="keywordtype">double</span>)y_test.size(0);</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                <span class="keywordflow">if</span> (priorAccuracy == 0) {</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                    priorAccuracy = accuracy;</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                    improvement = accuracy - priorAccuracy;</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                }</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>                <span class="keywordflow">if</span> (improvement &lt; convergence_threshold) {</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                    <span class="comment">// VLOG_SCOPE_F(3, &quot;  (improvement&lt;threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                    tolerance++;</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>                    <span class="comment">// VLOG_SCOPE_F(3, &quot;* (improvement&gt;=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>                    tolerance = 0; <span class="comment">// Reset the counter if the model performs better</span></div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>                    numItemsPack = 0;</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>                }</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>                <span class="keywordflow">if</span> (convergence_best) {</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>                    <span class="comment">// Keep the best accuracy until now as the prior accuracy</span></div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                    priorAccuracy = std::max(accuracy, priorAccuracy);</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>                } <span class="keywordflow">else</span> {</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>                    <span class="comment">// Keep the last accuray obtained as the prior accuracy</span></div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>                    priorAccuracy = accuracy;</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>                }</div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>            }</div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>            <span class="comment">// VLOG_SCOPE_F(1, &quot;tolerance: %d featuresUsed.size: %zu features.size: %zu&quot;, tolerance, featuresUsed.size(), features.size());</span></div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>            finished = finished || tolerance &gt; maxTolerance || featuresUsed.size() == features.size();</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        }</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">if</span> (tolerance &gt; maxTolerance) {</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            <span class="keywordflow">if</span> (numItemsPack &lt; n_models) {</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>                notes.push_back(<span class="stringliteral">&quot;Convergence threshold reached &amp; &quot;</span> + std::to_string(numItemsPack) + <span class="stringliteral">&quot; models eliminated&quot;</span>);</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>                <span class="comment">// VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; %d models eliminated of %d&quot;, numItemsPack, n_models);</span></div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; numItemsPack; ++i) {</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>                    significanceModels.pop_back();</div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>                    models.pop_back();</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>                    n_models--;</div>
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-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>                notes.push_back(<span class="stringliteral">&quot;Convergence threshold reached &amp; 0 models eliminated&quot;</span>);</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>                <span class="comment">// VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; 0 models eliminated n_models=%d numItemsPack=%d&quot;, n_models, numItemsPack);</span></div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            }</div>
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-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="keywordflow">if</span> (featuresUsed.size() != features.size()) {</div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            notes.push_back(<span class="stringliteral">&quot;Used features in train: &quot;</span> + std::to_string(featuresUsed.size()) + <span class="stringliteral">&quot; of &quot;</span> + std::to_string(features.size()));</div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            status = WARNING;</div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        }</div>
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-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    }</div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    std::vector&lt;std::string&gt; BoostAODE::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">return</span> Ensemble::graph(title);</div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    }</div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>}</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef BOOSTAODE_H</span></div>
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-  <div class="headertitle"><div class="title">Classifier.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;sstream&gt;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &quot;Classifier.h&quot;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span> </div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    <span class="keyword">const</span> std::string CLASSIFIER_NOT_FITTED = <span class="stringliteral">&quot;Classifier has not been fitted&quot;</span>;</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    Classifier&amp; Classifier::build(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    {</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        this-&gt;features = features;</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        this-&gt;className = className;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        this-&gt;states = states;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        m = dataset.size(1);</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        n = features.size();</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        checkFitParameters();</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keyword">auto</span> n_classes = states.at(className).size();</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        metrics = Metrics(dataset, features, className, n_classes);</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        model.initialize();</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        buildModel(weights);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        trainModel(weights);</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        fitted = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    }</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    <span class="keywordtype">void</span> Classifier::buildDataset(torch::Tensor&amp; ytmp)</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>    {</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        <span class="keywordflow">try</span> {</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>            <span class="keyword">auto</span> yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>            dataset = torch::cat({ dataset, yresized }, 0);</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        }</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception&amp; e) {</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>            std::stringstream oss;</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>            oss &lt;&lt; <span class="stringliteral">&quot;* Error in X and y dimensions *\n&quot;</span>;</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>            oss &lt;&lt; <span class="stringliteral">&quot;X dimensions: &quot;</span> &lt;&lt; dataset.sizes() &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>            oss &lt;&lt; <span class="stringliteral">&quot;y dimensions: &quot;</span> &lt;&lt; ytmp.sizes();</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>            <span class="keywordflow">throw</span> std::runtime_error(oss.str());</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        }</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    }</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    <span class="keywordtype">void</span> Classifier::trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    {</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        model.fit(dataset, weights, features, className, states);</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    }</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    <span class="comment">// X is nxm where n is the number of features and m the number of samples</span></div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    Classifier&amp; Classifier::fit(torch::Tensor&amp; X, torch::Tensor&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    {</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        dataset = X;</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>        buildDataset(y);</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="keywordflow">return</span> build(features, className, states, weights);</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    }</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <span class="comment">// X is nxm where n is the number of features and m the number of samples</span></div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    Classifier&amp; Classifier::fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    {</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        dataset = torch::zeros({ <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(X.size()), <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(X[0].size()) }, torch::kInt32);</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; X.size(); ++i) {</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>            dataset.index_put_({ i, <span class="stringliteral">&quot;...&quot;</span> }, torch::tensor(X[i], torch::kInt32));</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        }</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="keyword">auto</span> ytmp = torch::tensor(y, torch::kInt32);</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        buildDataset(ytmp);</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordflow">return</span> build(features, className, states, weights);</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    }</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    Classifier&amp; Classifier::fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    {</div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        this-&gt;dataset = dataset;</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <span class="keywordflow">return</span> build(features, className, states, weights);</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    }</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    Classifier&amp; Classifier::fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    {</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        this-&gt;dataset = dataset;</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordflow">return</span> build(features, className, states, weights);</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keywordtype">void</span> Classifier::checkFitParameters()</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    {</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keywordflow">if</span> (torch::is_floating_point(dataset)) {</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;dataset (X, y) must be of type Integer&quot;</span>);</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        }</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordflow">if</span> (dataset.size(0) - 1 != features.size()) {</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Classifier: X &quot;</span> + std::to_string(dataset.size(0) - 1) + <span class="stringliteral">&quot; and features &quot;</span> + std::to_string(features.size()) + <span class="stringliteral">&quot; must have the same number of features&quot;</span>);</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        }</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="keywordflow">if</span> (states.find(className) == states.end()) {</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;class name not found in states&quot;</span>);</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        }</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : features) {</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            <span class="keywordflow">if</span> (states.find(feature) == states.end()) {</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;feature [&quot;</span> + feature + <span class="stringliteral">&quot;] not found in states&quot;</span>);</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            }</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        }</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    }</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    torch::Tensor Classifier::predict(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>    {</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        }</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">return</span> model.predict(X);</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    std::vector&lt;int&gt; Classifier::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    {</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        }</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keyword">auto</span> m_ = X[0].size();</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <span class="keyword">auto</span> n_ = X.size();</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_; i++) {</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        }</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keyword">auto</span> yp = model.predict(Xd);</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        <span class="keywordflow">return</span> yp;</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    }</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    torch::Tensor Classifier::predict_proba(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    {</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        }</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        <span class="keywordflow">return</span> model.predict_proba(X);</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    }</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    std::vector&lt;std::vector&lt;double&gt;&gt; Classifier::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    {</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>            <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        }</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        <span class="keyword">auto</span> m_ = X[0].size();</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keyword">auto</span> n_ = X.size();</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="comment">// Convert to nxm vector</span></div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_; i++) {</div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>            Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        }</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <span class="keyword">auto</span> yp = model.predict_proba(Xd);</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">return</span> yp;</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    }</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    <span class="keywordtype">float</span> Classifier::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    {</div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        torch::Tensor y_pred = predict(X);</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keywordflow">return</span> (y_pred == y).sum().item&lt;<span class="keywordtype">float</span>&gt;() / y.size(0);</div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    }</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    <span class="keywordtype">float</span> Classifier::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</div>
-<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    {</div>
-<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        }</div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <span class="keywordflow">return</span> model.score(X, y);</div>
-<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    }</div>
-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    std::vector&lt;std::string&gt; Classifier::show()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">return</span> model.show();</div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
-<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    <span class="keywordtype">void</span> Classifier::addNodes()</div>
-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    {</div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="comment">// Add all nodes to the network</span></div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>&amp; feature : features) {</div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            model.addNode(feature);</div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        }</div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        model.addNode(className);</div>
-<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    }</div>
-<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    <span class="keywordtype">int</span> Classifier::getNumberOfNodes()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        <span class="comment">// Features does not include class</span></div>
-<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <span class="keywordflow">return</span> fitted ? model.getFeatures().size() : 0;</div>
-<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>    }</div>
-<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    <span class="keywordtype">int</span> Classifier::getNumberOfEdges()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <span class="keywordflow">return</span> fitted ? model.getNumEdges() : 0;</div>
-<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    }</div>
-<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    <span class="keywordtype">int</span> Classifier::getNumberOfStates()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">return</span> fitted ? model.getStates() : 0;</div>
-<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    }</div>
-<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    <span class="keywordtype">int</span> Classifier::getClassNumStates()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        <span class="keywordflow">return</span> fitted ? model.getClassNumStates() : 0;</div>
-<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    }</div>
-<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    std::vector&lt;std::string&gt; Classifier::topological_order()</div>
-<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    {</div>
-<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        <span class="keywordflow">return</span> model.topological_sort();</div>
-<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>    }</div>
-<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    std::string Classifier::dump_cpt()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        <span class="keywordflow">return</span> model.dump_cpt();</div>
-<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>    }</div>
-<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    <span class="keywordtype">void</span> Classifier::setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters)</div>
-<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    {</div>
-<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        <span class="keywordflow">if</span> (!hyperparameters.empty()) {</div>
-<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid hyperparameters&quot;</span> + hyperparameters.dump());</div>
-<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        }</div>
-<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    }</div>
-<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>}</div>
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-  <div class="headertitle"><div class="title">Classifier.h</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef CLASSIFIER_H</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#define CLASSIFIER_H</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;torch/torch.h&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;bayesnet/utils/BayesMetrics.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &quot;bayesnet/network/Network.h&quot;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &quot;bayesnet/BaseClassifier.h&quot;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="foldopen" id="foldopen00015" data-start="{" data-end="};">
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_classifier.html">   15</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_base_classifier.html">BaseClassifier</a> {</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>(<a class="code hl_class" href="classbayesnet_1_1_network.html">Network</a> model);</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_classifier.html">~Classifier</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>&amp; fit(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>&amp; fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>&amp; fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        <span class="keywordtype">void</span> addNodes();</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        <span class="keywordtype">int</span> getNumberOfNodes() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        <span class="keywordtype">int</span> getNumberOfEdges() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        <span class="keywordtype">int</span> getNumberOfStates() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        <span class="keywordtype">int</span> getClassNumStates() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        torch::Tensor predict(torch::Tensor&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        std::vector&lt;int&gt; predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        torch::Tensor predict_proba(torch::Tensor&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        std::vector&lt;std::vector&lt;double&gt;&gt; predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        status_t getStatus()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> status; }</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::string getVersion()<span class="keyword"> override </span>{ <span class="keywordflow">return</span> { project_version.begin(), project_version.end() }; };</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        <span class="keywordtype">float</span> score(torch::Tensor&amp; X, torch::Tensor&amp; y) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="keywordtype">float</span> score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        std::vector&lt;std::string&gt; show() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        std::vector&lt;std::string&gt; topological_order()  <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        std::vector&lt;std::string&gt; getNotes()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> notes; }</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        std::string dump_cpt() <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters) <span class="keyword">override</span>; <span class="comment">//For classifiers that don&#39;t have hyperparameters</span></div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        <span class="keywordtype">bool</span> fitted;</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m, n; <span class="comment">// m: number of samples, n: number of features</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        <a class="code hl_class" href="classbayesnet_1_1_network.html">Network</a> model;</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        Metrics metrics;</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        std::vector&lt;std::string&gt; features;</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        std::string className;</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        std::map&lt;std::string, std::vector&lt;int&gt;&gt; states;</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        torch::Tensor dataset; <span class="comment">// (n+1)xm tensor</span></div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        status_t status = NORMAL;</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        std::vector&lt;std::string&gt; notes; <span class="comment">// Used to store messages occurred during the fit process</span></div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>        <span class="keywordtype">void</span> checkFitParameters();</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <span class="keyword">virtual</span> <span class="keywordtype">void</span> buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights) = 0;</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="keywordtype">void</span> trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        <span class="keywordtype">void</span> buildDataset(torch::Tensor&amp; y);</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        <a class="code hl_class" href="classbayesnet_1_1_classifier.html">Classifier</a>&amp; build(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    };</div>
-</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>}</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="preprocessor">#endif</span></div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span> </div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span> </div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span> </div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span> </div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span> </div>
-<div class="ttc" id="aclassbayesnet_1_1_base_classifier_html"><div class="ttname"><a href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a></div><div class="ttdef"><b>Definition</b> <a href="_base_classifier_8h_source.html#l00013">BaseClassifier.h:13</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_classifier_html"><div class="ttname"><a href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></div><div class="ttdef"><b>Definition</b> <a href="#l00015">Classifier.h:15</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_network_html"><div class="ttname"><a href="classbayesnet_1_1_network.html">bayesnet::Network</a></div><div class="ttdef"><b>Definition</b> <a href="_network_8h_source.html#l00015">Network.h:15</a></div></div>
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-  <div class="headertitle"><div class="title">Ensemble.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;Ensemble.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span> </div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    Ensemble::Ensemble(<span class="keywordtype">bool</span> predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    {</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    };</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    <span class="keyword">const</span> std::string ENSEMBLE_NOT_FITTED = <span class="stringliteral">&quot;Ensemble has not been fitted&quot;</span>;</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    <span class="keywordtype">void</span> Ensemble::trainModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        n_models = models.size();</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>            <span class="comment">// fit with std::vectors</span></div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>            models[i]-&gt;fit(dataset, features, className, states);</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        }</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    }</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>    std::vector&lt;int&gt; Ensemble::compute_arg_max(std::vector&lt;std::vector&lt;double&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    {</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        std::vector&lt;int&gt; y_pred;</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; X.size(); ++i) {</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>            <span class="keyword">auto</span> max = std::max_element(X[i].begin(), X[i].end());</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>            y_pred.push_back(std::distance(X[i].begin(), max));</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        }</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordflow">return</span> y_pred;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    }</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    torch::Tensor Ensemble::compute_arg_max(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    {</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="keyword">auto</span> y_pred = torch::argmax(X, 1);</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordflow">return</span> y_pred;</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    }</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    torch::Tensor Ensemble::voting(torch::Tensor&amp; votes)</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    {</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="comment">// Convert m x n_models tensor to a m x n_class_states with voting probabilities</span></div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        <span class="keyword">auto</span> y_pred_ = votes.accessor&lt;int, 2&gt;();</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        std::vector&lt;int&gt; y_pred_final;</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keywordtype">int</span> numClasses = states.at(className).size();</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        <span class="comment">// votes is m x n_models with the prediction of every model for each sample</span></div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keyword">auto</span> result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; votes.size(0); ++i) {</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>            <span class="comment">// n_votes store in each index (value of class) the significance added by each model</span></div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>            <span class="comment">// i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions</span></div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>            std::vector&lt;double&gt; n_votes(numClasses, 0.0);</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; n_models; ++j) {</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>                n_votes[y_pred_[i][j]] += significanceModels.at(j);</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>            }</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>            result[i] = torch::tensor(n_votes);</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        }</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        <span class="comment">// To only do one division and gain precision</span></div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        result /= sum;</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    }</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    {</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>            <span class="keywordflow">throw</span> std::logic_error(ENSEMBLE_NOT_FITTED);</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        }</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordflow">return</span> predict_voting ? predict_average_voting(X) : predict_average_proba(X);</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    }</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    torch::Tensor Ensemble::predict_proba(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    {</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>            <span class="keywordflow">throw</span> std::logic_error(ENSEMBLE_NOT_FITTED);</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        }</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <span class="keywordflow">return</span> predict_voting ? predict_average_voting(X) : predict_average_proba(X);</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    }</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    std::vector&lt;int&gt; Ensemble::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    {</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <span class="keyword">auto</span> res = predict_proba(X);</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordflow">return</span> compute_arg_max(res);</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    torch::Tensor Ensemble::predict(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    {</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keyword">auto</span> res = predict_proba(X);</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <span class="keywordflow">return</span> compute_arg_max(res);</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    }</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    torch::Tensor Ensemble::predict_average_proba(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    {</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <span class="keyword">auto</span> n_states = models[0]-&gt;getClassNumStates();</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keyword">auto</span> threads{ std::vector&lt;std::thread&gt;() };</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        std::mutex mtx;</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            threads.push_back(std::thread([&amp;, i]() {</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>                <span class="keyword">auto</span> ypredict = models[i]-&gt;predict_proba(X);</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>                std::lock_guard&lt;std::mutex&gt; lock(mtx);</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>                y_pred += ypredict * significanceModels[i];</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>                }));</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        }</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; thread : threads) {</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            thread.join();</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        }</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        y_pred /= sum;</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <span class="keywordflow">return</span> y_pred;</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    }</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    {</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keyword">auto</span> n_states = models[0]-&gt;getClassNumStates();</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        std::vector&lt;std::vector&lt;double&gt;&gt; y_pred(X[0].size(), std::vector&lt;double&gt;(n_states, 0.0));</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keyword">auto</span> threads{ std::vector&lt;std::thread&gt;() };</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        std::mutex mtx;</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            threads.push_back(std::thread([&amp;, i]() {</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                <span class="keyword">auto</span> ypredict = models[i]-&gt;predict_proba(X);</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                assert(ypredict.size() == y_pred.size());</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                assert(ypredict[0].size() == y_pred[0].size());</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                std::lock_guard&lt;std::mutex&gt; lock(mtx);</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                <span class="comment">// Multiply each prediction by the significance of the model and then add it to the final prediction</span></div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; ypredict.size(); ++j) {</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>                    std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                        [significanceModels = significanceModels[i]](<span class="keywordtype">double</span> x, <span class="keywordtype">double</span> y) { <span class="keywordflow">return</span> x + y * significanceModels; });</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                }</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                }));</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        }</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; thread : threads) {</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            thread.join();</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        }</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="comment">//Divide each element of the prediction by the sum of the significances</span></div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; y_pred.size(); ++j) {</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](<span class="keywordtype">double</span> x) { <span class="keywordflow">return</span> x / sum; });</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        }</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <span class="keywordflow">return</span> y_pred;</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    }</div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_voting(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    {</div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        torch::Tensor Xt = bayesnet::vectorToTensor(X, <span class="keyword">false</span>);</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <span class="keyword">auto</span> y_pred = predict_average_voting(Xt);</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        std::vector&lt;std::vector&lt;double&gt;&gt; result = tensorToVectorDouble(y_pred);</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    }</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    torch::Tensor Ensemble::predict_average_voting(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    {</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="comment">// Build a m x n_models tensor with the predictions of each model</span></div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keyword">auto</span> threads{ std::vector&lt;std::thread&gt;() };</div>
-<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        std::mutex mtx;</div>
-<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            threads.push_back(std::thread([&amp;, i]() {</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>                <span class="keyword">auto</span> ypredict = models[i]-&gt;predict(X);</div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>                std::lock_guard&lt;std::mutex&gt; lock(mtx);</div>
-<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                y_pred.index_put_({ <span class="stringliteral">&quot;...&quot;</span>, i }, ypredict);</div>
-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>                }));</div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        }</div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; thread : threads) {</div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>            thread.join();</div>
-<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        }</div>
-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        <span class="keywordflow">return</span> voting(y_pred);</div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    }</div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="keywordtype">float</span> Ensemble::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>    {</div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <span class="keyword">auto</span> y_pred = predict(X);</div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        <span class="keywordtype">int</span> correct = 0;</div>
-<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; y_pred.size(0); ++i) {</div>
-<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            <span class="keywordflow">if</span> (y_pred[i].item&lt;int&gt;() == y[i].item&lt;int&gt;()) {</div>
-<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>                correct++;</div>
-<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            }</div>
-<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        }</div>
-<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <span class="keywordflow">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size(0);</div>
-<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    }</div>
-<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    <span class="keywordtype">float</span> Ensemble::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</div>
-<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    {</div>
-<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="keyword">auto</span> y_pred = predict(X);</div>
-<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        <span class="keywordtype">int</span> correct = 0;</div>
-<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; y_pred.size(); ++i) {</div>
-<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>            <span class="keywordflow">if</span> (y_pred[i] == y[i]) {</div>
-<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>                correct++;</div>
-<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            }</div>
-<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        }</div>
-<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        <span class="keywordflow">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size();</div>
-<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    }</div>
-<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    std::vector&lt;std::string&gt; Ensemble::show()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        <span class="keyword">auto</span> result = std::vector&lt;std::string&gt;();</div>
-<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <span class="keyword">auto</span> res = models[i]-&gt;show();</div>
-<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            result.insert(result.end(), res.begin(), res.end());</div>
-<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        }</div>
-<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    }</div>
-<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    std::vector&lt;std::string&gt; Ensemble::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        <span class="keyword">auto</span> result = std::vector&lt;std::string&gt;();</div>
-<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>            <span class="keyword">auto</span> res = models[i]-&gt;graph(title + <span class="stringliteral">&quot;_&quot;</span> + std::to_string(i));</div>
-<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            result.insert(result.end(), res.begin(), res.end());</div>
-<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        }</div>
-<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    }</div>
-<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>    <span class="keywordtype">int</span> Ensemble::getNumberOfNodes()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        <span class="keywordtype">int</span> nodes = 0;</div>
-<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>            nodes += models[i]-&gt;getNumberOfNodes();</div>
-<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        }</div>
-<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        <span class="keywordflow">return</span> nodes;</div>
-<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    }</div>
-<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    <span class="keywordtype">int</span> Ensemble::getNumberOfEdges()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        <span class="keywordtype">int</span> edges = 0;</div>
-<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>            edges += models[i]-&gt;getNumberOfEdges();</div>
-<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        }</div>
-<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        <span class="keywordflow">return</span> edges;</div>
-<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>    }</div>
-<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>    <span class="keywordtype">int</span> Ensemble::getNumberOfStates()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="keywordtype">int</span> nstates = 0;</div>
-<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; n_models; ++i) {</div>
-<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>            nstates += models[i]-&gt;getNumberOfStates();</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;KDB.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span>    KDB::KDB(<span class="keywordtype">int</span> k, <span class="keywordtype">float</span> theta) : Classifier(Network()), k(k), theta(theta)</div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    {</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>        validHyperparameters = { <span class="stringliteral">&quot;k&quot;</span>, <span class="stringliteral">&quot;theta&quot;</span> };</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    }</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    <span class="keywordtype">void</span> KDB::setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters_)</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    {</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="keyword">auto</span> hyperparameters = hyperparameters_;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;k&quot;</span>)) {</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>            k = hyperparameters[<span class="stringliteral">&quot;k&quot;</span>];</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>            hyperparameters.erase(<span class="stringliteral">&quot;k&quot;</span>);</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        }</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">&quot;theta&quot;</span>)) {</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>            theta = hyperparameters[<span class="stringliteral">&quot;theta&quot;</span>];</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>            hyperparameters.erase(<span class="stringliteral">&quot;theta&quot;</span>);</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        }</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        Classifier::setHyperparameters(hyperparameters);</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>    }</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    <span class="keywordtype">void</span> KDB::buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    {</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        <span class="comment">/*</span></div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment">        1. For each feature Xi, compute mutual information, I(X;C),</span></div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment">        where C is the class.</span></div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">        2. Compute class conditional mutual information I(Xi;XjIC), f or each</span></div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">        pair of features Xi and Xj, where i#j.</span></div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="comment">        3. Let the used variable list, S, be empty.</span></div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="comment">        4. Let the DAG network being constructed, BN, begin with a single</span></div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment">        class node, C.</span></div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">        5. Repeat until S includes all domain features</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">        5.1. Select feature Xmax which is not in S and has the largest value</span></div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">        I(Xmax;C).</span></div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">        5.2. Add a node to BN representing Xmax.</span></div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">        5.3. Add an arc from C to Xmax in BN.</span></div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">        5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">        the highest value for I(Xmax;X,jC).</span></div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">        5.5. Add Xmax to S.</span></div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">        Compute the conditional probabilility infered by the structure of BN by</span></div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">        using counts from DB, and output BN.</span></div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">        */</span></div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <span class="comment">// 1. For each feature Xi, compute mutual information, I(X;C),</span></div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        <span class="comment">// where C is the class.</span></div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        addNodes();</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>        <span class="keyword">const</span> torch::Tensor&amp; y = dataset.index({ -1, <span class="stringliteral">&quot;...&quot;</span> });</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        std::vector&lt;double&gt; mi;</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; features.size(); i++) {</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>            torch::Tensor firstFeature = dataset.index({ i, <span class="stringliteral">&quot;...&quot;</span> });</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>            mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        }</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <span class="comment">// 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span></div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        <span class="keyword">auto</span> conditionalEdgeWeights = metrics.conditionalEdge(weights);</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="comment">// 3. Let the used variable list, S, be empty.</span></div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        std::vector&lt;int&gt; S;</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <span class="comment">// 4. Let the DAG network being constructed, BN, begin with a single</span></div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="comment">// class node, C.</span></div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        <span class="comment">// 5. Repeat until S includes all domain features</span></div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="comment">// 5.1. Select feature Xmax which is not in S and has the largest value</span></div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="comment">// I(Xmax;C).</span></div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        <span class="keyword">auto</span> order = argsort(mi);</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> idx : order) {</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>            <span class="comment">// 5.2. Add a node to BN representing Xmax.</span></div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>            <span class="comment">// 5.3. Add an arc from C to Xmax in BN.</span></div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>            model.addEdge(className, features[idx]);</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>            <span class="comment">// 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span></div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>            <span class="comment">// the highest value for I(Xmax;X,jC).</span></div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            add_m_edges(idx, S, conditionalEdgeWeights);</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>            <span class="comment">// 5.5. Add Xmax to S.</span></div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>            S.push_back(idx);</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        }</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keywordtype">void</span> KDB::add_m_edges(<span class="keywordtype">int</span> idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    {</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keyword">auto</span> n_edges = std::min(k, <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(S.size()));</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <span class="keyword">auto</span> cond_w = clone(weights);</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <span class="keywordtype">bool</span> exit_cond = k == 0;</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordtype">int</span> num = 0;</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="keywordflow">while</span> (!exit_cond) {</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            <span class="keyword">auto</span> max_minfo = argmax(cond_w.index({ idx, <span class="stringliteral">&quot;...&quot;</span> })).item&lt;<span class="keywordtype">int</span>&gt;();</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            <span class="keyword">auto</span> belongs = find(S.begin(), S.end(), max_minfo) != S.end();</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <span class="keywordflow">if</span> (belongs &amp;&amp; cond_w.index({ idx, max_minfo }).item&lt;<span class="keywordtype">float</span>&gt;() &gt; theta) {</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>                <span class="keywordflow">try</span> {</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>                    model.addEdge(features[max_minfo], features[idx]);</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>                    num++;</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>                }</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>                <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::invalid_argument&amp; e) {</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>                    <span class="comment">// Loops are not allowed</span></div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>                }</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>            }</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            cond_w.index_put_({ idx, max_minfo }, -1);</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <span class="keyword">auto</span> candidates_mask = cond_w.index({ idx, <span class="stringliteral">&quot;...&quot;</span> }).gt(theta);</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            <span class="keyword">auto</span> candidates = candidates_mask.nonzero();</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>            exit_cond = num == n_edges || candidates.size(0) == 0;</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        }</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    std::vector&lt;std::string&gt; KDB::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        std::string header{ title };</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keywordflow">if</span> (title == <span class="stringliteral">&quot;KDB&quot;</span>) {</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>            header += <span class="stringliteral">&quot; (k=&quot;</span> + std::to_string(k) + <span class="stringliteral">&quot;, theta=&quot;</span> + std::to_string(theta) + <span class="stringliteral">&quot;)&quot;</span>;</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        }</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <span class="keywordflow">return</span> model.graph(header);</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    }</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>}</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        <span class="keywordtype">void</span> setHyperparameters(<span class="keyword">const</span> nlohmann::json&amp; hyperparameters_) <span class="keyword">override</span>;</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &quot;Proposal.h&quot;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;KDB.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="keyword">namespace </span>bayesnet {</div>
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-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keyword">explicit</span> <a class="code hl_class" href="classbayesnet_1_1_k_d_b_ld.html">KDBLd</a>(<span class="keywordtype">int</span> k);</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_k_d_b_ld.html">~KDBLd</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <a class="code hl_class" href="classbayesnet_1_1_k_d_b_ld.html">KDBLd</a>&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; name = <span class="stringliteral">&quot;KDB&quot;</span>) <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        torch::Tensor predict(torch::Tensor&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <span class="keyword">static</span> <span class="keyword">inline</span> std::string version() { <span class="keywordflow">return</span> <span class="stringliteral">&quot;0.0.1&quot;</span>; };</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    };</div>
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-<div class="ttc" id="aclassbayesnet_1_1_k_d_b_html"><div class="ttname"><a href="classbayesnet_1_1_k_d_b.html">bayesnet::KDB</a></div><div class="ttdef"><b>Definition</b> <a href="_k_d_b_8h_source.html#l00013">KDB.h:13</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_k_d_b_ld_html"><div class="ttname"><a href="classbayesnet_1_1_k_d_b_ld.html">bayesnet::KDBLd</a></div><div class="ttdef"><b>Definition</b> <a href="#l00013">KDBLd.h:13</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_proposal_html"><div class="ttname"><a href="classbayesnet_1_1_proposal.html">bayesnet::Proposal</a></div><div class="ttdef"><b>Definition</b> <a href="_proposal_8h_source.html#l00017">Proposal.h:17</a></div></div>
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docs/manual/_network_8cc_source.html

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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;thread&gt;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;mutex&gt;</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;sstream&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;Network.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    {</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    }</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    Network::Network(<span class="keywordtype">float</span> maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span> </div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    }</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    Network::Network(<span class="keyword">const</span> Network&amp; other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    {</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        <span class="keywordflow">if</span> (samples.defined())</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>            samples = samples.clone();</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>&amp; node : other.nodes) {</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>            nodes[node.first] = std::make_unique&lt;Node&gt;(*node.second);</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        }</div>
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-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    <span class="keywordtype">void</span> Network::initialize()</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    {</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        features.clear();</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        className = <span class="stringliteral">&quot;&quot;</span>;</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        classNumStates = 0;</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        fitted = <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        nodes.clear();</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        samples = torch::Tensor();</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    }</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    <span class="keywordtype">float</span> Network::getMaxThreads()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        <span class="keywordflow">return</span> maxThreads;</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    }</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    torch::Tensor&amp; Network::getSamples()</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    {</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        <span class="keywordflow">return</span> samples;</div>
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-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    {</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        <span class="keywordflow">if</span> (name == <span class="stringliteral">&quot;&quot;</span>) {</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Node name cannot be empty&quot;</span>);</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        }</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        <span class="keywordflow">if</span> (nodes.find(name) != nodes.end()) {</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>            <span class="keywordflow">return</span>;</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        }</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="keywordflow">if</span> (find(features.begin(), features.end(), name) == features.end()) {</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>            features.push_back(name);</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        }</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        nodes[name] = std::make_unique&lt;Node&gt;(name);</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    }</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    std::vector&lt;std::string&gt; Network::getFeatures()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        <span class="keywordflow">return</span> features;</div>
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-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <span class="keywordtype">int</span> Network::getClassNumStates()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordflow">return</span> classNumStates;</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    }</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <span class="keywordtype">int</span> Network::getStates()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        <span class="keywordtype">int</span> result = 0;</div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; node : nodes) {</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>            result += node.second-&gt;getNumStates();</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        }</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    }</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    std::string Network::getClassName()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordflow">return</span> className;</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keywordtype">bool</span> Network::isCyclic(<span class="keyword">const</span> std::string&amp; nodeId, std::unordered_set&lt;std::string&gt;&amp; visited, std::unordered_set&lt;std::string&gt;&amp; recStack)</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    {</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keywordflow">if</span> (visited.find(nodeId) == visited.end()) <span class="comment">// if node hasn&#39;t been visited yet</span></div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        {</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            visited.insert(nodeId);</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            recStack.insert(nodeId);</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            <span class="keywordflow">for</span> (Node* child : nodes[nodeId]-&gt;getChildren()) {</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>                <span class="keywordflow">if</span> (visited.find(child-&gt;getName()) == visited.end() &amp;&amp; isCyclic(child-&gt;getName(), visited, recStack))</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>                    <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>                <span class="keywordflow">if</span> (recStack.find(child-&gt;getName()) != recStack.end())</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>                    <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            }</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        }</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        recStack.erase(nodeId); <span class="comment">// remove node from recursion stack before function ends</span></div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    }</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <span class="keywordtype">void</span> Network::addEdge(<span class="keyword">const</span> std::string&amp; parent, <span class="keyword">const</span> std::string&amp; child)</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    {</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordflow">if</span> (nodes.find(parent) == nodes.end()) {</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Parent node &quot;</span> + parent + <span class="stringliteral">&quot; does not exist&quot;</span>);</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        }</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        <span class="keywordflow">if</span> (nodes.find(child) == nodes.end()) {</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Child node &quot;</span> + child + <span class="stringliteral">&quot; does not exist&quot;</span>);</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        }</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <span class="comment">// Temporarily add edge to check for cycles</span></div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        nodes[parent]-&gt;addChild(nodes[child].get());</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        nodes[child]-&gt;addParent(nodes[parent].get());</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        std::unordered_set&lt;std::string&gt; visited;</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        std::unordered_set&lt;std::string&gt; recStack;</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">if</span> (isCyclic(nodes[child]-&gt;getName(), visited, recStack)) <span class="comment">// if adding this edge forms a cycle</span></div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        {</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="comment">// remove problematic edge</span></div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            nodes[parent]-&gt;removeChild(nodes[child].get());</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            nodes[child]-&gt;removeParent(nodes[parent].get());</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Adding this edge forms a cycle in the graph.&quot;</span>);</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        }</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    }</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    std::map&lt;std::string, std::unique_ptr&lt;Node&gt;&gt;&amp; Network::getNodes()</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    {</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        <span class="keywordflow">return</span> nodes;</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>    }</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>    <span class="keywordtype">void</span> Network::checkFitData(<span class="keywordtype">int</span> n_samples, <span class="keywordtype">int</span> n_features, <span class="keywordtype">int</span> n_samples_y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    {</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        <span class="keywordflow">if</span> (weights.size(0) != n_samples) {</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Weights (&quot;</span> + std::to_string(weights.size(0)) + <span class="stringliteral">&quot;) must have the same number of elements as samples (&quot;</span> + std::to_string(n_samples) + <span class="stringliteral">&quot;) in Network::fit&quot;</span>);</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        }</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <span class="keywordflow">if</span> (n_samples != n_samples_y) {</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;X and y must have the same number of samples in Network::fit (&quot;</span> + std::to_string(n_samples) + <span class="stringliteral">&quot; != &quot;</span> + std::to_string(n_samples_y) + <span class="stringliteral">&quot;)&quot;</span>);</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        }</div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">if</span> (n_features != featureNames.size()) {</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;X and features must have the same number of features in Network::fit (&quot;</span> + std::to_string(n_features) + <span class="stringliteral">&quot; != &quot;</span> + std::to_string(featureNames.size()) + <span class="stringliteral">&quot;)&quot;</span>);</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        }</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <span class="keywordflow">if</span> (features.size() == 0) {</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;The network has not been initialized. You must call addNode() before calling fit()&quot;</span>);</div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        }</div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        <span class="keywordflow">if</span> (n_features != features.size() - 1) {</div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;X and local features must have the same number of features in Network::fit (&quot;</span> + std::to_string(n_features) + <span class="stringliteral">&quot; != &quot;</span> + std::to_string(features.size() - 1) + <span class="stringliteral">&quot;)&quot;</span>);</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        }</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">if</span> (find(features.begin(), features.end(), className) == features.end()) {</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Class Name not found in Network::features&quot;</span>);</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        }</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; feature : featureNames) {</div>
-<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            <span class="keywordflow">if</span> (find(features.begin(), features.end(), feature) == features.end()) {</div>
-<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Feature &quot;</span> + feature + <span class="stringliteral">&quot; not found in Network::features&quot;</span>);</div>
-<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>            }</div>
-<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <span class="keywordflow">if</span> (states.find(feature) == states.end()) {</div>
-<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Feature &quot;</span> + feature + <span class="stringliteral">&quot; not found in states&quot;</span>);</div>
-<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            }</div>
-<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        }</div>
-<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    }</div>
-<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    <span class="keywordtype">void</span> Network::setStates(<span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    {</div>
-<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="comment">// Set states to every Node in the network</span></div>
-<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        for_each(features.begin(), features.end(), [<span class="keyword">this</span>, &amp;states](<span class="keyword">const</span> std::string&amp; feature) {</div>
-<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            nodes.at(feature)-&gt;setNumStates(states.at(feature).size());</div>
-<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>            });</div>
-<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        classNumStates = nodes.at(className)-&gt;getNumStates();</div>
-<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    }</div>
-<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    <span class="comment">// X comes in nxm, where n is the number of features and m the number of samples</span></div>
-<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> torch::Tensor&amp; X, <span class="keyword">const</span> torch::Tensor&amp; y, <span class="keyword">const</span> torch::Tensor&amp; weights, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>    {</div>
-<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);</div>
-<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        this-&gt;className = className;</div>
-<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        torch::Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);</div>
-<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        samples = torch::cat({ X , ytmp }, 0);</div>
-<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; featureNames.size(); ++i) {</div>
-<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            <span class="keyword">auto</span> row_feature = X.index({ i, <span class="stringliteral">&quot;...&quot;</span> });</div>
-<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        }</div>
-<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        completeFit(states, weights);</div>
-<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    }</div>
-<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> torch::Tensor&amp; samples, <span class="keyword">const</span> torch::Tensor&amp; weights, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    {</div>
-<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);</div>
-<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        this-&gt;className = className;</div>
-<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        this-&gt;samples = samples;</div>
-<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        completeFit(states, weights);</div>
-<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    }</div>
-<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    <span class="comment">// input_data comes in nxm, where n is the number of features and m the number of samples</span></div>
-<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>    <span class="keywordtype">void</span> Network::fit(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp; input_data, <span class="keyword">const</span> std::vector&lt;int&gt;&amp; labels, <span class="keyword">const</span> std::vector&lt;double&gt;&amp; weights_, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states)</div>
-<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>    {</div>
-<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        <span class="keyword">const</span> torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);</div>
-<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);</div>
-<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        this-&gt;className = className;</div>
-<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        <span class="comment">// Build tensor of samples (nxm) (n+1 because of the class)</span></div>
-<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        samples = torch::zeros({ <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(input_data.size() + 1), <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(input_data[0].size()) }, torch::kInt32);</div>
-<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; featureNames.size(); ++i) {</div>
-<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            samples.index_put_({ i, <span class="stringliteral">&quot;...&quot;</span> }, torch::tensor(input_data[i], torch::kInt32));</div>
-<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        }</div>
-<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        samples.index_put_({ -1, <span class="stringliteral">&quot;...&quot;</span> }, torch::tensor(labels, torch::kInt32));</div>
-<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        completeFit(states, weights);</div>
-<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    }</div>
-<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    <span class="keywordtype">void</span> Network::completeFit(<span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    {</div>
-<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        setStates(states);</div>
-<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        laplaceSmoothing = 1.0 / samples.size(1); <span class="comment">// To use in CPT computation</span></div>
-<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        std::vector&lt;std::thread&gt; threads;</div>
-<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; node : nodes) {</div>
-<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            threads.emplace_back([<span class="keyword">this</span>, &amp;node, &amp;weights]() {</div>
-<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>                node.second-&gt;computeCPT(samples, features, laplaceSmoothing, weights);</div>
-<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>                });</div>
-<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        }</div>
-<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; thread : threads) {</div>
-<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            thread.join();</div>
-<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        }</div>
-<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        fitted = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    }</div>
-<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    torch::Tensor Network::predict_tensor(<span class="keyword">const</span> torch::Tensor&amp; samples, <span class="keyword">const</span> <span class="keywordtype">bool</span> proba)</div>
-<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    {</div>
-<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>            <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;You must call fit() before calling predict()&quot;</span>);</div>
-<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        }</div>
-<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        torch::Tensor result;</div>
-<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);</div>
-<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; samples.size(1); ++i) {</div>
-<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>            <span class="keyword">const</span> torch::Tensor sample = samples.index({ <span class="stringliteral">&quot;...&quot;</span>, i });</div>
-<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>            <span class="keyword">auto</span> psample = predict_sample(sample);</div>
-<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>            <span class="keyword">auto</span> temp = torch::tensor(psample, torch::kFloat64);</div>
-<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            <span class="comment">//            result.index_put_({ i, &quot;...&quot; }, torch::tensor(predict_sample(sample), torch::kFloat64));</span></div>
-<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>            result.index_put_({ i, <span class="stringliteral">&quot;...&quot;</span> }, temp);</div>
-<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        }</div>
-<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <span class="keywordflow">if</span> (proba)</div>
-<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>            <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="keywordflow">return</span> result.argmax(1);</div>
-<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>    }</div>
-<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>    <span class="comment">// Return mxn tensor of probabilities</span></div>
-<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>    torch::Tensor Network::predict_proba(<span class="keyword">const</span> torch::Tensor&amp; samples)</div>
-<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>    {</div>
-<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        <span class="keywordflow">return</span> predict_tensor(samples, <span class="keyword">true</span>);</div>
-<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>    }</div>
-<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span> </div>
-<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>    <span class="comment">// Return mxn tensor of probabilities</span></div>
-<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>    torch::Tensor Network::predict(<span class="keyword">const</span> torch::Tensor&amp; samples)</div>
-<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>    {</div>
-<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        <span class="keywordflow">return</span> predict_tensor(samples, <span class="keyword">false</span>);</div>
-<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>    }</div>
-<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span> </div>
-<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>    <span class="comment">// Return mx1 std::vector of predictions</span></div>
-<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>    <span class="comment">// tsamples is nxm std::vector of samples</span></div>
-<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>    std::vector&lt;int&gt; Network::predict(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp; tsamples)</div>
-<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    {</div>
-<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;You must call fit() before calling predict()&quot;</span>);</div>
-<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        }</div>
-<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        std::vector&lt;int&gt; predictions;</div>
-<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        std::vector&lt;int&gt; sample;</div>
-<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; tsamples[0].size(); ++row) {</div>
-<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>            sample.clear();</div>
-<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = 0; col &lt; tsamples.size(); ++col) {</div>
-<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>                sample.push_back(tsamples[col][row]);</div>
-<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            }</div>
-<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>            std::vector&lt;double&gt; classProbabilities = predict_sample(sample);</div>
-<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>            <span class="comment">// Find the class with the maximum posterior probability</span></div>
-<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>            <span class="keyword">auto</span> maxElem = max_element(classProbabilities.begin(), classProbabilities.end());</div>
-<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>            <span class="keywordtype">int</span> predictedClass = distance(classProbabilities.begin(), maxElem);</div>
-<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>            predictions.push_back(predictedClass);</div>
-<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        }</div>
-<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <span class="keywordflow">return</span> predictions;</div>
-<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>    }</div>
-<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>    <span class="comment">// Return mxn std::vector of probabilities</span></div>
-<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>    <span class="comment">// tsamples is nxm std::vector of samples</span></div>
-<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>    std::vector&lt;std::vector&lt;double&gt;&gt; Network::predict_proba(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp; tsamples)</div>
-<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>    {</div>
-<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        <span class="keywordflow">if</span> (!fitted) {</div>
-<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>            <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;You must call fit() before calling predict_proba()&quot;</span>);</div>
-<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        }</div>
-<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        std::vector&lt;std::vector&lt;double&gt;&gt; predictions;</div>
-<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        std::vector&lt;int&gt; sample;</div>
-<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; tsamples[0].size(); ++row) {</div>
-<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            sample.clear();</div>
-<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = 0; col &lt; tsamples.size(); ++col) {</div>
-<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>                sample.push_back(tsamples[col][row]);</div>
-<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>            }</div>
-<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>            predictions.push_back(predict_sample(sample));</div>
-<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        }</div>
-<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        <span class="keywordflow">return</span> predictions;</div>
-<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>    }</div>
-<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>    <span class="keywordtype">double</span> Network::score(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp; tsamples, <span class="keyword">const</span> std::vector&lt;int&gt;&amp; labels)</div>
-<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>    {</div>
-<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        std::vector&lt;int&gt; y_pred = predict(tsamples);</div>
-<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        <span class="keywordtype">int</span> correct = 0;</div>
-<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; y_pred.size(); ++i) {</div>
-<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>            <span class="keywordflow">if</span> (y_pred[i] == labels[i]) {</div>
-<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>                correct++;</div>
-<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>            }</div>
-<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        }</div>
-<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>        <span class="keywordflow">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size();</div>
-<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>    }</div>
-<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>    <span class="comment">// Return 1xn std::vector of probabilities</span></div>
-<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>    std::vector&lt;double&gt; Network::predict_sample(<span class="keyword">const</span> std::vector&lt;int&gt;&amp; sample)</div>
-<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>    {</div>
-<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>        <span class="comment">// Ensure the sample size is equal to the number of features</span></div>
-<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>        <span class="keywordflow">if</span> (sample.size() != features.size() - 1) {</div>
-<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Sample size (&quot;</span> + std::to_string(sample.size()) +</div>
-<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>                <span class="stringliteral">&quot;) does not match the number of features (&quot;</span> + std::to_string(features.size() - 1) + <span class="stringliteral">&quot;)&quot;</span>);</div>
-<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>        }</div>
-<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>        std::map&lt;std::string, int&gt; evidence;</div>
-<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; sample.size(); ++i) {</div>
-<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>            evidence[features[i]] = sample[i];</div>
-<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>        }</div>
-<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>        <span class="keywordflow">return</span> exactInference(evidence);</div>
-<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>    }</div>
-<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>    <span class="comment">// Return 1xn std::vector of probabilities</span></div>
-<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>    std::vector&lt;double&gt; Network::predict_sample(<span class="keyword">const</span> torch::Tensor&amp; sample)</div>
-<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>    {</div>
-<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>        <span class="comment">// Ensure the sample size is equal to the number of features</span></div>
-<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>        <span class="keywordflow">if</span> (sample.size(0) != features.size() - 1) {</div>
-<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Sample size (&quot;</span> + std::to_string(sample.size(0)) +</div>
-<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>                <span class="stringliteral">&quot;) does not match the number of features (&quot;</span> + std::to_string(features.size() - 1) + <span class="stringliteral">&quot;)&quot;</span>);</div>
-<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>        }</div>
-<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>        std::map&lt;std::string, int&gt; evidence;</div>
-<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; sample.size(0); ++i) {</div>
-<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>            evidence[features[i]] = sample[i].item&lt;<span class="keywordtype">int</span>&gt;();</div>
-<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        }</div>
-<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <span class="keywordflow">return</span> exactInference(evidence);</div>
-<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>    }</div>
-<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>    <span class="keywordtype">double</span> Network::computeFactor(std::map&lt;std::string, int&gt;&amp; completeEvidence)</div>
-<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>    {</div>
-<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>        <span class="keywordtype">double</span> result = 1.0;</div>
-<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; node : getNodes()) {</div>
-<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>            result *= node.second-&gt;getFactorValue(completeEvidence);</div>
-<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>        }</div>
-<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>    }</div>
-<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>    std::vector&lt;double&gt; Network::exactInference(std::map&lt;std::string, int&gt;&amp; evidence)</div>
-<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>    {</div>
-<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>        std::vector&lt;double&gt; result(classNumStates, 0.0);</div>
-<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>        std::vector&lt;std::thread&gt; threads;</div>
-<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        std::mutex mtx;</div>
-<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; classNumStates; ++i) {</div>
-<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>            threads.emplace_back([<span class="keyword">this</span>, &amp;result, &amp;evidence, i, &amp;mtx]() {</div>
-<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>                <span class="keyword">auto</span> completeEvidence = std::map&lt;std::string, int&gt;(evidence);</div>
-<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>                completeEvidence[getClassName()] = i;</div>
-<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>                <span class="keywordtype">double</span> factor = computeFactor(completeEvidence);</div>
-<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>                std::lock_guard&lt;std::mutex&gt; lock(mtx);</div>
-<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>                result[i] = factor;</div>
-<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>                });</div>
-<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>        }</div>
-<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; thread : threads) {</div>
-<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>            thread.join();</div>
-<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        }</div>
-<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>        <span class="comment">// Normalize result</span></div>
-<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>        <span class="keywordtype">double</span> sum = accumulate(result.begin(), result.end(), 0.0);</div>
-<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>        transform(result.begin(), result.end(), result.begin(), [sum](<span class="keyword">const</span> <span class="keywordtype">double</span>&amp; value) { return value / sum; });</div>
-<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>    }</div>
-<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>    std::vector&lt;std::string&gt; Network::show()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>        std::vector&lt;std::string&gt; result;</div>
-<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>        <span class="comment">// Draw the network</span></div>
-<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; node : nodes) {</div>
-<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>            std::string line = node.first + <span class="stringliteral">&quot; -&gt; &quot;</span>;</div>
-<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> child : node.second-&gt;getChildren()) {</div>
-<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>                line += child-&gt;getName() + <span class="stringliteral">&quot;, &quot;</span>;</div>
-<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>            }</div>
-<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>            result.push_back(line);</div>
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-<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>    std::vector&lt;std::string&gt; Network::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span><span class="keyword">    </span>{</div>
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-<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>        <span class="keyword">auto</span> prefix = <span class="stringliteral">&quot;digraph BayesNet {\nlabel=&lt;BayesNet &quot;</span>;</div>
-<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>        <span class="keyword">auto</span> suffix = <span class="stringliteral">&quot;&gt;\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n&quot;</span>;</div>
-<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>        std::string header = prefix + title + suffix;</div>
-<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>        output.push_back(header);</div>
-<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; node : nodes) {</div>
-<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>            <span class="keyword">auto</span> result = node.second-&gt;graph(className);</div>
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-<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>    std::vector&lt;std::pair&lt;std::string, std::string&gt;&gt; Network::getEdges()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>        <span class="keyword">auto</span> edges = std::vector&lt;std::pair&lt;std::string, std::string&gt;&gt;();</div>
-<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>        <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>&amp; node : nodes) {</div>
-<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>            <span class="keyword">auto</span> head = node.first;</div>
-<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>            <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>&amp; child : node.second-&gt;getChildren()) {</div>
-<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>                <span class="keyword">auto</span> tail = child-&gt;getName();</div>
-<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>                edges.push_back({ head, tail });</div>
-<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>            }</div>
-<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>        }</div>
-<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>        <span class="keywordflow">return</span> edges;</div>
-<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>    }</div>
-<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>    <span class="keywordtype">int</span> Network::getNumEdges()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>        <span class="keywordflow">return</span> getEdges().size();</div>
-<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>    }</div>
-<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>    std::vector&lt;std::string&gt; Network::topological_sort()</div>
-<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>    {</div>
-<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>        <span class="comment">/* Check if al the fathers of every node are before the node */</span></div>
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-<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>        result.erase(remove(result.begin(), result.end(), className), result.end());</div>
-<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>        <span class="keywordtype">bool</span> ending{ <span class="keyword">false</span> };</div>
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-<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>            ending = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : features) {</div>
-<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>                <span class="keyword">auto</span> fathers = nodes[feature]-&gt;getParents();</div>
-<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span>                <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>&amp; father : fathers) {</div>
-<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span>                    <span class="keyword">auto</span> fatherName = father-&gt;getName();</div>
-<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span>                    <span class="keywordflow">if</span> (fatherName == className) {</div>
-<div class="line"><a id="l00400" name="l00400"></a><span class="lineno">  400</span>                        <span class="keywordflow">continue</span>;</div>
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-<div class="line"><a id="l00407" name="l00407"></a><span class="lineno">  407</span>                            <span class="keywordflow">if</span> (distance(it, it2) &lt; 0) {</div>
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-<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>            oss &lt;&lt; <span class="stringliteral">&quot;* &quot;</span> &lt;&lt; node.first &lt;&lt; <span class="stringliteral">&quot;: (&quot;</span> &lt;&lt; node.second-&gt;getNumStates() &lt;&lt; <span class="stringliteral">&quot;) : &quot;</span> &lt;&lt; node.second-&gt;getCPT().sizes() &lt;&lt; std::endl;</div>
-<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>            oss &lt;&lt; node.second-&gt;getCPT() &lt;&lt; std::endl;</div>
-<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>        }</div>
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-<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>    }</div>
-<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>}</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
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-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        <span class="keywordtype">int</span> getNumEdges() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        <span class="keywordtype">int</span> getClassNumStates() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        std::string getClassName() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        <span class="comment">/*</span></div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">        Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.</span></div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">        */</span></div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="keywordtype">void</span> fit(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp; input_data, <span class="keyword">const</span> std::vector&lt;int&gt;&amp; labels, <span class="keyword">const</span> std::vector&lt;double&gt;&amp; weights, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states);</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordtype">void</span> fit(<span class="keyword">const</span> torch::Tensor&amp; X, <span class="keyword">const</span> torch::Tensor&amp; y, <span class="keyword">const</span> torch::Tensor&amp; weights, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states);</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        <span class="keywordtype">void</span> fit(<span class="keyword">const</span> torch::Tensor&amp; samples, <span class="keyword">const</span> torch::Tensor&amp; weights, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states);</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        std::vector&lt;int&gt; predict(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp;); <span class="comment">// Return mx1 std::vector of predictions</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        torch::Tensor predict(<span class="keyword">const</span> torch::Tensor&amp;); <span class="comment">// Return mx1 tensor of predictions</span></div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>        torch::Tensor predict_tensor(<span class="keyword">const</span> torch::Tensor&amp; samples, <span class="keyword">const</span> <span class="keywordtype">bool</span> proba);</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>        std::vector&lt;std::vector&lt;double&gt;&gt; predict_proba(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp;); <span class="comment">// Return mxn std::vector of probabilities</span></div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>        torch::Tensor predict_proba(<span class="keyword">const</span> torch::Tensor&amp;); <span class="comment">// Return mxn tensor of probabilities</span></div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keywordtype">double</span> score(<span class="keyword">const</span> std::vector&lt;std::vector&lt;int&gt;&gt;&amp;, <span class="keyword">const</span> std::vector&lt;int&gt;&amp;);</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        std::vector&lt;std::string&gt; topological_sort();</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        std::vector&lt;std::string&gt; show() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; title) <span class="keyword">const</span>; <span class="comment">// Returns a std::vector of std::strings representing the graph in graphviz format</span></div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="keywordtype">void</span> initialize();</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        std::string dump_cpt() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <span class="keyword">inline</span> std::string version() { <span class="keywordflow">return</span>  { project_version.begin(), project_version.end() }; }</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        std::map&lt;std::string, std::unique_ptr&lt;Node&gt;&gt; nodes;</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>        <span class="keywordtype">bool</span> fitted;</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <span class="keywordtype">float</span> maxThreads = 0.95;</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <span class="keywordtype">int</span> classNumStates;</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        std::vector&lt;std::string&gt; features; <span class="comment">// Including classname</span></div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        std::string className;</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        <span class="keywordtype">double</span> laplaceSmoothing;</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        torch::Tensor samples; <span class="comment">// n+1xm tensor used to fit the model</span></div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        <span class="keywordtype">bool</span> isCyclic(<span class="keyword">const</span> std::string&amp;, std::unordered_set&lt;std::string&gt;&amp;, std::unordered_set&lt;std::string&gt;&amp;);</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        std::vector&lt;double&gt; predict_sample(<span class="keyword">const</span> std::vector&lt;int&gt;&amp;);</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        std::vector&lt;double&gt; predict_sample(<span class="keyword">const</span> torch::Tensor&amp;);</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        std::vector&lt;double&gt; exactInference(std::map&lt;std::string, int&gt;&amp;);</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="keywordtype">double</span> computeFactor(std::map&lt;std::string, int&gt;&amp;);</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        <span class="keywordtype">void</span> completeFit(<span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordtype">void</span> checkFitData(<span class="keywordtype">int</span> n_features, <span class="keywordtype">int</span> n_samples, <span class="keywordtype">int</span> n_samples_y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; featureNames, <span class="keyword">const</span> std::string&amp; className, <span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <span class="keyword">const</span> torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordtype">void</span> setStates(<span class="keyword">const</span> std::map&lt;std::string, std::vector&lt;int&gt;&gt;&amp;);</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    };</div>
-</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>}</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="preprocessor">#endif</span></div>
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-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;Node.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span> </div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    Node::Node(<span class="keyword">const</span> std::string&amp; name)</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>        : name(name)</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    {</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    }</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>    <span class="keywordtype">void</span> Node::clear()</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    {</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        parents.clear();</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        children.clear();</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        cpTable = torch::Tensor();</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        dimensions.clear();</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        numStates = 0;</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    }</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    std::string Node::getName()<span class="keyword"> const</span></div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        <span class="keywordflow">return</span> name;</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>    }</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>    <span class="keywordtype">void</span> Node::addParent(Node* parent)</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    {</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        parents.push_back(parent);</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    }</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>    <span class="keywordtype">void</span> Node::removeParent(Node* parent)</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    {</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        parents.erase(std::remove(parents.begin(), parents.end(), parent), parents.end());</div>
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-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>    <span class="keywordtype">void</span> Node::removeChild(Node* child)</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>    {</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        children.erase(std::remove(children.begin(), children.end(), child), children.end());</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    }</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    <span class="keywordtype">void</span> Node::addChild(Node* child)</div>
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-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    {</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>        <span class="keywordflow">return</span> parents;</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    }</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    std::vector&lt;Node*&gt;&amp; Node::getChildren()</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    {</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <span class="keywordflow">return</span> children;</div>
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-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    <span class="keywordtype">int</span> Node::getNumStates()<span class="keyword"> const</span></div>
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-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <span class="keywordflow">return</span> numStates;</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    }</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <span class="keywordtype">void</span> Node::setNumStates(<span class="keywordtype">int</span> numStates)</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    {</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        this-&gt;numStates = numStates;</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    }</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    torch::Tensor&amp; Node::getCPT()</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    {</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        <span class="keywordflow">return</span> cpTable;</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    }</div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <span class="comment">/*</span></div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">     The MinFill criterion is a heuristic for variable elimination.</span></div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">     The variable that minimizes the number of edges that need to be added to the graph to make it triangulated.</span></div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">     This is done by counting the number of edges that need to be added to the graph if the variable is eliminated.</span></div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">     The variable with the minimum number of edges is chosen.</span></div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">     Here this is done computing the length of the combinations of the node neighbors taken 2 by 2.</span></div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">    */</span></div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    <span class="keywordtype">unsigned</span> Node::minFill()</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    {</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        std::unordered_set&lt;std::string&gt; neighbors;</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> child : children) {</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            neighbors.emplace(child-&gt;getName());</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        }</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> parent : parents) {</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>            neighbors.emplace(parent-&gt;getName());</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        }</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        <span class="keyword">auto</span> source = std::vector&lt;std::string&gt;(neighbors.begin(), neighbors.end());</div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordflow">return</span> combinations(source).size();</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    }</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    std::vector&lt;std::pair&lt;std::string, std::string&gt;&gt; Node::combinations(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; source)</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    {</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        std::vector&lt;std::pair&lt;std::string, std::string&gt;&gt; result;</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; source.size(); ++i) {</div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            std::string temp = source[i];</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = i + 1; j &lt; source.size(); ++j) {</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>                result.push_back({ temp, source[j] });</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            }</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        }</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        <span class="keywordflow">return</span> result;</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    }</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keywordtype">void</span> Node::computeCPT(<span class="keyword">const</span> torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> <span class="keywordtype">double</span> laplaceSmoothing, <span class="keyword">const</span> torch::Tensor&amp; weights)</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    {</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        dimensions.clear();</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        <span class="comment">// Get dimensions of the CPT</span></div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        dimensions.push_back(numStates);</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; parent) { <span class="keywordflow">return</span> parent-&gt;getNumStates(); });</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        <span class="comment">// Create a tensor of zeros with the dimensions of the CPT</span></div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="comment">// Fill table with counts</span></div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <span class="keyword">auto</span> pos = find(features.begin(), features.end(), name);</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <span class="keywordflow">if</span> (pos == features.end()) {</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;Feature &quot;</span> + name + <span class="stringliteral">&quot; not found in dataset&quot;</span>);</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        }</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keywordtype">int</span> name_index = pos - features.begin();</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n_sample = 0; n_sample &lt; dataset.size(1); ++n_sample) {</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            c10::List&lt;c10::optional&lt;at::Tensor&gt;&gt; coordinates;</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            coordinates.push_back(dataset.index({ name_index, n_sample }));</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> parent : parents) {</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>                pos = find(features.begin(), features.end(), parent-&gt;getName());</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                <span class="keywordflow">if</span> (pos == features.end()) {</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                    <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;Feature parent &quot;</span> + parent-&gt;getName() + <span class="stringliteral">&quot; not found in dataset&quot;</span>);</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                }</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                <span class="keywordtype">int</span> parent_index = pos - features.begin();</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                coordinates.push_back(dataset.index({ parent_index, n_sample }));</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            }</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            <span class="comment">// Increment the count of the corresponding coordinate</span></div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item&lt;<span class="keywordtype">double</span>&gt;());</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        }</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="comment">// Normalize the counts</span></div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        cpTable = cpTable / cpTable.sum(0);</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    }</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    <span class="keywordtype">float</span> Node::getFactorValue(std::map&lt;std::string, int&gt;&amp; evidence)</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    {</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        c10::List&lt;c10::optional&lt;at::Tensor&gt;&gt; coordinates;</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="comment">// following predetermined order of indices in the cpTable (see Node.h)</span></div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        coordinates.push_back(at::tensor(evidence[name]));</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&amp;evidence](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; parent) { return at::tensor(evidence[parent-&gt;getName()]); });</div>
-<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keywordflow">return</span> cpTable.index({ coordinates }).item&lt;float&gt;();</div>
-<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>    }</div>
-<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    std::vector&lt;std::string&gt; Node::graph(<span class="keyword">const</span> std::string&amp; className)</div>
-<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    {</div>
-<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        <span class="keyword">auto</span> output = std::vector&lt;std::string&gt;();</div>
-<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keyword">auto</span> suffix = name == className ? <span class="stringliteral">&quot;, fontcolor=red, fillcolor=lightblue, style=filled &quot;</span> : <span class="stringliteral">&quot;&quot;</span>;</div>
-<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        output.push_back(name + <span class="stringliteral">&quot; [shape=circle&quot;</span> + suffix + <span class="stringliteral">&quot;] \n&quot;</span>);</div>
-<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        transform(children.begin(), children.end(), back_inserter(output), [<span class="keyword">this</span>](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; child) { <span class="keywordflow">return</span> name + <span class="stringliteral">&quot; -&gt; &quot;</span> + child-&gt;getName(); });</div>
-<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">return</span> output;</div>
-<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    }</div>
-<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>}</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <span class="keywordtype">void</span> removeParent(<a class="code hl_class" href="classbayesnet_1_1_node.html">Node</a>*);</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <span class="keywordtype">void</span> removeChild(<a class="code hl_class" href="classbayesnet_1_1_node.html">Node</a>*);</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        std::string getName() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        std::vector&lt;Node*&gt;&amp; getParents();</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        std::vector&lt;Node*&gt;&amp; getChildren();</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        torch::Tensor&amp; getCPT();</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        <span class="keywordtype">void</span> computeCPT(<span class="keyword">const</span> torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> <span class="keywordtype">double</span> laplaceSmoothing, <span class="keyword">const</span> torch::Tensor&amp; weights);</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        <span class="keywordtype">int</span> getNumStates() <span class="keyword">const</span>;</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        <span class="keywordtype">void</span> setNumStates(<span class="keywordtype">int</span>);</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        <span class="keywordtype">unsigned</span> minFill();</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; clasName); <span class="comment">// Returns a std::vector of std::strings representing the graph in graphviz format</span></div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keywordtype">float</span> getFactorValue(std::map&lt;std::string, int&gt;&amp;);</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::string name;</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        std::vector&lt;Node*&gt; parents;</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        std::vector&lt;Node*&gt; children;</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="keywordtype">int</span> numStates = 0; <span class="comment">// number of states of the variable</span></div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        torch::Tensor cpTable; <span class="comment">// Order of indices is 0-&gt; node variable, 1-&gt; 1st parent, 2-&gt; 2nd parent, ...</span></div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        std::vector&lt;int64_t&gt; dimensions; <span class="comment">// dimensions of the cpTable</span></div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        std::vector&lt;std::pair&lt;std::string, std::string&gt;&gt; combinations(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp;);</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>    };</div>
-</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>}</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#endif</span></div>
-<div class="ttc" id="aclassbayesnet_1_1_node_html"><div class="ttname"><a href="classbayesnet_1_1_node.html">bayesnet::Node</a></div><div class="ttdef"><b>Definition</b> <a href="#l00014">Node.h:14</a></div></div>
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-  <div class="headertitle"><div class="title">Proposal.cc</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;Proposal.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span>    Proposal::Proposal(torch::Tensor&amp; dataset_, std::vector&lt;std::string&gt;&amp; features_, std::string&amp; className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    Proposal::~Proposal()</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    {</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; [key, value] : discretizers) {</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>            <span class="keyword">delete</span> value;</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>        }</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    }</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    <span class="keywordtype">void</span> Proposal::checkInput(<span class="keyword">const</span> torch::Tensor&amp; X, <span class="keyword">const</span> torch::Tensor&amp; y)</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    {</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <span class="keywordflow">if</span> (!torch::is_floating_point(X)) {</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;X must be a floating point tensor&quot;</span>);</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        }</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keywordflow">if</span> (torch::is_floating_point(y)) {</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;y must be an integer tensor&quot;</span>);</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        }</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    }</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>    map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::localDiscretizationProposal(<span class="keyword">const</span> map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; oldStates, Network&amp; model)</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>    {</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        <span class="comment">// order of local discretization is important. no good 0, 1, 2...</span></div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>        <span class="comment">// although we rediscretize features after the local discretization of every feature</span></div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        <span class="keyword">auto</span> order = model.topological_sort();</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        <span class="keyword">auto</span>&amp; nodes = model.getNodes();</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        map&lt;std::string, std::vector&lt;int&gt;&gt; states = oldStates;</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::vector&lt;int&gt; indicesToReDiscretize;</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        <span class="keywordtype">bool</span> upgrade = <span class="keyword">false</span>; <span class="comment">// Flag to check if we need to upgrade the model</span></div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : order) {</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>            <span class="keyword">auto</span> nodeParents = nodes[feature]-&gt;getParents();</div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>            <span class="keywordflow">if</span> (nodeParents.size() &lt; 2) <span class="keywordflow">continue</span>; <span class="comment">// Only has class as parent</span></div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>            upgrade = <span class="keyword">true</span>;</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>            <span class="keywordtype">int</span> index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>            indicesToReDiscretize.push_back(index); <span class="comment">// We need to re-discretize this feature</span></div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>            std::vector&lt;std::string&gt; parents;</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>            transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; p) { <span class="keywordflow">return</span> p-&gt;getName(); });</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>            <span class="comment">// Remove class as parent as it will be added later</span></div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>            parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>            <span class="comment">// Get the indices of the parents</span></div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>            std::vector&lt;int&gt; indices;</div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>            indices.push_back(-1); <span class="comment">// Add class index</span></div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>            transform(parents.begin(), parents.end(), back_inserter(indices), [&amp;](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; p) {<span class="keywordflow">return</span> find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>            <span class="comment">// Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)</span></div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>            std::vector&lt;std::string&gt; yJoinParents(Xf.size(1));</div>
-<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> idx : indices) {</div>
-<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; Xf.size(1); ++i) {</div>
-<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>                    yJoinParents[i] += to_string(pDataset.index({ idx, i }).item&lt;<span class="keywordtype">int</span>&gt;());</div>
-<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>                }</div>
-<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>            }</div>
-<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>            <span class="keyword">auto</span> yxv = factorize(yJoinParents);</div>
-<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>            <span class="keyword">auto</span> xvf_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</div>
-<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>            <span class="keyword">auto</span> xvf = std::vector&lt;mdlp::precision_t&gt;(xvf_ptr, xvf_ptr + Xf.size(1));</div>
-<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>            discretizers[feature]-&gt;fit(xvf, yxv);</div>
-<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        }</div>
-<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        <span class="keywordflow">if</span> (upgrade) {</div>
-<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>            <span class="comment">// Discretize again X (only the affected indices) with the new fitted discretizers</span></div>
-<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> index : indicesToReDiscretize) {</div>
-<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>                <span class="keyword">auto</span> Xt_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</div>
-<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>                <span class="keyword">auto</span> Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</div>
-<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>                pDataset.index_put_({ index, <span class="stringliteral">&quot;...&quot;</span> }, torch::tensor(discretizers[pFeatures[index]]-&gt;transform(Xt)));</div>
-<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>                <span class="keyword">auto</span> xStates = std::vector&lt;int&gt;(discretizers[pFeatures[index]]-&gt;getCutPoints().size() + 1);</div>
-<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>                iota(xStates.begin(), xStates.end(), 0);</div>
-<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>                <span class="comment">//Update new states of the feature/node</span></div>
-<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>                states[pFeatures[index]] = xStates;</div>
-<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>            }</div>
-<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>            <span class="keyword">const</span> torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</div>
-<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>            model.fit(pDataset, weights, pFeatures, pClassName, states);</div>
-<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        }</div>
-<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        <span class="keywordflow">return</span> states;</div>
-<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
-<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::fit_local_discretization(<span class="keyword">const</span> torch::Tensor&amp; y)</div>
-<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    {</div>
-<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        <span class="comment">// Discretize the continuous input data and build pDataset (Classifier::dataset)</span></div>
-<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordtype">int</span> m = Xf.size(1);</div>
-<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keywordtype">int</span> n = Xf.size(0);</div>
-<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        map&lt;std::string, std::vector&lt;int&gt;&gt; states;</div>
-<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</div>
-<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keyword">auto</span> yv = std::vector&lt;int&gt;(y.data_ptr&lt;<span class="keywordtype">int</span>&gt;(), y.data_ptr&lt;<span class="keywordtype">int</span>&gt;() + y.size(0));</div>
-<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="comment">// discretize input data by feature(row)</span></div>
-<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; pFeatures.size(); ++i) {</div>
-<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            <span class="keyword">auto</span>* discretizer = <span class="keyword">new</span> mdlp::CPPFImdlp();</div>
-<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <span class="keyword">auto</span> Xt_ptr = Xf.index({ i }).data_ptr&lt;float&gt;();</div>
-<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            <span class="keyword">auto</span> Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</div>
-<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            discretizer-&gt;fit(Xt, yv);</div>
-<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            pDataset.index_put_({ i, <span class="stringliteral">&quot;...&quot;</span> }, torch::tensor(discretizer-&gt;transform(Xt)));</div>
-<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            <span class="keyword">auto</span> xStates = std::vector&lt;int&gt;(discretizer-&gt;getCutPoints().size() + 1);</div>
-<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            iota(xStates.begin(), xStates.end(), 0);</div>
-<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>            states[pFeatures[i]] = xStates;</div>
-<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>            discretizers[pFeatures[i]] = discretizer;</div>
-<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        }</div>
-<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordtype">int</span> n_classes = torch::max(y).item&lt;<span class="keywordtype">int</span>&gt;() + 1;</div>
-<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        <span class="keyword">auto</span> yStates = std::vector&lt;int&gt;(n_classes);</div>
-<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        iota(yStates.begin(), yStates.end(), 0);</div>
-<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        states[pClassName] = yStates;</div>
-<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        pDataset.index_put_({ n, <span class="stringliteral">&quot;...&quot;</span> }, y);</div>
-<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <span class="keywordflow">return</span> states;</div>
-<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    }</div>
-<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    torch::Tensor Proposal::prepareX(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    {</div>
-<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keyword">auto</span> Xtd = torch::zeros_like(X, torch::kInt32);</div>
-<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; X.size(0); ++i) {</div>
-<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            <span class="keyword">auto</span> Xt = std::vector&lt;float&gt;(X[i].data_ptr&lt;float&gt;(), X[i].data_ptr&lt;float&gt;() + X.size(1));</div>
-<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            <span class="keyword">auto</span> Xd = discretizers[pFeatures[i]]-&gt;transform(Xt);</div>
-<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</div>
-<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        }</div>
-<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        <span class="keywordflow">return</span> Xtd;</div>
-<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    }</div>
-<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    std::vector&lt;int&gt; Proposal::factorize(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; labels_t)</div>
-<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    {</div>
-<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        std::vector&lt;int&gt; yy;</div>
-<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        yy.reserve(labels_t.size());</div>
-<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        std::map&lt;std::string, int&gt; labelMap;</div>
-<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        <span class="keywordtype">int</span> i = 0;</div>
-<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string&amp; label : labels_t) {</div>
-<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>            <span class="keywordflow">if</span> (labelMap.find(label) == labelMap.end()) {</div>
-<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>                labelMap[label] = i++;</div>
-<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>                <span class="keywordtype">bool</span> allDigits = std::all_of(label.begin(), label.end(), ::isdigit);</div>
-<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            }</div>
-<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            yy.push_back(labelMap[label]);</div>
-<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        }</div>
-<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="keywordflow">return</span> yy;</div>
-<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    }</div>
-<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>}</div>
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-  <div class="headertitle"><div class="title">Proposal.h</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef PROPOSAL_H</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#define PROPOSAL_H</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;string&gt;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;map&gt;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &lt;torch/torch.h&gt;</span></div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &lt;CPPFImdlp.h&gt;</span></div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="preprocessor">#include &quot;bayesnet/network/Network.h&quot;</span></div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="preprocessor">#include &quot;Classifier.h&quot;</span></div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span> </div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="foldopen" id="foldopen00017" data-start="{" data-end="};">
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_proposal.html">   17</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_proposal.html">Proposal</a> {</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <a class="code hl_class" href="classbayesnet_1_1_proposal.html">Proposal</a>(torch::Tensor&amp; pDataset, std::vector&lt;std::string&gt;&amp; features_, std::string&amp; className_);</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_proposal.html">~Proposal</a>();</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    <span class="keyword">protected</span>:</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keywordtype">void</span> checkInput(<span class="keyword">const</span> torch::Tensor&amp; X, <span class="keyword">const</span> torch::Tensor&amp; y);</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        torch::Tensor prepareX(torch::Tensor&amp; X);</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        map&lt;std::string, std::vector&lt;int&gt;&gt; localDiscretizationProposal(<span class="keyword">const</span> map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states, <a class="code hl_class" href="classbayesnet_1_1_network.html">Network</a>&amp; model);</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        map&lt;std::string, std::vector&lt;int&gt;&gt; fit_local_discretization(<span class="keyword">const</span> torch::Tensor&amp; y);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        torch::Tensor Xf; <span class="comment">// X continuous nxm tensor</span></div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        torch::Tensor y; <span class="comment">// y discrete nx1 tensor</span></div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>        map&lt;std::string, mdlp::CPPFImdlp*&gt; discretizers;</div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    <span class="keyword">private</span>:</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>        std::vector&lt;int&gt; factorize(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; labels_t);</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        torch::Tensor&amp; pDataset; <span class="comment">// (n+1)xm tensor</span></div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        std::vector&lt;std::string&gt;&amp; pFeatures;</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        std::string&amp; pClassName;</div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    };</div>
-</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>}</div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#endif  </span></div>
-<div class="ttc" id="aclassbayesnet_1_1_network_html"><div class="ttname"><a href="classbayesnet_1_1_network.html">bayesnet::Network</a></div><div class="ttdef"><b>Definition</b> <a href="_network_8h_source.html#l00015">Network.h:15</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_proposal_html"><div class="ttname"><a href="classbayesnet_1_1_proposal.html">bayesnet::Proposal</a></div><div class="ttdef"><b>Definition</b> <a href="#l00017">Proposal.h:17</a></div></div>
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-  <div class="headertitle"><div class="title">SPODELd.cc</div></div>
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-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;SPODELd.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span>    SPODELd::SPODELd(<span class="keywordtype">int</span> root) : SPODE(root), Proposal(dataset, features, className) {}</div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    SPODELd&amp; SPODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features_, <span class="keyword">const</span> std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>    {</div>
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>        checkInput(X_, y_);</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>        Xf = X_;</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>        y = y_;</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keywordflow">return</span> commonFit(features_, className_, states_);</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    }</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span> </div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    SPODELd&amp; SPODELd::fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features_, <span class="keyword">const</span> std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    {</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        <span class="keywordflow">if</span> (!torch::is_floating_point(dataset)) {</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Dataset must be a floating point tensor&quot;</span>);</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>        }</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), <span class="stringliteral">&quot;...&quot;</span> }).clone();</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>        y = dataset.index({ -1, <span class="stringliteral">&quot;...&quot;</span> }).clone().to(torch::kInt32);</div>
-<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>        <span class="keywordflow">return</span> commonFit(features_, className_, states_);</div>
-<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>    }</div>
-<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span> </div>
-<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    SPODELd&amp; SPODELd::commonFit(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features_, <span class="keyword">const</span> std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</div>
-<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    {</div>
-<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>        features = features_;</div>
-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        className = className_;</div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        <span class="comment">// Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span></div>
-<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>        states = fit_local_discretization(y);</div>
-<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>        <span class="comment">// We have discretized the input data</span></div>
-<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>        <span class="comment">// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network</span></div>
-<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>        SPODE::fit(dataset, features, className, states);</div>
-<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        states = localDiscretizationProposal(states, model);</div>
-<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
-<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>    }</div>
-<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    torch::Tensor SPODELd::predict(torch::Tensor&amp; X)</div>
-<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    {</div>
-<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>        <span class="keyword">auto</span> Xt = prepareX(X);</div>
-<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        <span class="keywordflow">return</span> SPODE::predict(Xt);</div>
-<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    }</div>
-<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    std::vector&lt;std::string&gt; SPODELd::graph(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="keyword">    </span>{</div>
-<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>        <span class="keywordflow">return</span> SPODE::graph(name);</div>
-<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    }</div>
-<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>}</div>
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docs/manual/_s_p_o_d_e_ld_8h_source.html

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-  <div class="headertitle"><div class="title">SPODELd.h</div></div>
-</div><!--header-->
-<div class="contents">
-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
-<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#ifndef SPODELD_H</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#define SPODELD_H</span></div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &quot;SPODE.h&quot;</span></div>
-<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;Proposal.h&quot;</span></div>
-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
-<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="keyword">namespace </span>bayesnet {</div>
-<div class="foldopen" id="foldopen00013" data-start="{" data-end="};">
-<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"><a class="line" href="classbayesnet_1_1_s_p_o_d_e_ld.html">   13</a></span>    <span class="keyword">class </span><a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">SPODELd</a> : <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e.html">SPODE</a>, <span class="keyword">public</span> <a class="code hl_class" href="classbayesnet_1_1_proposal.html">Proposal</a> {</div>
-<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>    <span class="keyword">public</span>:</div>
-<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>        <span class="keyword">explicit</span> <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">SPODELd</a>(<span class="keywordtype">int</span> root);</div>
-<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        <span class="keyword">virtual</span> <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">~SPODELd</a>() = <span class="keywordflow">default</span>;</div>
-<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">SPODELd</a>&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">SPODELd</a>&amp; fit(torch::Tensor&amp; dataset, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        <a class="code hl_class" href="classbayesnet_1_1_s_p_o_d_e_ld.html">SPODELd</a>&amp; commonFit(<span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features, <span class="keyword">const</span> std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states);</div>
-<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        std::vector&lt;std::string&gt; graph(<span class="keyword">const</span> std::string&amp; name = <span class="stringliteral">&quot;SPODE&quot;</span>) <span class="keyword">const override</span>;</div>
-<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        torch::Tensor predict(torch::Tensor&amp; X) <span class="keyword">override</span>;</div>
-<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keyword">static</span> <span class="keyword">inline</span> std::string version() { <span class="keywordflow">return</span> <span class="stringliteral">&quot;0.0.1&quot;</span>; };</div>
-<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    };</div>
-</div>
-<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>}</div>
-<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="preprocessor">#endif </span><span class="comment">// !SPODELD_H</span></div>
-<div class="ttc" id="aclassbayesnet_1_1_proposal_html"><div class="ttname"><a href="classbayesnet_1_1_proposal.html">bayesnet::Proposal</a></div><div class="ttdef"><b>Definition</b> <a href="_proposal_8h_source.html#l00017">Proposal.h:17</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_s_p_o_d_e_html"><div class="ttname"><a href="classbayesnet_1_1_s_p_o_d_e.html">bayesnet::SPODE</a></div><div class="ttdef"><b>Definition</b> <a href="_s_p_o_d_e_8h_source.html#l00012">SPODE.h:12</a></div></div>
-<div class="ttc" id="aclassbayesnet_1_1_s_p_o_d_e_ld_html"><div class="ttname"><a href="classbayesnet_1_1_s_p_o_d_e_ld.html">bayesnet::SPODELd</a></div><div class="ttdef"><b>Definition</b> <a href="#l00013">SPODELd.h:13</a></div></div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;TAN.h&quot;</span></div>
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-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
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-<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">// ***************************************************************</span></div>
-<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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-<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
-<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &quot;TANLd.h&quot;</span></div>
-<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span> </div>
-<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">namespace </span>bayesnet {</div>
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-<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>    TANLd&amp; TANLd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; features_, <span class="keyword">const</span> std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</div>
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-<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>        <span class="comment">// Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span></div>
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-<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    std::vector&lt;std::string&gt; TANLd::graph(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div>
-<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="keyword">    </span>{</div>
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-  <div class="headertitle"><div class="title">bayesnet::A2DE Member List</div></div>
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-<div class="contents">
-
-<p>This is the complete list of members for <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>, including all inherited members.</p>
-<table class="directory">
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>A2DE</b>(bool predict_voting=false) (defined in <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a></td><td class="entry"></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>addNodes</b>() (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>buildDataset</b>(torch::Tensor &amp;y) (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>buildModel</b>(const torch::Tensor &amp;weights) override (defined in <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>checkFitParameters</b>() (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>Classifier</b>(Network model) (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>className</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>compute_arg_max</b>(torch::Tensor &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>compute_arg_max</b>(std::vector&lt; std::vector&lt; double &gt; &gt; &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>dataset</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>dump_cpt</b>() const override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>Ensemble</b>(bool predict_voting=true) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>features</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>fit</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X, std::vector&lt; int &gt; &amp;y, const std::vector&lt; std::string &gt; &amp;features, const std::string &amp;className, std::map&lt; std::string, std::vector&lt; int &gt; &gt; &amp;states) override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>fit</b>(torch::Tensor &amp;X, torch::Tensor &amp;y, const std::vector&lt; std::string &gt; &amp;features, const std::string &amp;className, std::map&lt; std::string, std::vector&lt; int &gt; &gt; &amp;states) override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>fit</b>(torch::Tensor &amp;dataset, const std::vector&lt; std::string &gt; &amp;features, const std::string &amp;className, std::map&lt; std::string, std::vector&lt; int &gt; &gt; &amp;states) override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>fit</b>(torch::Tensor &amp;dataset, const std::vector&lt; std::string &gt; &amp;features, const std::string &amp;className, std::map&lt; std::string, std::vector&lt; int &gt; &gt; &amp;states, const torch::Tensor &amp;weights) override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>getClassNumStates</b>() const override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>getNotes</b>() const override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>getNumberOfEdges</b>() const override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>getNumberOfNodes</b>() const override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>getNumberOfStates</b>() const override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>getStatus</b>() const override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>getValidHyperparameters</b>() (defined in <a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>getVersion</b>() override (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>graph</b>(const std::string &amp;title=&quot;A2DE&quot;) const override (defined in <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>m</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>metrics</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>model</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>models</b> (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>n</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>n_models</b> (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>notes</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>predict</b>(torch::Tensor &amp;X) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>predict</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>predict_average_proba</b>(torch::Tensor &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>predict_average_proba</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>predict_average_voting</b>(torch::Tensor &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>predict_average_voting</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>predict_proba</b>(torch::Tensor &amp;X) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>predict_proba</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>predict_voting</b> (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>score</b>(torch::Tensor &amp;X, torch::Tensor &amp;y) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>score</b>(std::vector&lt; std::vector&lt; int &gt; &gt; &amp;X, std::vector&lt; int &gt; &amp;y) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>setHyperparameters</b>(const nlohmann::json &amp;hyperparameters) override (defined in <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>show</b>() const override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>significanceModels</b> (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>states</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>status</b> (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>topological_order</b>() override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>trainModel</b>(const torch::Tensor &amp;weights) override (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>validHyperparameters</b> (defined in <a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>voting</b>(torch::Tensor &amp;votes) (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>~A2DE</b>() (defined in <a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_a2_d_e.html">bayesnet::A2DE</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>~BaseClassifier</b>()=default (defined in <a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_base_classifier.html">bayesnet::BaseClassifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>~Classifier</b>()=default (defined in <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
-  <tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>~Ensemble</b>()=default (defined in <a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a>)</td><td class="entry"><a class="el" href="classbayesnet_1_1_ensemble.html">bayesnet::Ensemble</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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-<p>Implements <a class="el" href="classbayesnet_1_1_classifier.html">bayesnet::Classifier</a>.</p>
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