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
@@ -18,3 +13,9 @@
         url = https://github.com/catchorg/Catch2.git
 	main = main
 	update = merge
+[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
@@ -25,8 +25,11 @@ set(CMAKE_CXX_EXTENSIONS                  OFF)
 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 "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors -fno-default-inline")
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+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} -Ofast")
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
 
 # Options
 # -------
@@ -46,11 +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)
 
@@ -60,10 +64,10 @@ endif (ENABLE_CLANG_TIDY)
 
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
+
 # include(FetchContent)
 add_git_submodule("lib/json")
 add_git_submodule("lib/mdlp")
-add_subdirectory("lib/Files")
 
 # Subdirectories
 # --------------
@@ -73,7 +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)
@@ -86,4 +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 $(genhtml) || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
-	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory html --title "BayesNet Coverage Report" -s -k -f --legend >/dev/null 2>&1;
-	@xdg-open html/index.html || open html/index.html 2>/dev/null
+	@which $(genhtml) >/dev/null || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
+	@if [ ! -d $(docsrcdir)/coverage ]; then mkdir -p $(docsrcdir)/coverage; fi
+	@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& 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 torch::Tensor& weights) = 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, 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, 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

@@ -1,6 +1,5 @@
 include_directories(
-    ${BayesNet_SOURCE_DIR}/lib/mdlp
-    ${BayesNet_SOURCE_DIR}/lib/Files
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}
@@ -10,4 +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(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& 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 torch::Tensor& weights) 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, 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 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, 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

@@ -4,7 +4,6 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <ArffFiles.h>
 #include "Proposal.h"
 
 namespace bayesnet {
@@ -54,8 +53,7 @@ namespace bayesnet {
                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
                 }
             }
-            auto arff = ArffFiles();
-            auto yxv = arff.factorize(yJoinParents);
+            auto yxv = factorize(yJoinParents);
             auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
             auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
             discretizers[feature]->fit(xvf, yxv);
@@ -72,7 +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;
     }
@@ -113,4 +111,19 @@ namespace bayesnet {
         }
         return Xtd;
     }
+    std::vector<int> Proposal::factorize(const std::vector<std::string>& labels_t)
+    {
+        std::vector<int> yy;
+        yy.reserve(labels_t.size());
+        std::map<std::string, int> labelMap;
+        int i = 0;
+        for (const std::string& label : labels_t) {
+            if (labelMap.find(label) == labelMap.end()) {
+                labelMap[label] = i++;
+                bool allDigits = std::all_of(label.begin(), label.end(), ::isdigit);
+            }
+            yy.push_back(labelMap[label]);
+        }
+        return yy;
+    }
 }

bayesnet/classifiers/Proposal.h

@@ -27,6 +27,7 @@ namespace bayesnet {
         torch::Tensor y; // y discrete nx1 tensor
         map<std::string, mdlp::CPPFImdlp*> discretizers;
     private:
+        std::vector<int> factorize(const std::vector<std::string>& labels_t);
         torch::Tensor& pDataset; // (n+1)xm tensor
         std::vector<std::string>& pFeatures;
         std::string& pClassName;

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& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
-        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
-        std::vector<std::string> graph(const std::string& name = "SPODE") const 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, const Smoothing_t smoothing) override;
+        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 = "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;
-        std::vector<std::string> graph(const std::string& name = "TAN") const 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 = "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/Boost.cc

@@ -0,0 +1,246 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include "Boost.h"
+
+namespace bayesnet {
+    Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
+        "predict_voting", "select_features", "block_update" };
+    }
+    void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("order")) {
+            std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
+            order_algorithm = hyperparameters["order"];
+            if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
+                throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
+            }
+            hyperparameters.erase("order");
+        }
+        if (hyperparameters.contains("convergence")) {
+            convergence = hyperparameters["convergence"];
+            hyperparameters.erase("convergence");
+        }
+        if (hyperparameters.contains("convergence_best")) {
+            convergence_best = hyperparameters["convergence_best"];
+            hyperparameters.erase("convergence_best");
+        }
+        if (hyperparameters.contains("bisection")) {
+            bisection = hyperparameters["bisection"];
+            hyperparameters.erase("bisection");
+        }
+        if (hyperparameters.contains("threshold")) {
+            threshold = hyperparameters["threshold"];
+            hyperparameters.erase("threshold");
+        }
+        if (hyperparameters.contains("maxTolerance")) {
+            maxTolerance = hyperparameters["maxTolerance"];
+            if (maxTolerance < 1 || maxTolerance > 4)
+                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
+            hyperparameters.erase("maxTolerance");
+        }
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        if (hyperparameters.contains("select_features")) {
+            auto selectedAlgorithm = hyperparameters["select_features"];
+            std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
+            selectFeatures = true;
+            select_features_algorithm = selectedAlgorithm;
+            if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
+                throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
+            }
+            hyperparameters.erase("select_features");
+        }
+        if (hyperparameters.contains("block_update")) {
+            block_update = hyperparameters["block_update"];
+            hyperparameters.erase("block_update");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void Boost::buildModel(const torch::Tensor& weights)
+    {
+        // Models shall be built in trainModel
+        models.clear();
+        significanceModels.clear();
+        n_models = 0;
+        // Prepare the validation dataset
+        auto y_ = dataset.index({ -1, "..." });
+        if (convergence) {
+            // Prepare train & validation sets from train data
+            auto fold = folding::StratifiedKFold(5, y_, 271);
+            auto [train, test] = fold.getFold(0);
+            auto train_t = torch::tensor(train);
+            auto test_t = torch::tensor(test);
+            // Get train and validation sets
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+            y_train = dataset.index({ -1, train_t });
+            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+            y_test = dataset.index({ -1, test_t });
+            dataset = X_train;
+            m = X_train.size(1);
+            auto n_classes = states.at(className).size();
+            // Build dataset with train data
+            buildDataset(y_train);
+            metrics = Metrics(dataset, features, className, n_classes);
+        } else {
+            // Use all data to train
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+            y_train = y_;
+        }
+    }
+    std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
+    {
+        int maxFeatures = 0;
+        if (select_features_algorithm == SelectFeatures.CFS) {
+            featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
+        } else if (select_features_algorithm == SelectFeatures.IWSS) {
+            if (threshold < 0 || threshold >0.5) {
+                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
+            }
+            featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+        } else if (select_features_algorithm == SelectFeatures.FCBF) {
+            if (threshold < 1e-7 || threshold > 1) {
+                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
+            }
+            featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+        }
+        featureSelector->fit();
+        auto featuresUsed = featureSelector->getFeatures();
+        delete featureSelector;
+        return featuresUsed;
+    }
+    std::tuple<torch::Tensor&, double, bool> Boost::update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
+    {
+        bool terminate = false;
+        double alpha_t = 0;
+        auto mask_wrong = ypred != ytrain;
+        auto mask_right = ypred == ytrain;
+        auto masked_weights = weights * mask_wrong.to(weights.dtype());
+        double epsilon_t = masked_weights.sum().item<double>();
+        if (epsilon_t > 0.5) {
+            // Inverse the weights policy (plot ln(wt))
+            // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
+            // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+            terminate = true;
+        } else {
+            double wt = (1 - epsilon_t) / epsilon_t;
+            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+            // Step 3.2: Update weights for next classifier
+            // Step 3.2.1: Update weights of wrong samples
+            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
+            // Step 3.2.2: Update weights of right samples
+            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
+            // Step 3.3: Normalise the weights
+            double totalWeights = torch::sum(weights).item<double>();
+            weights = weights / totalWeights;
+        }
+        return { weights, alpha_t, terminate };
+    }
+    std::tuple<torch::Tensor&, double, bool> Boost::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
+    {
+        /* Update Block algorithm
+            k = # of models in block
+            n_models = # of models in ensemble to make predictions
+            n_models_bak = # models saved
+            models = vector of models to make predictions
+            models_bak = models not used to make predictions
+            significances_bak = backup of significances vector
+
+            Case list
+            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
+            B) k = 1, n_models = n + 1	=> n_models = n + k
+            C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
+            D) k > 1, n_models = k		=> n = 0, n_models = n + k
+            E) k > 1, n_models = k + n	=> n_models = n + k
+
+            A, D) n=0, k > 0, n_models == k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Don’t move any classifiers out of models
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Don’t restore any classifiers to models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+
+            B, C, E) n > 0, k > 0, n_models == n + k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Move first n classifiers to models_bak
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Insert classifiers in models_bak to be the first n models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+        */
+        //
+        // Make predict with only the last k models
+        //
+        std::unique_ptr<Classifier> model;
+        std::vector<std::unique_ptr<Classifier>> models_bak;
+        // 1. n_models_bak <- n_models 2. significances_bak <- significances
+        auto significance_bak = significanceModels;
+        auto n_models_bak = n_models;
+        // 3. significances = vector(k, 1)
+        significanceModels = std::vector<double>(k, 1.0);
+        // 4. Move first n classifiers to models_bak
+        // backup the first n_models - k models (if n_models == k, don't backup any)
+        for (int i = 0; i < n_models - k; ++i) {
+            model = std::move(models[0]);
+            models.erase(models.begin());
+            models_bak.push_back(std::move(model));
+        }
+        assert(models.size() == k);
+        // 5. n_models <- k
+        n_models = k;
+        // 6. Make prediction, compute alpha, update weights
+        auto ypred = predict(X_train);
+        //
+        // Update weights
+        //
+        double alpha_t;
+        bool terminate;
+        std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
+        //
+        // Restore the models if needed
+        //
+        // 7. Insert classifiers in models_bak to be the first n models
+        // if n_models_bak == k, don't restore any, because none of them were moved
+        if (k != n_models_bak) {
+            // Insert in the same order as they were extracted
+            int bak_size = models_bak.size();
+            for (int i = 0; i < bak_size; ++i) {
+                model = std::move(models_bak[bak_size - 1 - i]);
+                models_bak.erase(models_bak.end() - 1);
+                models.insert(models.begin(), std::move(model));
+            }
+        }
+        // 8. significances <- significances_bak
+        significanceModels = significance_bak;
+        //
+        // Update the significance of the last k models
+        //
+        // 9. Update last k significances
+        for (int i = 0; i < k; ++i) {
+            significanceModels[n_models_bak - k + i] = alpha_t;
+        }
+        // 10. n_models <- n_models_bak
+        n_models = n_models_bak;
+        return { weights, alpha_t, terminate };
+    }
+}

bayesnet/ensembles/Boost.h

@@ -0,0 +1,52 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOST_H
+#define BOOST_H
+#include <string>
+#include <tuple>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include <torch/torch.h>
+#include "Ensemble.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
+namespace bayesnet {
+    const struct {
+        std::string CFS = "CFS";
+        std::string FCBF = "FCBF";
+        std::string IWSS = "IWSS";
+    }SelectFeatures;
+    const struct {
+        std::string ASC = "asc";
+        std::string DESC = "desc";
+        std::string RAND = "rand";
+    }Orders;
+    class Boost : public Ensemble {
+    public:
+        explicit Boost(bool predict_voting = false);
+        virtual ~Boost() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+    protected:
+        std::vector<int> featureSelection(torch::Tensor& weights_);
+        void buildModel(const torch::Tensor& weights) override;
+        std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
+        std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
+        torch::Tensor X_train, y_train, X_test, y_test;
+        // Hyperparameters
+        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
+        int maxTolerance = 3;
+        std::string order_algorithm; // order to process the KBest features asc, desc, rand
+        bool convergence = true; //if true, stop when the model does not improve
+        bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
+        bool selectFeatures = false; // if true, use feature selection
+        std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
+        FeatureSelect* featureSelector = nullptr;
+        double threshold = -1;
+        bool block_update = false;
+
+    };
+}
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -0,0 +1,170 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <set>
+#include <functional>
+#include <limits.h>
+#include <tuple>
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include "BoostA2DE.h"
+
+namespace bayesnet {
+
+    BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
+    {
+    }
+    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_);
+        if (featuresSelected.size() < 2) {
+            notes.push_back("No features selected in initialization");
+            status = ERROR;
+            return std::vector<int>();
+        }
+        for (int i = 0; i < featuresSelected.size() - 1; i++) {
+            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_, smoothing);
+                models.push_back(std::move(model));
+                significanceModels.push_back(1.0); // They will be updated later in trainModel
+                n_models++;
+            }
+        }
+        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, const Smoothing_t smoothing)
+    {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostA2DE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
+        fitted = true;
+        double alpha_t = 0;
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        if (selectFeatures) {
+            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
+            for (int i = 0; i < n_models; ++i) {
+                significanceModels[i] = alpha_t;
+            }
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == Orders.ASC;
+        std::mt19937 g{ 173 };
+        std::vector<std::pair<int, int>> pairSelection;
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
+            if (order_algorithm == Orders.RAND) {
+                std::shuffle(pairSelection.begin(), pairSelection.end(), g);
+            }
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            while (counter++ < k && pairSelection.size() > 0) {
+                auto feature_pair = pairSelection[0];
+                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_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    auto ypred = model->predict(X_train);
+                    // Step 3.1: Compute the classifier amout of say
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+                }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
+            }
+            if (block_update) {
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+            }
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = 0; i < numItemsPack; ++i) {
+                    significanceModels.pop_back();
+                    models.pop_back();
+                    n_models--;
+                }
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (pairSelection.size() > 0) {
+            notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+            status = WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+    }
+    std::vector<std::string> BoostA2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/BoostA2DE.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOSTA2DE_H
+#define BOOSTA2DE_H
+#include <string>
+#include <vector>
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Boost.h"
+namespace bayesnet {
+    class BoostA2DE : public Boost {
+    public:
+        explicit BoostA2DE(bool predict_voting = false);
+        virtual ~BoostA2DE() = default;
+        std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+    };
+}
+#endif

bayesnet/ensembles/BoostAODE.cc

@@ -4,275 +4,40 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <random> 
 #include <set>
 #include <functional>
 #include <limits.h>
 #include <tuple>
-#include <folding.hpp>
-#include "bayesnet/feature_selection/CFS.h"
-#include "bayesnet/feature_selection/FCBF.h"
-#include "bayesnet/feature_selection/IWSS.h"
 #include "BoostAODE.h"
-#include "lib/log/loguru.cpp"
 
 namespace bayesnet {
 
-    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
+    BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
-        validHyperparameters = {
-            "maxModels", "bisection", "order", "convergence", "convergence_best", "threshold",
-            "select_features", "maxTolerance", "predict_voting", "block_update"
-        };
-
     }
-    void BoostAODE::buildModel(const torch::Tensor& weights)
+    std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
     {
-        // Models shall be built in trainModel
-        models.clear();
-        significanceModels.clear();
-        n_models = 0;
-        // Prepare the validation dataset
-        auto y_ = dataset.index({ -1, "..." });
-        if (convergence) {
-            // Prepare train & validation sets from train data
-            auto fold = folding::StratifiedKFold(5, y_, 271);
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            // Build dataset with train data
-            buildDataset(y_train);
-            metrics = Metrics(dataset, features, className, n_classes);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
-        }
-    }
-    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
-    {
-        auto hyperparameters = hyperparameters_;
-        if (hyperparameters.contains("order")) {
-            std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
-            order_algorithm = hyperparameters["order"];
-            if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
-            }
-            hyperparameters.erase("order");
-        }
-        if (hyperparameters.contains("convergence")) {
-            convergence = hyperparameters["convergence"];
-            hyperparameters.erase("convergence");
-        }
-        if (hyperparameters.contains("convergence_best")) {
-            convergence_best = hyperparameters["convergence_best"];
-            hyperparameters.erase("convergence_best");
-        }
-        if (hyperparameters.contains("bisection")) {
-            bisection = hyperparameters["bisection"];
-            hyperparameters.erase("bisection");
-        }
-        if (hyperparameters.contains("threshold")) {
-            threshold = hyperparameters["threshold"];
-            hyperparameters.erase("threshold");
-        }
-        if (hyperparameters.contains("maxTolerance")) {
-            maxTolerance = hyperparameters["maxTolerance"];
-            if (maxTolerance < 1 || maxTolerance > 4)
-                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
-            hyperparameters.erase("maxTolerance");
-        }
-        if (hyperparameters.contains("predict_voting")) {
-            predict_voting = hyperparameters["predict_voting"];
-            hyperparameters.erase("predict_voting");
-        }
-        if (hyperparameters.contains("select_features")) {
-            auto selectedAlgorithm = hyperparameters["select_features"];
-            std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
-            selectFeatures = true;
-            select_features_algorithm = selectedAlgorithm;
-            if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
-            }
-            hyperparameters.erase("select_features");
-        }
-        if (hyperparameters.contains("block_update")) {
-            block_update = hyperparameters["block_update"];
-            hyperparameters.erase("block_update");
-        }
-        Classifier::setHyperparameters(hyperparameters);
-    }
-    std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
-    {
-        bool terminate = false;
-        double alpha_t = 0;
-        auto mask_wrong = ypred != ytrain;
-        auto mask_right = ypred == ytrain;
-        auto masked_weights = weights * mask_wrong.to(weights.dtype());
-        double epsilon_t = masked_weights.sum().item<double>();
-        if (epsilon_t > 0.5) {
-            // Inverse the weights policy (plot ln(wt))
-            // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
-            // learner is better than random guess" (Zhi-Hua Zhou, 2012)
-            terminate = true;
-        } else {
-            double wt = (1 - epsilon_t) / epsilon_t;
-            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
-            // Step 3.2: Update weights for next classifier
-            // Step 3.2.1: Update weights of wrong samples
-            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
-            // Step 3.2.2: Update weights of right samples
-            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
-            // Step 3.3: Normalise the weights
-            double totalWeights = torch::sum(weights).item<double>();
-            weights = weights / totalWeights;
-        }
-        return { weights, alpha_t, terminate };
-    }
-    std::tuple<torch::Tensor&, double, bool> BoostAODE::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
-    {
-        /* Update Block algorithm
-            k = # of models in block
-            n_models = # of models in ensemble to make predictions
-            n_models_bak = # models saved
-            models = vector of models to make predictions
-            models_bak = models not used to make predictions
-            significances_bak = backup of significances vector
-
-            Case list
-            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
-            B) k = 1, n_models = n + 1	=> n_models = n + k
-            C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
-            D) k > 1, n_models = k		=> n = 0, n_models = n + k
-            E) k > 1, n_models = k + n	=> n_models = n + k
-
-            A, D) n=0, k > 0, n_models == k
-            1. n_models_bak <- n_models
-            2. significances_bak <- significances
-            3. significances = vector(k, 1)
-            4. Don’t move any classifiers out of models
-            5. n_models <- k
-            6. Make prediction, compute alpha, update weights
-            7. Don’t restore any classifiers to models
-            8. significances <- significances_bak
-            9. Update last k significances
-            10. n_models <- n_models_bak
-
-            B, C, E) n > 0, k > 0, n_models == n + k
-            1. n_models_bak <- n_models
-            2. significances_bak <- significances
-            3. significances = vector(k, 1)
-            4. Move first n classifiers to models_bak
-            5. n_models <- k
-            6. Make prediction, compute alpha, update weights
-            7. Insert classifiers in models_bak to be the first n models
-            8. significances <- significances_bak
-            9. Update last k significances
-            10. n_models <- n_models_bak
-        */
-        //
-        // Make predict with only the last k models
-        //
-        std::unique_ptr<Classifier> model;
-        std::vector<std::unique_ptr<Classifier>> models_bak;
-        // 1. n_models_bak <- n_models 2. significances_bak <- significances
-        auto significance_bak = significanceModels;
-        auto n_models_bak = n_models;
-        // 3. significances = vector(k, 1)
-        significanceModels = std::vector<double>(k, 1.0);
-        // 4. Move first n classifiers to models_bak
-        // backup the first n_models - k models (if n_models == k, don't backup any)
-        for (int i = 0; i < n_models - k; ++i) {
-            model = std::move(models[0]);
-            models.erase(models.begin());
-            models_bak.push_back(std::move(model));
-        }
-        assert(models.size() == k);
-        // 5. n_models <- k
-        n_models = k;
-        // 6. Make prediction, compute alpha, update weights
-        auto ypred = predict(X_train);
-        //
-        // Update weights
-        //
-        double alpha_t;
-        bool terminate;
-        std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
-        //
-        // Restore the models if needed
-        //
-        // 7. Insert classifiers in models_bak to be the first n models
-        // if n_models_bak == k, don't restore any, because none of them were moved
-        if (k != n_models_bak) {
-            // Insert in the same order as they were extracted
-            int bak_size = models_bak.size();
-            for (int i = 0; i < bak_size; ++i) {
-                model = std::move(models_bak[bak_size - 1 - i]);
-                models_bak.erase(models_bak.end() - 1);
-                models.insert(models.begin(), std::move(model));
-            }
-        }
-        // 8. significances <- significances_bak
-        significanceModels = significance_bak;
-        //
-        // Update the significance of the last k models
-        //
-        // 9. Update last k significances
-        for (int i = 0; i < k; ++i) {
-            significanceModels[n_models_bak - k + i] = alpha_t;
-        }
-        // 10. n_models <- n_models_bak
-        n_models = n_models_bak;
-        return { weights, alpha_t, terminate };
-    }
-    std::vector<int> BoostAODE::initializeModels()
-    {
-        std::vector<int> featuresUsed;
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-        int maxFeatures = 0;
-        if (select_features_algorithm == SelectFeatures.CFS) {
-            featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
-        } else if (select_features_algorithm == SelectFeatures.IWSS) {
-            if (threshold < 0 || threshold >0.5) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
-            }
-            featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        } else if (select_features_algorithm == SelectFeatures.FCBF) {
-            if (threshold < 1e-7 || threshold > 1) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
-            }
-            featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        }
-        featureSelector->fit();
-        auto cfsFeatures = featureSelector->getFeatures();
-        auto scores = featureSelector->getScores();
-        for (const int& feature : cfsFeatures) {
-            featuresUsed.push_back(feature);
+        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++;
         }
-        notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
-        delete featureSelector;
-        return featuresUsed;
+        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
         //
-        loguru::set_thread_name("BoostAODE");
-        loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
-        loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
+        // loguru::set_thread_name("BoostAODE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
 
         // Algorithm based on the adaboost algorithm for classification
         // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
@@ -282,7 +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
@@ -318,13 +83,13 @@ namespace bayesnet {
             );
             int k = bisection ? pow(2, tolerance) : 1;
             int counter = 0; // The model counter of the current pack
-            VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
             while (counter++ < k && featureSelection.size() > 0) {
                 auto feature = featureSelection[0];
                 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);
@@ -337,7 +102,7 @@ namespace bayesnet {
                 models.push_back(std::move(model));
                 significanceModels.push_back(alpha_t);
                 n_models++;
-                VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
             }
             if (block_update) {
                 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -351,10 +116,10 @@ namespace bayesnet {
                     improvement = accuracy - priorAccuracy;
                 }
                 if (improvement < convergence_threshold) {
-                    VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance++;
                 } else {
-                    VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance = 0; // Reset the counter if the model performs better
                     numItemsPack = 0;
                 }
@@ -366,13 +131,13 @@ namespace bayesnet {
                     priorAccuracy = accuracy;
                 }
             }
-            VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
             finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
         }
         if (tolerance > maxTolerance) {
             if (numItemsPack < n_models) {
                 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-                VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
                 for (int i = 0; i < numItemsPack; ++i) {
                     significanceModels.pop_back();
                     models.pop_back();
@@ -380,7 +145,7 @@ namespace bayesnet {
                 }
             } else {
                 notes.push_back("Convergence threshold reached & 0 models eliminated");
-                VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
             }
         }
         if (featuresUsed.size() != features.size()) {

bayesnet/ensembles/BoostAODE.h

@@ -6,45 +6,21 @@
 
 #ifndef BOOSTAODE_H
 #define BOOSTAODE_H
-#include <map>
+#include <string>
+#include <vector>
 #include "bayesnet/classifiers/SPODE.h"
-#include "bayesnet/feature_selection/FeatureSelect.h"
-#include "Ensemble.h"
+#include "Boost.h"
+
 namespace bayesnet {
-    const struct {
-        std::string CFS = "CFS";
-        std::string FCBF = "FCBF";
-        std::string IWSS = "IWSS";
-    }SelectFeatures;
-    const struct {
-        std::string ASC = "asc";
-        std::string DESC = "desc";
-        std::string RAND = "rand";
-    }Orders;
-    class BoostAODE : public Ensemble {
+    class BoostAODE : public Boost {
     public:
         explicit BoostAODE(bool predict_voting = false);
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
-        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
     protected:
-        void buildModel(const torch::Tensor& weights) override;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
-        std::vector<int> initializeModels();
-        torch::Tensor X_train, y_train, X_test, y_test;
-        // Hyperparameters
-        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
-        int maxTolerance = 3;
-        std::string order_algorithm; // order to process the KBest features asc, desc, rand
-        bool convergence = true; //if true, stop when the model does not improve
-        bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
-        bool selectFeatures = false; // if true, use feature selection
-        std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
-        FeatureSelect* featureSelector = nullptr;
-        double threshold = -1;
-        bool block_update = false;
+        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);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred += ypredict * significanceModels[i];
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            auto ypredict = models[i]->predict_proba(X);
+            y_pred += ypredict * significanceModels[i];
         }
         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);
-                assert(ypredict.size() == y_pred.size());
-                assert(ypredict[0].size() == y_pred[0].size());
-                std::lock_guard<std::mutex> lock(mtx);
-                // Multiply each prediction by the significance of the model and then add it to the final prediction
-                for (auto j = 0; j < ypredict.size(); ++j) {
-                    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 ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.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
+            for (auto j = 0; j < ypredict.size(); ++j) {
+                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; });
+            }
         }
         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);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
         }
         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) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
-        maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
+    Network::Network(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
+        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]() {
-                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
-                });
+            semaphore.acquire();
+            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) {
-            const torch::Tensor sample = samples.index({ "...", i });
+        auto worker = [&](const torch::Tensor& sample, int 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);
-            // Find the class with the maximum posterior probability
-            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
-            int predictedClass = distance(classProbabilities.begin(), maxElem);
-            predictions.push_back(predictedClass);
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            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;
-        std::mutex mtx;
+        auto completeEvidence = std::map<std::string, int>(evidence);
         for (int i = 0; i < classNumStates; ++i) {
-            threads.emplace_back([this, &result, &evidence, i, &mtx]() {
-                auto completeEvidence = std::map<std::string, int>(evidence);
-                completeEvidence[getClassName()] = i;
-                double factor = computeFactor(completeEvidence);
-                std::lock_guard<std::mutex> lock(mtx);
-                result[i] = factor;
-                });
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            completeEvidence[getClassName()] = i;
+            double partial = 1.0;
+            for (auto& node : getNodes()) {
+                partial *= node.second->getFactorValue(completeEvidence);
+            }
+            result[i] = partial;
         }
         // 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 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& 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 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, 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, 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);
-        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 completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
+        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 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.push_back(dataset.index({ name_index, n_sample }));
+            coordinates.clear();
+            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");
-        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
+        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/BayesMetrics.cc

@@ -30,6 +30,53 @@ namespace bayesnet {
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
     }
+    std::vector<std::pair<int, int>> Metrics::SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending, unsigned k)
+    {
+        // Return the K Best features 
+        auto n = features.size();
+        // compute scores
+        scoresKPairs.clear();
+        pairsKBest.clear();
+        auto labels = samples.index({ -1, "..." });
+        for (int i = 0; i < n - 1; ++i) {
+            if (std::find(featuresExcluded.begin(), featuresExcluded.end(), i) != featuresExcluded.end()) {
+                continue;
+            }
+            for (int j = i + 1; j < n; ++j) {
+                if (std::find(featuresExcluded.begin(), featuresExcluded.end(), j) != featuresExcluded.end()) {
+                    continue;
+                }
+                auto key = std::make_pair(i, j);
+                auto value = conditionalMutualInformation(samples.index({ i, "..." }), samples.index({ j, "..." }), labels, weights);
+                scoresKPairs.push_back({ key, value });
+            }
+        }
+        // sort scores
+        if (ascending) {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second < b.second; });
+
+        } else {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second > b.second; });
+        }
+        for (auto& [pairs, score] : scoresKPairs) {
+            pairsKBest.push_back(pairs);
+        }
+        if (k != 0 && k < pairsKBest.size()) {
+            if (ascending) {
+                int limit = pairsKBest.size() - k;
+                for (int i = 0; i < limit; i++) {
+                    pairsKBest.erase(pairsKBest.begin());
+                    scoresKPairs.erase(scoresKPairs.begin());
+                }
+            } else {
+                pairsKBest.resize(k);
+                scoresKPairs.resize(k);
+            }
+        }
+        return pairsKBest;
+    }
     std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
     {
         // Return the K Best features 
@@ -69,7 +116,10 @@ namespace bayesnet {
     {
         return scoresKBest;
     }
-
+    std::vector<std::pair<std::pair<int, int>, double>> Metrics::getScoresKPairs() const
+    {
+        return scoresKPairs;
+    }
     torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights)
     {
         auto result = std::vector<double>();
@@ -148,24 +198,20 @@ namespace bayesnet {
         }
         return entropyValue;
     }
-    // H(Y|X,C) = sum_{x in X, c in C} p(x,c) H(Y|X=x,C=c)
+    // H(X|Y,C) = sum_{y in Y, c in C} p(x,c) H(X|Y=y,C=c)
     double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
     {
         // Ensure the tensors are of the same length
         assert(firstFeature.size(0) == secondFeature.size(0) && firstFeature.size(0) == labels.size(0) && firstFeature.size(0) == weights.size(0));
-
         // Convert tensors to vectors for easier processing
         auto firstFeatureData = firstFeature.accessor<int, 1>();
         auto secondFeatureData = secondFeature.accessor<int, 1>();
         auto labelsData = labels.accessor<int, 1>();
         auto weightsData = weights.accessor<double, 1>();
-
         int numSamples = firstFeature.size(0);
-
         // Maps for joint and marginal probabilities
         std::map<std::tuple<int, int, int>, double> jointCount;
         std::map<std::tuple<int, int>, double> marginalCount;
-
         // Compute joint and marginal counts
         for (int i = 0; i < numSamples; ++i) {
             auto keyJoint = std::make_tuple(firstFeatureData[i], labelsData[i], secondFeatureData[i]);
@@ -174,34 +220,29 @@ namespace bayesnet {
             jointCount[keyJoint] += weightsData[i];
             marginalCount[keyMarginal] += weightsData[i];
         }
-
         // Total weight sum
         double totalWeight = torch::sum(weights).item<double>();
         if (totalWeight == 0)
             return 0;
-
         // Compute the conditional entropy
         double conditionalEntropy = 0.0;
-
         for (const auto& [keyJoint, jointFreq] : jointCount) {
             auto [x, c, y] = keyJoint;
             auto keyMarginal = std::make_tuple(x, c);
-
-            double p_xc = marginalCount[keyMarginal] / totalWeight;
+            //double p_xc = marginalCount[keyMarginal] / totalWeight;
             double p_y_given_xc = jointFreq / marginalCount[keyMarginal];
-
             if (p_y_given_xc > 0) {
                 conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
             }
         }
         return conditionalEntropy;
     }
-    // I(X;Y) = H(Y) - H(Y|X)
+    // I(X;Y) = H(Y) - H(Y|X) ; I(X;Y) >= 0
     double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
-        return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
+        return std::max(entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights), 0.0);
     }
-    // I(X;Y|C) = H(Y|C) - H(Y|X,C)
+    // I(X;Y|C) = H(X|C) - H(X|Y,C) >= 0
     double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
     {
         return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);

bayesnet/utils/BayesMetrics.h

@@ -16,7 +16,9 @@ namespace bayesnet {
         Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
         Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
+        std::vector<std::pair<int, int>> SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending = false, unsigned k = 0);
         std::vector<double> getScoresKBest() const;
+        std::vector<std::pair<std::pair<int, int>, double>> getScoresKPairs() const;
         double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
         double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
         torch::Tensor conditionalEdge(const torch::Tensor& weights);
@@ -33,7 +35,7 @@ namespace bayesnet {
         std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
         {
             std::vector<std::pair<T, T>> result;
-            for (int i = 0; i < source.size(); ++i) {
+            for (int i = 0; i < source.size() - 1; ++i) {
                 T temp = source[i];
                 for (int j = i + 1; j < source.size(); ++j) {
                     result.push_back({ temp, source[j] });
@@ -52,6 +54,8 @@ namespace bayesnet {
         int classNumStates = 0;
         std::vector<double> scoresKBest;
         std::vector<int> featuresKBest; // sorted indices of the features
+        std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
+        std::vector<std::pair<std::pair<int, int>, double>> scoresKPairs;
         double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
     };
 }

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/BoostAODE.md

@@ -27,4 +27,4 @@ The hyperparameters defined in the algorithm are:
 
 ## Operation
 
-### [Algorithm](./algorithm.md)
+### [Base Algorithm](./algorithm.md)

html/bayesnet/BaseClassifier.h.func-c.html

@@ -1,90 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/BaseClassifier.h - functions</title>
-  <link rel="stylesheet" type="text/css" href="../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (<a href="BaseClassifier.h.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
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-            <td class="headerValue">BayesNet Coverage Report</td>
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-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
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-              
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-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">bayesnet::BaseClassifier::~BaseClassifier()</a></td>
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-              
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-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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html/bayesnet/BaseClassifier.h.func.html

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-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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-            <tr>
-              <td width="100%">
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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
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-          <tr>
-            <td class="headerItem">Legend:</td>
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-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
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-              
-              
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-            <tr>
-              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">bayesnet::BaseClassifier::~BaseClassifier()</a></td>
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-              <td class="coverFnHi">1680</td>
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-          <br>
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html/bayesnet/BaseClassifier.h.gcov.frameset.html

@@ -1,19 +0,0 @@
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html/bayesnet/BaseClassifier.h.gcov.html

@@ -1,129 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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-  <link rel="stylesheet" type="text/css" href="../gcov.css">
-</head>
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-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (source / <a href="BaseClassifier.h.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <table cellpadding=0 cellspacing=0 border=0>
-            <tr>
-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #pragma once</span>
-<span id="L8"><span class="lineNum">       8</span>              : #include &lt;vector&gt;</span>
-<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
-<span id="L10"><span class="lineNum">      10</span>              : #include &lt;nlohmann/json.hpp&gt;</span>
-<span id="L11"><span class="lineNum">      11</span>              : namespace bayesnet {</span>
-<span id="L12"><span class="lineNum">      12</span>              :     enum status_t { NORMAL, WARNING, ERROR };</span>
-<span id="L13"><span class="lineNum">      13</span>              :     class BaseClassifier {</span>
-<span id="L14"><span class="lineNum">      14</span>              :     public:</span>
-<span id="L15"><span class="lineNum">      15</span>              :         // X is nxm std::vector, y is nx1 std::vector</span>
-<span id="L16"><span class="lineNum">      16</span>              :         virtual BaseClassifier&amp; fit(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) = 0;</span>
-<span id="L17"><span class="lineNum">      17</span>              :         // X is nxm tensor, y is nx1 tensor</span>
-<span id="L18"><span class="lineNum">      18</span>              :         virtual BaseClassifier&amp; fit(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) = 0;</span>
-<span id="L19"><span class="lineNum">      19</span>              :         virtual BaseClassifier&amp; fit(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) = 0;</span>
-<span id="L20"><span class="lineNum">      20</span>              :         virtual BaseClassifier&amp; fit(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) = 0;</span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC tlaBgGNC">        1680 :         virtual ~BaseClassifier() = default;</span></span>
-<span id="L22"><span class="lineNum">      22</span>              :         torch::Tensor virtual predict(torch::Tensor&amp; X) = 0;</span>
-<span id="L23"><span class="lineNum">      23</span>              :         std::vector&lt;int&gt; virtual predict(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</span>
-<span id="L24"><span class="lineNum">      24</span>              :         torch::Tensor virtual predict_proba(torch::Tensor&amp; X) = 0;</span>
-<span id="L25"><span class="lineNum">      25</span>              :         std::vector&lt;std::vector&lt;double&gt;&gt; virtual predict_proba(std::vector&lt;std::vector&lt;int &gt;&gt;&amp; X) = 0;</span>
-<span id="L26"><span class="lineNum">      26</span>              :         status_t virtual getStatus() const = 0;</span>
-<span id="L27"><span class="lineNum">      27</span>              :         float virtual score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) = 0;</span>
-<span id="L28"><span class="lineNum">      28</span>              :         float virtual score(torch::Tensor&amp; X, torch::Tensor&amp; y) = 0;</span>
-<span id="L29"><span class="lineNum">      29</span>              :         int virtual getNumberOfNodes()const = 0;</span>
-<span id="L30"><span class="lineNum">      30</span>              :         int virtual getNumberOfEdges()const = 0;</span>
-<span id="L31"><span class="lineNum">      31</span>              :         int virtual getNumberOfStates() const = 0;</span>
-<span id="L32"><span class="lineNum">      32</span>              :         int virtual getClassNumStates() const = 0;</span>
-<span id="L33"><span class="lineNum">      33</span>              :         std::vector&lt;std::string&gt; virtual show() const = 0;</span>
-<span id="L34"><span class="lineNum">      34</span>              :         std::vector&lt;std::string&gt; virtual graph(const std::string&amp; title = &quot;&quot;) const = 0;</span>
-<span id="L35"><span class="lineNum">      35</span>              :         virtual std::string getVersion() = 0;</span>
-<span id="L36"><span class="lineNum">      36</span>              :         std::vector&lt;std::string&gt; virtual topological_order() = 0;</span>
-<span id="L37"><span class="lineNum">      37</span>              :         std::vector&lt;std::string&gt; virtual getNotes() const = 0;</span>
-<span id="L38"><span class="lineNum">      38</span>              :         std::string virtual dump_cpt()const = 0;</span>
-<span id="L39"><span class="lineNum">      39</span>              :         virtual void setHyperparameters(const nlohmann::json&amp; hyperparameters) = 0;</span>
-<span id="L40"><span class="lineNum">      40</span>              :         std::vector&lt;std::string&gt;&amp; getValidHyperparameters() { return validHyperparameters; }</span>
-<span id="L41"><span class="lineNum">      41</span>              :     protected:</span>
-<span id="L42"><span class="lineNum">      42</span>              :         virtual void trainModel(const torch::Tensor&amp; weights) = 0;</span>
-<span id="L43"><span class="lineNum">      43</span>              :         std::vector&lt;std::string&gt; validHyperparameters;</span>
-<span id="L44"><span class="lineNum">      44</span>              :     };</span>
-<span id="L45"><span class="lineNum">      45</span>              : }</span>
-        </pre>
-              </td>
-            </tr>
-          </table>
-          <br>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
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-</html>

html/bayesnet/BaseClassifier.h.gcov.overview.html

@@ -1,32 +0,0 @@
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html/bayesnet/classifiers/Classifier.cc.func-c.html

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-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (<a href="Classifier.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">126</td>
-            <td class="headerCovTableEntry">126</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">24</td>
-            <td class="headerCovTableEntry">24</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
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-              
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-              
-              <td class="coverFnHi">16</td>
-              
-              
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-              
-              <td class="coverFnHi">16</td>
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-              
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-              
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-              
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-              
-              <td class="coverFnHi">24</td>
-              
-              
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-              
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-              
-              
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-            <tr>
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-              
-              <td class="coverFnHi">128</td>
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-              
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-              
-              <td class="coverFnHi">136</td>
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-              
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-              
-              <td class="coverFnHi">332</td>
-              
-              
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-              
-              <td class="coverFnHi">332</td>
-              
-              
-            </tr>
-            <tr>
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-              
-              <td class="coverFnHi">340</td>
-              
-              
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-              
-              <td class="coverFnHi">348</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L122">bayesnet::Classifier::predict_proba(std::vector&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::allocator&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">548</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L72">bayesnet::Classifier::fit(at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;, at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">660</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L66">bayesnet::Classifier::fit(at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">852</td>
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-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L115">bayesnet::Classifier::predict_proba(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">1484</td>
-              
-              
-            </tr>
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-              
-              <td class="coverFnHi">1576</td>
-              
-              
-            </tr>
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-              
-              <td class="coverFnHi">1576</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L12">bayesnet::Classifier::build(std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;, at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">1760</td>
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-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L77">bayesnet::Classifier::checkFitParameters()</a></td>
-              
-              <td class="coverFnHi">1760</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L94">bayesnet::Classifier::predict(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">1844</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L10">bayesnet::Classifier::Classifier(bayesnet::Network)</a></td>
-              
-              <td class="coverFnHi">2240</td>
-              
-              
-            </tr>
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-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
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html/bayesnet/classifiers/Classifier.cc.func.html

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-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (<a href="Classifier.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">126</td>
-            <td class="headerCovTableEntry">126</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">24</td>
-            <td class="headerCovTableEntry">24</td>
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-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
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-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-          <center>
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-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="Classifier.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
-              
-              
-            </tr>
-            <tr>
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-              
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-              
-            </tr>
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-              
-            </tr>
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-              
-              <td class="coverFnHi">1760</td>
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-              
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-              
-            </tr>
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-              
-              
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-            <tr>
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-              
-              <td class="coverFnHi">4</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L47">bayesnet::Classifier::fit(at::Tensor&amp;, at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">128</td>
-              
-              
-            </tr>
-            <tr>
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-              
-              <td class="coverFnHi">852</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L72">bayesnet::Classifier::fit(at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;, at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">660</td>
-              
-              
-            </tr>
-            <tr>
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-              
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-              
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-              
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-              
-              
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-              
-              <td class="coverFnHi">332</td>
-              
-              
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-              
-              <td class="coverFnHi">24</td>
-              
-              
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-              
-              <td class="coverFnHi">1844</td>
-              
-              
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-              
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-              
-              <td class="coverFnHi">1484</td>
-              
-              
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-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L122">bayesnet::Classifier::predict_proba(std::vector&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::allocator&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt;&amp;)</a></td>
-              
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-              
-              <td class="coverFnHi">112</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L142">bayesnet::Classifier::score(std::vector&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::allocator&lt;std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt;&amp;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">16</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L186">bayesnet::Classifier::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json&lt;std::map, std::vector, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector&lt;unsigned char, std::allocator&lt;unsigned char&gt; &gt;, void&gt; const&amp;)</a></td>
-              
-              <td class="coverFnHi">92</td>
-              
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-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L149">bayesnet::Classifier::show[abi:cxx11]() const</a></td>
-              
-              <td class="coverFnHi">24</td>
-              
-              
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-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L178">bayesnet::Classifier::topological_order[abi:cxx11]()</a></td>
-              
-              <td class="coverFnHi">4</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L42">bayesnet::Classifier::trainModel(at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">1576</td>
-              
-              
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-          <br>
-          </center>
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-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
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-</html>

html/bayesnet/classifiers/Classifier.cc.gcov.frameset.html

@@ -1,19 +0,0 @@
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-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
-
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html/bayesnet/classifiers/Classifier.cc.gcov.html

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-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.cc</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
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-<body>
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-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (source / <a href="Classifier.cc.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">126</td>
-            <td class="headerCovTableEntry">126</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">24</td>
-            <td class="headerCovTableEntry">24</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
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-          </table>
-
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-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #include &lt;sstream&gt;</span>
-<span id="L8"><span class="lineNum">       8</span>              : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
-<span id="L9"><span class="lineNum">       9</span>              : #include &quot;Classifier.h&quot;</span>
-<span id="L10"><span class="lineNum">      10</span>              : </span>
-<span id="L11"><span class="lineNum">      11</span>              : namespace bayesnet {</span>
-<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC tlaBgGNC">        2240 :     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</span></span>
-<span id="L13"><span class="lineNum">      13</span>              :     const std::string CLASSIFIER_NOT_FITTED = &quot;Classifier has not been fitted&quot;;</span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">        1760 :     Classifier&amp; Classifier::build(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)</span></span>
-<span id="L15"><span class="lineNum">      15</span>              :     {</span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">        1760 :         this-&gt;features = features;</span></span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">        1760 :         this-&gt;className = className;</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">        1760 :         this-&gt;states = states;</span></span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">        1760 :         m = dataset.size(1);</span></span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">        1760 :         n = features.size();</span></span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">        1760 :         checkFitParameters();</span></span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">        1728 :         auto n_classes = states.at(className).size();</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">        1728 :         metrics = Metrics(dataset, features, className, n_classes);</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">        1728 :         model.initialize();</span></span>
-<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">        1728 :         buildModel(weights);</span></span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">        1728 :         trainModel(weights);</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">        1712 :         fitted = true;</span></span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">        1712 :         return *this;</span></span>
-<span id="L29"><span class="lineNum">      29</span>              :     }</span>
-<span id="L30"><span class="lineNum">      30</span> <span class="tlaGNC">         340 :     void Classifier::buildDataset(torch::Tensor&amp; ytmp)</span></span>
-<span id="L31"><span class="lineNum">      31</span>              :     {</span>
-<span id="L32"><span class="lineNum">      32</span>              :         try {</span>
-<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">         340 :             auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</span></span>
-<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">        1052 :             dataset = torch::cat({ dataset, yresized }, 0);</span></span>
-<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         340 :         }</span></span>
-<span id="L36"><span class="lineNum">      36</span> <span class="tlaGNC">          16 :         catch (const std::exception&amp; e) {</span></span>
-<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">          16 :             std::stringstream oss;</span></span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">          16 :             oss &lt;&lt; &quot;* Error in X and y dimensions *\n&quot;;</span></span>
-<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">          16 :             oss &lt;&lt; &quot;X dimensions: &quot; &lt;&lt; dataset.sizes() &lt;&lt; &quot;\n&quot;;</span></span>
-<span id="L40"><span class="lineNum">      40</span> <span class="tlaGNC">          16 :             oss &lt;&lt; &quot;y dimensions: &quot; &lt;&lt; ytmp.sizes();</span></span>
-<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">          16 :             throw std::runtime_error(oss.str());</span></span>
-<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">          32 :         }</span></span>
-<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">         680 :     }</span></span>
-<span id="L44"><span class="lineNum">      44</span> <span class="tlaGNC">        1576 :     void Classifier::trainModel(const torch::Tensor&amp; weights)</span></span>
-<span id="L45"><span class="lineNum">      45</span>              :     {</span>
-<span id="L46"><span class="lineNum">      46</span> <span class="tlaGNC">        1576 :         model.fit(dataset, weights, features, className, states);</span></span>
-<span id="L47"><span class="lineNum">      47</span> <span class="tlaGNC">        1576 :     }</span></span>
-<span id="L48"><span class="lineNum">      48</span>              :     // X is nxm where n is the number of features and m the number of samples</span>
-<span id="L49"><span class="lineNum">      49</span> <span class="tlaGNC">         128 :     Classifier&amp; Classifier::fit(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)</span></span>
-<span id="L50"><span class="lineNum">      50</span>              :     {</span>
-<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">         128 :         dataset = X;</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">         128 :         buildDataset(y);</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">         120 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
-<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">         208 :         return build(features, className, states, weights);</span></span>
-<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">         120 :     }</span></span>
-<span id="L56"><span class="lineNum">      56</span>              :     // X is nxm where n is the number of features and m the number of samples</span>
-<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">         136 :     Classifier&amp; Classifier::fit(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)</span></span>
-<span id="L58"><span class="lineNum">      58</span>              :     {</span>
-<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">         136 :         dataset = torch::zeros({ static_cast&lt;int&gt;(X.size()), static_cast&lt;int&gt;(X[0].size()) }, torch::kInt32);</span></span>
-<span id="L60"><span class="lineNum">      60</span> <span class="tlaGNC">         976 :         for (int i = 0; i &lt; X.size(); ++i) {</span></span>
-<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">        3360 :             dataset.index_put_({ i, &quot;...&quot; }, torch::tensor(X[i], torch::kInt32));</span></span>
-<span id="L62"><span class="lineNum">      62</span>              :         }</span>
-<span id="L63"><span class="lineNum">      63</span> <span class="tlaGNC">         136 :         auto ytmp = torch::tensor(y, torch::kInt32);</span></span>
-<span id="L64"><span class="lineNum">      64</span> <span class="tlaGNC">         136 :         buildDataset(ytmp);</span></span>
-<span id="L65"><span class="lineNum">      65</span> <span class="tlaGNC">         128 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
-<span id="L66"><span class="lineNum">      66</span> <span class="tlaGNC">         240 :         return build(features, className, states, weights);</span></span>
-<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">         992 :     }</span></span>
-<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">         852 :     Classifier&amp; Classifier::fit(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)</span></span>
-<span id="L69"><span class="lineNum">      69</span>              :     {</span>
-<span id="L70"><span class="lineNum">      70</span> <span class="tlaGNC">         852 :         this-&gt;dataset = dataset;</span></span>
-<span id="L71"><span class="lineNum">      71</span> <span class="tlaGNC">         852 :         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</span></span>
-<span id="L72"><span class="lineNum">      72</span> <span class="tlaGNC">        1704 :         return build(features, className, states, weights);</span></span>
-<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">         852 :     }</span></span>
-<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         660 :     Classifier&amp; Classifier::fit(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)</span></span>
-<span id="L75"><span class="lineNum">      75</span>              :     {</span>
-<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         660 :         this-&gt;dataset = dataset;</span></span>
-<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">         660 :         return build(features, className, states, weights);</span></span>
-<span id="L78"><span class="lineNum">      78</span>              :     }</span>
-<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">        1760 :     void Classifier::checkFitParameters()</span></span>
-<span id="L80"><span class="lineNum">      80</span>              :     {</span>
-<span id="L81"><span class="lineNum">      81</span> <span class="tlaGNC">        1760 :         if (torch::is_floating_point(dataset)) {</span></span>
-<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">           8 :             throw std::invalid_argument(&quot;dataset (X, y) must be of type Integer&quot;);</span></span>
-<span id="L83"><span class="lineNum">      83</span>              :         }</span>
-<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">        1752 :         if (dataset.size(0) - 1 != features.size()) {</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">           8 :             throw std::invalid_argument(&quot;Classifier: X &quot; + std::to_string(dataset.size(0) - 1) + &quot; and features &quot; + std::to_string(features.size()) + &quot; must have the same number of features&quot;);</span></span>
-<span id="L86"><span class="lineNum">      86</span>              :         }</span>
-<span id="L87"><span class="lineNum">      87</span> <span class="tlaGNC">        1744 :         if (states.find(className) == states.end()) {</span></span>
-<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">           8 :             throw std::invalid_argument(&quot;class name not found in states&quot;);</span></span>
-<span id="L89"><span class="lineNum">      89</span>              :         }</span>
-<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">       32996 :         for (auto feature : features) {</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">       31268 :             if (states.find(feature) == states.end()) {</span></span>
-<span id="L92"><span class="lineNum">      92</span> <span class="tlaGNC">           8 :                 throw std::invalid_argument(&quot;feature [&quot; + feature + &quot;] not found in states&quot;);</span></span>
-<span id="L93"><span class="lineNum">      93</span>              :             }</span>
-<span id="L94"><span class="lineNum">      94</span> <span class="tlaGNC">       31268 :         }</span></span>
-<span id="L95"><span class="lineNum">      95</span> <span class="tlaGNC">        1728 :     }</span></span>
-<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">        1844 :     torch::Tensor Classifier::predict(torch::Tensor&amp; X)</span></span>
-<span id="L97"><span class="lineNum">      97</span>              :     {</span>
-<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">        1844 :         if (!fitted) {</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">          16 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
-<span id="L100"><span class="lineNum">     100</span>              :         }</span>
-<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">        1828 :         return model.predict(X);</span></span>
-<span id="L102"><span class="lineNum">     102</span>              :     }</span>
-<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">          16 :     std::vector&lt;int&gt; Classifier::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
-<span id="L104"><span class="lineNum">     104</span>              :     {</span>
-<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">          16 :         if (!fitted) {</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">           8 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
-<span id="L107"><span class="lineNum">     107</span>              :         }</span>
-<span id="L108"><span class="lineNum">     108</span> <span class="tlaGNC">           8 :         auto m_ = X[0].size();</span></span>
-<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">           8 :         auto n_ = X.size();</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">           8 :         std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</span></span>
-<span id="L111"><span class="lineNum">     111</span> <span class="tlaGNC">          40 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
-<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">          64 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</span></span>
-<span id="L113"><span class="lineNum">     113</span>              :         }</span>
-<span id="L114"><span class="lineNum">     114</span> <span class="tlaGNC">           8 :         auto yp = model.predict(Xd);</span></span>
-<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">          16 :         return yp;</span></span>
-<span id="L116"><span class="lineNum">     116</span> <span class="tlaGNC">           8 :     }</span></span>
-<span id="L117"><span class="lineNum">     117</span> <span class="tlaGNC">        1484 :     torch::Tensor Classifier::predict_proba(torch::Tensor&amp; X)</span></span>
-<span id="L118"><span class="lineNum">     118</span>              :     {</span>
-<span id="L119"><span class="lineNum">     119</span> <span class="tlaGNC">        1484 :         if (!fitted) {</span></span>
-<span id="L120"><span class="lineNum">     120</span> <span class="tlaGNC">           8 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
-<span id="L121"><span class="lineNum">     121</span>              :         }</span>
-<span id="L122"><span class="lineNum">     122</span> <span class="tlaGNC">        1476 :         return model.predict_proba(X);</span></span>
-<span id="L123"><span class="lineNum">     123</span>              :     }</span>
-<span id="L124"><span class="lineNum">     124</span> <span class="tlaGNC">         548 :     std::vector&lt;std::vector&lt;double&gt;&gt; Classifier::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
-<span id="L125"><span class="lineNum">     125</span>              :     {</span>
-<span id="L126"><span class="lineNum">     126</span> <span class="tlaGNC">         548 :         if (!fitted) {</span></span>
-<span id="L127"><span class="lineNum">     127</span> <span class="tlaGNC">           8 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
-<span id="L128"><span class="lineNum">     128</span>              :         }</span>
-<span id="L129"><span class="lineNum">     129</span> <span class="tlaGNC">         540 :         auto m_ = X[0].size();</span></span>
-<span id="L130"><span class="lineNum">     130</span> <span class="tlaGNC">         540 :         auto n_ = X.size();</span></span>
-<span id="L131"><span class="lineNum">     131</span> <span class="tlaGNC">         540 :         std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</span></span>
-<span id="L132"><span class="lineNum">     132</span>              :         // Convert to nxm vector</span>
-<span id="L133"><span class="lineNum">     133</span> <span class="tlaGNC">        5040 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
-<span id="L134"><span class="lineNum">     134</span> <span class="tlaGNC">        9000 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</span></span>
-<span id="L135"><span class="lineNum">     135</span>              :         }</span>
-<span id="L136"><span class="lineNum">     136</span> <span class="tlaGNC">         540 :         auto yp = model.predict_proba(Xd);</span></span>
-<span id="L137"><span class="lineNum">     137</span> <span class="tlaGNC">        1080 :         return yp;</span></span>
-<span id="L138"><span class="lineNum">     138</span> <span class="tlaGNC">         540 :     }</span></span>
-<span id="L139"><span class="lineNum">     139</span> <span class="tlaGNC">         112 :     float Classifier::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</span></span>
-<span id="L140"><span class="lineNum">     140</span>              :     {</span>
-<span id="L141"><span class="lineNum">     141</span> <span class="tlaGNC">         112 :         torch::Tensor y_pred = predict(X);</span></span>
-<span id="L142"><span class="lineNum">     142</span> <span class="tlaGNC">         208 :         return (y_pred == y).sum().item&lt;float&gt;() / y.size(0);</span></span>
-<span id="L143"><span class="lineNum">     143</span> <span class="tlaGNC">         104 :     }</span></span>
-<span id="L144"><span class="lineNum">     144</span> <span class="tlaGNC">          16 :     float Classifier::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</span></span>
-<span id="L145"><span class="lineNum">     145</span>              :     {</span>
-<span id="L146"><span class="lineNum">     146</span> <span class="tlaGNC">          16 :         if (!fitted) {</span></span>
-<span id="L147"><span class="lineNum">     147</span> <span class="tlaGNC">           8 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</span></span>
-<span id="L148"><span class="lineNum">     148</span>              :         }</span>
-<span id="L149"><span class="lineNum">     149</span> <span class="tlaGNC">           8 :         return model.score(X, y);</span></span>
-<span id="L150"><span class="lineNum">     150</span>              :     }</span>
-<span id="L151"><span class="lineNum">     151</span> <span class="tlaGNC">          24 :     std::vector&lt;std::string&gt; Classifier::show() const</span></span>
-<span id="L152"><span class="lineNum">     152</span>              :     {</span>
-<span id="L153"><span class="lineNum">     153</span> <span class="tlaGNC">          24 :         return model.show();</span></span>
-<span id="L154"><span class="lineNum">     154</span>              :     }</span>
-<span id="L155"><span class="lineNum">     155</span> <span class="tlaGNC">        1576 :     void Classifier::addNodes()</span></span>
-<span id="L156"><span class="lineNum">     156</span>              :     {</span>
-<span id="L157"><span class="lineNum">     157</span>              :         // Add all nodes to the network</span>
-<span id="L158"><span class="lineNum">     158</span> <span class="tlaGNC">       30872 :         for (const auto&amp; feature : features) {</span></span>
-<span id="L159"><span class="lineNum">     159</span> <span class="tlaGNC">       29296 :             model.addNode(feature);</span></span>
-<span id="L160"><span class="lineNum">     160</span>              :         }</span>
-<span id="L161"><span class="lineNum">     161</span> <span class="tlaGNC">        1576 :         model.addNode(className);</span></span>
-<span id="L162"><span class="lineNum">     162</span> <span class="tlaGNC">        1576 :     }</span></span>
-<span id="L163"><span class="lineNum">     163</span> <span class="tlaGNC">         332 :     int Classifier::getNumberOfNodes() const</span></span>
-<span id="L164"><span class="lineNum">     164</span>              :     {</span>
-<span id="L165"><span class="lineNum">     165</span>              :         // Features does not include class</span>
-<span id="L166"><span class="lineNum">     166</span> <span class="tlaGNC">         332 :         return fitted ? model.getFeatures().size() : 0;</span></span>
-<span id="L167"><span class="lineNum">     167</span>              :     }</span>
-<span id="L168"><span class="lineNum">     168</span> <span class="tlaGNC">         332 :     int Classifier::getNumberOfEdges() const</span></span>
-<span id="L169"><span class="lineNum">     169</span>              :     {</span>
-<span id="L170"><span class="lineNum">     170</span> <span class="tlaGNC">         332 :         return fitted ? model.getNumEdges() : 0;</span></span>
-<span id="L171"><span class="lineNum">     171</span>              :     }</span>
-<span id="L172"><span class="lineNum">     172</span> <span class="tlaGNC">          24 :     int Classifier::getNumberOfStates() const</span></span>
-<span id="L173"><span class="lineNum">     173</span>              :     {</span>
-<span id="L174"><span class="lineNum">     174</span> <span class="tlaGNC">          24 :         return fitted ? model.getStates() : 0;</span></span>
-<span id="L175"><span class="lineNum">     175</span>              :     }</span>
-<span id="L176"><span class="lineNum">     176</span> <span class="tlaGNC">         348 :     int Classifier::getClassNumStates() const</span></span>
-<span id="L177"><span class="lineNum">     177</span>              :     {</span>
-<span id="L178"><span class="lineNum">     178</span> <span class="tlaGNC">         348 :         return fitted ? model.getClassNumStates() : 0;</span></span>
-<span id="L179"><span class="lineNum">     179</span>              :     }</span>
-<span id="L180"><span class="lineNum">     180</span> <span class="tlaGNC">           4 :     std::vector&lt;std::string&gt; Classifier::topological_order()</span></span>
-<span id="L181"><span class="lineNum">     181</span>              :     {</span>
-<span id="L182"><span class="lineNum">     182</span> <span class="tlaGNC">           4 :         return model.topological_sort();</span></span>
-<span id="L183"><span class="lineNum">     183</span>              :     }</span>
-<span id="L184"><span class="lineNum">     184</span> <span class="tlaGNC">           4 :     std::string Classifier::dump_cpt() const</span></span>
-<span id="L185"><span class="lineNum">     185</span>              :     {</span>
-<span id="L186"><span class="lineNum">     186</span> <span class="tlaGNC">           4 :         return model.dump_cpt();</span></span>
-<span id="L187"><span class="lineNum">     187</span>              :     }</span>
-<span id="L188"><span class="lineNum">     188</span> <span class="tlaGNC">          92 :     void Classifier::setHyperparameters(const nlohmann::json&amp; hyperparameters)</span></span>
-<span id="L189"><span class="lineNum">     189</span>              :     {</span>
-<span id="L190"><span class="lineNum">     190</span> <span class="tlaGNC">          92 :         if (!hyperparameters.empty()) {</span></span>
-<span id="L191"><span class="lineNum">     191</span> <span class="tlaGNC">           8 :             throw std::invalid_argument(&quot;Invalid hyperparameters&quot; + hyperparameters.dump());</span></span>
-<span id="L192"><span class="lineNum">     192</span>              :         }</span>
-<span id="L193"><span class="lineNum">     193</span> <span class="tlaGNC">          84 :     }</span></span>
-<span id="L194"><span class="lineNum">     194</span>              : }</span>
-        </pre>
-              </td>
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-          </table>
-          <br>
-
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-</html>

html/bayesnet/classifiers/Classifier.cc.gcov.overview.html

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html/bayesnet/classifiers/Classifier.h.func-c.html

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-            <tr>
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-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (<a href="Classifier.h.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
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html/bayesnet/classifiers/Classifier.h.func.html

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-            <td width="5%"></td>
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-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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-            <td class="headerValue">BayesNet Coverage Report</td>
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-            <td class="headerCovTableEntry">4</td>
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-          <tr>
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-            <td class="headerValue">2024-05-06 17:54:04</td>
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-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
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-              <td class="coverFn"><a href="Classifier.h.gcov.html#L36">bayesnet::Classifier::getNotes[abi:cxx11]() const</a></td>
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-              <td class="coverFnHi">80</td>
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-              <td class="coverFn"><a href="Classifier.h.gcov.html#L30">bayesnet::Classifier::getStatus() const</a></td>
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-              <td class="coverFnHi">32</td>
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html/bayesnet/classifiers/Classifier.h.gcov.frameset.html

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html/bayesnet/classifiers/Classifier.h.gcov.html

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-
-            <tr>
-              <td width="100%">
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-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (source / <a href="Classifier.h.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
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-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
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-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #ifndef CLASSIFIER_H</span>
-<span id="L8"><span class="lineNum">       8</span>              : #define CLASSIFIER_H</span>
-<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
-<span id="L10"><span class="lineNum">      10</span>              : #include &quot;bayesnet/utils/BayesMetrics.h&quot;</span>
-<span id="L11"><span class="lineNum">      11</span>              : #include &quot;bayesnet/network/Network.h&quot;</span>
-<span id="L12"><span class="lineNum">      12</span>              : #include &quot;bayesnet/BaseClassifier.h&quot;</span>
-<span id="L13"><span class="lineNum">      13</span>              : </span>
-<span id="L14"><span class="lineNum">      14</span>              : namespace bayesnet {</span>
-<span id="L15"><span class="lineNum">      15</span>              :     class Classifier : public BaseClassifier {</span>
-<span id="L16"><span class="lineNum">      16</span>              :     public:</span>
-<span id="L17"><span class="lineNum">      17</span>              :         Classifier(Network model);</span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC tlaBgGNC">        1680 :         virtual ~Classifier() = default;</span></span>
-<span id="L19"><span class="lineNum">      19</span>              :         Classifier&amp; fit(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;</span>
-<span id="L20"><span class="lineNum">      20</span>              :         Classifier&amp; fit(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;</span>
-<span id="L21"><span class="lineNum">      21</span>              :         Classifier&amp; fit(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;</span>
-<span id="L22"><span class="lineNum">      22</span>              :         Classifier&amp; fit(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;</span>
-<span id="L23"><span class="lineNum">      23</span>              :         void addNodes();</span>
-<span id="L24"><span class="lineNum">      24</span>              :         int getNumberOfNodes() const override;</span>
-<span id="L25"><span class="lineNum">      25</span>              :         int getNumberOfEdges() const override;</span>
-<span id="L26"><span class="lineNum">      26</span>              :         int getNumberOfStates() const override;</span>
-<span id="L27"><span class="lineNum">      27</span>              :         int getClassNumStates() const override;</span>
-<span id="L28"><span class="lineNum">      28</span>              :         torch::Tensor predict(torch::Tensor&amp; X) override;</span>
-<span id="L29"><span class="lineNum">      29</span>              :         std::vector&lt;int&gt; predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) override;</span>
-<span id="L30"><span class="lineNum">      30</span>              :         torch::Tensor predict_proba(torch::Tensor&amp; X) override;</span>
-<span id="L31"><span class="lineNum">      31</span>              :         std::vector&lt;std::vector&lt;double&gt;&gt; predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X) override;</span>
-<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         128 :         status_t getStatus() const override { return status; }</span></span>
-<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">          96 :         std::string getVersion() override { return { project_version.begin(), project_version.end() }; };</span></span>
-<span id="L34"><span class="lineNum">      34</span>              :         float score(torch::Tensor&amp; X, torch::Tensor&amp; y) override;</span>
-<span id="L35"><span class="lineNum">      35</span>              :         float score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y) override;</span>
-<span id="L36"><span class="lineNum">      36</span>              :         std::vector&lt;std::string&gt; show() const override;</span>
-<span id="L37"><span class="lineNum">      37</span>              :         std::vector&lt;std::string&gt; topological_order()  override;</span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">          80 :         std::vector&lt;std::string&gt; getNotes() const override { return notes; }</span></span>
-<span id="L39"><span class="lineNum">      39</span>              :         std::string dump_cpt() const override;</span>
-<span id="L40"><span class="lineNum">      40</span>              :         void setHyperparameters(const nlohmann::json&amp; hyperparameters) override; //For classifiers that don't have hyperparameters</span>
-<span id="L41"><span class="lineNum">      41</span>              :     protected:</span>
-<span id="L42"><span class="lineNum">      42</span>              :         bool fitted;</span>
-<span id="L43"><span class="lineNum">      43</span>              :         unsigned int m, n; // m: number of samples, n: number of features</span>
-<span id="L44"><span class="lineNum">      44</span>              :         Network model;</span>
-<span id="L45"><span class="lineNum">      45</span>              :         Metrics metrics;</span>
-<span id="L46"><span class="lineNum">      46</span>              :         std::vector&lt;std::string&gt; features;</span>
-<span id="L47"><span class="lineNum">      47</span>              :         std::string className;</span>
-<span id="L48"><span class="lineNum">      48</span>              :         std::map&lt;std::string, std::vector&lt;int&gt;&gt; states;</span>
-<span id="L49"><span class="lineNum">      49</span>              :         torch::Tensor dataset; // (n+1)xm tensor</span>
-<span id="L50"><span class="lineNum">      50</span>              :         status_t status = NORMAL;</span>
-<span id="L51"><span class="lineNum">      51</span>              :         std::vector&lt;std::string&gt; notes; // Used to store messages occurred during the fit process</span>
-<span id="L52"><span class="lineNum">      52</span>              :         void checkFitParameters();</span>
-<span id="L53"><span class="lineNum">      53</span>              :         virtual void buildModel(const torch::Tensor&amp; weights) = 0;</span>
-<span id="L54"><span class="lineNum">      54</span>              :         void trainModel(const torch::Tensor&amp; weights) override;</span>
-<span id="L55"><span class="lineNum">      55</span>              :         void buildDataset(torch::Tensor&amp; y);</span>
-<span id="L56"><span class="lineNum">      56</span>              :     private:</span>
-<span id="L57"><span class="lineNum">      57</span>              :         Classifier&amp; build(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);</span>
-<span id="L58"><span class="lineNum">      58</span>              :     };</span>
-<span id="L59"><span class="lineNum">      59</span>              : }</span>
-<span id="L60"><span class="lineNum">      60</span>              : #endif</span>
-<span id="L61"><span class="lineNum">      61</span>              : </span>
-<span id="L62"><span class="lineNum">      62</span>              : </span>
-<span id="L63"><span class="lineNum">      63</span>              : </span>
-<span id="L64"><span class="lineNum">      64</span>              : </span>
-<span id="L65"><span class="lineNum">      65</span>              : </span>
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html/bayesnet/classifiers/Classifier.h.gcov.overview.html

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html/bayesnet/classifiers/KDB.cc.func-c.html

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-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
-            <td class="headerCovTableEntry">54</td>
-            <td class="headerCovTableEntry">52</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">5</td>
-            <td class="headerCovTableEntry">5</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDB.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L101">bayesnet::KDB::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">8</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L13">bayesnet::KDB::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json&lt;std::map, std::vector, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector&lt;unsigned char, std::allocator&lt;unsigned char&gt; &gt;, void&gt; const&amp;)</a></td>
-              
-              <td class="coverFnHi">12</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">bayesnet::KDB::buildModel(at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">52</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
-              
-              <td class="coverFnHi">148</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">bayesnet::KDB::add_m_edges(int, std::vector&lt;int, std::allocator&lt;int&gt; &gt;&amp;, at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">344</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
-</body>
-</html>

html/bayesnet/classifiers/KDB.cc.func.html

@@ -1,118 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
-            <td class="headerCovTableEntry">54</td>
-            <td class="headerCovTableEntry">52</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">5</td>
-            <td class="headerCovTableEntry">5</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDB.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
-              
-              <td class="coverFnHi">148</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">bayesnet::KDB::add_m_edges(int, std::vector&lt;int, std::allocator&lt;int&gt; &gt;&amp;, at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">344</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">bayesnet::KDB::buildModel(at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">52</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L101">bayesnet::KDB::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">8</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L13">bayesnet::KDB::setHyperparameters(nlohmann::json_abi_v3_11_3::basic_json&lt;std::map, std::vector, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_3::adl_serializer, std::vector&lt;unsigned char, std::allocator&lt;unsigned char&gt; &gt;, void&gt; const&amp;)</a></td>
-              
-              <td class="coverFnHi">12</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
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-</body>
-</html>

html/bayesnet/classifiers/KDB.cc.gcov.frameset.html

@@ -1,19 +0,0 @@
-        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Frameset//EN">
-
-        <html lang="en">
-
-        <head>
-          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc</title>
-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
-
-        <frameset cols="120,*">
-          <frame src="KDB.cc.gcov.overview.html" name="overview">
-          <frame src="KDB.cc.gcov.html" name="source">
-          <noframes>
-            <center>Frames not supported by your browser!<br></center>
-          </noframes>
-        </frameset>
-
-        </html>

html/bayesnet/classifiers/KDB.cc.gcov.html

@@ -1,195 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (source / <a href="KDB.cc.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
-            <td class="headerCovTableEntry">54</td>
-            <td class="headerCovTableEntry">52</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">5</td>
-            <td class="headerCovTableEntry">5</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <table cellpadding=0 cellspacing=0 border=0>
-            <tr>
-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #include &quot;KDB.h&quot;</span>
-<span id="L8"><span class="lineNum">       8</span>              : </span>
-<span id="L9"><span class="lineNum">       9</span>              : namespace bayesnet {</span>
-<span id="L10"><span class="lineNum">      10</span> <span class="tlaGNC tlaBgGNC">         148 :     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)</span></span>
-<span id="L11"><span class="lineNum">      11</span>              :     {</span>
-<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC">         444 :         validHyperparameters = { &quot;k&quot;, &quot;theta&quot; };</span></span>
-<span id="L13"><span class="lineNum">      13</span>              : </span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         444 :     }</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">          12 :     void KDB::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
-<span id="L16"><span class="lineNum">      16</span>              :     {</span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">          12 :         auto hyperparameters = hyperparameters_;</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">          12 :         if (hyperparameters.contains(&quot;k&quot;)) {</span></span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">           4 :             k = hyperparameters[&quot;k&quot;];</span></span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">           4 :             hyperparameters.erase(&quot;k&quot;);</span></span>
-<span id="L21"><span class="lineNum">      21</span>              :         }</span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">          12 :         if (hyperparameters.contains(&quot;theta&quot;)) {</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">           4 :             theta = hyperparameters[&quot;theta&quot;];</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">           4 :             hyperparameters.erase(&quot;theta&quot;);</span></span>
-<span id="L25"><span class="lineNum">      25</span>              :         }</span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">          12 :         Classifier::setHyperparameters(hyperparameters);</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">          12 :     }</span></span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">          52 :     void KDB::buildModel(const torch::Tensor&amp; weights)</span></span>
-<span id="L29"><span class="lineNum">      29</span>              :     {</span>
-<span id="L30"><span class="lineNum">      30</span>              :         /*</span>
-<span id="L31"><span class="lineNum">      31</span>              :         1. For each feature Xi, compute mutual information, I(X;C),</span>
-<span id="L32"><span class="lineNum">      32</span>              :         where C is the class.</span>
-<span id="L33"><span class="lineNum">      33</span>              :         2. Compute class conditional mutual information I(Xi;XjIC), f or each</span>
-<span id="L34"><span class="lineNum">      34</span>              :         pair of features Xi and Xj, where i#j.</span>
-<span id="L35"><span class="lineNum">      35</span>              :         3. Let the used variable list, S, be empty.</span>
-<span id="L36"><span class="lineNum">      36</span>              :         4. Let the DAG network being constructed, BN, begin with a single</span>
-<span id="L37"><span class="lineNum">      37</span>              :         class node, C.</span>
-<span id="L38"><span class="lineNum">      38</span>              :         5. Repeat until S includes all domain features</span>
-<span id="L39"><span class="lineNum">      39</span>              :         5.1. Select feature Xmax which is not in S and has the largest value</span>
-<span id="L40"><span class="lineNum">      40</span>              :         I(Xmax;C).</span>
-<span id="L41"><span class="lineNum">      41</span>              :         5.2. Add a node to BN representing Xmax.</span>
-<span id="L42"><span class="lineNum">      42</span>              :         5.3. Add an arc from C to Xmax in BN.</span>
-<span id="L43"><span class="lineNum">      43</span>              :         5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span>
-<span id="L44"><span class="lineNum">      44</span>              :         the highest value for I(Xmax;X,jC).</span>
-<span id="L45"><span class="lineNum">      45</span>              :         5.5. Add Xmax to S.</span>
-<span id="L46"><span class="lineNum">      46</span>              :         Compute the conditional probabilility infered by the structure of BN by</span>
-<span id="L47"><span class="lineNum">      47</span>              :         using counts from DB, and output BN.</span>
-<span id="L48"><span class="lineNum">      48</span>              :         */</span>
-<span id="L49"><span class="lineNum">      49</span>              :         // 1. For each feature Xi, compute mutual information, I(X;C),</span>
-<span id="L50"><span class="lineNum">      50</span>              :         // where C is the class.</span>
-<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">          52 :         addNodes();</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">         156 :         const torch::Tensor&amp; y = dataset.index({ -1, &quot;...&quot; });</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">          52 :         std::vector&lt;double&gt; mi;</span></span>
-<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">         396 :         for (auto i = 0; i &lt; features.size(); i++) {</span></span>
-<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">        1032 :             torch::Tensor firstFeature = dataset.index({ i, &quot;...&quot; });</span></span>
-<span id="L56"><span class="lineNum">      56</span> <span class="tlaGNC">         344 :             mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</span></span>
-<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">         344 :         }</span></span>
-<span id="L58"><span class="lineNum">      58</span>              :         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span>
-<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">          52 :         auto conditionalEdgeWeights = metrics.conditionalEdge(weights);</span></span>
-<span id="L60"><span class="lineNum">      60</span>              :         // 3. Let the used variable list, S, be empty.</span>
-<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">          52 :         std::vector&lt;int&gt; S;</span></span>
-<span id="L62"><span class="lineNum">      62</span>              :         // 4. Let the DAG network being constructed, BN, begin with a single</span>
-<span id="L63"><span class="lineNum">      63</span>              :         // class node, C.</span>
-<span id="L64"><span class="lineNum">      64</span>              :         // 5. Repeat until S includes all domain features</span>
-<span id="L65"><span class="lineNum">      65</span>              :         // 5.1. Select feature Xmax which is not in S and has the largest value</span>
-<span id="L66"><span class="lineNum">      66</span>              :         // I(Xmax;C).</span>
-<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">          52 :         auto order = argsort(mi);</span></span>
-<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">         396 :         for (auto idx : order) {</span></span>
-<span id="L69"><span class="lineNum">      69</span>              :             // 5.2. Add a node to BN representing Xmax.</span>
-<span id="L70"><span class="lineNum">      70</span>              :             // 5.3. Add an arc from C to Xmax in BN.</span>
-<span id="L71"><span class="lineNum">      71</span> <span class="tlaGNC">         344 :             model.addEdge(className, features[idx]);</span></span>
-<span id="L72"><span class="lineNum">      72</span>              :             // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span>
-<span id="L73"><span class="lineNum">      73</span>              :             // the highest value for I(Xmax;X,jC).</span>
-<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         344 :             add_m_edges(idx, S, conditionalEdgeWeights);</span></span>
-<span id="L75"><span class="lineNum">      75</span>              :             // 5.5. Add Xmax to S.</span>
-<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         344 :             S.push_back(idx);</span></span>
-<span id="L77"><span class="lineNum">      77</span>              :         }</span>
-<span id="L78"><span class="lineNum">      78</span> <span class="tlaGNC">         448 :     }</span></span>
-<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         344 :     void KDB::add_m_edges(int idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights)</span></span>
-<span id="L80"><span class="lineNum">      80</span>              :     {</span>
-<span id="L81"><span class="lineNum">      81</span> <span class="tlaGNC">         344 :         auto n_edges = std::min(k, static_cast&lt;int&gt;(S.size()));</span></span>
-<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">         344 :         auto cond_w = clone(weights);</span></span>
-<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         344 :         bool exit_cond = k == 0;</span></span>
-<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         344 :         int num = 0;</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">        1004 :         while (!exit_cond) {</span></span>
-<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">        2640 :             auto max_minfo = argmax(cond_w.index({ idx, &quot;...&quot; })).item&lt;int&gt;();</span></span>
-<span id="L87"><span class="lineNum">      87</span> <span class="tlaGNC">         660 :             auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();</span></span>
-<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">        1764 :             if (belongs &amp;&amp; cond_w.index({ idx, max_minfo }).item&lt;float&gt;() &gt; theta) {</span></span>
-<span id="L89"><span class="lineNum">      89</span>              :                 try {</span>
-<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">         320 :                     model.addEdge(features[max_minfo], features[idx]);</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">         320 :                     num++;</span></span>
-<span id="L92"><span class="lineNum">      92</span>              :                 }</span>
-<span id="L93"><span class="lineNum">      93</span> <span class="tlaUNC tlaBgUNC">           0 :                 catch (const std::invalid_argument&amp; e) {</span></span>
-<span id="L94"><span class="lineNum">      94</span>              :                     // Loops are not allowed</span>
-<span id="L95"><span class="lineNum">      95</span> <span class="tlaUNC">           0 :                 }</span></span>
-<span id="L96"><span class="lineNum">      96</span>              :             }</span>
-<span id="L97"><span class="lineNum">      97</span> <span class="tlaGNC tlaBgGNC">        2640 :             cond_w.index_put_({ idx, max_minfo }, -1);</span></span>
-<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">        1980 :             auto candidates_mask = cond_w.index({ idx, &quot;...&quot; }).gt(theta);</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">         660 :             auto candidates = candidates_mask.nonzero();</span></span>
-<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">         660 :             exit_cond = num == n_edges || candidates.size(0) == 0;</span></span>
-<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         660 :         }</span></span>
-<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">        2692 :     }</span></span>
-<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">           8 :     std::vector&lt;std::string&gt; KDB::graph(const std::string&amp; title) const</span></span>
-<span id="L104"><span class="lineNum">     104</span>              :     {</span>
-<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">           8 :         std::string header{ title };</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">           8 :         if (title == &quot;KDB&quot;) {</span></span>
-<span id="L107"><span class="lineNum">     107</span> <span class="tlaGNC">           8 :             header += &quot; (k=&quot; + std::to_string(k) + &quot;, theta=&quot; + std::to_string(theta) + &quot;)&quot;;</span></span>
-<span id="L108"><span class="lineNum">     108</span>              :         }</span>
-<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">          16 :         return model.graph(header);</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">           8 :     }</span></span>
-<span id="L111"><span class="lineNum">     111</span>              : }</span>
-        </pre>
-              </td>
-            </tr>
-          </table>
-          <br>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
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html/bayesnet/classifiers/KDB.cc.gcov.overview.html

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-          <img src="KDB.cc.gcov.png" width=80 height=110 alt="Overview" border=0 usemap="#overview">
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html/bayesnet/classifiers/KDB.h.func-c.html

@@ -1,90 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
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-
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-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
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-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDB.h.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
-              
-              <td class="coverFnHi">44</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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html/bayesnet/classifiers/KDB.h.func.html

@@ -1,90 +0,0 @@
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-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDB.h.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
-              
-              <td class="coverFnHi">44</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-
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-</html>

html/bayesnet/classifiers/KDB.h.gcov.frameset.html

@@ -1,19 +0,0 @@
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-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
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-        <frameset cols="120,*">
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-          <frame src="KDB.h.gcov.html" name="source">
-          <noframes>
-            <center>Frames not supported by your browser!<br></center>
-          </noframes>
-        </frameset>
-
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html/bayesnet/classifiers/KDB.h.gcov.html

@@ -1,111 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.h</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (source / <a href="KDB.h.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <table cellpadding=0 cellspacing=0 border=0>
-            <tr>
-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #ifndef KDB_H</span>
-<span id="L8"><span class="lineNum">       8</span>              : #define KDB_H</span>
-<span id="L9"><span class="lineNum">       9</span>              : #include &lt;torch/torch.h&gt;</span>
-<span id="L10"><span class="lineNum">      10</span>              : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
-<span id="L11"><span class="lineNum">      11</span>              : #include &quot;Classifier.h&quot;</span>
-<span id="L12"><span class="lineNum">      12</span>              : namespace bayesnet {</span>
-<span id="L13"><span class="lineNum">      13</span>              :     class KDB : public Classifier {</span>
-<span id="L14"><span class="lineNum">      14</span>              :     private:</span>
-<span id="L15"><span class="lineNum">      15</span>              :         int k;</span>
-<span id="L16"><span class="lineNum">      16</span>              :         float theta;</span>
-<span id="L17"><span class="lineNum">      17</span>              :         void add_m_edges(int idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights);</span>
-<span id="L18"><span class="lineNum">      18</span>              :     protected:</span>
-<span id="L19"><span class="lineNum">      19</span>              :         void buildModel(const torch::Tensor&amp; weights) override;</span>
-<span id="L20"><span class="lineNum">      20</span>              :     public:</span>
-<span id="L21"><span class="lineNum">      21</span>              :         explicit KDB(int k, float theta = 0.03);</span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC tlaBgGNC">          44 :         virtual ~KDB() = default;</span></span>
-<span id="L23"><span class="lineNum">      23</span>              :         void setHyperparameters(const nlohmann::json&amp; hyperparameters_) override;</span>
-<span id="L24"><span class="lineNum">      24</span>              :         std::vector&lt;std::string&gt; graph(const std::string&amp; name = &quot;KDB&quot;) const override;</span>
-<span id="L25"><span class="lineNum">      25</span>              :     };</span>
-<span id="L26"><span class="lineNum">      26</span>              : }</span>
-<span id="L27"><span class="lineNum">      27</span>              : #endif</span>
-        </pre>
-              </td>
-            </tr>
-          </table>
-          <br>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
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-</html>

html/bayesnet/classifiers/KDB.h.gcov.overview.html

@@ -1,27 +0,0 @@
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-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
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-          <map name="overview">
-            <area shape="rect" coords="0,0,79,3" href="KDB.h.gcov.html#L1" target="source" alt="overview">
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-          </map>
-
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-          <a href="KDB.h.gcov.html#top" target="source">Top</a><br><br>
-          <img src="KDB.h.gcov.png" width=80 height=26 alt="Overview" border=0 usemap="#overview">
-          </center>
-        </body>
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html/bayesnet/classifiers/KDBLd.cc.func-c.html

@@ -1,111 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">17</td>
-            <td class="headerCovTableEntry">17</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDBLd.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">bayesnet::KDBLd::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">4</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">16</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">bayesnet::KDBLd::fit(at::Tensor&amp;, at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">20</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
-              
-              <td class="coverFnHi">68</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
-</body>
-</html>

html/bayesnet/classifiers/KDBLd.cc.func.html

@@ -1,111 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">17</td>
-            <td class="headerCovTableEntry">17</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="KDBLd.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
-              
-              <td class="coverFnHi">68</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">bayesnet::KDBLd::fit(at::Tensor&amp;, at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;, std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">20</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">bayesnet::KDBLd::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">4</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">16</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
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-</html>

html/bayesnet/classifiers/KDBLd.cc.gcov.frameset.html

@@ -1,19 +0,0 @@
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-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
-
-        <frameset cols="120,*">
-          <frame src="KDBLd.cc.gcov.overview.html" name="overview">
-          <frame src="KDBLd.cc.gcov.html" name="source">
-          <noframes>
-            <center>Frames not supported by your browser!<br></center>
-          </noframes>
-        </frameset>
-
-        </html>

html/bayesnet/classifiers/KDBLd.cc.gcov.html

@@ -1,119 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (source / <a href="KDBLd.cc.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">17</td>
-            <td class="headerCovTableEntry">17</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">4</td>
-            <td class="headerCovTableEntry">4</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <table cellpadding=0 cellspacing=0 border=0>
-            <tr>
-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #include &quot;KDBLd.h&quot;</span>
-<span id="L8"><span class="lineNum">       8</span>              : </span>
-<span id="L9"><span class="lineNum">       9</span>              : namespace bayesnet {</span>
-<span id="L10"><span class="lineNum">      10</span> <span class="tlaGNC tlaBgGNC">          68 :     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
-<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC">          20 :     KDBLd&amp; KDBLd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
-<span id="L12"><span class="lineNum">      12</span>              :     {</span>
-<span id="L13"><span class="lineNum">      13</span> <span class="tlaGNC">          20 :         checkInput(X_, y_);</span></span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">          20 :         features = features_;</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">          20 :         className = className_;</span></span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">          20 :         Xf = X_;</span></span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">          20 :         y = y_;</span></span>
-<span id="L18"><span class="lineNum">      18</span>              :         // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">          20 :         states = fit_local_discretization(y);</span></span>
-<span id="L20"><span class="lineNum">      20</span>              :         // We have discretized the input data</span>
-<span id="L21"><span class="lineNum">      21</span>              :         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network</span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">          20 :         KDB::fit(dataset, features, className, states);</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">          20 :         states = localDiscretizationProposal(states, model);</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          20 :         return *this;</span></span>
-<span id="L25"><span class="lineNum">      25</span>              :     }</span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">          16 :     torch::Tensor KDBLd::predict(torch::Tensor&amp; X)</span></span>
-<span id="L27"><span class="lineNum">      27</span>              :     {</span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">          16 :         auto Xt = prepareX(X);</span></span>
-<span id="L29"><span class="lineNum">      29</span> <span class="tlaGNC">          32 :         return KDB::predict(Xt);</span></span>
-<span id="L30"><span class="lineNum">      30</span> <span class="tlaGNC">          16 :     }</span></span>
-<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">           4 :     std::vector&lt;std::string&gt; KDBLd::graph(const std::string&amp; name) const</span></span>
-<span id="L32"><span class="lineNum">      32</span>              :     {</span>
-<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">           4 :         return KDB::graph(name);</span></span>
-<span id="L34"><span class="lineNum">      34</span>              :     }</span>
-<span id="L35"><span class="lineNum">      35</span>              : }</span>
-        </pre>
-              </td>
-            </tr>
-          </table>
-          <br>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
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html/bayesnet/classifiers/KDBLd.cc.gcov.overview.html

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html/bayesnet/classifiers/KDBLd.h.func-c.html

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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="KDBLd.h.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
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-              
-              
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-            <tr>
-              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
-              
-              <td class="coverFnHi">20</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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html/bayesnet/classifiers/KDBLd.h.func.html

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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-            </tr>
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-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
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-              
-              
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-            <tr>
-              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
-              
-              <td class="coverFnHi">20</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
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html/bayesnet/classifiers/KDBLd.h.gcov.frameset.html

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-          </noframes>
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html/bayesnet/classifiers/KDBLd.h.gcov.html

@@ -1,108 +0,0 @@
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-            <tr>
-              <td width="100%">
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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-              </td>
-            </tr>
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-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #ifndef KDBLD_H</span>
-<span id="L8"><span class="lineNum">       8</span>              : #define KDBLD_H</span>
-<span id="L9"><span class="lineNum">       9</span>              : #include &quot;Proposal.h&quot;</span>
-<span id="L10"><span class="lineNum">      10</span>              : #include &quot;KDB.h&quot;</span>
-<span id="L11"><span class="lineNum">      11</span>              : </span>
-<span id="L12"><span class="lineNum">      12</span>              : namespace bayesnet {</span>
-<span id="L13"><span class="lineNum">      13</span>              :     class KDBLd : public KDB, public Proposal {</span>
-<span id="L14"><span class="lineNum">      14</span>              :     private:</span>
-<span id="L15"><span class="lineNum">      15</span>              :     public:</span>
-<span id="L16"><span class="lineNum">      16</span>              :         explicit KDBLd(int k);</span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC tlaBgGNC">          20 :         virtual ~KDBLd() = default;</span></span>
-<span id="L18"><span class="lineNum">      18</span>              :         KDBLd&amp; fit(torch::Tensor&amp; X, torch::Tensor&amp; y, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states) override;</span>
-<span id="L19"><span class="lineNum">      19</span>              :         std::vector&lt;std::string&gt; graph(const std::string&amp; name = &quot;KDB&quot;) const override;</span>
-<span id="L20"><span class="lineNum">      20</span>              :         torch::Tensor predict(torch::Tensor&amp; X) override;</span>
-<span id="L21"><span class="lineNum">      21</span>              :         static inline std::string version() { return &quot;0.0.1&quot;; };</span>
-<span id="L22"><span class="lineNum">      22</span>              :     };</span>
-<span id="L23"><span class="lineNum">      23</span>              : }</span>
-<span id="L24"><span class="lineNum">      24</span>              : #endif // !KDBLD_H</span>
-        </pre>
-              </td>
-            </tr>
-          </table>
-          <br>
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html/bayesnet/classifiers/KDBLd.h.gcov.overview.html

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html/bayesnet/classifiers/Proposal.cc.func-c.html

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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
-            <td class="headerCovTableEntry">86</td>
-            <td class="headerCovTableEntry">84</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">8</td>
-            <td class="headerCovTableEntry">8</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="Proposal.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">168</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
-              
-              <td class="coverFnHi">200</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)</a></td>
-              
-              <td class="coverFnHi">212</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">bayesnet::Proposal::checkInput(at::Tensor const&amp;, at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">228</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">bayesnet::Proposal::fit_local_discretization[abi:cxx11](at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">232</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">bayesnet::Proposal::Proposal(at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt;&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">424</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">auto bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)::{lambda(auto:1 const&amp;)#2}::operator()&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">1372</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">auto bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)::{lambda(auto:1 const&amp;)#1}::operator()&lt;bayesnet::Node*&gt;(bayesnet::Node* const&amp;) const</a></td>
-              
-              <td class="coverFnHi">2696</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
-</body>
-</html>

html/bayesnet/classifiers/Proposal.cc.func.html

@@ -1,139 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (<a href="Proposal.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
-            <td class="headerCovTableEntry">86</td>
-            <td class="headerCovTableEntry">84</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">8</td>
-            <td class="headerCovTableEntry">8</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <center>
-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><a href="Proposal.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">auto bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)::{lambda(auto:1 const&amp;)#1}::operator()&lt;bayesnet::Node*&gt;(bayesnet::Node* const&amp;) const</a></td>
-              
-              <td class="coverFnHi">2696</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">auto bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)::{lambda(auto:1 const&amp;)#2}::operator()&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">1372</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">bayesnet::Proposal::Proposal(at::Tensor&amp;, std::vector&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::allocator&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt; &gt;&amp;, std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;&amp;)</a></td>
-              
-              <td class="coverFnHi">424</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">bayesnet::Proposal::checkInput(at::Tensor const&amp;, at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">228</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">bayesnet::Proposal::fit_local_discretization[abi:cxx11](at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">232</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">bayesnet::Proposal::localDiscretizationProposal(std::map&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt;, std::vector&lt;int, std::allocator&lt;int&gt; &gt;, std::less&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; &gt;, std::allocator&lt;std::pair&lt;std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const, std::vector&lt;int, std::allocator&lt;int&gt; &gt; &gt; &gt; &gt; const&amp;, bayesnet::Network&amp;)</a></td>
-              
-              <td class="coverFnHi">212</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&amp;)</a></td>
-              
-              <td class="coverFnHi">168</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
-              
-              <td class="coverFnHi">200</td>
-              
-              
-            </tr>
-          </table>
-          <br>
-          </center>
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-            <tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
-          </table>
-          <br>
-
-</body>
-</html>

html/bayesnet/classifiers/Proposal.cc.gcov.frameset.html

@@ -1,19 +0,0 @@
-        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Frameset//EN">
-
-        <html lang="en">
-
-        <head>
-          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc</title>
-          <link rel="stylesheet" type="text/css" href="../../gcov.css">
-        </head>
-
-        <frameset cols="120,*">
-          <frame src="Proposal.cc.gcov.overview.html" name="overview">
-          <frame src="Proposal.cc.gcov.html" name="source">
-          <noframes>
-            <center>Frames not supported by your browser!<br></center>
-          </noframes>
-        </frameset>
-
-        </html>

html/bayesnet/classifiers/Proposal.cc.gcov.html

@@ -1,200 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-
-<html lang="en">
-
-<head>
-  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
-
-<body>
-
-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
-            <tr><td class="title">LCOV - code coverage report</td></tr>
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (source / <a href="Proposal.cc.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
-            <td class="headerCovTableEntry">86</td>
-            <td class="headerCovTableEntry">84</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">8</td>
-            <td class="headerCovTableEntry">8</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
-              </td>
-            </tr>
-
-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-          </table>
-
-          <table cellpadding=0 cellspacing=0 border=0>
-            <tr>
-              <td><br></td>
-            </tr>
-            <tr>
-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #include &lt;ArffFiles.h&gt;</span>
-<span id="L8"><span class="lineNum">       8</span>              : #include &quot;Proposal.h&quot;</span>
-<span id="L9"><span class="lineNum">       9</span>              : </span>
-<span id="L10"><span class="lineNum">      10</span>              : namespace bayesnet {</span>
-<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC tlaBgGNC">         424 :     Proposal::Proposal(torch::Tensor&amp; dataset_, std::vector&lt;std::string&gt;&amp; features_, std::string&amp; className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</span></span>
-<span id="L12"><span class="lineNum">      12</span> <span class="tlaGNC">         200 :     Proposal::~Proposal()</span></span>
-<span id="L13"><span class="lineNum">      13</span>              :     {</span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">        1896 :         for (auto&amp; [key, value] : discretizers) {</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">        1696 :             delete value;</span></span>
-<span id="L16"><span class="lineNum">      16</span>              :         }</span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">         200 :     }</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">         228 :     void Proposal::checkInput(const torch::Tensor&amp; X, const torch::Tensor&amp; y)</span></span>
-<span id="L19"><span class="lineNum">      19</span>              :     {</span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">         228 :         if (!torch::is_floating_point(X)) {</span></span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaUNC tlaBgUNC">           0 :             throw std::invalid_argument(&quot;X must be a floating point tensor&quot;);</span></span>
-<span id="L22"><span class="lineNum">      22</span>              :         }</span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC tlaBgGNC">         228 :         if (torch::is_floating_point(y)) {</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaUNC tlaBgUNC">           0 :             throw std::invalid_argument(&quot;y must be an integer tensor&quot;);</span></span>
-<span id="L25"><span class="lineNum">      25</span>              :         }</span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC tlaBgGNC">         228 :     }</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">         212 :     map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::localDiscretizationProposal(const map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; oldStates, Network&amp; model)</span></span>
-<span id="L28"><span class="lineNum">      28</span>              :     {</span>
-<span id="L29"><span class="lineNum">      29</span>              :         // order of local discretization is important. no good 0, 1, 2...</span>
-<span id="L30"><span class="lineNum">      30</span>              :         // although we rediscretize features after the local discretization of every feature</span>
-<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">         212 :         auto order = model.topological_sort();</span></span>
-<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         212 :         auto&amp; nodes = model.getNodes();</span></span>
-<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">         212 :         map&lt;std::string, std::vector&lt;int&gt;&gt; states = oldStates;</span></span>
-<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">         212 :         std::vector&lt;int&gt; indicesToReDiscretize;</span></span>
-<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         212 :         bool upgrade = false; // Flag to check if we need to upgrade the model</span></span>
-<span id="L36"><span class="lineNum">      36</span> <span class="tlaGNC">        1776 :         for (auto feature : order) {</span></span>
-<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">        1564 :             auto nodeParents = nodes[feature]-&gt;getParents();</span></span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">        1564 :             if (nodeParents.size() &lt; 2) continue; // Only has class as parent</span></span>
-<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">        1324 :             upgrade = true;</span></span>
-<span id="L40"><span class="lineNum">      40</span> <span class="tlaGNC">        1324 :             int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</span></span>
-<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">        1324 :             indicesToReDiscretize.push_back(index); // We need to re-discretize this feature</span></span>
-<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">        1324 :             std::vector&lt;std::string&gt; parents;</span></span>
-<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">        4020 :             transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto&amp; p) { return p-&gt;getName(); });</span></span>
-<span id="L44"><span class="lineNum">      44</span>              :             // Remove class as parent as it will be added later</span>
-<span id="L45"><span class="lineNum">      45</span> <span class="tlaGNC">        1324 :             parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</span></span>
-<span id="L46"><span class="lineNum">      46</span>              :             // Get the indices of the parents</span>
-<span id="L47"><span class="lineNum">      47</span> <span class="tlaGNC">        1324 :             std::vector&lt;int&gt; indices;</span></span>
-<span id="L48"><span class="lineNum">      48</span> <span class="tlaGNC">        1324 :             indices.push_back(-1); // Add class index</span></span>
-<span id="L49"><span class="lineNum">      49</span> <span class="tlaGNC">        2696 :             transform(parents.begin(), parents.end(), back_inserter(indices), [&amp;](const auto&amp; p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</span></span>
-<span id="L50"><span class="lineNum">      50</span>              :             // 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>
-<span id="L51"><span class="lineNum">      51</span> <span class="tlaGNC">        1324 :             std::vector&lt;std::string&gt; yJoinParents(Xf.size(1));</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">        4020 :             for (auto idx : indices) {</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">      958640 :                 for (int i = 0; i &lt; Xf.size(1); ++i) {</span></span>
-<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">     2867832 :                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item&lt;int&gt;());</span></span>
-<span id="L55"><span class="lineNum">      55</span>              :                 }</span>
-<span id="L56"><span class="lineNum">      56</span>              :             }</span>
-<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">        1324 :             auto arff = ArffFiles();</span></span>
-<span id="L58"><span class="lineNum">      58</span> <span class="tlaGNC">        1324 :             auto yxv = arff.factorize(yJoinParents);</span></span>
-<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">        2648 :             auto xvf_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
-<span id="L60"><span class="lineNum">      60</span> <span class="tlaGNC">        1324 :             auto xvf = std::vector&lt;mdlp::precision_t&gt;(xvf_ptr, xvf_ptr + Xf.size(1));</span></span>
-<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">        1324 :             discretizers[feature]-&gt;fit(xvf, yxv);</span></span>
-<span id="L62"><span class="lineNum">      62</span> <span class="tlaGNC">        1804 :         }</span></span>
-<span id="L63"><span class="lineNum">      63</span> <span class="tlaGNC">         212 :         if (upgrade) {</span></span>
-<span id="L64"><span class="lineNum">      64</span>              :             // Discretize again X (only the affected indices) with the new fitted discretizers</span>
-<span id="L65"><span class="lineNum">      65</span> <span class="tlaGNC">        1536 :             for (auto index : indicesToReDiscretize) {</span></span>
-<span id="L66"><span class="lineNum">      66</span> <span class="tlaGNC">        2648 :                 auto Xt_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
-<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">        1324 :                 auto Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
-<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">        5296 :                 pDataset.index_put_({ index, &quot;...&quot; }, torch::tensor(discretizers[pFeatures[index]]-&gt;transform(Xt)));</span></span>
-<span id="L69"><span class="lineNum">      69</span> <span class="tlaGNC">        1324 :                 auto xStates = std::vector&lt;int&gt;(discretizers[pFeatures[index]]-&gt;getCutPoints().size() + 1);</span></span>
-<span id="L70"><span class="lineNum">      70</span> <span class="tlaGNC">        1324 :                 iota(xStates.begin(), xStates.end(), 0);</span></span>
-<span id="L71"><span class="lineNum">      71</span>              :                 //Update new states of the feature/node</span>
-<span id="L72"><span class="lineNum">      72</span> <span class="tlaGNC">        1324 :                 states[pFeatures[index]] = xStates;</span></span>
-<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">        1324 :             }</span></span>
-<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         212 :             const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</span></span>
-<span id="L75"><span class="lineNum">      75</span> <span class="tlaGNC">         212 :             model.fit(pDataset, weights, pFeatures, pClassName, states);</span></span>
-<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         212 :         }</span></span>
-<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">         424 :         return states;</span></span>
-<span id="L78"><span class="lineNum">      78</span> <span class="tlaGNC">      960128 :     }</span></span>
-<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         232 :     map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::fit_local_discretization(const torch::Tensor&amp; y)</span></span>
-<span id="L80"><span class="lineNum">      80</span>              :     {</span>
-<span id="L81"><span class="lineNum">      81</span>              :         // Discretize the continuous input data and build pDataset (Classifier::dataset)</span>
-<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">         232 :         int m = Xf.size(1);</span></span>
-<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         232 :         int n = Xf.size(0);</span></span>
-<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         232 :         map&lt;std::string, std::vector&lt;int&gt;&gt; states;</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">         232 :         pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</span></span>
-<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">         232 :         auto yv = std::vector&lt;int&gt;(y.data_ptr&lt;int&gt;(), y.data_ptr&lt;int&gt;() + y.size(0));</span></span>
-<span id="L87"><span class="lineNum">      87</span>              :         // discretize input data by feature(row)</span>
-<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">        1944 :         for (auto i = 0; i &lt; pFeatures.size(); ++i) {</span></span>
-<span id="L89"><span class="lineNum">      89</span> <span class="tlaGNC">        1712 :             auto* discretizer = new mdlp::CPPFImdlp();</span></span>
-<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">        3424 :             auto Xt_ptr = Xf.index({ i }).data_ptr&lt;float&gt;();</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">        1712 :             auto Xt = std::vector&lt;float&gt;(Xt_ptr, Xt_ptr + Xf.size(1));</span></span>
-<span id="L92"><span class="lineNum">      92</span> <span class="tlaGNC">        1712 :             discretizer-&gt;fit(Xt, yv);</span></span>
-<span id="L93"><span class="lineNum">      93</span> <span class="tlaGNC">        6848 :             pDataset.index_put_({ i, &quot;...&quot; }, torch::tensor(discretizer-&gt;transform(Xt)));</span></span>
-<span id="L94"><span class="lineNum">      94</span> <span class="tlaGNC">        1712 :             auto xStates = std::vector&lt;int&gt;(discretizer-&gt;getCutPoints().size() + 1);</span></span>
-<span id="L95"><span class="lineNum">      95</span> <span class="tlaGNC">        1712 :             iota(xStates.begin(), xStates.end(), 0);</span></span>
-<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">        1712 :             states[pFeatures[i]] = xStates;</span></span>
-<span id="L97"><span class="lineNum">      97</span> <span class="tlaGNC">        1712 :             discretizers[pFeatures[i]] = discretizer;</span></span>
-<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">        1712 :         }</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">         232 :         int n_classes = torch::max(y).item&lt;int&gt;() + 1;</span></span>
-<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">         232 :         auto yStates = std::vector&lt;int&gt;(n_classes);</span></span>
-<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         232 :         iota(yStates.begin(), yStates.end(), 0);</span></span>
-<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">         232 :         states[pClassName] = yStates;</span></span>
-<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">         696 :         pDataset.index_put_({ n, &quot;...&quot; }, y);</span></span>
-<span id="L104"><span class="lineNum">     104</span> <span class="tlaGNC">         464 :         return states;</span></span>
-<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">        3888 :     }</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">         168 :     torch::Tensor Proposal::prepareX(torch::Tensor&amp; X)</span></span>
-<span id="L107"><span class="lineNum">     107</span>              :     {</span>
-<span id="L108"><span class="lineNum">     108</span> <span class="tlaGNC">         168 :         auto Xtd = torch::zeros_like(X, torch::kInt32);</span></span>
-<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">        1376 :         for (int i = 0; i &lt; X.size(0); ++i) {</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">        1208 :             auto Xt = std::vector&lt;float&gt;(X[i].data_ptr&lt;float&gt;(), X[i].data_ptr&lt;float&gt;() + X.size(1));</span></span>
-<span id="L111"><span class="lineNum">     111</span> <span class="tlaGNC">        1208 :             auto Xd = discretizers[pFeatures[i]]-&gt;transform(Xt);</span></span>
-<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">        3624 :             Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</span></span>
-<span id="L113"><span class="lineNum">     113</span> <span class="tlaGNC">        1208 :         }</span></span>
-<span id="L114"><span class="lineNum">     114</span> <span class="tlaGNC">         336 :         return Xtd;</span></span>
-<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">        1376 :     }</span></span>
-<span id="L116"><span class="lineNum">     116</span>              : }</span>
-        </pre>
-              </td>
-            </tr>
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html/bayesnet/classifiers/Proposal.cc.gcov.overview.html

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html/bayesnet/classifiers/SPODE.cc.func-c.html

@@ -1,104 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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-<html lang="en">
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-  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.cc - functions</title>
-  <link rel="stylesheet" type="text/css" href="../../gcov.css">
-</head>
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-          <table width="100%" border=0 cellspacing=0 cellpadding=0>
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-
-            <tr>
-              <td width="100%">
-                <table cellpadding=1 border=0 width="100%">
-          <tr>
-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (<a href="SPODE.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">10</td>
-            <td class="headerCovTableEntry">10</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">3</td>
-            <td class="headerCovTableEntry">3</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
-            <td></td>
-          </tr>
-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-                </table>
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-            <tr><td><br></td></tr>
-            <tr>
-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><a href="SPODE.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
-              
-              <td class="tableHead">Hit count <span  title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L24">bayesnet::SPODE::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">68</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">1016</td>
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-              
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html/bayesnet/classifiers/SPODE.cc.func.html

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-            <tr>
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-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (<a href="SPODE.cc.gcov.html">source</a> / functions)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">10</td>
-            <td class="headerCovTableEntry">10</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">3</td>
-            <td class="headerCovTableEntry">3</td>
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-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
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-          <table cellpadding=1 cellspacing=1 border=0>
-            <tr><td><br></td></tr>
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-              <td class="tableHead">Function Name <span  title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
-              
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-              
-              
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-            <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">bayesnet::SPODE::SPODE(int)</a></td>
-              
-              <td class="coverFnHi">1124</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&amp;)</a></td>
-              
-              <td class="coverFnHi">1016</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L24">bayesnet::SPODE::graph(std::__cxx11::basic_string&lt;char, std::char_traits&lt;char&gt;, std::allocator&lt;char&gt; &gt; const&amp;) const</a></td>
-              
-              <td class="coverFnHi">68</td>
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-              
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html/bayesnet/classifiers/SPODE.cc.gcov.frameset.html

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html/bayesnet/classifiers/SPODE.cc.gcov.html

@@ -1,115 +0,0 @@
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-            <tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
-
-            <tr>
-              <td width="100%">
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-            <td width="10%" class="headerItem">Current view:</td>
-            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (source / <a href="SPODE.cc.func-c.html">functions</a>)</span></td>
-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">10</td>
-            <td class="headerCovTableEntry">10</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">3</td>
-            <td class="headerCovTableEntry">3</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
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-            <td></td>
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-                  <tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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-              <td>
-<pre class="sourceHeading">            Line data    Source code</pre>
-<pre class="source">
-<span id="L1"><span class="lineNum">       1</span>              : // ***************************************************************</span>
-<span id="L2"><span class="lineNum">       2</span>              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
-<span id="L3"><span class="lineNum">       3</span>              : // SPDX-FileType: SOURCE</span>
-<span id="L4"><span class="lineNum">       4</span>              : // SPDX-License-Identifier: MIT</span>
-<span id="L5"><span class="lineNum">       5</span>              : // ***************************************************************</span>
-<span id="L6"><span class="lineNum">       6</span>              : </span>
-<span id="L7"><span class="lineNum">       7</span>              : #include &quot;SPODE.h&quot;</span>
-<span id="L8"><span class="lineNum">       8</span>              : </span>
-<span id="L9"><span class="lineNum">       9</span>              : namespace bayesnet {</span>
-<span id="L10"><span class="lineNum">      10</span>              : </span>
-<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC tlaBgGNC">        1124 :     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}</span></span>
-<span id="L12"><span class="lineNum">      12</span>              : </span>
-<span id="L13"><span class="lineNum">      13</span> <span class="tlaGNC">        1016 :     void SPODE::buildModel(const torch::Tensor&amp; weights)</span></span>
-<span id="L14"><span class="lineNum">      14</span>              :     {</span>
-<span id="L15"><span class="lineNum">      15</span>              :         // 0. Add all nodes to the model</span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">        1016 :         addNodes();</span></span>
-<span id="L17"><span class="lineNum">      17</span>              :         // 1. Add edges from the class node to all other nodes</span>
-<span id="L18"><span class="lineNum">      18</span>              :         // 2. Add edges from the root node to all other nodes</span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">       25680 :         for (int i = 0; i &lt; static_cast&lt;int&gt;(features.size()); ++i) {</span></span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">       24664 :             model.addEdge(className, features[i]);</span></span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">       24664 :             if (i != root) {</span></span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">       23648 :                 model.addEdge(features[root], features[i]);</span></span>
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-<span id="L24"><span class="lineNum">      24</span>              :         }</span>
-<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">        1016 :     }</span></span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">          68 :     std::vector&lt;std::string&gt; SPODE::graph(const std::string&amp; name) const</span></span>
-<span id="L27"><span class="lineNum">      27</span>              :     {</span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">          68 :         return model.graph(name);</span></span>
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-<span id="L30"><span class="lineNum">      30</span>              : </span>
-<span id="L31"><span class="lineNum">      31</span>              : }</span>
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html/bayesnet/classifiers/SPODE.cc.gcov.overview.html

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html/bayesnet/classifiers/SPODE.h.func-c.html

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-            <td width="5%"></td>
-            <td width="5%"></td>
-            <td width="5%" class="headerCovTableHead">Coverage</td>
-            <td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
-            <td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test:</td>
-            <td class="headerValue">BayesNet Coverage Report</td>
-            <td></td>
-            <td class="headerItem">Lines:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
-          </tr>
-          <tr>
-            <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-05-06 17:54:04</td>
-            <td></td>
-            <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
-            <td class="headerCovTableEntry">1</td>
-            <td class="headerCovTableEntry">1</td>
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-          <tr>
-            <td class="headerItem">Legend:</td>
-            <td class="headerValueLeg">            Lines:
-            <span class="coverLegendCov">hit</span>
-            <span class="coverLegendNoCov">not hit</span>
-</td>
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