Complete Conditional Mutual Information and test

Fix update_coverage.py with lcov2.1 output

.devcontainer/Dockerfile

@@ -0,0 +1,57 @@
+FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
+
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.22.2"
+
+# Optionally install the cmake for vcpkg
+COPY ./reinstall-cmake.sh /tmp/
+
+RUN if [ "${REINSTALL_CMAKE_VERSION_FROM_SOURCE}" != "none" ]; then \
+    chmod +x /tmp/reinstall-cmake.sh && /tmp/reinstall-cmake.sh ${REINSTALL_CMAKE_VERSION_FROM_SOURCE}; \
+    fi \
+    && rm -f /tmp/reinstall-cmake.sh
+
+
+# [Optional] Uncomment this section to install additional vcpkg ports.
+# RUN su vscode -c "${VCPKG_ROOT}/vcpkg install <your-port-name-here>"
+
+# [Optional] Uncomment this section to install additional packages.
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -y install --no-install-recommends wget software-properties-common libdatetime-perl libcapture-tiny-perl libdatetime-format-dateparse-perl libgd-perl
+
+# Add PPA for GCC 13
+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
+
+# Install lcov 2.1
+RUN wget --quiet https://github.com/linux-test-project/lcov/releases/download/v2.1/lcov-2.1.tar.gz && \
+    tar -xvf lcov-2.1.tar.gz && \
+    cd lcov-2.1 && \
+    make install
+RUN rm lcov-2.1.tar.gz
+RUN rm -fr lcov-2.1
+
+# Install Miniconda
+RUN mkdir -p /opt/conda 
+RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
+    bash /opt/conda/miniconda.sh -b -p /opt/miniconda
+
+# Add conda to PATH
+ENV PATH=/opt/miniconda/bin:$PATH
+
+# add CXX and CC to the environment with gcc 13
+ENV CXX=/usr/bin/g++-13
+ENV CC=/usr/bin/gcc-13
+
+# link the last gcov version
+RUN rm /usr/bin/gcov
+RUN ln -s /usr/bin/gcov-13 /usr/bin/gcov
+
+# change ownership of /opt/miniconda to vscode user
+RUN chown -R vscode:vscode /opt/miniconda
+
+USER vscode
+RUN conda init
+RUN conda install -y -c conda-forge yaml pytorch

.devcontainer/devcontainer.json

@@ -0,0 +1,37 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/cpp
+{
+	"name": "C++",
+	"build": {
+		"dockerfile": "Dockerfile"
+	},
+	// "features": {
+	// 	"ghcr.io/devcontainers/features/conda:1": {}
+	// }
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+	// Use 'postCreateCommand' to run commands after the container is created.
+	"postCreateCommand": "make release && make debug && echo 'Done!'",
+	// Configure tool-specific properties.
+	// "customizations": {},
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"ms-vscode.cpptools",
+				"ms-vscode.cpptools-extension-pack",
+				"ms-vscode.cpptools-themes",
+				"ms-vscode.cmake-tools",
+				"ms-azuretools.vscode-docker",
+				"jbenden.c-cpp-flylint",
+				"matepek.vscode-catch2-test-adapter",
+				"GitHub.copilot"
+			]
+		}
+	}
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

.devcontainer/reinstall-cmake.sh

@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+#-------------------------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See https://go.microsoft.com/fwlink/?linkid=2090316 for license information.
+#-------------------------------------------------------------------------------------------------------------
+#
+set -e
+
+CMAKE_VERSION=${1:-"none"}
+
+if [ "${CMAKE_VERSION}" = "none" ]; then
+    echo "No CMake version specified, skipping CMake reinstallation"
+    exit 0
+fi
+
+# Cleanup temporary directory and associated files when exiting the script.
+cleanup() {
+    EXIT_CODE=$?
+    set +e
+    if [[ -n "${TMP_DIR}" ]]; then
+        echo "Executing cleanup of tmp files"
+        rm -Rf "${TMP_DIR}"
+    fi
+    exit $EXIT_CODE
+}
+trap cleanup EXIT
+
+
+echo "Installing CMake..."
+apt-get -y purge --auto-remove cmake
+mkdir -p /opt/cmake
+
+architecture=$(dpkg --print-architecture)
+case "${architecture}" in
+    arm64)
+        ARCH=aarch64 ;;
+    amd64)
+        ARCH=x86_64 ;;
+    *)
+        echo "Unsupported architecture ${architecture}."
+        exit 1
+        ;;
+esac
+
+CMAKE_BINARY_NAME="cmake-${CMAKE_VERSION}-linux-${ARCH}.sh"
+CMAKE_CHECKSUM_NAME="cmake-${CMAKE_VERSION}-SHA-256.txt"
+TMP_DIR=$(mktemp -d -t cmake-XXXXXXXXXX)
+
+echo "${TMP_DIR}"
+cd "${TMP_DIR}"
+
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_BINARY_NAME}" -O
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_CHECKSUM_NAME}" -O
+
+sha256sum -c --ignore-missing "${CMAKE_CHECKSUM_NAME}"
+sh "${TMP_DIR}/${CMAKE_BINARY_NAME}" --prefix=/opt/cmake --skip-license
+
+ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
+ln -s /opt/cmake/bin/ctest /usr/local/bin/ctest

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for more information:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# https://containers.dev/guide/dependabot
+
+version: 2
+updates:
+ - package-ecosystem: "devcontainers"
+   directory: "/"
+   schedule:
+     interval: weekly

.gitignore

@@ -39,4 +39,5 @@ cmake-build*/**
 puml/**
 .vscode/settings.json
 sample/build
+**/.DS_Store
 

.gitmodules

@@ -13,3 +13,8 @@
 	url = https://github.com/rmontanana/folding
 	main = main
 	update = merge
+[submodule "tests/lib/catch2"]
+	path = tests/lib/catch2
+        url = https://github.com/catchorg/Catch2.git
+	main = main
+	update = merge

.sonarlint/connectedMode.json

@@ -0,0 +1,4 @@
+{
+    "sonarCloudOrganization": "rmontanana",
+    "projectKey": "rmontanana_BayesNet"
+}

.vscode/launch.json

@@ -16,7 +16,7 @@
             "name": "test",
             "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
             "args": [
-                "\"Bisection Best\""
+                "[Node]"
             ],
             "cwd": "${workspaceFolder}/build_debug/tests"
         },

CHANGELOG.md

@@ -9,16 +9,24 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Add the Library logo generated with <https://openart.ai> to README.md
-- Add link to the coverage report in the README.md coverage label.
-- Add the *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
+- Library logo generated with <https://openart.ai> to README.md
+- Link to the coverage report in the README.md coverage label.
+- *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.
+- A2DE & SPnDE tests.
+- Add tests to reach 99% of coverage.
+- Add tests to check the correct version of the mdlp, folding and json libraries.
 
 ### Internal
 
-- Refactor library ArffFile to limit the number of samples with a parameter.
-- Refactor tests libraries location to test/lib
+- 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.
 - Remove conditionalEdgeWeights method in BayesMetrics.
+- Refactor Coverage Report generation.
+- Add devcontainer to work on apple silicon.
+- Change build cmake folder names to Debug & Release.
 
 ## [1.0.5] 2024-04-20
 

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.5
+  VERSION 1.0.5.1
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -25,8 +25,9 @@ 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 "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -O0 -g")
+set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors -fno-default-inline")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+
 # Options
 # -------
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
@@ -60,8 +61,9 @@ endif (ENABLE_CLANG_TIDY)
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
 # include(FetchContent)
-add_git_submodule("lib/mdlp")
 add_git_submodule("lib/json")
+add_git_submodule("lib/mdlp")
+add_subdirectory("lib/Files")
 
 # Subdirectories
 # --------------
@@ -72,8 +74,7 @@ add_subdirectory(bayesnet)
 # -------
 if (ENABLE_TESTING)
 MESSAGE("Testing enabled")
-  add_subdirectory("tests/lib/catch2")  
-  add_subdirectory(tests/lib/Files)
+  add_subdirectory(tests/lib/catch2)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)

Makefile

@@ -2,14 +2,13 @@ SHELL := /bin/bash
 .DEFAULT_GOAL := help
 .PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge
 
-f_release = build_release
-f_debug = build_debug
+f_release = build_Release
+f_debug = build_Debug
 f_diagrams = diagrams
 app_targets = BayesNet
 test_targets = TestBayesNet
 clang-uml = clang-uml
 plantuml = plantuml
-gcovr = gcovr
 lcov = lcov
 genhtml = genhtml
 dot = dot
@@ -115,24 +114,24 @@ 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 $(gcovr) || (echo ">>> Please install gcovr"; exit 1)
 	@which $(lcov) || (echo ">>> Please install lcov"; exit 1)
-	@which $(genhtml) || (echo ">>> Please install lcov"; exit 1)
-	@$(MAKE) test
-	@$(gcovr) $(f_debug)/tests
+	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
 	@echo ">>> Building report..."
 	@cd $(f_debug)/tests; \
-	$(lcov) --directory . --capture --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --directory CMakeFiles --capture --demangle-cpp --ignore-errors source,source --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
 	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
-	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1
-	@$(genhtml) $(f_debug)/tests/coverage.info --output-directory html >/dev/null 2>&1;
+	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/opt/miniconda/*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --summary coverage.info
 	@$(MAKE) updatebadge
 	@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
 	@echo ">>> Done";
 

README.md

@@ -7,7 +7,7 @@
 [![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-97,1%25-green)](html/index.html)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,0%25-green)](html/index.html)
 
 Bayesian Network Classifiers using libtorch from scratch
 
@@ -61,7 +61,25 @@ make sample fname=tests/data/glass.arff
 
 ## Models
 
-### [BoostAODE](docs/BoostAODE.md)
+#### - TAN
+
+#### - KDB
+
+#### - SPODE
+
+#### - AODE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+### With Local Discretization
+
+#### - TANLd
+
+#### - KDBLd
+
+#### - SPODELd
+
+#### - AODELd
 
 ## Diagrams
 

bayesnet/classifiers/SPnDE.cc

@@ -0,0 +1,38 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPnDE.h"
+
+namespace bayesnet {
+
+    SPnDE::SPnDE(std::vector<int> parents) : Classifier(Network()), parents(parents) {}
+
+    void SPnDE::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        std::vector<int> attributes;
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            if (std::find(parents.begin(), parents.end(), i) == parents.end()) {
+                attributes.push_back(i);
+            }
+        }
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the parents nodes to all other nodes
+        for (const auto& attribute : attributes) {
+            model.addEdge(className, features[attribute]);
+            for (const auto& root : parents) {
+
+                model.addEdge(features[root], features[attribute]);
+            }
+        }
+    }
+    std::vector<std::string> SPnDE::graph(const std::string& name) const
+    {
+        return model.graph(name);
+    }
+
+}

bayesnet/classifiers/SPnDE.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPnDE_H
+#define SPnDE_H
+#include <vector>
+#include "Classifier.h"
+
+namespace bayesnet {
+    class SPnDE : public Classifier {
+    public:
+        explicit SPnDE(std::vector<int> parents);
+        virtual ~SPnDE() = default;
+        std::vector<std::string> graph(const std::string& name = "SPnDE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        std::vector<int> parents;
+
+
+    };
+}
+#endif

bayesnet/ensembles/A2DE.cc

@@ -0,0 +1,40 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "A2DE.h"
+
+namespace bayesnet {
+    A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+    }
+    void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void A2DE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size() - 1; ++i) {
+            for (int j = i + 1; j < features.size(); ++j) {
+                auto model = std::make_unique<SPnDE>(std::vector<int>({ i, j }));
+                models.push_back(std::move(model));
+            }
+        }
+        n_models = static_cast<unsigned>(models.size());
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> A2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/A2DE.h

@@ -0,0 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef A2DE_H
+#define A2DE_H
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Ensemble.h"
+namespace bayesnet {
+    class A2DE : public Ensemble {
+    public:
+        A2DE(bool predict_voting = false);
+        virtual ~A2DE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "A2DE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

bayesnet/network/Network.cc

@@ -410,11 +410,7 @@ namespace bayesnet {
                                 result.insert(it2, fatherName);
                                 ending = false;
                             }
-                        } else {
-                            throw std::logic_error("Error in topological sort because of node " + feature + " is not in result");
                         }
-                    } else {
-                        throw std::logic_error("Error in topological sort because of node father " + fatherName + " is not in result");
                     }
                 }
             }

bayesnet/network/Node.cc

@@ -9,7 +9,7 @@
 namespace bayesnet {
 
     Node::Node(const std::string& name)
-        : name(name), numStates(0), cpTable(torch::Tensor()), parents(std::vector<Node*>()), children(std::vector<Node*>())
+        : name(name)
     {
     }
     void Node::clear()
@@ -96,7 +96,6 @@ namespace bayesnet {
         // 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;
         // Fill table with counts

bayesnet/network/Node.h

@@ -12,14 +12,6 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class Node {
-    private:
-        std::string name;
-        std::vector<Node*> parents;
-        std::vector<Node*> children;
-        int numStates; // number of states of the variable
-        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
-        std::vector<int64_t> dimensions; // dimensions of the cpTable
-        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     public:
         explicit Node(const std::string&);
         void clear();
@@ -37,6 +29,14 @@ namespace bayesnet {
         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>&);
+    private:
+        std::string name;
+        std::vector<Node*> parents;
+        std::vector<Node*> children;
+        int numStates = 0; // number of states of the variable
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
+        std::vector<int64_t> dimensions; // dimensions of the cpTable
+        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     };
 }
 #endif

bayesnet/utils/BayesMetrics.cc

@@ -4,6 +4,9 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <map>
+#include <unordered_map>
+#include <tuple>
 #include "Mst.h"
 #include "BayesMetrics.h"
 namespace bayesnet {
@@ -105,6 +108,8 @@ namespace bayesnet {
         }
         return matrix;
     }
+    // Measured in nats (natural logarithm (log) base e)
+    // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
     double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
         torch::Tensor counts = feature.bincount(weights);
@@ -143,11 +148,64 @@ namespace bayesnet {
         }
         return entropyValue;
     }
+    // H(Y|X,C) = sum_{x in X, c in C} p(x,c) H(Y|X=x,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]);
+            auto keyMarginal = std::make_tuple(firstFeatureData[i], labelsData[i]);
+
+            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_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)
     double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
         return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
     }
+    // I(X;Y|C) = H(Y|C) - H(Y|X,C)
+    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);
+    }
     /*
     Compute the maximum spanning tree considering the weights as distances
     and the indices of the weights as nodes of this square matrix using

bayesnet/utils/BayesMetrics.h

@@ -18,12 +18,16 @@ namespace bayesnet {
         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
         std::vector<double> getScoresKBest() 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);
         std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        // Measured in nats (natural logarithm (log) base e)
+        // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
     protected:
         torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
         std::string className;
-        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
         std::vector<std::string> features;
         template <class T>
         std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)

docs/BoostAODE.md

@@ -5,7 +5,7 @@
 The hyperparameters defined in the algorithm are:
 
 - ***bisection*** (*boolean*): If set to true allows the algorithm to add *k* models at once (as specified in the algorithm) to the ensemble. Default value: *true*.
-- ***biesection_best*** (*boolean*): If set to *true*, the algorithm will take as *priorAccuracy* the best accuracy computed. If set to *false⁺ it will take the last accuracy as *priorAccuracy*. Default value: *false*.
+- ***bisection_best*** (*boolean*): If set to *true*, the algorithm will take as *priorAccuracy* the best accuracy computed. If set to *false⁺ it will take the last accuracy as *priorAccuracy*. Default value: *false*.
 
 - ***order*** (*{"asc", "desc", "rand"}*): Sets the order (ascending/descending/random) in which dataset variables will be processed to choose the parents of the *SPODEs*. Default value: *"desc"*.
 

gcovr.cfg

@@ -1,5 +0,0 @@
-filter = bayesnet/
-exclude-directories = build_debug/lib/
-exclude = bayesnet/utils/loguru.*
-print-summary = yes
-sort = uncovered-percent

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/BaseClassifier.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/BaseClassifier.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (<a href="BaseClassifier.h.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">bayesnet::BaseClassifier::~BaseClassifier()</a></td>
               
-              <td class="coverFnHi">241</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
-              
-              <td class="coverFnAliasLo">0</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">241</td>
+              <td class="coverFnHi">1680</td>
               
               
             </tr>

html/bayesnet/BaseClassifier.h.func.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/BaseClassifier.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/BaseClassifier.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (<a href="BaseClassifier.h.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
+              <td class="coverFn"><a href="BaseClassifier.h.gcov.html#L19">bayesnet::BaseClassifier::~BaseClassifier()</a></td>
               
-              <td class="coverFnHi">241</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD0Ev</a></td>
-              
-              <td class="coverFnAliasLo">0</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="BaseClassifier.h.gcov.html#L19">_ZN8bayesnet14BaseClassifierD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">241</td>
+              <td class="coverFnHi">1680</td>
               
               
             </tr>

html/bayesnet/BaseClassifier.h.gcov.frameset.html

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

html/bayesnet/BaseClassifier.h.gcov.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/BaseClassifier.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/BaseClassifier.h</title>
   <link rel="stylesheet" type="text/css" href="../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (source / <a href="BaseClassifier.h.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryLo">50.0&nbsp;%</td>
-            <td class="headerCovTableEntry">2</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>
@@ -80,7 +88,7 @@
 <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">         241 :         virtual ~BaseClassifier() = default;</span></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>

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

@@ -0,0 +1,32 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/BaseClassifier.h</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="BaseClassifier.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="BaseClassifier.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="BaseClassifier.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="BaseClassifier.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="BaseClassifier.h.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="BaseClassifier.h.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="BaseClassifier.h.gcov.html#L13" target="source" alt="overview">
+            <area shape="rect" coords="0,28,79,31" href="BaseClassifier.h.gcov.html#L17" target="source" alt="overview">
+            <area shape="rect" coords="0,32,79,35" href="BaseClassifier.h.gcov.html#L21" target="source" alt="overview">
+            <area shape="rect" coords="0,36,79,39" href="BaseClassifier.h.gcov.html#L25" target="source" alt="overview">
+            <area shape="rect" coords="0,40,79,43" href="BaseClassifier.h.gcov.html#L29" target="source" alt="overview">
+            <area shape="rect" coords="0,44,79,47" href="BaseClassifier.h.gcov.html#L33" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="BaseClassifier.h.gcov.html#top" target="source">Top</a><br><br>
+          <img src="BaseClassifier.h.gcov.png" width=80 height=44 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (<a href="Classifier.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,170 +71,170 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L178">_ZN8bayesnet10Classifier17topological_orderB5cxx11Ev</a></td>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L182">bayesnet::Classifier::dump_cpt[abi:cxx11]() const</a></td>
               
-              <td class="coverFnHi">11</td>
+              <td class="coverFnHi">4</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L182">_ZNK8bayesnet10Classifier8dump_cptB5cxx11Ev</a></td>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L178">bayesnet::Classifier::topological_order[abi:cxx11]()</a></td>
               
-              <td class="coverFnHi">11</td>
+              <td class="coverFnHi">4</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L142">_ZN8bayesnet10Classifier5scoreERSt6vectorIS1_IiSaIiEESaIS3_EERS3_</a></td>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L101">bayesnet::Classifier::predict(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">44</td>
+              <td class="coverFnHi">16</td>
               
               
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-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L101">_ZN8bayesnet10Classifier7predictERSt6vectorIS1_IiSaIiEESaIS3_EE</a></td>
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-              <td class="coverFnHi">44</td>
+              <td class="coverFnHi">16</td>
               
               
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-              <td class="coverFn"><a href="Classifier.cc.gcov.html#L170">_ZNK8bayesnet10Classifier17getNumberOfStatesEv</a></td>
+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L170">bayesnet::Classifier::getNumberOfStates() const</a></td>
               
-              <td class="coverFnHi">66</td>
+              <td class="coverFnHi">24</td>
               
               
             </tr>
             <tr>
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html/bayesnet/classifiers/Classifier.cc.func.html

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 <head>
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-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.cc - functions</title>
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 </head>
 
@@ -19,7 +19,7 @@
                 <table cellpadding=1 border=0 width="100%">
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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>
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+              <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>
               
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+              <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>
               
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+              <td class="coverFn"><a href="Classifier.cc.gcov.html#L55">bayesnet::Classifier::fit(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;, 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>
               
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html/bayesnet/classifiers/Classifier.cc.gcov.frameset.html

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

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Classifier.cc<span style="font-size: 80%;"> (source / <a href="Classifier.cc.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -71,188 +79,188 @@
 <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">        4750 :     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</span></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">        3413 :     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="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">        3413 :         this-&gt;features = features;</span></span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">        3413 :         this-&gt;className = className;</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">        3413 :         this-&gt;states = states;</span></span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">        3413 :         m = dataset.size(1);</span></span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">        3413 :         n = features.size();</span></span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">        3413 :         checkFitParameters();</span></span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">        3325 :         auto n_classes = states.at(className).size();</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">        3325 :         metrics = Metrics(dataset, features, className, n_classes);</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">        3325 :         model.initialize();</span></span>
-<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">        3325 :         buildModel(weights);</span></span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">        3325 :         trainModel(weights);</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">        3277 :         fitted = true;</span></span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">        3277 :         return *this;</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">         888 :     void Classifier::buildDataset(torch::Tensor&amp; ytmp)</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">         888 :             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">        2752 :             dataset = torch::cat({ dataset, yresized }, 0);</span></span>
-<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         888 :         }</span></span>
-<span id="L36"><span class="lineNum">      36</span> <span class="tlaGNC">          44 :         catch (const std::exception&amp; e) {</span></span>
-<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">          44 :             std::stringstream oss;</span></span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">          44 :             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">          44 :             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">          44 :             oss &lt;&lt; &quot;y dimensions: &quot; &lt;&lt; ytmp.sizes();</span></span>
-<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">          44 :             throw std::runtime_error(oss.str());</span></span>
-<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">          88 :         }</span></span>
-<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">        1776 :     }</span></span>
-<span id="L44"><span class="lineNum">      44</span> <span class="tlaGNC">        2951 :     void Classifier::trainModel(const torch::Tensor&amp; weights)</span></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">        2951 :         model.fit(dataset, weights, features, className, states);</span></span>
-<span id="L47"><span class="lineNum">      47</span> <span class="tlaGNC">        2951 :     }</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">         322 :     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="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">         322 :         dataset = X;</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">         322 :         buildDataset(y);</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">         300 :         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">         512 :         return build(features, className, states, weights);</span></span>
-<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">         300 :     }</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">         360 :     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="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">         360 :         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">        5883 :         for (int i = 0; i &lt; X.size(); ++i) {</span></span>
-<span id="L61"><span class="lineNum">      61</span> <span class="tlaGNC">       22092 :             dataset.index_put_({ i, &quot;...&quot; }, torch::tensor(X[i], torch::kInt32));</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">         360 :         auto ytmp = torch::tensor(y, torch::kInt32);</span></span>
-<span id="L64"><span class="lineNum">      64</span> <span class="tlaGNC">         360 :         buildDataset(ytmp);</span></span>
-<span id="L65"><span class="lineNum">      65</span> <span class="tlaGNC">         338 :         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">         628 :         return build(features, className, states, weights);</span></span>
-<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">        5931 :     }</span></span>
-<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">        1089 :     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="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">        1089 :         this-&gt;dataset = dataset;</span></span>
-<span id="L71"><span class="lineNum">      71</span> <span class="tlaGNC">        1089 :         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">        2178 :         return build(features, className, states, weights);</span></span>
-<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">        1089 :     }</span></span>
-<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">        1686 :     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="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">        1686 :         this-&gt;dataset = dataset;</span></span>
-<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">        1686 :         return build(features, className, states, weights);</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">        3413 :     void Classifier::checkFitParameters()</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">        3413 :         if (torch::is_floating_point(dataset)) {</span></span>
-<span id="L82"><span class="lineNum">      82</span> <span class="tlaGNC">          22 :             throw std::invalid_argument(&quot;dataset (X, y) must be of type Integer&quot;);</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">        3391 :         if (dataset.size(0) - 1 != features.size()) {</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">          22 :             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="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">        3369 :         if (states.find(className) == states.end()) {</span></span>
-<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">          22 :             throw std::invalid_argument(&quot;class name not found in states&quot;);</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">      124581 :         for (auto feature : features) {</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">      121256 :             if (states.find(feature) == states.end()) {</span></span>
-<span id="L92"><span class="lineNum">      92</span> <span class="tlaGNC">          22 :                 throw std::invalid_argument(&quot;feature [&quot; + feature + &quot;] not found in states&quot;);</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">      121256 :         }</span></span>
-<span id="L95"><span class="lineNum">      95</span> <span class="tlaGNC">        3325 :     }</span></span>
-<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">        3262 :     torch::Tensor Classifier::predict(torch::Tensor&amp; X)</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">        3262 :         if (!fitted) {</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">          44 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</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">        3218 :         return model.predict(X);</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">          44 :     std::vector&lt;int&gt; Classifier::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</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">          44 :         if (!fitted) {</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">          22 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</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">          22 :         auto m_ = X[0].size();</span></span>
-<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">          22 :         auto n_ = X.size();</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">          22 :         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">         110 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
-<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">         176 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</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">          22 :         auto yp = model.predict(Xd);</span></span>
-<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">          44 :         return yp;</span></span>
-<span id="L116"><span class="lineNum">     116</span> <span class="tlaGNC">          22 :     }</span></span>
-<span id="L117"><span class="lineNum">     117</span> <span class="tlaGNC">        3562 :     torch::Tensor Classifier::predict_proba(torch::Tensor&amp; X)</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">        3562 :         if (!fitted) {</span></span>
-<span id="L120"><span class="lineNum">     120</span> <span class="tlaGNC">          22 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</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">        3540 :         return model.predict_proba(X);</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">         766 :     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="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">         766 :         if (!fitted) {</span></span>
-<span id="L127"><span class="lineNum">     127</span> <span class="tlaGNC">          22 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</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">         744 :         auto m_ = X[0].size();</span></span>
-<span id="L130"><span class="lineNum">     130</span> <span class="tlaGNC">         744 :         auto n_ = X.size();</span></span>
-<span id="L131"><span class="lineNum">     131</span> <span class="tlaGNC">         744 :         std::vector&lt;std::vector&lt;int&gt;&gt; Xd(n_, std::vector&lt;int&gt;(m_, 0));</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">        9722 :         for (auto i = 0; i &lt; n_; i++) {</span></span>
-<span id="L134"><span class="lineNum">     134</span> <span class="tlaGNC">       17956 :             Xd[i] = std::vector&lt;int&gt;(X[i].begin(), X[i].end());</span></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">         744 :         auto yp = model.predict_proba(Xd);</span></span>
-<span id="L137"><span class="lineNum">     137</span> <span class="tlaGNC">        1488 :         return yp;</span></span>
-<span id="L138"><span class="lineNum">     138</span> <span class="tlaGNC">         744 :     }</span></span>
-<span id="L139"><span class="lineNum">     139</span> <span class="tlaGNC">         308 :     float Classifier::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</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">         308 :         torch::Tensor y_pred = predict(X);</span></span>
-<span id="L142"><span class="lineNum">     142</span> <span class="tlaGNC">         572 :         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">         286 :     }</span></span>
-<span id="L144"><span class="lineNum">     144</span> <span class="tlaGNC">          44 :     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="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">          44 :         if (!fitted) {</span></span>
-<span id="L147"><span class="lineNum">     147</span> <span class="tlaGNC">          22 :             throw std::logic_error(CLASSIFIER_NOT_FITTED);</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">          22 :         return model.score(X, y);</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">          66 :     std::vector&lt;std::string&gt; Classifier::show() const</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">          66 :         return model.show();</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">        2951 :     void Classifier::addNodes()</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">      116009 :         for (const auto&amp; feature : features) {</span></span>
-<span id="L159"><span class="lineNum">     159</span> <span class="tlaGNC">      113058 :             model.addNode(feature);</span></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">        2951 :         model.addNode(className);</span></span>
-<span id="L162"><span class="lineNum">     162</span> <span class="tlaGNC">        2951 :     }</span></span>
-<span id="L163"><span class="lineNum">     163</span> <span class="tlaGNC">         475 :     int Classifier::getNumberOfNodes() const</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">         475 :         return fitted ? model.getFeatures().size() : 0;</span></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">         475 :     int Classifier::getNumberOfEdges() const</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">         475 :         return fitted ? model.getNumEdges() : 0;</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">          66 :     int Classifier::getNumberOfStates() const</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">          66 :         return fitted ? model.getStates() : 0;</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">         877 :     int Classifier::getClassNumStates() const</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">         877 :         return fitted ? model.getClassNumStates() : 0;</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">          11 :     std::vector&lt;std::string&gt; Classifier::topological_order()</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">          11 :         return model.topological_sort();</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">          11 :     std::string Classifier::dump_cpt() const</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">          11 :         return model.dump_cpt();</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">         231 :     void Classifier::setHyperparameters(const nlohmann::json&amp; hyperparameters)</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">         231 :         if (!hyperparameters.empty()) {</span></span>
-<span id="L191"><span class="lineNum">     191</span> <span class="tlaGNC">          22 :             throw std::invalid_argument(&quot;Invalid hyperparameters&quot; + hyperparameters.dump());</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">         209 :     }</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>

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

@@ -0,0 +1,69 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.cc</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
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html/bayesnet/classifiers/Classifier.h.func-c.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Classifier.h<span style="font-size: 80%;"> (<a href="Classifier.h.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
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html/bayesnet/classifiers/Classifier.h.func.html

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 <head>
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-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.h - functions</title>
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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.h<span style="font-size: 80%;"> (<a href="Classifier.h.gcov.html">source</a> / functions)</span></td>
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html/bayesnet/classifiers/Classifier.h.gcov.frameset.html

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

@@ -4,7 +4,7 @@
 
 <head>
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-  <title>LCOV - coverage.info - bayesnet/classifiers/Classifier.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Classifier.h</title>
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@@ -19,7 +19,7 @@
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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.h<span style="font-size: 80%;"> (source / <a href="Classifier.h.func-c.html">functions</a>)</span></td>
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+            <td class="headerValue">2024-05-06 17:54:04</td>
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+            <span class="coverLegendNoCov">not hit</span>
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 <span id="L15"><span class="lineNum">      15</span>              :     class Classifier : public BaseClassifier {</span>
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 <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>

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

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+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
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-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.cc - functions</title>
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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> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></td>
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+              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
               
-              <td class="coverFnHi">407</td>
+              <td class="coverFnHi">148</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">_ZN8bayesnet3KDB11add_m_edgesEiRSt6vectorIiSaIiEERN2at6TensorE</a></td>
+              <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">946</td>
+              <td class="coverFnHi">344</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (<a href="KDB.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,37 +71,37 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">_ZN8bayesnet3KDB10buildModelERKN2at6TensorE</a></td>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">bayesnet::KDB::KDB(int, float)</a></td>
               
-              <td class="coverFnHi">143</td>
+              <td class="coverFnHi">148</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L77">_ZN8bayesnet3KDB11add_m_edgesEiRSt6vectorIiSaIiEERN2at6TensorE</a></td>
+              <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">946</td>
+              <td class="coverFnHi">344</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L13">_ZN8bayesnet3KDB18setHyperparametersERKN8nlohmann16json_abi_v3_11_310basic_jsonISt3mapSt6vectorNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEblmdSaNS2_14adl_serializerES5_IhSaIhEEvEE</a></td>
+              <td class="coverFn"><a href="KDB.cc.gcov.html#L26">bayesnet::KDB::buildModel(at::Tensor const&amp;)</a></td>
               
-              <td class="coverFnHi">33</td>
+              <td class="coverFnHi">52</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L8">_ZN8bayesnet3KDBC2Eif</a></td>
+              <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">407</td>
+              <td class="coverFnHi">8</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.cc.gcov.html#L101">_ZNK8bayesnet3KDB5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <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">22</td>
+              <td class="coverFnHi">12</td>
               
               
             </tr>

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

@@ -0,0 +1,19 @@
+        <!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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDB.cc<span style="font-size: 80%;"> (source / <a href="KDB.cc.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">96.3&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -69,25 +77,25 @@
 <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">         407 :     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)</span></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">        1221 :         validHyperparameters = { &quot;k&quot;, &quot;theta&quot; };</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">        1221 :     }</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">          33 :     void KDB::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</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">          33 :         auto hyperparameters = hyperparameters_;</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">          33 :         if (hyperparameters.contains(&quot;k&quot;)) {</span></span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">          11 :             k = hyperparameters[&quot;k&quot;];</span></span>
-<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">          11 :             hyperparameters.erase(&quot;k&quot;);</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">          33 :         if (hyperparameters.contains(&quot;theta&quot;)) {</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">          11 :             theta = hyperparameters[&quot;theta&quot;];</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          11 :             hyperparameters.erase(&quot;theta&quot;);</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">          33 :         Classifier::setHyperparameters(hyperparameters);</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">          33 :     }</span></span>
-<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">         143 :     void KDB::buildModel(const torch::Tensor&amp; weights)</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>
@@ -110,66 +118,66 @@
 <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">         143 :         addNodes();</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">         429 :         const torch::Tensor&amp; y = dataset.index({ -1, &quot;...&quot; });</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">         143 :         std::vector&lt;double&gt; mi;</span></span>
-<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">        1089 :         for (auto i = 0; i &lt; features.size(); i++) {</span></span>
-<span id="L55"><span class="lineNum">      55</span> <span class="tlaGNC">        2838 :             torch::Tensor firstFeature = dataset.index({ i, &quot;...&quot; });</span></span>
-<span id="L56"><span class="lineNum">      56</span> <span class="tlaGNC">         946 :             mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</span></span>
-<span id="L57"><span class="lineNum">      57</span> <span class="tlaGNC">         946 :         }</span></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">         143 :         auto conditionalEdgeWeights = metrics.conditionalEdge(weights);</span></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">         143 :         std::vector&lt;int&gt; S;</span></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">         143 :         auto order = argsort(mi);</span></span>
-<span id="L68"><span class="lineNum">      68</span> <span class="tlaGNC">        1089 :         for (auto idx : order) {</span></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">         946 :             model.addEdge(className, features[idx]);</span></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">         946 :             add_m_edges(idx, S, conditionalEdgeWeights);</span></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">         946 :             S.push_back(idx);</span></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">        1232 :     }</span></span>
-<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         946 :     void KDB::add_m_edges(int idx, std::vector&lt;int&gt;&amp; S, torch::Tensor&amp; weights)</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">         946 :         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">         946 :         auto cond_w = clone(weights);</span></span>
-<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         946 :         bool exit_cond = k == 0;</span></span>
-<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         946 :         int num = 0;</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">        2761 :         while (!exit_cond) {</span></span>
-<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">        7260 :             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">        1815 :             auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();</span></span>
-<span id="L88"><span class="lineNum">      88</span> <span class="tlaGNC">        4851 :             if (belongs &amp;&amp; cond_w.index({ idx, max_minfo }).item&lt;float&gt;() &gt; theta) {</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">         880 :                     model.addEdge(features[max_minfo], features[idx]);</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">         880 :                     num++;</span></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">        7260 :             cond_w.index_put_({ idx, max_minfo }, -1);</span></span>
-<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">        5445 :             auto candidates_mask = cond_w.index({ idx, &quot;...&quot; }).gt(theta);</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">        1815 :             auto candidates = candidates_mask.nonzero();</span></span>
-<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">        1815 :             exit_cond = num == n_edges || candidates.size(0) == 0;</span></span>
-<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">        1815 :         }</span></span>
-<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">        7403 :     }</span></span>
-<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">          22 :     std::vector&lt;std::string&gt; KDB::graph(const std::string&amp; title) const</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">          22 :         std::string header{ title };</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">          22 :         if (title == &quot;KDB&quot;) {</span></span>
-<span id="L107"><span class="lineNum">     107</span> <span class="tlaGNC">          22 :             header += &quot; (k=&quot; + std::to_string(k) + &quot;, theta=&quot; + std::to_string(theta) + &quot;)&quot;;</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">          44 :         return model.graph(header);</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">          22 :     }</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>

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

@@ -0,0 +1,48 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.cc</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="KDB.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="KDB.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="KDB.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="KDB.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="KDB.cc.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="KDB.cc.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="KDB.cc.gcov.html#L13" target="source" alt="overview">
+            <area shape="rect" coords="0,28,79,31" href="KDB.cc.gcov.html#L17" target="source" alt="overview">
+            <area shape="rect" coords="0,32,79,35" href="KDB.cc.gcov.html#L21" target="source" alt="overview">
+            <area shape="rect" coords="0,36,79,39" href="KDB.cc.gcov.html#L25" target="source" alt="overview">
+            <area shape="rect" coords="0,40,79,43" href="KDB.cc.gcov.html#L29" target="source" alt="overview">
+            <area shape="rect" coords="0,44,79,47" href="KDB.cc.gcov.html#L33" target="source" alt="overview">
+            <area shape="rect" coords="0,48,79,51" href="KDB.cc.gcov.html#L37" target="source" alt="overview">
+            <area shape="rect" coords="0,52,79,55" href="KDB.cc.gcov.html#L41" target="source" alt="overview">
+            <area shape="rect" coords="0,56,79,59" href="KDB.cc.gcov.html#L45" target="source" alt="overview">
+            <area shape="rect" coords="0,60,79,63" href="KDB.cc.gcov.html#L49" target="source" alt="overview">
+            <area shape="rect" coords="0,64,79,67" href="KDB.cc.gcov.html#L53" target="source" alt="overview">
+            <area shape="rect" coords="0,68,79,71" href="KDB.cc.gcov.html#L57" target="source" alt="overview">
+            <area shape="rect" coords="0,72,79,75" href="KDB.cc.gcov.html#L61" target="source" alt="overview">
+            <area shape="rect" coords="0,76,79,79" href="KDB.cc.gcov.html#L65" target="source" alt="overview">
+            <area shape="rect" coords="0,80,79,83" href="KDB.cc.gcov.html#L69" target="source" alt="overview">
+            <area shape="rect" coords="0,84,79,87" href="KDB.cc.gcov.html#L73" target="source" alt="overview">
+            <area shape="rect" coords="0,88,79,91" href="KDB.cc.gcov.html#L77" target="source" alt="overview">
+            <area shape="rect" coords="0,92,79,95" href="KDB.cc.gcov.html#L81" target="source" alt="overview">
+            <area shape="rect" coords="0,96,79,99" href="KDB.cc.gcov.html#L85" target="source" alt="overview">
+            <area shape="rect" coords="0,100,79,103" href="KDB.cc.gcov.html#L89" target="source" alt="overview">
+            <area shape="rect" coords="0,104,79,107" href="KDB.cc.gcov.html#L93" target="source" alt="overview">
+            <area shape="rect" coords="0,108,79,111" href="KDB.cc.gcov.html#L97" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="KDB.cc.gcov.html#top" target="source">Top</a><br><br>
+          <img src="KDB.cc.gcov.png" width=80 height=110 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              <td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
               
-              <td class="coverFnHi">11</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">2</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">9</td>
+              <td class="coverFnHi">44</td>
               
               
             </tr>

html/bayesnet/classifiers/KDB.h.func.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (<a href="KDB.h.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
+              <td class="coverFn"><a href="KDB.h.gcov.html#L20">bayesnet::KDB::~KDB()</a></td>
               
-              <td class="coverFnHi">11</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">2</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDB.h.gcov.html#L20">_ZN8bayesnet3KDBD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">9</td>
+              <td class="coverFnHi">44</td>
               
               
             </tr>

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

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

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDB.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.h</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDB.h<span style="font-size: 80%;"> (source / <a href="KDB.h.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -81,7 +89,7 @@
 <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">          11 :         virtual ~KDB() = default;</span></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>

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

@@ -0,0 +1,27 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDB.h</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="KDB.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="KDB.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="KDB.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="KDB.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="KDB.h.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="KDB.h.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="KDB.h.gcov.html#L13" target="source" alt="overview">
+          </map>
+
+          <center>
+          <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>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,30 +71,30 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">_ZNK8bayesnet5KDBLd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <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">11</td>
+              <td class="coverFnHi">4</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">_ZN8bayesnet5KDBLd7predictERN2at6TensorE</a></td>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnHi">44</td>
+              <td class="coverFnHi">16</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">_ZN8bayesnet5KDBLd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <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">55</td>
+              <td class="coverFnHi">20</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">_ZN8bayesnet5KDBLdC2Ei</a></td>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
               
-              <td class="coverFnHi">187</td>
+              <td class="coverFnHi">68</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (<a href="KDBLd.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,30 +71,30 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L9">_ZN8bayesnet5KDBLd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">bayesnet::KDBLd::KDBLd(int)</a></td>
               
-              <td class="coverFnHi">55</td>
+              <td class="coverFnHi">68</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">_ZN8bayesnet5KDBLd7predictERN2at6TensorE</a></td>
+              <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">44</td>
+              <td class="coverFnHi">20</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L8">_ZN8bayesnet5KDBLdC2Ei</a></td>
+              <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">187</td>
+              <td class="coverFnHi">4</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L29">_ZNK8bayesnet5KDBLd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <td class="coverFn"><a href="KDBLd.cc.gcov.html#L24">bayesnet::KDBLd::predict(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnHi">11</td>
+              <td class="coverFnHi">16</td>
               
               
             </tr>

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

@@ -0,0 +1,19 @@
+        <!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/KDBLd.cc</title>
+          <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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (source / <a href="KDBLd.cc.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -69,30 +77,30 @@
 <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">         187 :     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
-<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC">          55 :     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="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">          55 :         checkInput(X_, y_);</span></span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">          55 :         features = features_;</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">          55 :         className = className_;</span></span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">          55 :         Xf = X_;</span></span>
-<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">          55 :         y = y_;</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">          55 :         states = fit_local_discretization(y);</span></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">          55 :         KDB::fit(dataset, features, className, states);</span></span>
-<span id="L23"><span class="lineNum">      23</span> <span class="tlaGNC">          55 :         states = localDiscretizationProposal(states, model);</span></span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          55 :         return *this;</span></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">          44 :     torch::Tensor KDBLd::predict(torch::Tensor&amp; X)</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">          44 :         auto Xt = prepareX(X);</span></span>
-<span id="L29"><span class="lineNum">      29</span> <span class="tlaGNC">          88 :         return KDB::predict(Xt);</span></span>
-<span id="L30"><span class="lineNum">      30</span> <span class="tlaGNC">          44 :     }</span></span>
-<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">          11 :     std::vector&lt;std::string&gt; KDBLd::graph(const std::string&amp; name) const</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">          11 :         return KDB::graph(name);</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>

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

@@ -0,0 +1,29 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.cc</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="KDBLd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="KDBLd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="KDBLd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="KDBLd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="KDBLd.cc.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="KDBLd.cc.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="KDBLd.cc.gcov.html#L13" target="source" alt="overview">
+            <area shape="rect" coords="0,28,79,31" href="KDBLd.cc.gcov.html#L17" target="source" alt="overview">
+            <area shape="rect" coords="0,32,79,35" href="KDBLd.cc.gcov.html#L21" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="KDBLd.cc.gcov.html#top" target="source">Top</a><br><br>
+          <img src="KDBLd.cc.gcov.png" width=80 height=34 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></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.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
               
-              <td class="coverFnHi">5</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">2</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">3</td>
+              <td class="coverFnHi">20</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></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.h<span style="font-size: 80%;"> (<a href="KDBLd.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
+              <td class="coverFn"><a href="KDBLd.h.gcov.html#L15">bayesnet::KDBLd::~KDBLd()</a></td>
               
-              <td class="coverFnHi">5</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">2</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="KDBLd.h.gcov.html#L15">_ZN8bayesnet5KDBLdD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">3</td>
+              <td class="coverFnHi">20</td>
               
               
             </tr>

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

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

html/bayesnet/classifiers/KDBLd.h.gcov.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/KDBLd.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.h</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - KDBLd.h<span style="font-size: 80%;"> (source / <a href="KDBLd.h.func-c.html">functions</a>)</span></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.h<span style="font-size: 80%;"> (source / <a href="KDBLd.h.func-c.html">functions</a>)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -76,7 +84,7 @@
 <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">           5 :         virtual ~KDBLd() = default;</span></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>

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

@@ -0,0 +1,26 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.h</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="KDBLd.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="KDBLd.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="KDBLd.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="KDBLd.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="KDBLd.h.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="KDBLd.h.gcov.html#L9" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="KDBLd.h.gcov.html#top" target="source">Top</a><br><br>
+          <img src="KDBLd.h.gcov.png" width=80 height=23 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Proposal.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (<a href="Proposal.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
-            <td class="headerCovTableEntry">9</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>
@@ -63,72 +71,58 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">_ZN8bayesnet8Proposal8prepareXERN2at6TensorE</a></td>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnHi">462</td>
+              <td class="coverFnHi">168</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
               
-              <td class="coverFnHi">550</td>
+              <td class="coverFnHi">200</td>
               
               
             </tr>
             <tr>
-              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              <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="coverFnAliasLo">0</td>
+              <td class="coverFnHi">212</td>
               
               
             </tr>
             <tr>
-              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD2Ev</a></td>
+              <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="coverFnAliasHi">550</td>
+              <td class="coverFnHi">228</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">_ZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkE</a></td>
+              <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">583</td>
+              <td class="coverFnHi">232</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">_ZN8bayesnet8Proposal10checkInputERKN2at6TensorES4_</a></td>
+              <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">627</td>
+              <td class="coverFnHi">424</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">_ZN8bayesnet8Proposal24fit_local_discretizationB5cxx11ERKN2at6TensorE</a></td>
+              <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">638</td>
+              <td class="coverFnHi">1372</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">_ZN8bayesnet8ProposalC2ERN2at6TensorERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERSA_</a></td>
+              <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">1166</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E0_clIS7_EEDaSO_</a></td>
-              
-              <td class="coverFnHi">3773</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E_clIPNS_4NodeEEEDaSO_</a></td>
-              
-              <td class="coverFnHi">7414</td>
+              <td class="coverFnHi">2696</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Proposal.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (<a href="Proposal.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
-            <td class="headerCovTableEntry">9</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>
@@ -63,72 +71,58 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L16">_ZN8bayesnet8Proposal10checkInputERKN2at6TensorES4_</a></td>
+              <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">627</td>
+              <td class="coverFnHi">2696</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L77">_ZN8bayesnet8Proposal24fit_local_discretizationB5cxx11ERKN2at6TensorE</a></td>
+              <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">638</td>
+              <td class="coverFnHi">1372</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L25">_ZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkE</a></td>
+              <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">583</td>
+              <td class="coverFnHi">424</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">_ZN8bayesnet8Proposal8prepareXERN2at6TensorE</a></td>
+              <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">462</td>
+              <td class="coverFnHi">228</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L9">_ZN8bayesnet8ProposalC2ERN2at6TensorERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERSA_</a></td>
+              <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">1166</td>
+              <td class="coverFnHi">232</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              <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">550</td>
+              <td class="coverFnHi">212</td>
               
               
             </tr>
             <tr>
-              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD0Ev</a></td>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L104">bayesnet::Proposal::prepareX(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnAliasLo">0</td>
+              <td class="coverFnHi">168</td>
               
               
             </tr>
             <tr>
-              <td class="coverFnAlias"><a href="Proposal.cc.gcov.html#L10">_ZN8bayesnet8ProposalD2Ev</a></td>
+              <td class="coverFn"><a href="Proposal.cc.gcov.html#L10">bayesnet::Proposal::~Proposal()</a></td>
               
-              <td class="coverFnAliasHi">550</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L47">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E0_clIS7_EEDaSO_</a></td>
-              
-              <td class="coverFnHi">3773</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFn"><a href="Proposal.cc.gcov.html#L41">_ZZN8bayesnet8Proposal27localDiscretizationProposalERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIiSaIiEESt4lessIS7_ESaISt4pairIKS7_SA_EEERNS_7NetworkEENKUlRKT_E_clIPNS_4NodeEEEDaSO_</a></td>
-              
-              <td class="coverFnHi">7414</td>
+              <td class="coverFnHi">200</td>
               
               
             </tr>

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

@@ -0,0 +1,19 @@
+        <!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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/Proposal.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/Proposal.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - Proposal.cc<span style="font-size: 80%;"> (source / <a href="Proposal.cc.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">97.7&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</td>
+            <td class="headerValue">2024-05-06 17:54:04</td>
             <td></td>
             <td class="headerItem">Functions:</td>
-            <td class="headerCovTableEntryMed">88.9&nbsp;%</td>
-            <td class="headerCovTableEntry">9</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>
@@ -70,111 +78,111 @@
 <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">        1166 :     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">         550 :     Proposal::~Proposal()</span></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">        5214 :         for (auto&amp; [key, value] : discretizers) {</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">        4664 :             delete value;</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">         550 :     }</span></span>
-<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">         627 :     void Proposal::checkInput(const torch::Tensor&amp; X, const torch::Tensor&amp; y)</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">         627 :         if (!torch::is_floating_point(X)) {</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">         627 :         if (torch::is_floating_point(y)) {</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">         627 :     }</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">         583 :     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="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">         583 :         auto order = model.topological_sort();</span></span>
-<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         583 :         auto&amp; nodes = model.getNodes();</span></span>
-<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">         583 :         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">         583 :         std::vector&lt;int&gt; indicesToReDiscretize;</span></span>
-<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">         583 :         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">        4884 :         for (auto feature : order) {</span></span>
-<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">        4301 :             auto nodeParents = nodes[feature]-&gt;getParents();</span></span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">        4301 :             if (nodeParents.size() &lt; 2) continue; // Only has class as parent</span></span>
-<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">        3641 :             upgrade = true;</span></span>
-<span id="L40"><span class="lineNum">      40</span> <span class="tlaGNC">        3641 :             int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</span></span>
-<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">        3641 :             indicesToReDiscretize.push_back(index); // We need to re-discretize this feature</span></span>
-<span id="L42"><span class="lineNum">      42</span> <span class="tlaGNC">        3641 :             std::vector&lt;std::string&gt; parents;</span></span>
-<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">       11055 :             transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto&amp; p) { return p-&gt;getName(); });</span></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">        3641 :             parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</span></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">        3641 :             std::vector&lt;int&gt; indices;</span></span>
-<span id="L48"><span class="lineNum">      48</span> <span class="tlaGNC">        3641 :             indices.push_back(-1); // Add class index</span></span>
-<span id="L49"><span class="lineNum">      49</span> <span class="tlaGNC">        7414 :             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="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">        3641 :             std::vector&lt;std::string&gt; yJoinParents(Xf.size(1));</span></span>
-<span id="L52"><span class="lineNum">      52</span> <span class="tlaGNC">       11055 :             for (auto idx : indices) {</span></span>
-<span id="L53"><span class="lineNum">      53</span> <span class="tlaGNC">     2636260 :                 for (int i = 0; i &lt; Xf.size(1); ++i) {</span></span>
-<span id="L54"><span class="lineNum">      54</span> <span class="tlaGNC">     7886538 :                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item&lt;int&gt;());</span></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">        3641 :             auto arff = ArffFiles();</span></span>
-<span id="L58"><span class="lineNum">      58</span> <span class="tlaGNC">        3641 :             auto yxv = arff.factorize(yJoinParents);</span></span>
-<span id="L59"><span class="lineNum">      59</span> <span class="tlaGNC">        7282 :             auto xvf_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
-<span id="L60"><span class="lineNum">      60</span> <span class="tlaGNC">        3641 :             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">        3641 :             discretizers[feature]-&gt;fit(xvf, yxv);</span></span>
-<span id="L62"><span class="lineNum">      62</span> <span class="tlaGNC">        4961 :         }</span></span>
-<span id="L63"><span class="lineNum">      63</span> <span class="tlaGNC">         583 :         if (upgrade) {</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">        4224 :             for (auto index : indicesToReDiscretize) {</span></span>
-<span id="L66"><span class="lineNum">      66</span> <span class="tlaGNC">        7282 :                 auto Xt_ptr = Xf.index({ index }).data_ptr&lt;float&gt;();</span></span>
-<span id="L67"><span class="lineNum">      67</span> <span class="tlaGNC">        3641 :                 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">       14564 :                 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">        3641 :                 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">        3641 :                 iota(xStates.begin(), xStates.end(), 0);</span></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">        3641 :                 states[pFeatures[index]] = xStates;</span></span>
-<span id="L73"><span class="lineNum">      73</span> <span class="tlaGNC">        3641 :             }</span></span>
-<span id="L74"><span class="lineNum">      74</span> <span class="tlaGNC">         583 :             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">         583 :             model.fit(pDataset, weights, pFeatures, pClassName, states);</span></span>
-<span id="L76"><span class="lineNum">      76</span> <span class="tlaGNC">         583 :         }</span></span>
-<span id="L77"><span class="lineNum">      77</span> <span class="tlaGNC">        1166 :         return states;</span></span>
-<span id="L78"><span class="lineNum">      78</span> <span class="tlaGNC">     2640352 :     }</span></span>
-<span id="L79"><span class="lineNum">      79</span> <span class="tlaGNC">         638 :     map&lt;std::string, std::vector&lt;int&gt;&gt; Proposal::fit_local_discretization(const torch::Tensor&amp; y)</span></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">         638 :         int m = Xf.size(1);</span></span>
-<span id="L83"><span class="lineNum">      83</span> <span class="tlaGNC">         638 :         int n = Xf.size(0);</span></span>
-<span id="L84"><span class="lineNum">      84</span> <span class="tlaGNC">         638 :         map&lt;std::string, std::vector&lt;int&gt;&gt; states;</span></span>
-<span id="L85"><span class="lineNum">      85</span> <span class="tlaGNC">         638 :         pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</span></span>
-<span id="L86"><span class="lineNum">      86</span> <span class="tlaGNC">         638 :         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="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">        5346 :         for (auto i = 0; i &lt; pFeatures.size(); ++i) {</span></span>
-<span id="L89"><span class="lineNum">      89</span> <span class="tlaGNC">        4708 :             auto* discretizer = new mdlp::CPPFImdlp();</span></span>
-<span id="L90"><span class="lineNum">      90</span> <span class="tlaGNC">        9416 :             auto Xt_ptr = Xf.index({ i }).data_ptr&lt;float&gt;();</span></span>
-<span id="L91"><span class="lineNum">      91</span> <span class="tlaGNC">        4708 :             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">        4708 :             discretizer-&gt;fit(Xt, yv);</span></span>
-<span id="L93"><span class="lineNum">      93</span> <span class="tlaGNC">       18832 :             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">        4708 :             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">        4708 :             iota(xStates.begin(), xStates.end(), 0);</span></span>
-<span id="L96"><span class="lineNum">      96</span> <span class="tlaGNC">        4708 :             states[pFeatures[i]] = xStates;</span></span>
-<span id="L97"><span class="lineNum">      97</span> <span class="tlaGNC">        4708 :             discretizers[pFeatures[i]] = discretizer;</span></span>
-<span id="L98"><span class="lineNum">      98</span> <span class="tlaGNC">        4708 :         }</span></span>
-<span id="L99"><span class="lineNum">      99</span> <span class="tlaGNC">         638 :         int n_classes = torch::max(y).item&lt;int&gt;() + 1;</span></span>
-<span id="L100"><span class="lineNum">     100</span> <span class="tlaGNC">         638 :         auto yStates = std::vector&lt;int&gt;(n_classes);</span></span>
-<span id="L101"><span class="lineNum">     101</span> <span class="tlaGNC">         638 :         iota(yStates.begin(), yStates.end(), 0);</span></span>
-<span id="L102"><span class="lineNum">     102</span> <span class="tlaGNC">         638 :         states[pClassName] = yStates;</span></span>
-<span id="L103"><span class="lineNum">     103</span> <span class="tlaGNC">        1914 :         pDataset.index_put_({ n, &quot;...&quot; }, y);</span></span>
-<span id="L104"><span class="lineNum">     104</span> <span class="tlaGNC">        1276 :         return states;</span></span>
-<span id="L105"><span class="lineNum">     105</span> <span class="tlaGNC">       10692 :     }</span></span>
-<span id="L106"><span class="lineNum">     106</span> <span class="tlaGNC">         462 :     torch::Tensor Proposal::prepareX(torch::Tensor&amp; X)</span></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">         462 :         auto Xtd = torch::zeros_like(X, torch::kInt32);</span></span>
-<span id="L109"><span class="lineNum">     109</span> <span class="tlaGNC">        3784 :         for (int i = 0; i &lt; X.size(0); ++i) {</span></span>
-<span id="L110"><span class="lineNum">     110</span> <span class="tlaGNC">        3322 :             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">        3322 :             auto Xd = discretizers[pFeatures[i]]-&gt;transform(Xt);</span></span>
-<span id="L112"><span class="lineNum">     112</span> <span class="tlaGNC">        9966 :             Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</span></span>
-<span id="L113"><span class="lineNum">     113</span> <span class="tlaGNC">        3322 :         }</span></span>
-<span id="L114"><span class="lineNum">     114</span> <span class="tlaGNC">         462 :         return Xtd;</span></span>
-<span id="L115"><span class="lineNum">     115</span> <span class="tlaGNC">        3322 :     }</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>

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

@@ -0,0 +1,49 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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+          <center>
+          <a href="Proposal.cc.gcov.html#top" target="source">Top</a><br><br>
+          <img src="Proposal.cc.gcov.png" width=80 height=115 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (<a href="SPODE.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,23 +71,23 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L24">_ZNK8bayesnet5SPODE5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <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">187</td>
+              <td class="coverFnHi">68</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">_ZN8bayesnet5SPODE10buildModelERKN2at6TensorE</a></td>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&amp;)</a></td>
               
-              <td class="coverFnHi">2665</td>
+              <td class="coverFnHi">1016</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">_ZN8bayesnet5SPODEC2Ei</a></td>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">bayesnet::SPODE::SPODE(int)</a></td>
               
-              <td class="coverFnHi">2962</td>
+              <td class="coverFnHi">1124</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (<a href="SPODE.cc.gcov.html">source</a> / functions)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -63,23 +71,23 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">_ZN8bayesnet5SPODE10buildModelERKN2at6TensorE</a></td>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">bayesnet::SPODE::SPODE(int)</a></td>
               
-              <td class="coverFnHi">2665</td>
+              <td class="coverFnHi">1124</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L9">_ZN8bayesnet5SPODEC2Ei</a></td>
+              <td class="coverFn"><a href="SPODE.cc.gcov.html#L11">bayesnet::SPODE::buildModel(at::Tensor const&amp;)</a></td>
               
-              <td class="coverFnHi">2962</td>
+              <td class="coverFnHi">1016</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.cc.gcov.html#L24">_ZNK8bayesnet5SPODE5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <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">187</td>
+              <td class="coverFnHi">68</td>
               
               
             </tr>

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

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

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.cc<span style="font-size: 80%;"> (source / <a href="SPODE.cc.func-c.html">functions</a>)</span></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>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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>
@@ -70,24 +78,24 @@
 <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">        2962 :     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}</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">        2665 :     void SPODE::buildModel(const torch::Tensor&amp; weights)</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">        2665 :         addNodes();</span></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">      113941 :         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">      111276 :             model.addEdge(className, features[i]);</span></span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">      111276 :             if (i != root) {</span></span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">      108611 :                 model.addEdge(features[root], features[i]);</span></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>
 <span id="L23"><span class="lineNum">      23</span>              :             }</span>
 <span id="L24"><span class="lineNum">      24</span>              :         }</span>
-<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">        2665 :     }</span></span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">         187 :     std::vector&lt;std::string&gt; SPODE::graph(const std::string&amp; name) const</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">         187 :         return model.graph(name);</span></span>
+<span id="L28"><span class="lineNum">      28</span> <span class="tlaGNC">          68 :         return model.graph(name);</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>              : }</span>

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

@@ -0,0 +1,28 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.cc</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="SPODE.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="SPODE.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="SPODE.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="SPODE.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="SPODE.cc.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="SPODE.cc.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="SPODE.cc.gcov.html#L13" target="source" alt="overview">
+            <area shape="rect" coords="0,28,79,31" href="SPODE.cc.gcov.html#L17" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="SPODE.cc.gcov.html#top" target="source">Top</a><br><br>
+          <img src="SPODE.cc.gcov.png" width=80 height=30 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.h<span style="font-size: 80%;"> (<a href="SPODE.h.gcov.html">source</a> / functions)</span></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.h<span style="font-size: 80%;"> (<a href="SPODE.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED0Ev</a></td>
+              <td class="coverFn"><a href="SPODE.h.gcov.html#L17">bayesnet::SPODE::~SPODE()</a></td>
               
-              <td class="coverFnHi">337</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED0Ev</a></td>
-              
-              <td class="coverFnAliasHi">148</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED2Ev</a></td>
-              
-              <td class="coverFnAliasHi">189</td>
+              <td class="coverFnHi">1836</td>
               
               
             </tr>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.h<span style="font-size: 80%;"> (<a href="SPODE.h.gcov.html">source</a> / functions)</span></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.h<span style="font-size: 80%;"> (<a href="SPODE.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED0Ev</a></td>
+              <td class="coverFn"><a href="SPODE.h.gcov.html#L17">bayesnet::SPODE::~SPODE()</a></td>
               
-              <td class="coverFnHi">337</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED0Ev</a></td>
-              
-              <td class="coverFnAliasHi">148</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODE.h.gcov.html#L17">_ZN8bayesnet5SPODED2Ev</a></td>
-              
-              <td class="coverFnAliasHi">189</td>
+              <td class="coverFnHi">1836</td>
               
               
             </tr>

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

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

html/bayesnet/classifiers/SPODE.h.gcov.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODE.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.h</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODE.h<span style="font-size: 80%;"> (source / <a href="SPODE.h.func-c.html">functions</a>)</span></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.h<span style="font-size: 80%;"> (source / <a href="SPODE.h.func-c.html">functions</a>)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -78,7 +86,7 @@
 <span id="L16"><span class="lineNum">      16</span>              :         void buildModel(const torch::Tensor&amp; weights) override;</span>
 <span id="L17"><span class="lineNum">      17</span>              :     public:</span>
 <span id="L18"><span class="lineNum">      18</span>              :         explicit SPODE(int root);</span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC tlaBgGNC">         337 :         virtual ~SPODE() = default;</span></span>
+<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC tlaBgGNC">        1836 :         virtual ~SPODE() = default;</span></span>
 <span id="L20"><span class="lineNum">      20</span>              :         std::vector&lt;std::string&gt; graph(const std::string&amp; name = &quot;SPODE&quot;) const override;</span>
 <span id="L21"><span class="lineNum">      21</span>              :     };</span>
 <span id="L22"><span class="lineNum">      22</span>              : }</span>

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

@@ -0,0 +1,26 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODE.h</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="SPODE.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="SPODE.h.gcov.html#L1" target="source" alt="overview">
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+            <area shape="rect" coords="0,20,79,23" href="SPODE.h.gcov.html#L9" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="SPODE.h.gcov.html#top" target="source">Top</a><br><br>
+          <img src="SPODE.h.gcov.png" width=80 height=22 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (<a href="SPODELd.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (<a href="SPODELd.cc.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">6</td>
             <td class="headerCovTableEntry">6</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>
@@ -63,44 +71,44 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">_ZN8bayesnet7SPODELd3fitERN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">bayesnet::SPODELd::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">22</td>
+              <td class="coverFnHi">8</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">_ZNK8bayesnet7SPODELd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">bayesnet::SPODELd::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">99</td>
+              <td class="coverFnHi">36</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">_ZN8bayesnet7SPODELd7predictERN2at6TensorE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">bayesnet::SPODELd::predict(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnHi">374</td>
+              <td class="coverFnHi">136</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">_ZN8bayesnet7SPODELd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">bayesnet::SPODELd::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">462</td>
+              <td class="coverFnHi">168</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">_ZN8bayesnet7SPODELd9commonFitERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EERKS7_RSt3mapIS7_S1_IiSaIiEESt4lessIS7_ESaISt4pairISC_SG_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">bayesnet::SPODELd::commonFit(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">473</td>
+              <td class="coverFnHi">172</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">_ZN8bayesnet7SPODELdC2Ei</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">bayesnet::SPODELd::SPODELd(int)</a></td>
               
-              <td class="coverFnHi">605</td>
+              <td class="coverFnHi">220</td>
               
               
             </tr>

html/bayesnet/classifiers/SPODELd.cc.func.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.cc - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.cc - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (<a href="SPODELd.cc.gcov.html">source</a> / functions)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (<a href="SPODELd.cc.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">6</td>
             <td class="headerCovTableEntry">6</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>
@@ -63,44 +71,44 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">_ZN8bayesnet7SPODELd3fitERN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">bayesnet::SPODELd::SPODELd(int)</a></td>
               
-              <td class="coverFnHi">22</td>
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             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">_ZN8bayesnet7SPODELd3fitERN2at6TensorES3_RKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISA_EERKSA_RSt3mapISA_S4_IiSaIiEESt4lessISA_ESaISt4pairISF_SJ_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">bayesnet::SPODELd::commonFit(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">462</td>
+              <td class="coverFnHi">172</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">_ZN8bayesnet7SPODELd7predictERN2at6TensorE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L9">bayesnet::SPODELd::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">374</td>
+              <td class="coverFnHi">168</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L27">_ZN8bayesnet7SPODELd9commonFitERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EERKS7_RSt3mapIS7_S1_IiSaIiEESt4lessIS7_ESaISt4pairISC_SG_EEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L17">bayesnet::SPODELd::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">473</td>
+              <td class="coverFnHi">8</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L8">_ZN8bayesnet7SPODELdC2Ei</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">bayesnet::SPODELd::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">605</td>
+              <td class="coverFnHi">36</td>
               
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L44">_ZNK8bayesnet7SPODELd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
+              <td class="coverFn"><a href="SPODELd.cc.gcov.html#L39">bayesnet::SPODELd::predict(at::Tensor&amp;)</a></td>
               
-              <td class="coverFnHi">99</td>
+              <td class="coverFnHi">136</td>
               
               
             </tr>

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

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

html/bayesnet/classifiers/SPODELd.cc.gcov.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.cc</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.cc</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (source / <a href="SPODELd.cc.func-c.html">functions</a>)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (source / <a href="SPODELd.cc.func-c.html">functions</a>)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">6</td>
             <td class="headerCovTableEntry">6</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>
@@ -69,45 +77,45 @@
 <span id="L7"><span class="lineNum">       7</span>              : #include &quot;SPODELd.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">         605 :     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
-<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC">         462 :     SPODELd&amp; SPODELd::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="L10"><span class="lineNum">      10</span> <span class="tlaGNC tlaBgGNC">         220 :     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
+<span id="L11"><span class="lineNum">      11</span> <span class="tlaGNC">         168 :     SPODELd&amp; SPODELd::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">         462 :         checkInput(X_, y_);</span></span>
-<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         462 :         Xf = X_;</span></span>
-<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">         462 :         y = y_;</span></span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">         462 :         return commonFit(features_, className_, states_);</span></span>
+<span id="L13"><span class="lineNum">      13</span> <span class="tlaGNC">         168 :         checkInput(X_, y_);</span></span>
+<span id="L14"><span class="lineNum">      14</span> <span class="tlaGNC">         168 :         Xf = X_;</span></span>
+<span id="L15"><span class="lineNum">      15</span> <span class="tlaGNC">         168 :         y = y_;</span></span>
+<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC">         168 :         return commonFit(features_, className_, states_);</span></span>
 <span id="L17"><span class="lineNum">      17</span>              :     }</span>
 <span id="L18"><span class="lineNum">      18</span>              : </span>
-<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">          22 :     SPODELd&amp; SPODELd::fit(torch::Tensor&amp; dataset, 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="L19"><span class="lineNum">      19</span> <span class="tlaGNC">           8 :     SPODELd&amp; SPODELd::fit(torch::Tensor&amp; dataset, 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="L20"><span class="lineNum">      20</span>              :     {</span>
-<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">          22 :         if (!torch::is_floating_point(dataset)) {</span></span>
-<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">          11 :             throw std::runtime_error(&quot;Dataset must be a floating point tensor&quot;);</span></span>
+<span id="L21"><span class="lineNum">      21</span> <span class="tlaGNC">           8 :         if (!torch::is_floating_point(dataset)) {</span></span>
+<span id="L22"><span class="lineNum">      22</span> <span class="tlaGNC">           4 :             throw std::runtime_error(&quot;Dataset must be a floating point tensor&quot;);</span></span>
 <span id="L23"><span class="lineNum">      23</span>              :         }</span>
-<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          44 :         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; }).clone();</span></span>
-<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">          33 :         y = dataset.index({ -1, &quot;...&quot; }).clone().to(torch::kInt32);</span></span>
-<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">          11 :         return commonFit(features_, className_, states_);</span></span>
-<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">          33 :     }</span></span>
+<span id="L24"><span class="lineNum">      24</span> <span class="tlaGNC">          16 :         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; }).clone();</span></span>
+<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">          12 :         y = dataset.index({ -1, &quot;...&quot; }).clone().to(torch::kInt32);</span></span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">           4 :         return commonFit(features_, className_, states_);</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>
-<span id="L29"><span class="lineNum">      29</span> <span class="tlaGNC">         473 :     SPODELd&amp; SPODELd::commonFit(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="L29"><span class="lineNum">      29</span> <span class="tlaGNC">         172 :     SPODELd&amp; SPODELd::commonFit(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="L30"><span class="lineNum">      30</span>              :     {</span>
-<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">         473 :         features = features_;</span></span>
-<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         473 :         className = className_;</span></span>
+<span id="L31"><span class="lineNum">      31</span> <span class="tlaGNC">         172 :         features = features_;</span></span>
+<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         172 :         className = className_;</span></span>
 <span id="L33"><span class="lineNum">      33</span>              :         // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
-<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">         473 :         states = fit_local_discretization(y);</span></span>
+<span id="L34"><span class="lineNum">      34</span> <span class="tlaGNC">         172 :         states = fit_local_discretization(y);</span></span>
 <span id="L35"><span class="lineNum">      35</span>              :         // We have discretized the input data</span>
 <span id="L36"><span class="lineNum">      36</span>              :         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network</span>
-<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">         473 :         SPODE::fit(dataset, features, className, states);</span></span>
-<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">         473 :         states = localDiscretizationProposal(states, model);</span></span>
-<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">         473 :         return *this;</span></span>
+<span id="L37"><span class="lineNum">      37</span> <span class="tlaGNC">         172 :         SPODE::fit(dataset, features, className, states);</span></span>
+<span id="L38"><span class="lineNum">      38</span> <span class="tlaGNC">         172 :         states = localDiscretizationProposal(states, model);</span></span>
+<span id="L39"><span class="lineNum">      39</span> <span class="tlaGNC">         172 :         return *this;</span></span>
 <span id="L40"><span class="lineNum">      40</span>              :     }</span>
-<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">         374 :     torch::Tensor SPODELd::predict(torch::Tensor&amp; X)</span></span>
+<span id="L41"><span class="lineNum">      41</span> <span class="tlaGNC">         136 :     torch::Tensor SPODELd::predict(torch::Tensor&amp; X)</span></span>
 <span id="L42"><span class="lineNum">      42</span>              :     {</span>
-<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">         374 :         auto Xt = prepareX(X);</span></span>
-<span id="L44"><span class="lineNum">      44</span> <span class="tlaGNC">         748 :         return SPODE::predict(Xt);</span></span>
-<span id="L45"><span class="lineNum">      45</span> <span class="tlaGNC">         374 :     }</span></span>
-<span id="L46"><span class="lineNum">      46</span> <span class="tlaGNC">          99 :     std::vector&lt;std::string&gt; SPODELd::graph(const std::string&amp; name) const</span></span>
+<span id="L43"><span class="lineNum">      43</span> <span class="tlaGNC">         136 :         auto Xt = prepareX(X);</span></span>
+<span id="L44"><span class="lineNum">      44</span> <span class="tlaGNC">         272 :         return SPODE::predict(Xt);</span></span>
+<span id="L45"><span class="lineNum">      45</span> <span class="tlaGNC">         136 :     }</span></span>
+<span id="L46"><span class="lineNum">      46</span> <span class="tlaGNC">          36 :     std::vector&lt;std::string&gt; SPODELd::graph(const std::string&amp; name) const</span></span>
 <span id="L47"><span class="lineNum">      47</span>              :     {</span>
-<span id="L48"><span class="lineNum">      48</span> <span class="tlaGNC">          99 :         return SPODE::graph(name);</span></span>
+<span id="L48"><span class="lineNum">      48</span> <span class="tlaGNC">          36 :         return SPODE::graph(name);</span></span>
 <span id="L49"><span class="lineNum">      49</span>              :     }</span>
 <span id="L50"><span class="lineNum">      50</span>              : }</span>
         </pre>

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

@@ -0,0 +1,33 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.cc</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="SPODELd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="SPODELd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,8,79,11" href="SPODELd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,12,79,15" href="SPODELd.cc.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,16,79,19" href="SPODELd.cc.gcov.html#L5" target="source" alt="overview">
+            <area shape="rect" coords="0,20,79,23" href="SPODELd.cc.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="SPODELd.cc.gcov.html#L13" target="source" alt="overview">
+            <area shape="rect" coords="0,28,79,31" href="SPODELd.cc.gcov.html#L17" target="source" alt="overview">
+            <area shape="rect" coords="0,32,79,35" href="SPODELd.cc.gcov.html#L21" target="source" alt="overview">
+            <area shape="rect" coords="0,36,79,39" href="SPODELd.cc.gcov.html#L25" target="source" alt="overview">
+            <area shape="rect" coords="0,40,79,43" href="SPODELd.cc.gcov.html#L29" target="source" alt="overview">
+            <area shape="rect" coords="0,44,79,47" href="SPODELd.cc.gcov.html#L33" target="source" alt="overview">
+            <area shape="rect" coords="0,48,79,51" href="SPODELd.cc.gcov.html#L37" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="SPODELd.cc.gcov.html#top" target="source">Top</a><br><br>
+          <img src="SPODELd.cc.gcov.png" width=80 height=49 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (<a href="SPODELd.h.gcov.html">source</a> / functions)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (<a href="SPODELd.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD0Ev</a></td>
+              <td class="coverFn"><a href="SPODELd.h.gcov.html#L14">bayesnet::SPODELd::~SPODELd()</a></td>
               
-              <td class="coverFnHi">80</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">39</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">41</td>
+              <td class="coverFnHi">320</td>
               
               
             </tr>

html/bayesnet/classifiers/SPODELd.h.func.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.h - functions</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.h - functions</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (<a href="SPODELd.h.gcov.html">source</a> / functions)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (<a href="SPODELd.h.gcov.html">source</a> / functions)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -63,23 +71,9 @@
               
             </tr>
             <tr>
-              <td class="coverFn"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD0Ev</a></td>
+              <td class="coverFn"><a href="SPODELd.h.gcov.html#L14">bayesnet::SPODELd::~SPODELd()</a></td>
               
-              <td class="coverFnHi">80</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD0Ev</a></td>
-              
-              <td class="coverFnAliasHi">39</td>
-              
-              
-            </tr>
-            <tr>
-              <td class="coverFnAlias"><a href="SPODELd.h.gcov.html#L14">_ZN8bayesnet7SPODELdD2Ev</a></td>
-              
-              <td class="coverFnAliasHi">41</td>
+              <td class="coverFnHi">320</td>
               
               
             </tr>

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

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

html/bayesnet/classifiers/SPODELd.h.gcov.html

@@ -4,7 +4,7 @@
 
 <head>
   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-  <title>LCOV - coverage.info - bayesnet/classifiers/SPODELd.h</title>
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.h</title>
   <link rel="stylesheet" type="text/css" href="../../gcov.css">
 </head>
 
@@ -19,7 +19,7 @@
                 <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">top level</a> - <a href="index.html">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (source / <a href="SPODELd.h.func-c.html">functions</a>)</span></td>
+            <td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.h<span style="font-size: 80%;"> (source / <a href="SPODELd.h.func-c.html">functions</a>)</span></td>
             <td width="5%"></td>
             <td width="5%"></td>
             <td width="5%" class="headerCovTableHead">Coverage</td>
@@ -28,7 +28,7 @@
           </tr>
           <tr>
             <td class="headerItem">Test:</td>
-            <td class="headerValue">coverage.info</td>
+            <td class="headerValue">BayesNet Coverage Report</td>
             <td></td>
             <td class="headerItem">Lines:</td>
             <td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -37,12 +37,20 @@
           </tr>
           <tr>
             <td class="headerItem">Test Date:</td>
-            <td class="headerValue">2024-04-29 20:48:03</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">2</td>
-            <td class="headerCovTableEntry">2</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>
@@ -75,7 +83,7 @@
 <span id="L13"><span class="lineNum">      13</span>              :     class SPODELd : public SPODE, public Proposal {</span>
 <span id="L14"><span class="lineNum">      14</span>              :     public:</span>
 <span id="L15"><span class="lineNum">      15</span>              :         explicit SPODELd(int root);</span>
-<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC tlaBgGNC">          80 :         virtual ~SPODELd() = default;</span></span>
+<span id="L16"><span class="lineNum">      16</span> <span class="tlaGNC tlaBgGNC">         320 :         virtual ~SPODELd() = default;</span></span>
 <span id="L17"><span class="lineNum">      17</span>              :         SPODELd&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="L18"><span class="lineNum">      18</span>              :         SPODELd&amp; fit(torch::Tensor&amp; dataset, 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>              :         SPODELd&amp; commonFit(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>

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

@@ -0,0 +1,27 @@
+        <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+
+        <html lang="en">
+
+        <head>
+          <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.h</title>
+          <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+          <link rel="stylesheet" type="text/css" href="../../gcov.css">
+        </head>
+
+        <body>
+          <map name="overview">
+            <area shape="rect" coords="0,0,79,3" href="SPODELd.h.gcov.html#L1" target="source" alt="overview">
+            <area shape="rect" coords="0,4,79,7" href="SPODELd.h.gcov.html#L1" target="source" alt="overview">
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+            <area shape="rect" coords="0,20,79,23" href="SPODELd.h.gcov.html#L9" target="source" alt="overview">
+            <area shape="rect" coords="0,24,79,27" href="SPODELd.h.gcov.html#L13" target="source" alt="overview">
+          </map>
+
+          <center>
+          <a href="SPODELd.h.gcov.html#top" target="source">Top</a><br><br>
+          <img src="SPODELd.h.gcov.png" width=80 height=24 alt="Overview" border=0 usemap="#overview">
+          </center>
+        </body>
+        </html>

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

@@ -0,0 +1,104 @@
+<!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/SPnDE.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> - SPnDE.cc<span style="font-size: 80%;"> (<a href="SPnDE.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">14</td>
+            <td class="headerCovTableEntry">14</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>
+              </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="SPnDE.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="SPnDE.cc.gcov.html#L31">bayesnet::SPnDE::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">24</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPnDE.cc.gcov.html#L9">bayesnet::SPnDE::SPnDE(std::vector&lt;int, std::allocator&lt;int&gt; &gt;)</a></td>
+              
+              <td class="coverFnHi">456</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPnDE.cc.gcov.html#L11">bayesnet::SPnDE::buildModel(at::Tensor const&amp;)</a></td>
+              
+              <td class="coverFnHi">456</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/SPnDE.cc.func.html

@@ -0,0 +1,104 @@
+<!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/SPnDE.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> - SPnDE.cc<span style="font-size: 80%;"> (<a href="SPnDE.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">14</td>
+            <td class="headerCovTableEntry">14</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>
+              </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="SPnDE.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="SPnDE.cc.gcov.html#L9">bayesnet::SPnDE::SPnDE(std::vector&lt;int, std::allocator&lt;int&gt; &gt;)</a></td>
+              
+              <td class="coverFnHi">456</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPnDE.cc.gcov.html#L11">bayesnet::SPnDE::buildModel(at::Tensor const&amp;)</a></td>
+              
+              <td class="coverFnHi">456</td>
+              
+              
+            </tr>
+            <tr>
+              <td class="coverFn"><a href="SPnDE.cc.gcov.html#L31">bayesnet::SPnDE::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">24</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/SPnDE.cc.gcov.frameset.html

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

html/bayesnet/classifiers/SPnDE.cc.gcov.html

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+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+  <title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPnDE.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> - SPnDE.cc<span style="font-size: 80%;"> (source / <a href="SPnDE.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">14</td>
+            <td class="headerCovTableEntry">14</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>
+              </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;SPnDE.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">         456 :     SPnDE::SPnDE(std::vector&lt;int&gt; parents) : Classifier(Network()), parents(parents) {}</span></span>
+<span id="L12"><span class="lineNum">      12</span>              : </span>
+<span id="L13"><span class="lineNum">      13</span> <span class="tlaGNC">         456 :     void SPnDE::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">         456 :         addNodes();</span></span>
+<span id="L17"><span class="lineNum">      17</span> <span class="tlaGNC">         456 :         std::vector&lt;int&gt; attributes;</span></span>
+<span id="L18"><span class="lineNum">      18</span> <span class="tlaGNC">        4440 :         for (int i = 0; i &lt; static_cast&lt;int&gt;(features.size()); ++i) {</span></span>
+<span id="L19"><span class="lineNum">      19</span> <span class="tlaGNC">        3984 :             if (std::find(parents.begin(), parents.end(), i) == parents.end()) {</span></span>
+<span id="L20"><span class="lineNum">      20</span> <span class="tlaGNC">        3072 :                 attributes.push_back(i);</span></span>
+<span id="L21"><span class="lineNum">      21</span>              :             }</span>
+<span id="L22"><span class="lineNum">      22</span>              :         }</span>
+<span id="L23"><span class="lineNum">      23</span>              :         // 1. Add edges from the class node to all other nodes</span>
+<span id="L24"><span class="lineNum">      24</span>              :         // 2. Add edges from the parents nodes to all other nodes</span>
+<span id="L25"><span class="lineNum">      25</span> <span class="tlaGNC">        3528 :         for (const auto&amp; attribute : attributes) {</span></span>
+<span id="L26"><span class="lineNum">      26</span> <span class="tlaGNC">        3072 :             model.addEdge(className, features[attribute]);</span></span>
+<span id="L27"><span class="lineNum">      27</span> <span class="tlaGNC">        9216 :             for (const auto&amp; root : parents) {</span></span>
+<span id="L28"><span class="lineNum">      28</span>              : </span>
+<span id="L29"><span class="lineNum">      29</span> <span class="tlaGNC">        6144 :                 model.addEdge(features[root], features[attribute]);</span></span>
+<span id="L30"><span class="lineNum">      30</span>              :             }</span>
+<span id="L31"><span class="lineNum">      31</span>              :         }</span>
+<span id="L32"><span class="lineNum">      32</span> <span class="tlaGNC">         456 :     }</span></span>
+<span id="L33"><span class="lineNum">      33</span> <span class="tlaGNC">          24 :     std::vector&lt;std::string&gt; SPnDE::graph(const std::string&amp; name) const</span></span>
+<span id="L34"><span class="lineNum">      34</span>              :     {</span>
+<span id="L35"><span class="lineNum">      35</span> <span class="tlaGNC">          24 :         return model.graph(name);</span></span>
+<span id="L36"><span class="lineNum">      36</span>              :     }</span>
+<span id="L37"><span class="lineNum">      37</span>              : </span>
+<span id="L38"><span class="lineNum">      38</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>
+          </table>
+          <br>
+
+</body>
+</html>