Refactor project for setuptools

Makefile

@@ -25,7 +25,7 @@ push:  ## Push code with tags
 build:  ## Build package
 	rm -fr dist/*
 	rm -fr build/*
-	python -m build
+	python -m build --wheel
 
 buildext:  ## Build extension
 	rm -fr dist/*

pyproject.toml

@@ -4,7 +4,8 @@ requires = ["setuptools", "cython", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [tool.setuptools]
-packages = ["fimdlp"]
+#packages = ["fimdlp"]
+package-dir = { "" = "src" }
 license-files = ["LICENSE"]
 
 [tool.setuptools.dynamic]
@@ -18,7 +19,7 @@ authors = [
 ]
 dynamic = ['version']
 dependencies = ["numpy", "joblib"]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Science/Research",
@@ -40,7 +41,7 @@ Home = "https://github.com/doctorado-ml/FImdlp"
 
 [tool.black]
 line-length = 79
-target_version = ['py38', 'py39', 'py310']
+target_version = ['py39', 'py310']
 include = '\.pyi?$'
 exclude = '''
 /(

setup.py

@@ -9,11 +9,11 @@ from setuptools import Extension, setup
 setup(
     ext_modules=[
         Extension(
-            name="cppfimdlp",
+            name="fimdlp.cppfimdlp",
             sources=[
-                "fimdlp/cfimdlp.pyx",
-                "fimdlp/cppmdlp/CPPFImdlp.cpp",
-                "fimdlp/cppmdlp/Metrics.cpp",
+                "src/fimdlp/cfimdlp.pyx",
+                "src/fimdlp/cppmdlp/CPPFImdlp.cpp",
+                "src/fimdlp/cppmdlp/Metrics.cpp",
             ],
             language="c++",
             include_dirs=["fimdlp"],

src/fimdlp/cppmdlp/.gitignore

@@ -0,0 +1,36 @@
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+**/build
+**/lcoverage
+.idea
+cmake-*

src/fimdlp/cppmdlp/CMakeLists.txt

@@ -0,0 +1,7 @@
+cmake_minimum_required(VERSION 3.24)
+project(mdlp)
+
+set(CMAKE_CXX_STANDARD 17)
+
+add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
+

src/fimdlp/cppmdlp/CPPFImdlp.cpp

@@ -0,0 +1,160 @@
+#include <numeric>
+#include <algorithm>
+#include <set>
+#include <cmath>
+#include "CPPFImdlp.h"
+#include "Metrics.h"
+
+namespace mdlp {
+    CPPFImdlp::CPPFImdlp(bool proposal):proposal(proposal), indices(indices_t()), X(samples_t()), y(labels_t()), metrics(Metrics(y, indices))
+    {
+    }
+    CPPFImdlp::~CPPFImdlp()
+        = default;
+
+    CPPFImdlp& CPPFImdlp::fit(samples_t& X_, labels_t& y_)
+    {
+        X = X_;
+        y = y_;
+        cutPoints.clear();
+        if (X.size() != y.size()) {
+            throw invalid_argument("X and y must have the same size");
+        }
+        if (X.size() == 0 || y.size() == 0) {
+            throw invalid_argument("X and y must have at least one element");
+        }
+        indices = sortIndices(X_);
+        metrics.setData(y, indices);
+        if (proposal)
+            computeCutPointsProposal();
+        else
+            computeCutPoints(0, X.size());
+        return *this;
+    }
+    void CPPFImdlp::computeCutPoints(size_t start, size_t end)
+    {
+        int cut;
+        if (end - start < 2)
+            return;
+        cut = getCandidate(start, end);
+        if (cut == -1 || !mdlp(start, cut, end)) {
+            // cut.value == -1 means that there is no candidate in the interval
+            // No boundary found, so we add both ends of the interval as cutpoints
+            // because they were selected by the algorithm before
+            if (start != 0)
+                cutPoints.push_back((X[indices[start]] + X[indices[start - 1]]) / 2);
+            if (end != X.size())
+                cutPoints.push_back((X[indices[end]] + X[indices[end - 1]]) / 2);
+            return;
+        }
+        computeCutPoints(start, cut);
+        computeCutPoints(cut, end);
+    }
+    void CPPFImdlp::computeCutPointsOriginal(size_t start, size_t end)
+    {
+        precision_t cut;
+        if (end - start < 2)
+            return;
+        cut = getCandidate(start, end);
+        if (cut == -1)
+            return;
+        if (mdlp(start, cut, end)) {
+            cutPoints.push_back((X[indices[cut]] + X[indices[cut - 1]]) / 2);
+        }
+        computeCutPointsOriginal(start, cut);
+        computeCutPointsOriginal(cut, end);
+    }
+    void CPPFImdlp::computeCutPointsProposal()
+    {
+        precision_t xPrev, xCur, xPivot, cutPoint;
+        int yPrev, yCur, yPivot;
+        size_t idx, numElements, start;
+
+        xCur = xPrev = X[indices[0]];
+        yCur = yPrev = y[indices[0]];
+        numElements = indices.size() - 1;
+        idx = start = 0;
+        while (idx < numElements) {
+            xPivot = xCur;
+            yPivot = yCur;
+            // Read the same values and check class changes
+            do {
+                idx++;
+                xCur = X[indices[idx]];
+                yCur = y[indices[idx]];
+                if (yCur != yPivot && xCur == xPivot) {
+                    yPivot = -1;
+                }
+            }
+            while (idx < numElements && xCur == xPivot);
+            // Check if the class changed and there are more than 1 element
+            if ((idx - start > 1) && (yPivot == -1 || yPrev != yCur) && mdlp(start, idx, indices.size())) {
+                start = idx;
+                cutPoint = (xPrev + xCur) / 2;
+                cutPoints.push_back(cutPoint);
+            }
+            yPrev = yPivot;
+            xPrev = xPivot;
+        }
+    }
+    long int CPPFImdlp::getCandidate(size_t start, size_t end)
+    {
+        long int candidate = -1, elements = end - start;
+        precision_t entropy_left, entropy_right, minEntropy = numeric_limits<precision_t>::max();
+        for (auto idx = start + 1; idx < end; idx++) {
+            // Cutpoints are always on boudndaries
+            if (y[indices[idx]] == y[indices[idx - 1]])
+                continue;
+            entropy_left = precision_t(idx - start) / elements * metrics.entropy(start, idx);
+            entropy_right = precision_t(end - idx) / elements * metrics.entropy(idx, end);
+            if (entropy_left + entropy_right < minEntropy) {
+                minEntropy = entropy_left + entropy_right;
+                candidate = idx;
+            }
+        }
+        return candidate;
+    }
+    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
+    {
+        int k, k1, k2;
+        precision_t ig, delta;
+        precision_t ent, ent1, ent2;
+        auto N = precision_t(end - start);
+        if (N < 2) {
+            return false;
+        }
+        k = metrics.computeNumClasses(start, end);
+        k1 = metrics.computeNumClasses(start, cut);
+        k2 = metrics.computeNumClasses(cut, end);
+        ent = metrics.entropy(start, end);
+        ent1 = metrics.entropy(start, cut);
+        ent2 = metrics.entropy(cut, end);
+        ig = metrics.informationGain(start, cut, end);
+        delta = log2(pow(3, precision_t(k)) - 2) -
+            (precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2);
+        precision_t term = 1 / N * (log2(N - 1) + delta);
+        return ig > term;
+    }
+    cutPoints_t CPPFImdlp::getCutPoints()
+    {
+        // Remove duplicates and sort
+        cutPoints_t output(cutPoints.size());
+        set<precision_t> s;
+        unsigned size = cutPoints.size();
+        for (unsigned i = 0; i < size; i++)
+            s.insert(cutPoints[i]);
+        output.assign(s.begin(), s.end());
+        sort(output.begin(), output.end());
+        return output;
+    }
+    // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
+    indices_t CPPFImdlp::sortIndices(samples_t& X_)
+    {
+        indices_t idx(X_.size());
+        iota(idx.begin(), idx.end(), 0);
+        for (size_t i = 0; i < X_.size(); i++)
+            sort(idx.begin(), idx.end(), [&X_](size_t i1, size_t i2)
+                { return X_[i1] < X_[i2]; });
+        return idx;
+    }
+}

src/fimdlp/cppmdlp/CPPFImdlp.h

@@ -0,0 +1,33 @@
+#ifndef CPPFIMDLP_H
+#define CPPFIMDLP_H
+#include "typesFImdlp.h"
+#include "Metrics.h"
+#include <utility>
+namespace mdlp {
+    class CPPFImdlp {
+    protected:
+        bool proposal;
+        indices_t indices; // sorted indices to use with X and y
+        samples_t X;
+        labels_t y;
+        Metrics metrics;
+        cutPoints_t cutPoints;
+
+        static indices_t sortIndices(samples_t&);
+        void computeCutPoints(size_t, size_t);
+        long int getCandidate(size_t, size_t);
+        bool mdlp(size_t, size_t, size_t);
+
+        // Original algorithm
+        void computeCutPointsOriginal(size_t, size_t);
+        bool goodCut(size_t, size_t, size_t);
+        void computeCutPointsProposal();
+
+    public:
+        CPPFImdlp(bool);
+        ~CPPFImdlp();
+        CPPFImdlp& fit(samples_t&, labels_t&);
+        samples_t getCutPoints();
+    };
+}
+#endif

src/fimdlp/cppmdlp/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Ricardo Montañana Gómez
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/fimdlp/cppmdlp/Metrics.cpp

@@ -0,0 +1,65 @@
+#include "Metrics.h"
+#include <set>
+#include <cmath>
+using namespace std;
+namespace mdlp {
+    Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_), numClasses(computeNumClasses(0, indices.size())), entropyCache(cacheEnt_t()), igCache(cacheIg_t())
+    {
+    }
+    int Metrics::computeNumClasses(size_t start, size_t end)
+    {
+        set<int> nClasses;
+        for (auto i = start; i < end; ++i) {
+            nClasses.insert(y[indices[i]]);
+        }
+        return nClasses.size();
+    }
+    void Metrics::setData(labels_t& y_, indices_t& indices_)
+    {
+        indices = indices_;
+        y = y_;
+        numClasses = computeNumClasses(0, indices.size());
+        entropyCache.clear();
+        igCache.clear();
+    }
+    precision_t Metrics::entropy(size_t start, size_t end)
+    {
+        precision_t p, ventropy = 0;
+        int nElements = 0;
+        labels_t counts(numClasses + 1, 0);
+        if (end - start < 2)
+            return 0;
+        if (entropyCache.find(make_tuple(start, end)) != entropyCache.end()) {
+            return entropyCache[make_tuple(start, end)];
+        }
+        for (auto i = &indices[start]; i != &indices[end]; ++i) {
+            counts[y[*i]]++;
+            nElements++;
+        }
+        for (auto count : counts) {
+            if (count > 0) {
+                p = (precision_t)count / nElements;
+                ventropy -= p * log2(p);
+            }
+        }
+        entropyCache[make_tuple(start, end)] = ventropy;
+        return ventropy;
+    }
+    precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)
+    {
+        precision_t iGain;
+        precision_t entropyInterval, entropyLeft, entropyRight;
+        int nElementsLeft = cut - start, nElementsRight = end - cut;
+        int nElements = end - start;
+        if (igCache.find(make_tuple(start, cut, end)) != igCache.end()) {
+            return igCache[make_tuple(start, cut, end)];
+        }
+        entropyInterval = entropy(start, end);
+        entropyLeft = entropy(start, cut);
+        entropyRight = entropy(cut, end);
+        iGain = entropyInterval - ((precision_t)nElementsLeft * entropyLeft + (precision_t)nElementsRight * entropyRight) / nElements;
+        igCache[make_tuple(start, cut, end)] = iGain;
+        return iGain;
+    }
+
+}

src/fimdlp/cppmdlp/Metrics.h

@@ -0,0 +1,20 @@
+#ifndef CCMETRICS_H
+#define CCMETRICS_H
+#include "typesFImdlp.h"
+namespace mdlp {
+    class Metrics {
+    protected:
+        labels_t& y;
+        indices_t& indices;
+        int numClasses;
+        cacheEnt_t entropyCache;
+        cacheIg_t igCache;
+    public:
+        Metrics(labels_t&, indices_t&);
+        void setData(labels_t&, indices_t&);
+        int computeNumClasses(size_t, size_t);
+        precision_t entropy(size_t, size_t);
+        precision_t informationGain(size_t, size_t, size_t);
+    };
+}
+#endif

src/fimdlp/cppmdlp/README.md

@@ -0,0 +1,2 @@
+# mdlp
+Discretization algorithm based on the paper by Fayyad & Irani Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning

src/fimdlp/cppmdlp/sample/ArffFiles.cpp

@@ -0,0 +1,117 @@
+#include "ArffFiles.h"
+
+#include <fstream>
+#include <sstream>
+#include <map>
+#include <iostream>
+
+using namespace std;
+
+ArffFiles::ArffFiles()
+{
+}
+vector<string> ArffFiles::getLines()
+{
+    return lines;
+}
+unsigned long int ArffFiles::getSize()
+{
+    return lines.size();
+}
+vector<tuple<string, string>> ArffFiles::getAttributes()
+{
+    return attributes;
+}
+string ArffFiles::getClassName()
+{
+    return className;
+}
+string ArffFiles::getClassType()
+{
+    return classType;
+}
+vector<vector<float>>& ArffFiles::getX()
+{
+    return X;
+}
+vector<int>& ArffFiles::getY()
+{
+    return y;
+}
+void ArffFiles::load(string fileName, bool classLast)
+{
+    ifstream file(fileName);
+    string keyword, attribute, type;
+    if (file.is_open()) {
+        string line;
+        while (getline(file, line)) {
+            if (line[0] == '%' || line.empty() || line == "\r" || line == " ") {
+                continue;
+            }
+            if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
+                stringstream ss(line);
+                ss >> keyword >> attribute >> type;
+                attributes.push_back(make_tuple(attribute, type));
+                continue;
+            }
+            if (line[0] == '@') {
+                continue;
+            }
+            lines.push_back(line);
+        }
+        file.close();
+        if (attributes.empty())
+            throw invalid_argument("No attributes found");
+        if (classLast) {
+            className = get<0>(attributes.back());
+            classType = get<1>(attributes.back());
+            attributes.pop_back();
+        } else {
+            className = get<0>(attributes.front());
+            classType = get<1>(attributes.front());
+            attributes.erase(attributes.begin());
+        }
+        generateDataset(classLast);
+    } else
+        throw invalid_argument("Unable to open file");
+}
+void ArffFiles::generateDataset(bool classLast)
+{
+    X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
+    vector<string> yy = vector<string>(lines.size(), "");
+    int labelIndex = classLast ? attributes.size() : 0;
+    for (int i = 0; i < lines.size(); i++) {
+        stringstream ss(lines[i]);
+        string value;
+        int pos = 0, xIndex = 0;
+        while (getline(ss, value, ',')) {
+            if (pos++ == labelIndex) {
+                yy[i] = value;
+            } else {
+                X[xIndex++][i] = stof(value);
+            }
+        }
+    }
+    y = factorize(yy);
+}
+string ArffFiles::trim(const string& source)
+{
+    string s(source);
+    s.erase(0, s.find_first_not_of(" \n\r\t"));
+    s.erase(s.find_last_not_of(" \n\r\t") + 1);
+    return s;
+}
+vector<int> ArffFiles::factorize(const vector<string>& labels_t)
+{
+    vector<int> yy;
+    yy.reserve(labels_t.size());
+    map<string, int> labelMap;
+    int i = 0;
+    for (string label : labels_t) {
+        if (labelMap.find(label) == labelMap.end()) {
+            labelMap[label] = i++;
+        }
+        yy.push_back(labelMap[label]);
+    }
+    return yy;
+}

src/fimdlp/cppmdlp/sample/ArffFiles.h

@@ -0,0 +1,28 @@
+#ifndef ARFFFILES_H
+#define ARFFFILES_H
+#include <string>
+#include <vector>
+#include <tuple>
+using namespace std;
+class ArffFiles {
+private:
+    vector<string> lines;
+    vector<tuple<string, string>> attributes;
+    string className, classType;
+    vector<vector<float>> X;
+    vector<int> y;
+    void generateDataset(bool);
+public:
+    ArffFiles();
+    void load(string, bool = true);
+    vector<string> getLines();
+    unsigned long int getSize();
+    string getClassName();
+    string getClassType();
+    string trim(const string&);
+    vector<vector<float>>& getX();
+    vector<int>& getY();
+    vector<tuple<string, string>> getAttributes();
+    vector<int> factorize(const vector<string>& labels_t);
+};
+#endif

src/fimdlp/cppmdlp/sample/CMakeLists.txt

@@ -0,0 +1,6 @@
+cmake_minimum_required(VERSION 3.24)
+project(main)
+
+set(CMAKE_CXX_STANDARD 17)
+
+add_executable(sample sample.cpp ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)

src/fimdlp/cppmdlp/sample/sample.cpp

@@ -0,0 +1,54 @@
+#include "ArffFiles.h"
+#include <iostream>
+#include <vector>
+#include <iomanip>
+#include "../CPPFImdlp.h"
+
+using namespace std;
+
+int main(int argc, char** argv)
+{
+    ArffFiles file;
+    vector<string> lines;
+    string path = "../../tests/datasets/";
+    map<string, bool > datasets = {
+        {"mfeat-factors", true},
+        {"iris", true},
+        {"letter", true},
+        {"kdd_JapaneseVowels", false}
+    };
+    if (argc != 2 || datasets.find(argv[1]) == datasets.end()) {
+        cout << "Usage: " << argv[0] << " {mfeat-factors, iris, letter, kdd_JapaneseVowels}" << endl;
+        return 1;
+    }
+
+    file.load(path + argv[1] + ".arff", datasets[argv[1]]);
+    auto attributes = file.getAttributes();
+    int items = file.getSize();
+    cout << "Number of lines: " << items << endl;
+    cout << "Attributes: " << endl;
+    for (auto attribute : attributes) {
+        cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
+    }
+    cout << "Class name: " << file.getClassName() << endl;
+    cout << "Class type: " << file.getClassType() << endl;
+    cout << "Data: " << endl;
+    vector<vector<float>>& X = file.getX();
+    vector<int>& y = file.getY();
+    for (int i = 0; i < 50; i++) {
+        for (auto feature : X) {
+            cout << fixed << setprecision(1) << feature[i] << " ";
+        }
+        cout << y[i] << endl;
+    }
+    mdlp::CPPFImdlp test = mdlp::CPPFImdlp(false);
+    for (auto i = 0; i < attributes.size(); i++) {
+        cout << "Cut points for " << get<0>(attributes[i]) << endl;
+        cout << "--------------------------" << setprecision(3) << endl;
+        test.fit(X[i], y);
+        for (auto item : test.getCutPoints()) {
+            cout << item << endl;
+        }
+    }
+    return 0;
+}

src/fimdlp/cppmdlp/tests/.gitignore

@@ -0,0 +1,2 @@
+build
+build/*

src/fimdlp/cppmdlp/tests/CMakeLists.txt

@@ -0,0 +1,32 @@
+cmake_minimum_required(VERSION 3.14)
+project(FImdlp)
+
+# GoogleTest requires at least C++14
+set(CMAKE_CXX_STANDARD 14)
+include(FetchContent)
+
+include_directories(${GTEST_INCLUDE_DIRS})
+
+FetchContent_Declare(
+  googletest
+  URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
+)
+# For Windows: Prevent overriding the parent project's compiler/linker settings
+set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
+FetchContent_MakeAvailable(googletest)
+
+enable_testing()
+
+add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
+add_executable(FImdlp_unittest ../CPPFImdlp.cpp ../Metrics.cpp FImdlp_unittest.cpp)
+target_link_libraries(Metrics_unittest GTest::gtest_main)
+target_link_libraries(FImdlp_unittest GTest::gtest_main)
+target_compile_options(Metrics_unittest PRIVATE --coverage)
+target_compile_options(FImdlp_unittest PRIVATE --coverage)
+target_link_options(Metrics_unittest PRIVATE --coverage)
+target_link_options(FImdlp_unittest PRIVATE --coverage)
+
+include(GoogleTest)
+gtest_discover_tests(Metrics_unittest)
+gtest_discover_tests(FImdlp_unittest)
+

src/fimdlp/cppmdlp/tests/FImdlp_unittest.cpp

@@ -0,0 +1,141 @@
+#include "gtest/gtest.h"
+#include "../Metrics.h"
+#include "../CPPFImdlp.h"
+#include <iostream>
+
+namespace mdlp {
+    class TestFImdlp : public CPPFImdlp, public testing::Test {
+    public:
+        precision_t precision = 0.000001;
+
+        TestFImdlp() : CPPFImdlp(false) {}
+
+        void SetUp() {
+            //    5.0, 5.1, 5.1, 5.1, 5.2, 5.3, 5.6, 5.7, 5.9, 6.0]
+            //(5.0, 1) (5.1, 1) (5.1, 2) (5.1, 2) (5.2, 1) (5.3, 1) (5.6, 2) (5.7, 1) (5.9, 2) (6.0, 2)
+            X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
+            y = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
+            proposal = false;
+            fit(X, y);
+        }
+
+        void setProposal(bool value) {
+            proposal = value;
+        }
+
+        // void initIndices()
+        // {
+        //     indices = indices_t();
+        // }
+        void checkSortedVector() {
+            indices_t testSortedIndices = sortIndices(X);
+            precision_t prev = X[testSortedIndices[0]];
+            for (auto i = 0; i < X.size(); ++i) {
+                EXPECT_EQ(testSortedIndices[i], indices[i]);
+                EXPECT_LE(prev, X[testSortedIndices[i]]);
+                prev = X[testSortedIndices[i]];
+            }
+        }
+
+        void checkCutPoints(cutPoints_t &expected) {
+            int expectedSize = expected.size();
+            EXPECT_EQ(cutPoints.size(), expectedSize);
+            for (auto i = 0; i < cutPoints.size(); i++) {
+                EXPECT_NEAR(cutPoints[i], expected[i], precision);
+            }
+        }
+
+        template<typename T, typename A>
+        void checkVectors(std::vector<T, A> const &expected, std::vector<T, A> const &computed) {
+            EXPECT_EQ(expected.size(), computed.size());
+            ASSERT_EQ(expected.size(), computed.size());
+            for (auto i = 0; i < expected.size(); i++) {
+                EXPECT_NEAR(expected[i], computed[i],precision);
+            }
+        }
+    };
+
+    TEST_F(TestFImdlp, FitErrorEmptyDataset) {
+        X = samples_t();
+        y = labels_t();
+        EXPECT_THROW(fit(X, y), std::invalid_argument);
+    }
+
+    TEST_F(TestFImdlp, FitErrorDifferentSize) {
+        X = {1, 2, 3};
+        y = {1, 2};
+        EXPECT_THROW(fit(X, y), std::invalid_argument);
+    }
+
+    TEST_F(TestFImdlp, SortIndices) {
+        X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
+        indices = {4, 3, 6, 8, 2, 1, 5, 0, 9, 7};
+        checkSortedVector();
+        X = {5.77, 5.88, 5.99};
+        indices = {0, 1, 2};
+        checkSortedVector();
+        X = {5.33, 5.22, 5.11};
+        indices = {2, 1, 0};
+        checkSortedVector();
+    }
+
+    TEST_F(TestFImdlp, TestDataset) {
+        proposal = false;
+        fit(X, y);
+        computeCutPointsOriginal(0, 10);
+        cutPoints_t expected = {5.6499996185302734};
+        vector<precision_t> computed = getCutPoints();
+        computed = getCutPoints();
+        int expectedSize = expected.size();
+        EXPECT_EQ(computed.size(), expected.size());
+        for (auto i = 0; i < expectedSize; i++) {
+            EXPECT_NEAR(computed[i], expected[i], precision);
+        }
+    }
+
+    TEST_F(TestFImdlp, ComputeCutPointsOriginal) {
+        cutPoints_t expected = {5.65};
+        proposal = false;
+        computeCutPointsOriginal(0, 10);
+        checkCutPoints(expected);
+    }
+
+    TEST_F(TestFImdlp, ComputeCutPointsOriginalGCase) {
+        cutPoints_t expected;
+        proposal = false;
+        expected = {2};
+        samples_t X_ = {0, 1, 2, 2};
+        labels_t y_ = {1, 1, 1, 2};
+        fit(X_, y_);
+        checkCutPoints(expected);
+    }
+
+    TEST_F(TestFImdlp, ComputeCutPointsProposal) {
+        proposal = true;
+        cutPoints_t expected;
+        expected = {};
+        fit(X, y);
+        computeCutPointsProposal();
+        checkCutPoints(expected);
+    }
+
+    TEST_F(TestFImdlp, ComputeCutPointsProposalGCase) {
+        cutPoints_t expected;
+        expected = {1.5};
+        proposal = true;
+        samples_t X_ = {0, 1, 2, 2};
+        labels_t y_ = {1, 1, 1, 2};
+        fit(X_, y_);
+        checkCutPoints(expected);
+    }
+
+    TEST_F(TestFImdlp, GetCutPoints) {
+        samples_t computed, expected = {5.65};
+        proposal = false;
+        computeCutPointsOriginal(0, 10);
+        computed = getCutPoints();
+        for (auto item: cutPoints)
+            cout << setprecision(6) << item << endl;
+        checkVectors(expected, computed);
+    }
+}

src/fimdlp/cppmdlp/tests/Metrics_unittest.cpp

@@ -0,0 +1,43 @@
+#include "gtest/gtest.h"
+#include "../Metrics.h"
+
+
+namespace mdlp {
+    class TestMetrics: public Metrics, public testing::Test {
+    public:
+        labels_t y;
+        samples_t X;
+        indices_t indices;
+        precision_t precision = 0.000001;
+
+        TestMetrics(): Metrics(y, indices) {}
+        void SetUp()
+        {
+            y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
+            indices = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+            setData(y, indices);
+        }
+    };
+    TEST_F(TestMetrics, NumClasses)
+    {
+        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
+        EXPECT_EQ(1, computeNumClasses(4, 8));
+        EXPECT_EQ(2, computeNumClasses(0, 10));
+        EXPECT_EQ(2, computeNumClasses(8, 10));
+    }
+    TEST_F(TestMetrics, Entropy)
+    {
+        EXPECT_EQ(1, entropy(0, 10));
+        EXPECT_EQ(0, entropy(0, 5));
+        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
+        setData(y, indices);
+        ASSERT_NEAR(0.468996, entropy(0, 10), precision);
+    }
+    TEST_F(TestMetrics, InformationGain)
+    {
+        ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
+        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
+        setData(y, indices);
+        ASSERT_NEAR(0.108032, informationGain(0, 5, 10), precision);
+    }
+}

src/fimdlp/cppmdlp/tests/cover

@@ -0,0 +1,4 @@
+rm -fr lcoverage/*
+lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
+genhtml lcoverage/main_coverage.info --output-directory lcoverage
+open lcoverage/index.html

src/fimdlp/cppmdlp/tests/datasets/iris.arff

@@ -0,0 +1,225 @@
+% 1. Title: Iris Plants Database
+% 
+% 2. Sources:
+%      (a) Creator: R.A. Fisher
+%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+%      (c) Date: July, 1988
+% 
+% 3. Past Usage:
+%    - Publications: too many to mention!!!  Here are a few.
+%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
+%       Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
+%       to Mathematical Statistics" (John Wiley, NY, 1950).
+%    2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
+%       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+%    3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+%       Structure and Classification Rule for Recognition in Partially Exposed
+%       Environments".  IEEE Transactions on Pattern Analysis and Machine
+%       Intelligence, Vol. PAMI-2, No. 1, 67-71.
+%       -- Results:
+%          -- very low misclassification rates (0% for the setosa class)
+%    4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
+%       Transactions on Information Theory, May 1972, 431-433.
+%       -- Results:
+%          -- very low misclassification rates again
+%    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
+%       conceptual clustering system finds 3 classes in the data.
+% 
+% 4. Relevant Information:
+%    --- This is perhaps the best known database to be found in the pattern
+%        recognition literature.  Fisher's paper is a classic in the field
+%        and is referenced frequently to this day.  (See Duda & Hart, for
+%        example.)  The data set contains 3 classes of 50 instances each,
+%        where each class refers to a type of iris plant.  One class is
+%        linearly separable from the other 2; the latter are NOT linearly
+%        separable from each other.
+%    --- Predicted attribute: class of iris plant.
+%    --- This is an exceedingly simple domain.
+% 
+% 5. Number of Instances: 150 (50 in each of three classes)
+% 
+% 6. Number of Attributes: 4 numeric, predictive attributes and the class
+% 
+% 7. Attribute Information:
+%    1. sepal length in cm
+%    2. sepal width in cm
+%    3. petal length in cm
+%    4. petal width in cm
+%    5. class: 
+%       -- Iris Setosa
+%       -- Iris Versicolour
+%       -- Iris Virginica
+% 
+% 8. Missing Attribute Values: None
+% 
+% Summary Statistics:
+%  	           Min  Max   Mean    SD   Class Correlation
+%    sepal length: 4.3  7.9   5.84  0.83    0.7826   
+%     sepal width: 2.0  4.4   3.05  0.43   -0.4194
+%    petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
+%     petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
+% 
+% 9. Class Distribution: 33.3% for each of 3 classes.
+
+@RELATION iris
+
+@ATTRIBUTE sepallength	REAL
+@ATTRIBUTE sepalwidth 	REAL
+@ATTRIBUTE petallength 	REAL
+@ATTRIBUTE petalwidth	REAL
+@ATTRIBUTE class 	{Iris-setosa,Iris-versicolor,Iris-virginica}
+
+@DATA
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+%
+%
+%

src/fimdlp/cppmdlp/tests/test

@@ -0,0 +1,12 @@
+cmake -S . -B build -Wno-dev 
+if test $? -ne 0; then
+   echo "Error in creating build commands."
+   exit 1
+fi
+cmake --build build
+if test $? -ne 0; then
+   echo "Error in build command."
+   exit 1
+fi
+cd build
+ctest --output-on-failure

src/fimdlp/cppmdlp/typesFImdlp.h

@@ -0,0 +1,16 @@
+#ifndef TYPES_H
+#define TYPES_H
+#include <vector>
+#include <map>
+
+using namespace std;
+namespace mdlp {
+    typedef float precision_t;
+    typedef vector<precision_t> samples_t;
+    typedef vector<int> labels_t;
+    typedef vector<size_t> indices_t;
+    typedef vector<precision_t> cutPoints_t;
+    typedef map<tuple<int, int>, precision_t> cacheEnt_t;
+    typedef map<tuple<int, int, int>, precision_t> cacheIg_t;
+}
+#endif