Update Discretizer version

* Add better check in testKBins.py

* Add Discretizer base class for Both discretizers

* Refactor order of constructors init

.devcontainer/Dockerfile

@@ -0,0 +1,16 @@
+FROM mcr.microsoft.com/devcontainers/cpp:0-ubuntu-22.04
+
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -y install --no-install-recommends \
+    python3 \
+    python3-pip \
+    lcov \
+    cmake \
+    && apt-get autoremove -y && apt-get clean -y && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install --no-cache-dir \
+    cpplint \
+    cmake-format\
+    gcovr
+# [Optional] Uncomment this section to install additional vcpkg ports.
+# RUN su vscode -c "${VCPKG_ROOT}/vcpkg install <your-port-name-here>"

.devcontainer/devcontainer.json

@@ -0,0 +1,32 @@
+// 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 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": "gcc -v",
+	// Configure tool-specific properties.
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"ms-vscode.cpptools",
+				"ms-vscode.cpptools-extension-pack",
+				"ms-vscode.cpptools-themes",
+				"jbenden.c-cpp-flylint",
+				"matepek.vscode-catch2-test-adapter",
+				"ms-vscode.cmake-tools",
+				"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/workflows/build.yml

@@ -13,11 +13,11 @@ jobs:
     env:
       BUILD_WRAPPER_OUT_DIR: build_wrapper_output_directory # Directory where build-wrapper output will be placed
     steps:
-      - uses: actions/checkout@v3.2.0
+      - uses: actions/checkout@v4.1.6
         with:
           fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis
       - name: Install sonar-scanner and build-wrapper
-        uses: SonarSource/sonarcloud-github-c-cpp@v1
+        uses: SonarSource/sonarcloud-github-c-cpp@v2
       - name: Install lcov & gcovr
         run: |
           sudo apt-get -y install lcov
@@ -30,8 +30,7 @@ jobs:
           make
           ctest -C Release --output-on-failure --test-dir tests
           cd ..
-          # gcovr -f CPPFImdlp.cpp -f Metrics.cpp --merge-mode-functions=separate --txt --sonarqube=coverage.xml
-          gcovr -f CPPFImdlp.cpp -f Metrics.cpp  --txt --sonarqube=coverage.xml
+          gcovr -f CPPFImdlp.cpp -f Metrics.cpp  -f BinDisc.cpp --txt --sonarqube=coverage.xml
       - name: Run sonar-scanner
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -31,9 +31,10 @@
 *.out
 *.app
 **/build
+build_Debug
+build_Release
 **/lcoverage
 .idea
 cmake-*
 **/CMakeFiles
-.vscode/*
 **/gcovr-report

.vscode/launch.json

@@ -5,20 +5,38 @@
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "lldb samplex",
-            "type": "lldb",
+            "name": "C++ Launch config",
+            "type": "cppdbg",
             "request": "launch",
-            "targetArchitecture": "arm64",
-            "program": "${workspaceRoot}/sample/build/sample",
-            "args": [
-                "-f",
-                "glass"
-            ],
+            "program": "${workspaceFolder}/tests/build/BinDisc_unittest",
+            "cwd": "${workspaceFolder}/tests/build",
+            "args": [],
+            "launchCompleteCommand": "exec-run",
             "stopAtEntry": false,
-            "cwd": "${workspaceRoot}/sample/build/",
-            "environment": [],
-            "externalConsole": false,
-            "MIMode": "lldb"
-        }
+            "linux": {
+                "MIMode": "gdb",
+                "miDebuggerPath": "/usr/bin/gdb",
+                "setupCommands": [
+                    {
+                        "description": "Enable pretty-printing for gdb",
+                        "text": "-enable-pretty-printing",
+                        "ignoreFailures": true
+                    },
+                    {
+                        "description": "Auto load symbols when loading an .so file",
+                        "text": "set auto-solib-add",
+                        "ignoreFailures": false
+                    }
+                ]
+            },
+            "osx": {
+                "type": "lldb",
+                "MIMode": "lldb"
+            },
+            "windows": {
+                "MIMode": "gdb",
+                "miDebuggerPath": "C:\\MinGw\\bin\\gdb.exe"
+            }
+        },
     ]
 }

.vscode/settings.json

@@ -5,5 +5,89 @@
     },
     "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools",
     "cmake.configureOnOpen": true,
-    "sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json"
+    "sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json",
+    "files.associations": {
+        "*.rmd": "markdown",
+        "*.py": "python",
+        "vector": "cpp",
+        "__bit_reference": "cpp",
+        "__bits": "cpp",
+        "__config": "cpp",
+        "__debug": "cpp",
+        "__errc": "cpp",
+        "__hash_table": "cpp",
+        "__locale": "cpp",
+        "__mutex_base": "cpp",
+        "__node_handle": "cpp",
+        "__nullptr": "cpp",
+        "__split_buffer": "cpp",
+        "__string": "cpp",
+        "__threading_support": "cpp",
+        "__tuple": "cpp",
+        "array": "cpp",
+        "atomic": "cpp",
+        "bitset": "cpp",
+        "cctype": "cpp",
+        "chrono": "cpp",
+        "clocale": "cpp",
+        "cmath": "cpp",
+        "compare": "cpp",
+        "complex": "cpp",
+        "concepts": "cpp",
+        "cstdarg": "cpp",
+        "cstddef": "cpp",
+        "cstdint": "cpp",
+        "cstdio": "cpp",
+        "cstdlib": "cpp",
+        "cstring": "cpp",
+        "ctime": "cpp",
+        "cwchar": "cpp",
+        "cwctype": "cpp",
+        "exception": "cpp",
+        "initializer_list": "cpp",
+        "ios": "cpp",
+        "iosfwd": "cpp",
+        "istream": "cpp",
+        "limits": "cpp",
+        "locale": "cpp",
+        "memory": "cpp",
+        "mutex": "cpp",
+        "new": "cpp",
+        "optional": "cpp",
+        "ostream": "cpp",
+        "ratio": "cpp",
+        "sstream": "cpp",
+        "stdexcept": "cpp",
+        "streambuf": "cpp",
+        "string": "cpp",
+        "string_view": "cpp",
+        "system_error": "cpp",
+        "tuple": "cpp",
+        "type_traits": "cpp",
+        "typeinfo": "cpp",
+        "unordered_map": "cpp",
+        "variant": "cpp",
+        "algorithm": "cpp",
+        "iostream": "cpp",
+        "iomanip": "cpp",
+        "numeric": "cpp",
+        "set": "cpp",
+        "__tree": "cpp",
+        "deque": "cpp",
+        "list": "cpp",
+        "map": "cpp",
+        "unordered_set": "cpp",
+        "any": "cpp",
+        "condition_variable": "cpp",
+        "forward_list": "cpp",
+        "fstream": "cpp",
+        "stack": "cpp",
+        "thread": "cpp",
+        "__memory": "cpp",
+        "filesystem": "cpp",
+        "*.toml": "toml",
+        "utility": "cpp",
+        "span": "cpp",
+        "*.tcc": "cpp"
+    }
 }

.vscode/tasks.json

@@ -0,0 +1,26 @@
+{
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"type": "cmake",
+			"label": "CMake: build",
+			"command": "build",
+			"targets": [
+				"all"
+			],
+			"group": {
+				"kind": "build",
+				"isDefault": true
+			},
+			"problemMatcher": [],
+			"detail": "CMake template build task"
+		},
+		{
+			"type": "cmake",
+			"label": "CMake: configure",
+			"command": "configure",
+			"problemMatcher": [],
+			"detail": "CMake template configure task"
+		}
+	]
+}

BinDisc.cpp

@@ -0,0 +1,99 @@
+#include <algorithm>
+#include <limits>
+#include <cmath>
+#include "BinDisc.h"
+#include <iostream>
+#include <string>
+
+namespace mdlp {
+
+    BinDisc::BinDisc(int n_bins, strategy_t strategy) :
+        Discretizer(), n_bins{ n_bins }, strategy{ strategy }
+    {
+        if (n_bins < 3) {
+            throw std::invalid_argument("n_bins must be greater than 2");
+        }
+    }
+    BinDisc::~BinDisc() = default;
+    void BinDisc::fit(samples_t& X)
+    {
+        // y is included for compatibility with the Discretizer interface
+        cutPoints.clear();
+        if (X.empty()) {
+            cutPoints.push_back(std::numeric_limits<precision_t>::max());
+            return;
+        }
+        if (strategy == strategy_t::QUANTILE) {
+            fit_quantile(X);
+        } else if (strategy == strategy_t::UNIFORM) {
+            fit_uniform(X);
+        }
+    }
+    void BinDisc::fit(samples_t& X, labels_t& y)
+    {
+        fit(X);
+    }
+    std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
+    {
+        // Doesn't include end point as it is not needed
+        if (start == end) {
+            return { 0 };
+        }
+        precision_t delta = (end - start) / static_cast<precision_t>(num - 1);
+        std::vector<precision_t> linspc;
+        for (size_t i = 0; i < num - 1; ++i) {
+            precision_t val = start + delta * static_cast<precision_t>(i);
+            linspc.push_back(val);
+        }
+        return linspc;
+    }
+    size_t clip(const size_t n, size_t lower, size_t upper)
+    {
+        return std::max(lower, std::min(n, upper));
+    }
+    std::vector<precision_t> percentile(samples_t& data, std::vector<precision_t>& percentiles)
+    {
+        // Implementation taken from https://dpilger26.github.io/NumCpp/doxygen/html/percentile_8hpp_source.html
+        std::vector<precision_t> results;
+        results.reserve(percentiles.size());
+        for (auto percentile : percentiles) {
+            const size_t i = static_cast<size_t>(std::floor(static_cast<double>(data.size() - 1) * percentile / 100.));
+            const auto indexLower = clip(i, 0, data.size() - 1);
+            const double percentI = static_cast<double>(indexLower) / static_cast<double>(data.size() - 1);
+            const double fraction =
+                (percentile / 100.0 - percentI) /
+                (static_cast<double>(indexLower + 1) / static_cast<double>(data.size() - 1) - percentI);
+            const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction;
+            if (value != results.back())
+                results.push_back(value);
+        }
+        return results;
+    }
+    void BinDisc::fit_quantile(samples_t& X)
+    {
+        auto quantiles = linspace(0.0, 100.0, n_bins + 1);
+        auto data = X;
+        std::sort(data.begin(), data.end());
+        if (data.front() == data.back() || data.size() == 1) {
+            // if X is constant
+            cutPoints.push_back(std::numeric_limits<precision_t>::max());
+            return;
+        }
+        cutPoints = percentile(data, quantiles);
+        normalizeCutPoints();
+    }
+    void BinDisc::fit_uniform(samples_t& X)
+    {
+
+        auto minmax = std::minmax_element(X.begin(), X.end());
+        cutPoints = linspace(*minmax.first, *minmax.second, n_bins + 1);
+        normalizeCutPoints();
+    }
+    void BinDisc::normalizeCutPoints()
+    {
+        // Add max value to the end
+        cutPoints.push_back(std::numeric_limits<precision_t>::max());
+        // Remove first as it is not needed
+        cutPoints.erase(cutPoints.begin());
+    }
+}

BinDisc.h

@@ -0,0 +1,29 @@
+#ifndef BINDISC_H
+#define BINDISC_H
+
+#include "typesFImdlp.h"
+#include "Discretizer.h"
+#include <string>
+
+namespace mdlp {
+
+    enum class strategy_t {
+        UNIFORM,
+        QUANTILE
+    };
+    class BinDisc : public Discretizer {
+    public:
+        BinDisc(int n_bins = 3, strategy_t strategy = strategy_t::UNIFORM);
+        ~BinDisc();
+        // y is included for compatibility with the Discretizer interface
+        void fit(samples_t& X_, labels_t& y) override;
+        void fit(samples_t& X);
+    private:
+        void fit_uniform(samples_t&);
+        void fit_quantile(samples_t&);
+        void normalizeCutPoints();
+        int n_bins;
+        strategy_t strategy;
+    };
+}
+#endif

CMakeLists.txt

@@ -7,7 +7,7 @@ endif ()
 
 set(CMAKE_CXX_STANDARD 11)
 
-add_library(mdlp CPPFImdlp.cpp Metrics.cpp sample/sample.cpp)
+add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
 add_subdirectory(sample)
 add_subdirectory(tests)
 

CPPFImdlp.cpp

@@ -3,20 +3,19 @@
 #include <set>
 #include <cmath>
 #include "CPPFImdlp.h"
-#include "Metrics.h"
 
 namespace mdlp {
 
-    CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) : min_length(min_length_),
-                                                                               max_depth(max_depth_),
-                                                                               proposed_cuts(proposed) {
+    CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) :
+        Discretizer(),
+        min_length(min_length_),
+        max_depth(max_depth_),
+        proposed_cuts(proposed)
+    {
     }
 
-    CPPFImdlp::CPPFImdlp() = default;
-
-    CPPFImdlp::~CPPFImdlp() = default;
-
-    size_t CPPFImdlp::compute_max_num_cut_points() const {
+    size_t CPPFImdlp::compute_max_num_cut_points() const
+    {
         // Set the actual maximum number of cut points as a number or as a percentage of the number of samples
         if (proposed_cuts == 0) {
             return numeric_limits<size_t>::max();
@@ -29,11 +28,13 @@ namespace mdlp {
         return static_cast<size_t>(proposed_cuts);
     }
 
-    void CPPFImdlp::fit(samples_t &X_, labels_t &y_) {
+    void CPPFImdlp::fit(samples_t& X_, labels_t& y_)
+    {
         X = X_;
         y = y_;
         num_cut_points = compute_max_num_cut_points();
         depth = 0;
+        discretizedData.clear();
         cutPoints.clear();
         if (X.size() != y.size()) {
             throw invalid_argument("X and y must have the same size");
@@ -50,9 +51,17 @@ namespace mdlp {
         indices = sortIndices(X_, y_);
         metrics.setData(y, indices);
         computeCutPoints(0, X.size(), 1);
+        sort(cutPoints.begin(), cutPoints.end());
+        if (num_cut_points > 0) {
+            // Select the best (with lower entropy) cut points
+            while (cutPoints.size() > num_cut_points) {
+                resizeCutPoints();
+            }
+        }
     }
 
-    pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end) {
+    pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)
+    {
         size_t n;
         size_t m;
         size_t idxPrev = cut - 1 >= start ? cut - 1 : cut;
@@ -81,14 +90,13 @@ namespace mdlp {
         // Decide which values to use
         cut = cut + (backWall ? m + 1 : -n);
         actual = X[indices[cut]];
-        return {(actual + previous) / 2, cut};
+        return { (actual + previous) / 2, cut };
     }
 
-    void CPPFImdlp::computeCutPoints(size_t start, size_t end, int depth_) {
+    void CPPFImdlp::computeCutPoints(size_t start, size_t end, int depth_)
+    {
         size_t cut;
         pair<precision_t, size_t> result;
-        if (cutPoints.size() == num_cut_points)
-            return;
         // Check if the interval length and the depth are Ok
         if (end - start < min_length || depth_ > max_depth)
             return;
@@ -105,7 +113,8 @@ namespace mdlp {
         }
     }
 
-    size_t CPPFImdlp::getCandidate(size_t start, size_t end) {
+    size_t CPPFImdlp::getCandidate(size_t start, size_t end)
+    {
         /* Definition 1: A binary discretization for A is determined by selecting the cut point TA for which
         E(A, TA; S) is minimal amongst all the candidate cut points. */
         size_t candidate = numeric_limits<size_t>::max();
@@ -128,8 +137,8 @@ namespace mdlp {
             // Cutpoints are always on boundaries (definition 2)
             if (y[indices[idx]] == y[indices[idx - 1]])
                 continue;
-            entropy_left = precision_t(idx - start) / static_cast<float>(elements) * metrics.entropy(start, idx);
-            entropy_right = precision_t(end - idx) / static_cast<float>(elements) * metrics.entropy(idx, end);
+            entropy_left = precision_t(idx - start) / static_cast<precision_t>(elements) * metrics.entropy(start, idx);
+            entropy_right = precision_t(end - idx) / static_cast<precision_t>(elements) * metrics.entropy(idx, end);
             if (entropy_left + entropy_right < minEntropy) {
                 minEntropy = entropy_left + entropy_right;
                 candidate = idx;
@@ -138,7 +147,8 @@ namespace mdlp {
         return candidate;
     }
 
-    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end) {
+    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
+    {
         int k;
         int k1;
         int k2;
@@ -155,31 +165,46 @@ namespace mdlp {
         ent1 = metrics.entropy(start, cut);
         ent2 = metrics.entropy(cut, end);
         ig = metrics.informationGain(start, cut, end);
-        delta = static_cast<float>(log2(pow(3, precision_t(k)) - 2) -
-                                   (precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2));
+        delta = static_cast<precision_t>(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;
     }
 
     // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
-    indices_t CPPFImdlp::sortIndices(samples_t &X_, labels_t &y_) {
+    indices_t CPPFImdlp::sortIndices(samples_t& X_, labels_t& y_)
+    {
         indices_t idx(X_.size());
-        iota(idx.begin(), idx.end(), 0);
+        std::iota(idx.begin(), idx.end(), 0);
         stable_sort(idx.begin(), idx.end(), [&X_, &y_](size_t i1, size_t i2) {
             if (X_[i1] == X_[i2])
                 return y_[i1] < y_[i2];
             else
                 return X_[i1] < X_[i2];
-        });
+            });
         return idx;
     }
 
-    cutPoints_t CPPFImdlp::getCutPoints() {
-        sort(cutPoints.begin(), cutPoints.end());
-        return cutPoints;
+    void CPPFImdlp::resizeCutPoints()
+    {
+        //Compute entropy of each of the whole cutpoint set and discards the biggest value
+        precision_t maxEntropy = 0;
+        precision_t entropy;
+        size_t maxEntropyIdx = 0;
+        size_t begin = 0;
+        size_t end;
+        for (size_t idx = 0; idx < cutPoints.size(); idx++) {
+            end = begin;
+            while (X[indices[end]] < cutPoints[idx] && end < X.size())
+                end++;
+            entropy = metrics.entropy(begin, end);
+            if (entropy > maxEntropy) {
+                maxEntropy = entropy;
+                maxEntropyIdx = idx;
+            }
+            begin = end;
+        }
+        cutPoints.erase(cutPoints.begin() + static_cast<long>(maxEntropyIdx));
     }
 
-    int CPPFImdlp::get_depth() const {
-        return depth;
-    }
 }

CPPFImdlp.h

@@ -2,13 +2,20 @@
 #define CPPFIMDLP_H
 
 #include "typesFImdlp.h"
-#include "Metrics.h"
 #include <limits>
 #include <utility>
 #include <string>
+#include "Metrics.h"
+#include "Discretizer.h"
 
 namespace mdlp {
-    class CPPFImdlp {
+    class CPPFImdlp : public Discretizer {
+    public:
+        CPPFImdlp() = default;
+        CPPFImdlp(size_t min_length_, int max_depth_, float proposed);
+        virtual ~CPPFImdlp() = default;
+        void fit(samples_t& X_, labels_t& y_) override;
+        inline int get_depth() const { return depth; };
     protected:
         size_t min_length = 3;
         int depth = 0;
@@ -18,35 +25,14 @@ namespace mdlp {
         samples_t X = samples_t();
         labels_t y = labels_t();
         Metrics metrics = Metrics(y, indices);
-        cutPoints_t cutPoints;
         size_t num_cut_points = numeric_limits<size_t>::max();
-
         static indices_t sortIndices(samples_t&, labels_t&);
-
         void computeCutPoints(size_t, size_t, int);
-
+        void resizeCutPoints();
         bool mdlp(size_t, size_t, size_t);
-
         size_t getCandidate(size_t, size_t);
-
         size_t compute_max_num_cut_points() const;
-
         pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
-
-    public:
-        CPPFImdlp();
-
-        CPPFImdlp(size_t, int, float);
-
-        ~CPPFImdlp();
-
-        void fit(samples_t&, labels_t&);
-
-        cutPoints_t getCutPoints();
-
-        int get_depth() const;
-
-        static inline string version() { return "1.1.1"; };
     };
 }
 #endif

Discretizer.h

@@ -0,0 +1,31 @@
+#ifndef DISCRETIZER_H
+#define DISCRETIZER_H
+
+#include <string>
+#include <algorithm>
+#include "typesFImdlp.h"
+
+namespace mdlp {
+    class Discretizer {
+    public:
+        Discretizer() = default;
+        virtual ~Discretizer() = default;
+        virtual void fit(samples_t& X_, labels_t& y_) = 0;
+        inline cutPoints_t getCutPoints() const { return cutPoints; };
+        labels_t& transform(const samples_t& data)
+        {
+            discretizedData.clear();
+            discretizedData.reserve(data.size());
+            for (const precision_t& item : data) {
+                auto upper = std::upper_bound(cutPoints.begin(), cutPoints.end(), item);
+                discretizedData.push_back(upper - cutPoints.begin());
+            }
+            return discretizedData;
+        };
+        static inline std::string version() { return "1.2.0"; };
+    protected:
+        labels_t discretizedData = labels_t();
+        cutPoints_t cutPoints;
+    };
+}
+#endif

Metrics.cpp

@@ -4,11 +4,13 @@
 
 using namespace std;
 namespace mdlp {
-    Metrics::Metrics(labels_t &y_, indices_t &indices_) : y(y_), indices(indices_),
-                                                          numClasses(computeNumClasses(0, indices.size())) {
+    Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_),
+        numClasses(computeNumClasses(0, indices.size()))
+    {
     }
 
-    int Metrics::computeNumClasses(size_t start, size_t end) {
+    int Metrics::computeNumClasses(size_t start, size_t end)
+    {
         set<int> nClasses;
         for (auto i = start; i < end; ++i) {
             nClasses.insert(y[indices[i]]);
@@ -16,7 +18,8 @@ namespace mdlp {
         return static_cast<int>(nClasses.size());
     }
 
-    void Metrics::setData(const labels_t &y_, const indices_t &indices_) {
+    void Metrics::setData(const labels_t& y_, const indices_t& indices_)
+    {
         indices = indices_;
         y = y_;
         numClasses = computeNumClasses(0, indices.size());
@@ -24,21 +27,22 @@ namespace mdlp {
         igCache.clear();
     }
 
-    precision_t Metrics::entropy(size_t start, size_t end) {
+    precision_t Metrics::entropy(size_t start, size_t end)
+    {
         precision_t p;
         precision_t ventropy = 0;
         int nElements = 0;
         labels_t counts(numClasses + 1, 0);
         if (end - start < 2)
             return 0;
-        if (entropyCache.find({start, end}) != entropyCache.end()) {
+        if (entropyCache.find({ start, end }) != entropyCache.end()) {
             return entropyCache[{start, end}];
         }
         for (auto i = &indices[start]; i != &indices[end]; ++i) {
             counts[y[*i]]++;
             nElements++;
         }
-        for (auto count: counts) {
+        for (auto count : counts) {
             if (count > 0) {
                 p = static_cast<precision_t>(count) / static_cast<precision_t>(nElements);
                 ventropy -= p * log2(p);
@@ -48,7 +52,8 @@ namespace mdlp {
         return ventropy;
     }
 
-    precision_t Metrics::informationGain(size_t start, size_t cut, size_t end) {
+    precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)
+    {
         precision_t iGain;
         precision_t entropyInterval;
         precision_t entropyLeft;
@@ -63,9 +68,9 @@ namespace mdlp {
         entropyLeft = entropy(start, cut);
         entropyRight = entropy(cut, end);
         iGain = entropyInterval -
-                (static_cast<precision_t>(nElementsLeft) * entropyLeft +
-                 static_cast<precision_t>(nElementsRight) * entropyRight) /
-                static_cast<precision_t>(nElements);
+            (static_cast<precision_t>(nElementsLeft) * entropyLeft +
+                static_cast<precision_t>(nElementsRight) * entropyRight) /
+            static_cast<precision_t>(nElements);
         igCache[make_tuple(start, cut, end)] = iGain;
         return iGain;
     }

Metrics.h

@@ -6,20 +6,16 @@
 namespace mdlp {
     class Metrics {
     protected:
-        labels_t &y;
-        indices_t &indices;
+        labels_t& y;
+        indices_t& indices;
         int numClasses;
         cacheEnt_t entropyCache = cacheEnt_t();
         cacheIg_t igCache = cacheIg_t();
     public:
-        Metrics(labels_t &, indices_t &);
-
-        void setData(const labels_t &, const indices_t &);
-
+        Metrics(labels_t&, indices_t&);
+        void setData(const labels_t&, const indices_t&);
         int computeNumClasses(size_t, size_t);
-
         precision_t entropy(size_t, size_t);
-
         precision_t informationGain(size_t, size_t, size_t);
     };
 }

README.md

@@ -2,7 +2,7 @@
 [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_mdlp&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
 [![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_mdlp&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
 
-# mdlp
+# <img src="logo.png" alt="logo" width="50"/> mdlp
 
 Discretization algorithm based on the paper by Fayyad &amp; Irani [Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning](https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf)
 
@@ -24,9 +24,8 @@ To run the sample, just execute the following commands:
 
 ```bash
 cd sample
-mkdir build
+cmake -B build
 cd build
-cmake ..
 make
 ./sample -f iris -m 2
 ./sample -h
@@ -34,7 +33,7 @@ make
 
 ## Test
 
-To run the tests, execute the following commands:
+To run the tests and see coverage (llvm & gcovr have to be installed), execute the following commands:
 
 ```bash
 cd tests

sample/CMakeLists.txt

@@ -1,4 +1,5 @@
-
 set(CMAKE_CXX_STANDARD 11)
 
+set(CMAKE_BUILD_TYPE Debug)
+
 add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)

sample/sample.cpp

@@ -14,31 +14,33 @@ using namespace mdlp;
 const string PATH = "../../tests/datasets/";
 
 /* print a description of all supported options */
-void usage(const char *path) {
+void usage(const char* path)
+{
     /* take only the last portion of the path */
-    const char *basename = strrchr(path, '/');
+    const char* basename = strrchr(path, '/');
     basename = basename ? basename + 1 : path;
 
     cout << "usage: " << basename << "[OPTION]" << endl;
     cout << "  -h, --help\t\t Print this help and exit." << endl;
     cout
-            << "  -f, --file[=FILENAME]\t {all, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
-            << endl;
+        << "  -f, --file[=FILENAME]\t {all, diabetes, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
+        << endl;
     cout << "  -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << endl;
     cout << "  -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << endl;
     cout
-            << "  -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 = any"
-            << endl;
+        << "  -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 -> any"
+        << endl;
     cout << "  -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << endl;
 }
 
-tuple<string, string, int, int, float> parse_arguments(int argc, char **argv) {
+tuple<string, string, int, int, float> parse_arguments(int argc, char** argv)
+{
     string file_name;
     string path = PATH;
     int max_depth = numeric_limits<int>::max();
     int min_length = 3;
     float max_cutpoints = 0;
-    const option long_options[] = {
+    const vector<struct option> long_options = {
             {"help",          no_argument,       nullptr, 'h'},
             {"file",          required_argument, nullptr, 'f'},
             {"path",          required_argument, nullptr, 'p'},
@@ -48,7 +50,7 @@ tuple<string, string, int, int, float> parse_arguments(int argc, char **argv) {
             {nullptr,         no_argument,       nullptr, 0}
     };
     while (true) {
-        const auto c = getopt_long(argc, argv, "hf:p:m:c:n:", long_options, nullptr);
+        const auto c = getopt_long(argc, argv, "hf:p:m:c:n:", long_options.data(), nullptr);
         if (c == -1)
             break;
         switch (c) {
@@ -86,60 +88,66 @@ tuple<string, string, int, int, float> parse_arguments(int argc, char **argv) {
     return make_tuple(file_name, path, max_depth, min_length, max_cutpoints);
 }
 
-void process_file(const string &path, const string &file_name, bool class_last, int max_depth, int min_length,
-                  float max_cutpoints) {
+void process_file(const string& path, const string& file_name, bool class_last, int max_depth, int min_length,
+    float max_cutpoints)
+{
     ArffFiles file;
 
     file.load(path + file_name + ".arff", class_last);
-    auto attributes = file.getAttributes();
-    auto items = file.getSize();
+    const auto attributes = file.getAttributes();
+    const auto items = file.getSize();
     cout << "Number of lines: " << items << endl;
     cout << "Attributes: " << endl;
-    for (auto attribute: attributes) {
+    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<samples_t> &X = file.getX();
-    labels_t &y = file.getY();
+    vector<samples_t>& X = file.getX();
+    labels_t& y = file.getY();
     for (int i = 0; i < 5; i++) {
-        for (auto feature: X) {
+        for (auto feature : X) {
             cout << fixed << setprecision(1) << feature[i] << " ";
         }
         cout << y[i] << endl;
     }
     auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
-    auto total = 0;
+    size_t total = 0;
     for (auto i = 0; i < attributes.size(); i++) {
         auto min_max = minmax_element(X[i].begin(), X[i].end());
-        cout << "Cut points for " << get<0>(attributes[i]) << endl;
-        cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
-        cout << "--------------------------" << setprecision(3) << endl;
+        cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
         test.fit(X[i], y);
-        for (auto item: test.getCutPoints()) {
-            cout << item << endl;
+        auto cut_points = test.getCutPoints();
+        for (auto item : cut_points) {
+            cout << item;
+            if (item != cut_points.back())
+                cout << ", ";
         }
         total += test.getCutPoints().size();
+        cout << "]" << endl;
+        cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
+        cout << "--------------------------" << endl;
     }
     cout << "Total cut points ...: " << total << endl;
     cout << "Total feature states: " << total + attributes.size() << endl;
 }
 
-void process_all_files(const map<string, bool> &datasets, const string &path, int max_depth, int min_length,
-                       float max_cutpoints) {
+void process_all_files(const map<string, bool>& datasets, const string& path, int max_depth, int min_length,
+    float max_cutpoints)
+{
     cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
-         << max_cutpoints << endl << endl;
+        << max_cutpoints << endl << endl;
     printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
     printf("==================== ==== ==== ========\n");
-    for (const auto &dataset: datasets) {
+    for (const auto& dataset : datasets) {
         ArffFiles file;
         file.load(path + dataset.first + ".arff", dataset.second);
         auto attributes = file.getAttributes();
-        vector<samples_t> &X = file.getX();
-        labels_t &y = file.getY();
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
         size_t timing = 0;
-        int cut_points = 0;
+        size_t cut_points = 0;
         for (auto i = 0; i < attributes.size(); i++) {
             auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
             std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
@@ -148,13 +156,15 @@ void process_all_files(const map<string, bool> &datasets, const string &path, in
             timing += std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count();
             cut_points += test.getCutPoints().size();
         }
-        printf("%-20s %4lu %4d %8zu\n", dataset.first.c_str(), attributes.size(), cut_points, timing);
+        printf("%-20s %4lu %4zu %8zu\n", dataset.first.c_str(), attributes.size(), cut_points, timing);
     }
 }
 
 
-int main(int argc, char **argv) {
+int main(int argc, char** argv)
+{
     map<string, bool> datasets = {
+            {"diabetes",           true},
             {"glass",              true},
             {"iris",               true},
             {"kdd_JapaneseVowels", false},

sonar-project.properties

@@ -2,9 +2,11 @@ sonar.projectKey=rmontanana_mdlp
 sonar.organization=rmontanana
 
 # This is the name and version displayed in the SonarCloud UI.
-#sonar.projectName=mdlp
-#sonar.projectVersion=1.0
-
+sonar.projectName=mdlp
+sonar.projectVersion=1.1.3
+# sonar.test.exclusions=tests/**
+# sonar.tests=tests/
+# sonar.coverage.exclusions=tests/**,sample/**
 # Path is relative to the sonar-project.properties file. Replace "\" by "/" on Windows.
 #sonar.sources=.
 

tests/ArffFiles.cpp

@@ -7,35 +7,43 @@ using namespace std;
 
 ArffFiles::ArffFiles() = default;
 
-vector<string> ArffFiles::getLines() const {
+vector<string> ArffFiles::getLines() const
+{
     return lines;
 }
 
-unsigned long int ArffFiles::getSize() const {
+unsigned long int ArffFiles::getSize() const
+{
     return lines.size();
 }
 
-vector<pair<string, string>> ArffFiles::getAttributes() const {
+vector<pair<string, string>> ArffFiles::getAttributes() const
+{
     return attributes;
 }
 
-string ArffFiles::getClassName() const {
+string ArffFiles::getClassName() const
+{
     return className;
 }
 
-string ArffFiles::getClassType() const {
+string ArffFiles::getClassType() const
+{
     return classType;
 }
 
-vector<vector<float>> &ArffFiles::getX() {
+vector<mdlp::samples_t>& ArffFiles::getX()
+{
     return X;
 }
 
-vector<int> &ArffFiles::getY() {
+vector<int>& ArffFiles::getY()
+{
     return y;
 }
 
-void ArffFiles::load(const string &fileName, bool classLast) {
+void ArffFiles::load(const string& fileName, bool classLast)
+{
     ifstream file(fileName);
     if (!file.is_open()) {
         throw invalid_argument("Unable to open file");
@@ -55,7 +63,7 @@ void ArffFiles::load(const string &fileName, bool classLast) {
             type = "";
             while (ss >> type_w)
                 type += type_w + " ";
-            attributes.emplace_back(attribute, trim(type));
+            attributes.emplace_back(trim(attribute), trim(type));
             continue;
         }
         if (line[0] == '@') {
@@ -79,8 +87,9 @@ void ArffFiles::load(const string &fileName, bool classLast) {
 
 }
 
-void ArffFiles::generateDataset(bool classLast) {
-    X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
+void ArffFiles::generateDataset(bool classLast)
+{
+    X = vector<mdlp::samples_t>(attributes.size(), mdlp::samples_t(lines.size()));
     auto yy = vector<string>(lines.size(), "");
     int labelIndex = classLast ? static_cast<int>(attributes.size()) : 0;
     for (size_t i = 0; i < lines.size(); i++) {
@@ -99,19 +108,21 @@ void ArffFiles::generateDataset(bool classLast) {
     y = factorize(yy);
 }
 
-string ArffFiles::trim(const string &source) {
+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);
+    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> ArffFiles::factorize(const vector<string>& labels_t)
+{
     vector<int> yy;
     yy.reserve(labels_t.size());
     map<string, int> labelMap;
     int i = 0;
-    for (const string &label: labels_t) {
+    for (const string& label : labels_t) {
         if (labelMap.find(label) == labelMap.end()) {
             labelMap[label] = i++;
         }

tests/ArffFiles.h

@@ -3,6 +3,7 @@
 
 #include <string>
 #include <vector>
+#include "../typesFImdlp.h"
 
 using namespace std;
 
@@ -12,33 +13,23 @@ private:
     vector<pair<string, string>> attributes;
     string className;
     string classType;
-    vector<vector<float>> X;
+    vector<mdlp::samples_t> X;
     vector<int> y;
 
     void generateDataset(bool);
 
 public:
     ArffFiles();
-
-    void load(const string &, bool = true);
-
+    void load(const string&, bool = true);
     vector<string> getLines() const;
-
     unsigned long int getSize() const;
-
     string getClassName() const;
-
     string getClassType() const;
-
-    static string trim(const string &);
-
-    vector<vector<float>> &getX();
-
-    vector<int> &getY();
-
+    static string trim(const string&);
+    vector<mdlp::samples_t>& getX();
+    vector<int>& getY();
     vector<pair<string, string>> getAttributes() const;
-
-    static vector<int> factorize(const vector<string> &labels_t);
+    static vector<int> factorize(const vector<string>& labels_t);
 };
 
 #endif

tests/BinDisc_unittest.cpp

@@ -0,0 +1,346 @@
+#include <fstream>
+#include <string>
+#include <iostream>
+#include "gtest/gtest.h"
+#include "ArffFiles.h"
+#include "../BinDisc.h"
+
+namespace mdlp {
+    const float margin = 1e-4;
+    static std::string set_data_path()
+    {
+        std::string path = "../datasets/";
+        std::ifstream file(path + "iris.arff");
+        if (file.is_open()) {
+            file.close();
+            return path;
+        }
+        return "../../tests/datasets/";
+    }
+    const std::string data_path = set_data_path();
+    class TestBinDisc3U : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc3U(int n_bins = 3) : BinDisc(n_bins, strategy_t::UNIFORM) {};
+    };
+    class TestBinDisc3Q : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc3Q(int n_bins = 3) : BinDisc(n_bins, strategy_t::QUANTILE) {};
+    };
+    class TestBinDisc4U : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc4U(int n_bins = 4) : BinDisc(n_bins, strategy_t::UNIFORM) {};
+    };
+    class TestBinDisc4Q : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc4Q(int n_bins = 4) : BinDisc(n_bins, strategy_t::QUANTILE) {};
+    };
+    TEST_F(TestBinDisc3U, Easy3BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
+        auto y = labels_t();
+        fit(X, y);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(3.66667, cuts.at(0), margin);
+        EXPECT_NEAR(6.33333, cuts.at(1), margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts.at(2));
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3Q, Easy3BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(3.666667, cuts[0], margin);
+        EXPECT_NEAR(6.333333, cuts[1], margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3U, X10BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_EQ(4.0, cuts[0]);
+        EXPECT_EQ(7.0, cuts[1]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3Q, X10BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_EQ(4, cuts[0]);
+        EXPECT_EQ(7, cuts[1]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3U, X11BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(4.33333, cuts[0], margin);
+        EXPECT_NEAR(7.66667, cuts[1], margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3U, X11BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(4.33333, cuts[0], margin);
+        EXPECT_NEAR(7.66667, cuts[1], margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3U, ConstantUniform)
+    {
+        samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 0, 0 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3Q, ConstantQuantile)
+    {
+        samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 0, 0 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc3U, EmptyUniform)
+    {
+        samples_t X = {};
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
+    }
+    TEST_F(TestBinDisc3Q, EmptyQuantile)
+    {
+        samples_t X = {};
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(1, cuts.size());
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
+    }
+    TEST(TestBinDisc3, ExceptionNumberBins)
+    {
+        EXPECT_THROW(BinDisc(2), std::invalid_argument);
+    }
+    TEST_F(TestBinDisc3U, EasyRepeated)
+    {
+        samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(1.66667, cuts[0], margin);
+        EXPECT_NEAR(2.33333, cuts[1], margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 2, 0, 0, 2, 0, 0, 2, 0, 0 };
+        EXPECT_EQ(expected, labels);
+        ASSERT_EQ(3.0, X[0]); // X is not modified
+    }
+    TEST_F(TestBinDisc3Q, EasyRepeated)
+    {
+        samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(2, cuts.size());
+        EXPECT_NEAR(1.66667, cuts[0], margin);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[1]);
+        auto labels = transform(X);
+        labels_t expected = { 1, 0, 0, 1, 0, 0, 1, 0, 0 };
+        EXPECT_EQ(expected, labels);
+        ASSERT_EQ(3.0, X[0]); // X is not modified
+    }
+    TEST_F(TestBinDisc4U, Easy4BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        ASSERT_EQ(3.75, cuts[0]);
+        EXPECT_EQ(6.5, cuts[1]);
+        EXPECT_EQ(9.25, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4Q, Easy4BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        ASSERT_EQ(3.75, cuts[0]);
+        EXPECT_EQ(6.5, cuts[1]);
+        EXPECT_EQ(9.25, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4U, X13BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.0, cuts[0]);
+        EXPECT_EQ(7.0, cuts[1]);
+        EXPECT_EQ(10.0, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4Q, X13BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.0, cuts[0]);
+        EXPECT_EQ(7.0, cuts[1]);
+        EXPECT_EQ(10.0, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4U, X14BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.25, cuts[0]);
+        EXPECT_EQ(7.5, cuts[1]);
+        EXPECT_EQ(10.75, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4Q, X14BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.25, cuts[0]);
+        EXPECT_EQ(7.5, cuts[1]);
+        EXPECT_EQ(10.75, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4U, X15BinsUniform)
+    {
+        samples_t X = { 15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.5, cuts[0]);
+        EXPECT_EQ(8, cuts[1]);
+        EXPECT_EQ(11.5, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4Q, X15BinsQuantile)
+    {
+        samples_t X = { 15.0, 13.0, 12.0, 14.0, 6.0, 1.0, 8.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(4.5, cuts[0]);
+        EXPECT_EQ(8, cuts[1]);
+        EXPECT_EQ(11.5, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4U, RepeatedValuesUniform)
+    {
+        samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
+        //               0    1     2   3    4    5    6    7    8    9
+        fit(X);
+        auto cuts = getCutPoints();
+        EXPECT_EQ(4, cuts.size());
+        EXPECT_EQ(1.0, cuts[0]);
+        EXPECT_EQ(2.0, cuts[1]);
+        ASSERT_EQ(3.0, cuts[2]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
+    {
+        samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
+        //               0    1     2   3    4    5    6    7    8    9
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_EQ(2.0, cuts[0]);
+        ASSERT_EQ(3.0, cuts[1]);
+        EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
+        auto labels = transform(X);
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 2 };
+        EXPECT_EQ(expected, labels);
+    }
+    TEST_F(TestBinDisc4U, irisUniform)
+    {
+        ArffFiles file;
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        fit(X[0]);
+        auto Xt = transform(X[0]);
+        labels_t expected = { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 1, 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1 };
+        EXPECT_EQ(expected, Xt);
+    }
+    TEST_F(TestBinDisc4Q, irisQuantile)
+    {
+        ArffFiles file;
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        fit(X[0]);
+        auto Xt = transform(X[0]);
+        labels_t expected = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
+        EXPECT_EQ(expected, Xt);
+    }
+}

tests/CMakeLists.txt

@@ -1,3 +1,4 @@
+cmake_minimum_required(VERSION 3.20)
 set(CMAKE_CXX_STANDARD 11)
 include(FetchContent)
 
@@ -15,15 +16,28 @@ FetchContent_MakeAvailable(googletest)
 enable_testing()
 
 add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
-add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.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)
+
+add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp)
+target_link_libraries(FImdlp_unittest GTest::gtest_main)
+target_compile_options(FImdlp_unittest PRIVATE --coverage)
 target_link_options(FImdlp_unittest PRIVATE --coverage)
 
+add_executable(BinDisc_unittest ../BinDisc.cpp ArffFiles.cpp BinDisc_unittest.cpp)
+target_link_libraries(BinDisc_unittest GTest::gtest_main)
+target_compile_options(BinDisc_unittest PRIVATE --coverage)
+target_link_options(BinDisc_unittest PRIVATE --coverage)
+
+add_executable(Discretizer_unittest ../BinDisc.cpp ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp Discretizer_unittest.cpp)
+target_link_libraries(Discretizer_unittest GTest::gtest_main)
+target_compile_options(Discretizer_unittest PRIVATE --coverage)
+target_link_options(Discretizer_unittest PRIVATE --coverage)
+
 include(GoogleTest)
+
 gtest_discover_tests(Metrics_unittest)
 gtest_discover_tests(FImdlp_unittest)
-
+gtest_discover_tests(BinDisc_unittest)
+gtest_discover_tests(Discretizer_unittest)

tests/Discretizer_unittest.cpp

@@ -0,0 +1,74 @@
+#include <fstream>
+#include <string>
+#include <iostream>
+#include "gtest/gtest.h"
+#include "ArffFiles.h"
+#include "../Discretizer.h"
+#include "../BinDisc.h"
+#include "../CPPFImdlp.h"
+
+namespace mdlp {
+    const float margin = 1e-4;
+    static std::string set_data_path()
+    {
+        std::string path = "../datasets/";
+        std::ifstream file(path + "iris.arff");
+        if (file.is_open()) {
+            file.close();
+            return path;
+        }
+        return "../../tests/datasets/";
+    }
+    const std::string data_path = set_data_path();
+
+    TEST(Discretizer, BinIrisUniform)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        auto y = labels_t();
+        disc->fit(X[0], y);
+        auto Xt = disc->transform(X[0]);
+        labels_t expected = { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 1, 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1 };
+        delete disc;
+        EXPECT_EQ(expected, Xt);
+    }
+    TEST(Discretizer, BinIrisQuantile)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        auto y = labels_t();
+        disc->fit(X[0], y);
+        auto Xt = disc->transform(X[0]);
+        labels_t expected = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
+        delete disc;
+        EXPECT_EQ(expected, Xt);
+    }
+    TEST(Discretizer, FImdlpIris)
+    {
+        labels_t expected = {
+            5, 3, 4, 4, 5, 5, 5, 5, 2, 4, 5, 5, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5,
+            5, 4, 5, 3, 5, 5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 4, 3, 5, 5, 0, 4, 5,
+            5, 3, 5, 4, 5, 4, 4, 4, 4, 0, 1, 1, 4, 0, 2, 0, 0, 3, 0, 2, 2, 4,
+            3, 0, 0, 0, 4, 1, 0, 1, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 3, 5, 4, 0,
+            3, 0, 0, 3, 0, 0, 0, 3, 2, 2, 0, 1, 4, 0, 3, 2, 3, 3, 0, 2, 0, 5,
+            4, 0, 3, 0, 1, 4, 3, 5, 0, 0, 4, 1, 1, 0, 4, 4, 1, 3, 1, 3, 1, 5,
+            1, 1, 0, 3, 5, 4, 3, 4, 4, 4, 0, 4, 4, 3, 0, 3, 5, 3
+        };
+        ArffFiles file;
+        Discretizer* disc = new CPPFImdlp();
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
+        disc->fit(X[1], y);
+        auto computed = disc->transform(X[1]);
+        delete disc;
+        EXPECT_EQ(computed.size(), expected.size());
+        for (unsigned long i = 0; i < computed.size(); i++) {
+            EXPECT_EQ(computed[i], expected[i]);
+        }
+    }
+}

tests/FImdlp_unittest.cpp

@@ -23,15 +23,17 @@ namespace mdlp {
 
         string data_path;
 
-        void SetUp() override {
-            X = {4.7f, 4.7f, 4.7f, 4.7f, 4.8f, 4.8f, 4.8f, 4.8f, 4.9f, 4.95f, 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f,
-                 6.0f, 5.1f, 5.9f};
-            y = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
+        void SetUp() override
+        {
+            X = { 4.7f, 4.7f, 4.7f, 4.7f, 4.8f, 4.8f, 4.8f, 4.8f, 4.9f, 4.95f, 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f,
+                 6.0f, 5.1f, 5.9f };
+            y = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
             fit(X, y);
             data_path = set_data_path();
         }
 
-        static string set_data_path() {
+        static string set_data_path()
+        {
             string path = "../datasets/";
             ifstream file(path + "iris.arff");
             if (file.is_open()) {
@@ -41,7 +43,8 @@ namespace mdlp {
             return "../../tests/datasets/";
         }
 
-        void checkSortedVector() {
+        void checkSortedVector()
+        {
             indices_t testSortedIndices = sortIndices(X, y);
             precision_t prev = X[testSortedIndices[0]];
             for (unsigned long i = 0; i < X.size(); ++i) {
@@ -51,7 +54,8 @@ namespace mdlp {
             }
         }
 
-        void checkCutPoints(cutPoints_t &computed, cutPoints_t &expected) const {
+        void checkCutPoints(cutPoints_t& computed, cutPoints_t& expected) const
+        {
             EXPECT_EQ(computed.size(), expected.size());
             for (unsigned long i = 0; i < computed.size(); i++) {
                 cout << "(" << computed[i] << ", " << expected[i] << ") ";
@@ -59,9 +63,10 @@ namespace mdlp {
             }
         }
 
-        bool test_result(const samples_t &X_, size_t cut, float midPoint, size_t limit, const string &title) {
+        bool test_result(const samples_t& X_, size_t cut, float midPoint, size_t limit, const string& title)
+        {
             pair<precision_t, size_t> result;
-            labels_t y_ = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+            labels_t y_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
             X = X_;
             y = y_;
             indices = sortIndices(X, y);
@@ -72,12 +77,13 @@ namespace mdlp {
             return true;
         }
 
-        void test_dataset(CPPFImdlp &test, const string &filename, vector<cutPoints_t> &expected,
-                          vector<int> &depths) const {
+        void test_dataset(CPPFImdlp& test, const string& filename, vector<cutPoints_t>& expected,
+            vector<int>& depths) const
+        {
             ArffFiles file;
             file.load(data_path + filename + ".arff", true);
-            vector<samples_t> &X = file.getX();
-            labels_t &y = file.getY();
+            vector<samples_t>& X = file.getX();
+            labels_t& y = file.getY();
             auto attributes = file.getAttributes();
             for (auto feature = 0; feature < attributes.size(); feature++) {
                 test.fit(X[feature], y);
@@ -90,82 +96,100 @@ namespace mdlp {
         }
     };
 
-    TEST_F(TestFImdlp, FitErrorEmptyDataset) {
+    TEST_F(TestFImdlp, FitErrorEmptyDataset)
+    {
         X = samples_t();
         y = labels_t();
         EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have at least one element");
     }
 
-    TEST_F(TestFImdlp, FitErrorDifferentSize) {
-        X = {1, 2, 3};
-        y = {1, 2};
+    TEST_F(TestFImdlp, FitErrorDifferentSize)
+    {
+        X = { 1, 2, 3 };
+        y = { 1, 2 };
         EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have the same size");
     }
 
-    TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth) {
+    TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth)
+    {
         auto testLength = CPPFImdlp(2, 10, 0);
         auto testDepth = CPPFImdlp(3, 0, 0);
-        X = {1, 2, 3};
-        y = {1, 2, 3};
+        X = { 1, 2, 3 };
+        y = { 1, 2, 3 };
         EXPECT_THROW_WITH_MESSAGE(testLength.fit(X, y), invalid_argument, "min_length must be greater than 2");
         EXPECT_THROW_WITH_MESSAGE(testDepth.fit(X, y), invalid_argument, "max_depth must be greater than 0");
     }
 
-    TEST_F(TestFImdlp, FitErrorMaxCutPoints) {
+    TEST_F(TestFImdlp, JoinFit)
+    {
+        samples_t X_ = { 1, 2, 2, 3, 4, 2, 3 };
+        labels_t y_ = { 0, 0, 1, 2, 3, 4, 5 };
+        cutPoints_t expected = { 1.5f, 2.5f };
+        fit(X_, y_);
+        auto computed = getCutPoints();
+        EXPECT_EQ(computed.size(), expected.size());
+        checkCutPoints(computed, expected);
+    }
+
+    TEST_F(TestFImdlp, FitErrorMaxCutPoints)
+    {
         auto testmin = CPPFImdlp(2, 10, -1);
         auto testmax = CPPFImdlp(3, 0, 200);
-        X = {1, 2, 3};
-        y = {1, 2, 3};
+        X = { 1, 2, 3 };
+        y = { 1, 2, 3 };
         EXPECT_THROW_WITH_MESSAGE(testmin.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
         EXPECT_THROW_WITH_MESSAGE(testmax.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
     }
 
-    TEST_F(TestFImdlp, SortIndices) {
-        X = {5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f, 6.0f, 5.1f, 5.9f};
-        y = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
-        indices = {4, 3, 6, 8, 2, 1, 5, 0, 9, 7};
+    TEST_F(TestFImdlp, SortIndices)
+    {
+        X = { 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f, 6.0f, 5.1f, 5.9f };
+        y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
+        indices = { 4, 3, 6, 8, 2, 1, 5, 0, 9, 7 };
         checkSortedVector();
-        X = {5.77f, 5.88f, 5.99f};
-        y = {1, 2, 1};
-        indices = {0, 1, 2};
+        X = { 5.77f, 5.88f, 5.99f };
+        y = { 1, 2, 1 };
+        indices = { 0, 1, 2 };
         checkSortedVector();
-        X = {5.33f, 5.22f, 5.11f};
-        y = {1, 2, 1};
-        indices = {2, 1, 0};
+        X = { 5.33f, 5.22f, 5.11f };
+        y = { 1, 2, 1 };
+        indices = { 2, 1, 0 };
         checkSortedVector();
-        X = {5.33f, 5.22f, 5.33f};
-        y = {2, 2, 1};
-        indices = {1, 2, 0};
+        X = { 5.33f, 5.22f, 5.33f };
+        y = { 2, 2, 1 };
+        indices = { 1, 2, 0 };
     }
 
-    TEST_F(TestFImdlp, TestShortDatasets) {
+    TEST_F(TestFImdlp, TestShortDatasets)
+    {
         vector<precision_t> computed;
-        X = {1};
-        y = {1};
+        X = { 1 };
+        y = { 1 };
         fit(X, y);
         computed = getCutPoints();
         EXPECT_EQ(computed.size(), 0);
-        X = {1, 3};
-        y = {1, 2};
+        X = { 1, 3 };
+        y = { 1, 2 };
         fit(X, y);
         computed = getCutPoints();
         EXPECT_EQ(computed.size(), 0);
-        X = {2, 4};
-        y = {1, 2};
+        X = { 2, 4 };
+        y = { 1, 2 };
         fit(X, y);
         computed = getCutPoints();
         EXPECT_EQ(computed.size(), 0);
-        X = {1, 2, 3};
-        y = {1, 2, 2};
+        X = { 1, 2, 3 };
+        y = { 1, 2, 2 };
         fit(X, y);
         computed = getCutPoints();
         EXPECT_EQ(computed.size(), 1);
         EXPECT_NEAR(computed[0], 1.5, precision);
     }
 
-    TEST_F(TestFImdlp, TestArtificialDataset) {
+    TEST_F(TestFImdlp, TestArtificialDataset)
+    {
         fit(X, y);
-        cutPoints_t expected = {5.05f};
+        cutPoints_t expected = { 5.05f };
         vector<precision_t> computed = getCutPoints();
         EXPECT_EQ(computed.size(), expected.size());
         for (unsigned long i = 0; i < computed.size(); i++) {
@@ -173,49 +197,53 @@ namespace mdlp {
         }
     }
 
-    TEST_F(TestFImdlp, TestIris) {
+    TEST_F(TestFImdlp, TestIris)
+    {
         vector<cutPoints_t> expected = {
                 {5.45f, 5.75f},
                 {2.75f, 2.85f, 2.95f, 3.05f, 3.35f},
                 {2.45f, 4.75f, 5.05f},
                 {0.8f,  1.75f}
         };
-        vector<int> depths = {3, 5, 4, 3};
+        vector<int> depths = { 3, 5, 4, 3 };
         auto test = CPPFImdlp();
         test_dataset(test, "iris", expected, depths);
     }
 
-    TEST_F(TestFImdlp, ComputeCutPointsGCase) {
+    TEST_F(TestFImdlp, ComputeCutPointsGCase)
+    {
         cutPoints_t expected;
-        expected = {1.5};
-        samples_t X_ = {0, 1, 2, 2, 2};
-        labels_t y_ = {1, 1, 1, 2, 2};
+        expected = { 1.5 };
+        samples_t X_ = { 0, 1, 2, 2, 2 };
+        labels_t y_ = { 1, 1, 1, 2, 2 };
         fit(X_, y_);
         auto computed = getCutPoints();
         checkCutPoints(computed, expected);
     }
 
-    TEST_F(TestFImdlp, ValueCutPoint) {
+    TEST_F(TestFImdlp, ValueCutPoint)
+    {
         // Case titles as stated in the doc
-        samples_t X1a{3.1f, 3.2f, 3.3f, 3.4f, 3.5f, 3.6f, 3.7f, 3.8f, 3.9f, 4.0f};
+        samples_t X1a{ 3.1f, 3.2f, 3.3f, 3.4f, 3.5f, 3.6f, 3.7f, 3.8f, 3.9f, 4.0f };
         test_result(X1a, 6, 7.3f / 2, 6, "1a");
-        samples_t X2a = {3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
+        samples_t X2a = { 3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
         test_result(X2a, 6, 7.1f / 2, 4, "2a");
-        samples_t X2b = {3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
+        samples_t X2b = { 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
         test_result(X2b, 6, 7.5f / 2, 7, "2b");
-        samples_t X3a = {3.f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
+        samples_t X3a = { 3.f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
         test_result(X3a, 4, 7.1f / 2, 4, "3a");
-        samples_t X3b = {3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f};
+        samples_t X3b = { 3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f };
         test_result(X3b, 4, 7.1f / 2, 4, "3b");
-        samples_t X4a = {3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.9f, 4.0f};
+        samples_t X4a = { 3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.9f, 4.0f };
         test_result(X4a, 4, 6.9f / 2, 2, "4a");
-        samples_t X4b = {3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
+        samples_t X4b = { 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
         test_result(X4b, 4, 7.5f / 2, 7, "4b");
-        samples_t X4c = {3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f};
+        samples_t X4c = { 3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f };
         test_result(X4c, 4, 6.9f / 2, 2, "4c");
     }
 
-    TEST_F(TestFImdlp, MaxDepth) {
+    TEST_F(TestFImdlp, MaxDepth)
+    {
         // Set max_depth to 1
         auto test = CPPFImdlp(3, 1, 0);
         vector<cutPoints_t> expected = {
@@ -224,11 +252,12 @@ namespace mdlp {
                 {2.45f},
                 {0.8f}
         };
-        vector<int> depths = {1, 1, 1, 1};
+        vector<int> depths = { 1, 1, 1, 1 };
         test_dataset(test, "iris", expected, depths);
     }
 
-    TEST_F(TestFImdlp, MinLength) {
+    TEST_F(TestFImdlp, MinLength)
+    {
         auto test = CPPFImdlp(75, 100, 0);
         // Set min_length to 75
         vector<cutPoints_t> expected = {
@@ -237,11 +266,12 @@ namespace mdlp {
                 {2.45f, 4.75f},
                 {0.8f,  1.75f}
         };
-        vector<int> depths = {3, 2, 2, 2};
+        vector<int> depths = { 3, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
     }
 
-    TEST_F(TestFImdlp, MinLengthMaxDepth) {
+    TEST_F(TestFImdlp, MinLengthMaxDepth)
+    {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 0);
         vector<cutPoints_t> expected = {
@@ -250,24 +280,27 @@ namespace mdlp {
                 {2.45f, 4.75f},
                 {0.8f,  1.75f}
         };
-        vector<int> depths = {2, 2, 2, 2};
+        vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
     }
 
-    TEST_F(TestFImdlp, MaxCutPointsInteger) {
+    TEST_F(TestFImdlp, MaxCutPointsInteger)
+    {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 1);
         vector<cutPoints_t> expected = {
                 {5.45f},
-                {3.35f},
+                {2.85f},
                 {2.45f},
                 {0.8f}
         };
-        vector<int> depths = {1, 1, 1, 1};
+        vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
+
     }
 
-    TEST_F(TestFImdlp, MaxCutPointsFloat) {
+    TEST_F(TestFImdlp, MaxCutPointsFloat)
+    {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 0.2f);
         vector<cutPoints_t> expected = {
@@ -276,23 +309,46 @@ namespace mdlp {
                 {2.45f, 4.75f},
                 {0.8f,  1.75f}
         };
-        vector<int> depths = {2, 2, 2, 2};
+        vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
     }
 
-    TEST_F(TestFImdlp, ProposedCuts) {
-        vector<pair<float, size_t>> proposed_list = {{0.1f,  2},
+    TEST_F(TestFImdlp, ProposedCuts)
+    {
+        vector<pair<float, size_t>> proposed_list = { {0.1f,  2},
                                                      {0.5f,  10},
                                                      {0.07f, 1},
                                                      {1.0f,  1},
-                                                     {2.0f,  2}};
+                                                     {2.0f,  2} };
         size_t expected;
         size_t computed;
-        for (auto proposed_item: proposed_list) {
+        for (auto proposed_item : proposed_list) {
             tie(proposed_cuts, expected) = proposed_item;
             computed = compute_max_num_cut_points();
             ASSERT_EQ(expected, computed);
         }
 
     }
+    TEST_F(TestFImdlp, TransformTest)
+    {
+        labels_t expected = {
+            5, 3, 4, 4, 5, 5, 5, 5, 2, 4, 5, 5, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5,
+            5, 4, 5, 3, 5, 5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 4, 3, 5, 5, 0, 4, 5,
+            5, 3, 5, 4, 5, 4, 4, 4, 4, 0, 1, 1, 4, 0, 2, 0, 0, 3, 0, 2, 2, 4,
+            3, 0, 0, 0, 4, 1, 0, 1, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 3, 5, 4, 0,
+            3, 0, 0, 3, 0, 0, 0, 3, 2, 2, 0, 1, 4, 0, 3, 2, 3, 3, 0, 2, 0, 5,
+            4, 0, 3, 0, 1, 4, 3, 5, 0, 0, 4, 1, 1, 0, 4, 4, 1, 3, 1, 3, 1, 5,
+            1, 1, 0, 3, 5, 4, 3, 4, 4, 4, 0, 4, 4, 3, 0, 3, 5, 3
+        };
+        ArffFiles file;
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
+        fit(X[1], y);
+        auto computed = transform(X[1]);
+        EXPECT_EQ(computed.size(), expected.size());
+        for (unsigned long i = 0; i < computed.size(); i++) {
+            EXPECT_EQ(computed[i], expected[i]);
+        }
+    }
 }

tests/Metrics_unittest.cpp

@@ -2,38 +2,51 @@
 #include "../Metrics.h"
 
 namespace mdlp {
-    class TestMetrics : public Metrics, public testing::Test {
+    class TestMetrics: public Metrics, public testing::Test {
     public:
-        labels_t y_ = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
-        indices_t indices_ = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+        labels_t y_ = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
+        indices_t indices_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
         precision_t precision = 0.000001f;
 
-        TestMetrics() : Metrics(y_, indices_) {};
+        TestMetrics(): Metrics(y_, indices_) {};
 
-        void SetUp() override {
+        void SetUp() override
+        {
             setData(y_, indices_);
         }
     };
 
-    TEST_F(TestMetrics, NumClasses) {
-        y = {1, 1, 1, 1, 1, 1, 1, 1, 2, 1};
+    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) {
+    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};
+        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
         setData(y, indices);
         ASSERT_NEAR(0.468996f, entropy(0, 10), precision);
     }
 
-    TEST_F(TestMetrics, InformationGain) {
+    TEST_F(TestMetrics, EntropyDouble)
+    {
+        y = { 0, 0, 1, 2, 3 };
+        samples_t expected_entropies = { 0.0, 0.0, 0.91829583, 1.5, 1.4575424759098898 };
+        for (auto idx = 0; idx < y.size(); ++idx) {
+            ASSERT_NEAR(expected_entropies[idx], entropy(0, idx + 1), precision);
+        }
+    }
+
+    TEST_F(TestMetrics, InformationGain)
+    {
         ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
         ASSERT_NEAR(1, informationGain(0, 5, 10), precision); // For cache
-        y = {1, 1, 1, 1, 1, 1, 1, 1, 2, 1};
+        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
         setData(y, indices);
         ASSERT_NEAR(0.108032f, informationGain(0, 5, 10), precision);
     }

tests/datasets/diabetes.arff

@@ -0,0 +1,863 @@
+% 1. Title: Pima Indians Diabetes Database
+% 
+% 2. Sources:
+%    (a) Original owners: National Institute of Diabetes and Digestive and
+%                         Kidney Diseases
+%    (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)
+%                           Research Center, RMI Group Leader
+%                           Applied Physics Laboratory
+%                           The Johns Hopkins University
+%                           Johns Hopkins Road
+%                           Laurel, MD 20707
+%                           (301) 953-6231
+%    (c) Date received: 9 May 1990
+% 
+% 3. Past Usage:
+%     1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
+%        Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
+%        the onset of diabetes mellitus.  In {\it Proceedings of the Symposium
+%        on Computer Applications and Medical Care} (pp. 261--265).  IEEE
+%        Computer Society Press.
+% 
+%        The diagnostic, binary-valued variable investigated is whether the
+%        patient shows signs of diabetes according to World Health Organization
+%        criteria (i.e., if the 2 hour post-load plasma glucose was at least 
+%        200 mg/dl at any survey  examination or if found during routine medical
+%        care).   The population lives near Phoenix, Arizona, USA.
+% 
+%        Results: Their ADAP algorithm makes a real-valued prediction between
+%        0 and 1.  This was transformed into a binary decision using a cutoff of 
+%        0.448.  Using 576 training instances, the sensitivity and specificity
+%        of their algorithm was 76% on the remaining 192 instances.
+% 
+% 4. Relevant Information:
+%       Several constraints were placed on the selection of these instances from
+%       a larger database.  In particular, all patients here are females at
+%       least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning
+%       routine that generates and executes digital analogs of perceptron-like
+%       devices.  It is a unique algorithm; see the paper for details.
+% 
+% 5. Number of Instances: 768
+% 
+% 6. Number of Attributes: 8 plus class 
+% 
+% 7. For Each Attribute: (all numeric-valued)
+%    1. Number of times pregnant
+%    2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
+%    3. Diastolic blood pressure (mm Hg)
+%    4. Triceps skin fold thickness (mm)
+%    5. 2-Hour serum insulin (mu U/ml)
+%    6. Body mass index (weight in kg/(height in m)^2)
+%    7. Diabetes pedigree function
+%    8. Age (years)
+%    9. Class variable (0 or 1)
+% 
+% 8. Missing Attribute Values: None
+% 
+% 9. Class Distribution: (class value 1 is interpreted as "tested positive for
+%    diabetes")
+% 
+%    Class Value  Number of instances
+%    0            500
+%    1            268
+% 
+% 10. Brief statistical analysis:
+% 
+%     Attribute number:    Mean:   Standard Deviation:
+%     1.                     3.8     3.4
+%     2.                   120.9    32.0
+%     3.                    69.1    19.4
+%     4.                    20.5    16.0
+%     5.                    79.8   115.2
+%     6.                    32.0     7.9
+%     7.                     0.5     0.3
+%     8.                    33.2    11.8
+% 
+% 
+%
+%
+%
+%
+% Relabeled values in attribute 'class'
+%    From: 0                       To: tested_negative     
+%    From: 1                       To: tested_positive     
+%
+@relation pima_diabetes
+@attribute 'preg' real
+@attribute 'plas' real
+@attribute 'pres' real
+@attribute 'skin' real
+@attribute 'insu' real
+@attribute 'mass' real
+@attribute 'pedi' real
+@attribute 'age' real
+@attribute 'class' { tested_negative, tested_positive}
+@data
+6,148,72,35,0,33.6,0.627,50,tested_positive
+1,85,66,29,0,26.6,0.351,31,tested_negative
+8,183,64,0,0,23.3,0.672,32,tested_positive
+1,89,66,23,94,28.1,0.167,21,tested_negative
+0,137,40,35,168,43.1,2.288,33,tested_positive
+5,116,74,0,0,25.6,0.201,30,tested_negative
+3,78,50,32,88,31,0.248,26,tested_positive
+10,115,0,0,0,35.3,0.134,29,tested_negative
+2,197,70,45,543,30.5,0.158,53,tested_positive
+8,125,96,0,0,0,0.232,54,tested_positive
+4,110,92,0,0,37.6,0.191,30,tested_negative
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+0,102,86,17,105,29.3,0.695,27,tested_negative
+4,128,70,0,0,34.3,0.303,24,tested_negative
+6,147,80,0,0,29.5,0.178,50,tested_positive
+4,90,0,0,0,28,0.61,31,tested_negative
+3,103,72,30,152,27.6,0.73,27,tested_negative
+2,157,74,35,440,39.4,0.134,30,tested_negative
+1,167,74,17,144,23.4,0.447,33,tested_positive
+0,179,50,36,159,37.8,0.455,22,tested_positive
+11,136,84,35,130,28.3,0.26,42,tested_positive
+0,107,60,25,0,26.4,0.133,23,tested_negative
+1,91,54,25,100,25.2,0.234,23,tested_negative
+1,117,60,23,106,33.8,0.466,27,tested_negative
+5,123,74,40,77,34.1,0.269,28,tested_negative
+2,120,54,0,0,26.8,0.455,27,tested_negative
+1,106,70,28,135,34.2,0.142,22,tested_negative
+2,155,52,27,540,38.7,0.24,25,tested_positive
+2,101,58,35,90,21.8,0.155,22,tested_negative
+1,120,80,48,200,38.9,1.162,41,tested_negative
+11,127,106,0,0,39,0.19,51,tested_negative
+3,80,82,31,70,34.2,1.292,27,tested_positive
+10,162,84,0,0,27.7,0.182,54,tested_negative
+1,199,76,43,0,42.9,1.394,22,tested_positive
+8,167,106,46,231,37.6,0.165,43,tested_positive
+9,145,80,46,130,37.9,0.637,40,tested_positive
+6,115,60,39,0,33.7,0.245,40,tested_positive
+1,112,80,45,132,34.8,0.217,24,tested_negative
+4,145,82,18,0,32.5,0.235,70,tested_positive
+10,111,70,27,0,27.5,0.141,40,tested_positive
+6,98,58,33,190,34,0.43,43,tested_negative
+9,154,78,30,100,30.9,0.164,45,tested_negative
+6,165,68,26,168,33.6,0.631,49,tested_negative
+1,99,58,10,0,25.4,0.551,21,tested_negative
+10,68,106,23,49,35.5,0.285,47,tested_negative
+3,123,100,35,240,57.3,0.88,22,tested_negative
+8,91,82,0,0,35.6,0.587,68,tested_negative
+6,195,70,0,0,30.9,0.328,31,tested_positive
+9,156,86,0,0,24.8,0.23,53,tested_positive
+0,93,60,0,0,35.3,0.263,25,tested_negative
+3,121,52,0,0,36,0.127,25,tested_positive
+2,101,58,17,265,24.2,0.614,23,tested_negative
+2,56,56,28,45,24.2,0.332,22,tested_negative
+0,162,76,36,0,49.6,0.364,26,tested_positive
+0,95,64,39,105,44.6,0.366,22,tested_negative
+4,125,80,0,0,32.3,0.536,27,tested_positive
+5,136,82,0,0,0,0.64,69,tested_negative
+2,129,74,26,205,33.2,0.591,25,tested_negative
+3,130,64,0,0,23.1,0.314,22,tested_negative
+1,107,50,19,0,28.3,0.181,29,tested_negative
+1,140,74,26,180,24.1,0.828,23,tested_negative
+1,144,82,46,180,46.1,0.335,46,tested_positive
+8,107,80,0,0,24.6,0.856,34,tested_negative
+13,158,114,0,0,42.3,0.257,44,tested_positive
+2,121,70,32,95,39.1,0.886,23,tested_negative
+7,129,68,49,125,38.5,0.439,43,tested_positive
+2,90,60,0,0,23.5,0.191,25,tested_negative
+7,142,90,24,480,30.4,0.128,43,tested_positive
+3,169,74,19,125,29.9,0.268,31,tested_positive
+0,99,0,0,0,25,0.253,22,tested_negative
+4,127,88,11,155,34.5,0.598,28,tested_negative
+4,118,70,0,0,44.5,0.904,26,tested_negative
+2,122,76,27,200,35.9,0.483,26,tested_negative
+6,125,78,31,0,27.6,0.565,49,tested_positive
+1,168,88,29,0,35,0.905,52,tested_positive
+2,129,0,0,0,38.5,0.304,41,tested_negative
+4,110,76,20,100,28.4,0.118,27,tested_negative
+6,80,80,36,0,39.8,0.177,28,tested_negative
+10,115,0,0,0,0,0.261,30,tested_positive
+2,127,46,21,335,34.4,0.176,22,tested_negative
+9,164,78,0,0,32.8,0.148,45,tested_positive
+2,93,64,32,160,38,0.674,23,tested_positive
+3,158,64,13,387,31.2,0.295,24,tested_negative
+5,126,78,27,22,29.6,0.439,40,tested_negative
+10,129,62,36,0,41.2,0.441,38,tested_positive
+0,134,58,20,291,26.4,0.352,21,tested_negative
+3,102,74,0,0,29.5,0.121,32,tested_negative
+7,187,50,33,392,33.9,0.826,34,tested_positive
+3,173,78,39,185,33.8,0.97,31,tested_positive
+10,94,72,18,0,23.1,0.595,56,tested_negative
+1,108,60,46,178,35.5,0.415,24,tested_negative
+5,97,76,27,0,35.6,0.378,52,tested_positive
+4,83,86,19,0,29.3,0.317,34,tested_negative
+1,114,66,36,200,38.1,0.289,21,tested_negative
+1,149,68,29,127,29.3,0.349,42,tested_positive
+5,117,86,30,105,39.1,0.251,42,tested_negative
+1,111,94,0,0,32.8,0.265,45,tested_negative
+4,112,78,40,0,39.4,0.236,38,tested_negative
+1,116,78,29,180,36.1,0.496,25,tested_negative
+0,141,84,26,0,32.4,0.433,22,tested_negative
+2,175,88,0,0,22.9,0.326,22,tested_negative
+2,92,52,0,0,30.1,0.141,22,tested_negative
+3,130,78,23,79,28.4,0.323,34,tested_positive
+8,120,86,0,0,28.4,0.259,22,tested_positive
+2,174,88,37,120,44.5,0.646,24,tested_positive
+2,106,56,27,165,29,0.426,22,tested_negative
+2,105,75,0,0,23.3,0.56,53,tested_negative
+4,95,60,32,0,35.4,0.284,28,tested_negative
+0,126,86,27,120,27.4,0.515,21,tested_negative
+8,65,72,23,0,32,0.6,42,tested_negative
+2,99,60,17,160,36.6,0.453,21,tested_negative
+1,102,74,0,0,39.5,0.293,42,tested_positive
+11,120,80,37,150,42.3,0.785,48,tested_positive
+3,102,44,20,94,30.8,0.4,26,tested_negative
+1,109,58,18,116,28.5,0.219,22,tested_negative
+9,140,94,0,0,32.7,0.734,45,tested_positive
+13,153,88,37,140,40.6,1.174,39,tested_negative
+12,100,84,33,105,30,0.488,46,tested_negative
+1,147,94,41,0,49.3,0.358,27,tested_positive
+1,81,74,41,57,46.3,1.096,32,tested_negative
+3,187,70,22,200,36.4,0.408,36,tested_positive
+6,162,62,0,0,24.3,0.178,50,tested_positive
+4,136,70,0,0,31.2,1.182,22,tested_positive
+1,121,78,39,74,39,0.261,28,tested_negative
+3,108,62,24,0,26,0.223,25,tested_negative
+0,181,88,44,510,43.3,0.222,26,tested_positive
+8,154,78,32,0,32.4,0.443,45,tested_positive
+1,128,88,39,110,36.5,1.057,37,tested_positive
+7,137,90,41,0,32,0.391,39,tested_negative
+0,123,72,0,0,36.3,0.258,52,tested_positive
+1,106,76,0,0,37.5,0.197,26,tested_negative
+6,190,92,0,0,35.5,0.278,66,tested_positive
+2,88,58,26,16,28.4,0.766,22,tested_negative
+9,170,74,31,0,44,0.403,43,tested_positive
+9,89,62,0,0,22.5,0.142,33,tested_negative
+10,101,76,48,180,32.9,0.171,63,tested_negative
+2,122,70,27,0,36.8,0.34,27,tested_negative
+5,121,72,23,112,26.2,0.245,30,tested_negative
+1,126,60,0,0,30.1,0.349,47,tested_positive
+1,93,70,31,0,30.4,0.315,23,tested_negative

tests/test

@@ -1,20 +1,18 @@
+#!/bin/bash
 if [ -d build ] ; then
 	rm -fr build
 fi
 if [ -d gcovr-report ] ; then
 	rm -fr gcovr-report
 fi
-cmake -S . -B build -Wno-dev 
+cmake -S . -B build -Wno-dev -DCMAKE_BUILD_TYPE=Debug -DCMAKE_CXX_FLAGS="--coverage" -DCMAKE_C_FLAGS="--coverage"
 cmake --build build
 cd build
 ctest --output-on-failure
 cd ..
-if [ ! -d gcovr-report ] ; then 
-	mkdir gcovr-report
-fi
-rm -fr gcovr-report/* 2>/dev/null
+mkdir gcovr-report
 #lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
 #lcov --remove lcoverage/main_coverage.info 'v1/*' '/Applications/*' '*/tests/*' --output-file lcoverage/main_coverage.info -q
 #lcov --list lcoverage/main_coverage.info
 cd ..
-gcovr  --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --txt --sonarqube=tests/gcovr-report/coverage.xml   
+gcovr  --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --gcov-filter "BinDisc.cpp" --gcov-filter "Discretizer.h" --txt --sonarqube=tests/gcovr-report/coverage.xml --exclude-noncode-lines

tests/testKbins.py

@@ -0,0 +1,412 @@
+from scipy.io.arff import loadarff
+from sklearn.preprocessing import KBinsDiscretizer
+
+
+def test(clf, X, expected, title):
+    X = [[x] for x in X]
+    clf.fit(X)
+    computed = [int(x[0]) for x in clf.transform(X)]
+    print(f"{title}")
+    print(f"{computed=}")
+    print(f"{expected=}")
+    assert computed == expected
+    print("-" * 80)
+
+
+# Test Uniform Strategy
+clf3u = KBinsDiscretizer(
+    n_bins=3, encode="ordinal", strategy="uniform", subsample=200_000
+)
+clf3q = KBinsDiscretizer(
+    n_bins=3, encode="ordinal", strategy="quantile", subsample=200_000
+)
+clf4u = KBinsDiscretizer(
+    n_bins=4, encode="ordinal", strategy="uniform", subsample=200_000
+)
+clf4q = KBinsDiscretizer(
+    n_bins=4, encode="ordinal", strategy="quantile", subsample=200_000
+)
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
+labels = [0, 0, 0, 1, 1, 1, 2, 2, 2]
+test(clf3u, X, labels, title="Easy3BinsUniform")
+test(clf3q, X, labels, title="Easy3BinsQuantile")
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+# En C++ se obtiene el mismo resultado en ambos, no como aquí
+labels2 = [0, 0, 0, 1, 1, 1, 1, 2, 2, 2]
+test(clf3u, X, labels, title="X10BinsUniform")
+test(clf3q, X, labels2, title="X10BinsQuantile")
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]
+labels = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+# En C++ se obtiene el mismo resultado en ambos, no como aquí
+# labels2 = [0, 0, 0, 1, 1, 1, 1, 2, 2, 2]
+test(clf3u, X, labels, title="X11BinsUniform")
+test(clf3q, X, labels, title="X11BinsQuantile")
+#
+X = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+labels = [0, 0, 0, 0, 0, 0]
+test(clf3u, X, labels, title="ConstantUniform")
+test(clf3q, X, labels, title="ConstantQuantile")
+#
+X = [3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]
+labels = [2, 0, 0, 2, 0, 0, 2, 0, 0]
+labels2 = [1, 0, 0, 1, 0, 0, 1, 0, 0]  # igual que en C++
+test(clf3u, X, labels, title="EasyRepeatedUniform")
+test(clf3q, X, labels2, title="EasyRepeatedQuantile")
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
+labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
+test(clf4u, X, labels, title="Easy4BinsUniform")
+test(clf4q, X, labels, title="Easy4BinsQuantile")
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0]
+labels = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3]
+test(clf4u, X, labels, title="X13BinsUniform")
+test(clf4q, X, labels, title="X13BinsQuantile")
+#
+X = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0]
+labels = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3]
+test(clf4u, X, labels, title="X14BinsUniform")
+test(clf4q, X, labels, title="X14BinsQuantile")
+#
+X1 = [15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0]
+X2 = [15.0, 13.0, 12.0, 14.0, 6.0, 1.0, 8.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0]
+labels1 = [3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0]
+labels2 = [3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0]
+test(clf4u, X1, labels1, title="X15BinsUniform")
+test(clf4q, X2, labels2, title="X15BinsQuantile")
+#
+X = [0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
+labels = [0, 1, 1, 1, 2, 2, 3, 3, 3, 3]
+test(clf4u, X, labels, title="RepeatedValuesUniform")
+test(clf4q, X, labels, title="RepeatedValuesQuantile")
+
+print(f"Uniform   {clf4u.bin_edges_=}")
+print(f"Quaintile {clf4q.bin_edges_=}")
+print("-" * 80)
+#
+data, meta = loadarff("tests/datasets/iris.arff")
+
+labelsu = [
+    0,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    0,
+    1,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    1,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    3,
+    2,
+    2,
+    1,
+    2,
+    1,
+    2,
+    0,
+    2,
+    0,
+    0,
+    1,
+    1,
+    1,
+    1,
+    2,
+    1,
+    1,
+    2,
+    1,
+    1,
+    1,
+    2,
+    1,
+    2,
+    2,
+    2,
+    2,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    2,
+    2,
+    1,
+    1,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    2,
+    0,
+    1,
+    2,
+    1,
+    3,
+    2,
+    2,
+    3,
+    0,
+    3,
+    2,
+    3,
+    2,
+    2,
+    2,
+    1,
+    1,
+    2,
+    2,
+    3,
+    3,
+    1,
+    2,
+    1,
+    3,
+    2,
+    2,
+    3,
+    2,
+    1,
+    2,
+    3,
+    3,
+    3,
+    2,
+    2,
+    1,
+    3,
+    2,
+    2,
+    1,
+    2,
+    2,
+    2,
+    1,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2,
+    1,
+]
+labelsq = [
+    1,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    2,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    0,
+    1,
+    0,
+    0,
+    0,
+    1,
+    1,
+    0,
+    0,
+    1,
+    1,
+    1,
+    0,
+    0,
+    1,
+    0,
+    0,
+    1,
+    0,
+    0,
+    0,
+    0,
+    1,
+    0,
+    1,
+    0,
+    1,
+    0,
+    3,
+    3,
+    3,
+    1,
+    3,
+    1,
+    2,
+    0,
+    3,
+    1,
+    0,
+    2,
+    2,
+    2,
+    1,
+    3,
+    1,
+    2,
+    2,
+    1,
+    2,
+    2,
+    2,
+    2,
+    3,
+    3,
+    3,
+    3,
+    2,
+    1,
+    1,
+    1,
+    2,
+    2,
+    1,
+    2,
+    3,
+    2,
+    1,
+    1,
+    1,
+    2,
+    2,
+    0,
+    1,
+    1,
+    1,
+    2,
+    1,
+    1,
+    2,
+    2,
+    3,
+    2,
+    3,
+    3,
+    0,
+    3,
+    3,
+    3,
+    3,
+    3,
+    3,
+    1,
+    2,
+    3,
+    3,
+    3,
+    3,
+    2,
+    3,
+    1,
+    3,
+    2,
+    3,
+    3,
+    2,
+    2,
+    3,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2,
+    3,
+    2,
+    3,
+    2,
+    3,
+    3,
+    3,
+    2,
+    3,
+    3,
+    3,
+    2,
+    3,
+    2,
+    2,
+]
+# test(clf4u, data["sepallength"], labelsu, title="IrisUniform")
+# test(clf4q, data["sepallength"], labelsq, title="IrisQuantile")
+sepallength = [[x] for x in data["sepallength"]]
+clf4u.fit(sepallength)
+clf4q.fit(sepallength)
+computedu = clf4u.transform(sepallength)
+computedq = clf4q.transform(sepallength)
+wrongu = 0
+wrongq = 0
+for i in range(len(labelsu)):
+    if labelsu[i] != computedu[i]:
+        wrongu += 1
+    if labelsq[i] != computedq[i]:
+        wrongq += 1
+print(f"Iris sepallength diff. between BinDisc & sklearn::KBins Uniform  ={wrongu:3d}")
+print(f"Iris sepallength diff. between BinDisc & sklearn::KBins Quantile ={wrongq:3d}")

typesFImdlp.h

@@ -1,5 +1,6 @@
 #ifndef TYPES_H
 #define TYPES_H
+
 #include <vector>
 #include <map>
 #include <stdexcept>
@@ -7,11 +8,11 @@
 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<pair<int, int>, precision_t> cacheEnt_t;
-    typedef map<tuple<int, int, int>, precision_t> cacheIg_t;
+    typedef std::vector<precision_t> samples_t;
+    typedef std::vector<int> labels_t;
+    typedef std::vector<size_t> indices_t;
+    typedef std::vector<precision_t> cutPoints_t;
+    typedef std::map<std::pair<int, int>, precision_t> cacheEnt_t;
+    typedef std::map<std::tuple<int, int, int>, precision_t> cacheIg_t;
 }
 #endif