Merge c488ace719 into 7b0673fd4b

Add fit_transform methods

.github/workflows/build.yml

@@ -22,15 +22,19 @@ jobs:
         run: |
           sudo apt-get -y install lcov
           sudo apt-get -y install gcovr
+      - name: Install Libtorch
+        run: |
+          wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcpu.zip
+          unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
       - name: Tests & build-wrapper
         run: |
-          cmake -S . -B build -Wno-dev 
+          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DENABLE_TESTING=ON
           build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
           cd build
           make
           ctest -C Release --output-on-failure --test-dir tests
           cd ..
-          gcovr -f CPPFImdlp.cpp -f Metrics.cpp  -f BinDisc.cpp --txt --sonarqube=coverage.xml
+          gcovr -f CPPFImdlp.cpp -f Metrics.cpp -f BinDisc.cpp -f Discretizer.cpp --txt --sonarqube=coverage.xml
       - name: Run sonar-scanner
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -33,6 +33,8 @@
 **/build
 build_Debug
 build_Release
+build_debug
+build_release
 **/lcoverage
 .idea
 cmake-*

.vscode/settings.json

@@ -88,6 +88,22 @@
         "*.toml": "toml",
         "utility": "cpp",
         "span": "cpp",
-        "*.tcc": "cpp"
+        "*.tcc": "cpp",
+        "bit": "cpp",
+        "charconv": "cpp",
+        "cinttypes": "cpp",
+        "codecvt": "cpp",
+        "functional": "cpp",
+        "iterator": "cpp",
+        "memory_resource": "cpp",
+        "random": "cpp",
+        "source_location": "cpp",
+        "format": "cpp",
+        "numbers": "cpp",
+        "semaphore": "cpp",
+        "stop_token": "cpp",
+        "text_encoding": "cpp",
+        "typeindex": "cpp",
+        "valarray": "cpp"
     }
 }

BinDisc.cpp

@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <limits>
 #include <cmath>
 #include "BinDisc.h"
 #include <iostream>
@@ -20,7 +19,8 @@ namespace mdlp {
         // y is included for compatibility with the Discretizer interface
         cutPoints.clear();
         if (X.empty()) {
-            cutPoints.push_back(std::numeric_limits<precision_t>::max());
+            cutPoints.push_back(0.0);
+            cutPoints.push_back(0.0);
             return;
         }
         if (strategy == strategy_t::QUANTILE) {
@@ -35,13 +35,12 @@ namespace mdlp {
     }
     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 };
+            return { start, end };
         }
         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) {
+        for (size_t i = 0; i < num; ++i) {
             precision_t val = start + delta * static_cast<precision_t>(i);
             linspc.push_back(val);
         }
@@ -55,17 +54,19 @@ namespace mdlp {
     {
         // Implementation taken from https://dpilger26.github.io/NumCpp/doxygen/html/percentile_8hpp_source.html
         std::vector<precision_t> results;
+        bool first = true;
         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 auto indexLower = clip(i, 0, data.size() - 2);
             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())
+            if (value != results.back() || first) // first needed as results.back() return is undefined for empty vectors
                 results.push_back(value);
+            first = false;
         }
         return results;
     }
@@ -75,25 +76,16 @@ namespace mdlp {
         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());
+            // if X is constant, pass any two given points that shall be ignored in transform
+            cutPoints.push_back(data.front());
+            cutPoints.push_back(data.front());
             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

@@ -6,7 +6,6 @@
 #include <string>
 
 namespace mdlp {
-
     enum class strategy_t {
         UNIFORM,
         QUANTILE
@@ -21,7 +20,6 @@ namespace mdlp {
     private:
         void fit_uniform(samples_t&);
         void fit_quantile(samples_t&);
-        void normalizeCutPoints();
         int n_bins;
         strategy_t strategy;
     };

CMakeLists.txt

@@ -1,13 +1,11 @@
 cmake_minimum_required(VERSION 3.20)
 project(mdlp)
-
-if (POLICY CMP0135)
-    cmake_policy(SET CMP0135 NEW)
-endif ()
-
-set(CMAKE_CXX_STANDARD 11)
-
-add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
+set(CMAKE_CXX_STANDARD 17)
+find_package(Torch REQUIRED)
+include_directories(${TORCH_INCLUDE_DIRS})
+add_library(mdlp CPPFImdlp.cpp Metrics.cpp BinDisc.cpp Discretizer.cpp)
+target_link_libraries(mdlp "${TORCH_LIBRARIES}")
 add_subdirectory(sample)
-add_subdirectory(tests)
-
+if (ENABLE_TESTING)
+    add_subdirectory(tests)
+endif(ENABLE_TESTING)

CPPFImdlp.cpp

@@ -25,7 +25,7 @@ namespace mdlp {
         }
         if (proposed_cuts < 1)
             return static_cast<size_t>(round(static_cast<float>(X.size()) * proposed_cuts));
-        return static_cast<size_t>(proposed_cuts);
+        return static_cast<size_t>(proposed_cuts); // The 2 extra cutpoints should not be considered here as this parameter is considered before they are added
     }
 
     void CPPFImdlp::fit(samples_t& X_, labels_t& y_)
@@ -58,6 +58,10 @@ namespace mdlp {
                 resizeCutPoints();
             }
         }
+        // Insert first & last X value to the cutpoints as them shall be ignored in transform
+        auto minmax = std::minmax_element(X.begin(), X.end());
+        cutPoints.push_back(*minmax.second);
+        cutPoints.insert(cutPoints.begin(), *minmax.first);
     }
 
     pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)

Discretizer.cpp

@@ -0,0 +1,51 @@
+#include "Discretizer.h"
+
+namespace mdlp {
+    labels_t& Discretizer::transform(const samples_t& data)
+    {
+        discretizedData.clear();
+        discretizedData.reserve(data.size());
+        // CutPoints always have more than two items
+        // Have to ignore first and last cut points provided
+        auto first = cutPoints.begin() + 1;
+        auto last = cutPoints.end() - 1;
+        for (const precision_t& item : data) {
+            auto upper = std::lower_bound(first, last, item);
+            int number = upper - first;
+            /*
+            OJO
+            */
+            if (number < 0)
+                throw std::runtime_error("number is less than 0 in discretizer::transform");
+            discretizedData.push_back(number);
+        }
+        return discretizedData;
+    }
+    labels_t& Discretizer::fit_transform(samples_t& X_, labels_t& y_)
+    {
+        fit(X_, y_);
+        return transform(X_);
+    }
+    void Discretizer::fit_t(torch::Tensor& X_, torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        fit(X, y);
+    }
+    torch::Tensor Discretizer::transform_t(torch::Tensor& X_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<float>(), X_.data_ptr<float>() + num_elements);
+        auto result = transform(X);
+        return torch::tensor(result, torch::kInt32);
+    }
+    torch::Tensor Discretizer::fit_transform_t(torch::Tensor& X_, torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        auto result = fit_transform(X, y);
+        return torch::tensor(result, torch::kInt32);
+    }
+}

Discretizer.h

@@ -3,6 +3,7 @@
 
 #include <string>
 #include <algorithm>
+#include <torch/torch.h>
 #include "typesFImdlp.h"
 
 namespace mdlp {
@@ -10,22 +11,17 @@ namespace mdlp {
     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"; };
+        virtual void fit(samples_t& X_, labels_t& y_) = 0;
+        labels_t& transform(const samples_t& data);
+        labels_t& fit_transform(samples_t& X_, labels_t& y_);
+        void fit_t(torch::Tensor& X_, torch::Tensor& y_);
+        torch::Tensor transform_t(torch::Tensor& X_);
+        torch::Tensor fit_transform_t(torch::Tensor& X_, torch::Tensor& y_);
+        static inline std::string version() { return "1.2.3"; };
     protected:
         labels_t discretizedData = labels_t();
-        cutPoints_t cutPoints;
+        cutPoints_t cutPoints; // At least two cutpoints must be provided, the first and the last will be ignored in transform
     };
 }
 #endif

Makefile

@@ -0,0 +1,13 @@
+SHELL := /bin/bash
+.DEFAULT_GOAL := build
+.PHONY: build test
+
+build: 
+	@if [ -d build_release ]; then rm -fr build_release; fi
+	@mkdir build_release
+	@cmake -B build_release -S . -DCMAKE_BUILD_TYPE=Release -DENABLE_TESTING=OFF
+	@cmake --build build_release
+
+test:
+	@echo "Testing..."
+	@cd tests && ./test

Metrics.cpp

@@ -4,8 +4,8 @@
 
 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()))
     {
     }
 

README.md

@@ -14,21 +14,27 @@ The implementation tries to mitigate the problem of different label values with
 Other features:
 
 - Intervals with the same value of the variable are not taken into account for cutpoints.
-- Intervals have to have more than two examples to be evaluated.
+- Intervals have to have more than two examples to be evaluated (mdlp).
 
-The algorithm returns the cut points for the variable.
+- The algorithm returns the cut points for the variable.
+
+- The transform method uses the cut points returning its index in the following way:
+
+        cut[i - 1] <= x < cut[i]
+
+    using the [std::upper_bound](https://en.cppreference.com/w/cpp/algorithm/upper_bound) method
+
+- K-Bins discretization is also implemented, and "quantile" and "uniform" strategies are available.
 
 ## Sample
 
 To run the sample, just execute the following commands:
 
 ```bash
-cd sample
-cmake -B build
-cd build
-make
-./sample -f iris -m 2
-./sample -h
+cmake -B build -S .
+cmake --build build
+build/sample/sample -f iris -m 2
+build/sample/sample -h
 ```
 
 ## Test
@@ -36,6 +42,5 @@ make
 To run the tests and see coverage (llvm & gcovr have to be installed), execute the following commands:
 
 ```bash
-cd tests
-./test
+make test
 ```

sample/.vscode/launch.json

@@ -1,21 +0,0 @@
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "lldb puro",
-            "type": "cppdbg",
-            // "targetArchitecture": "arm64",
-            "request": "launch",
-            "program": "${workspaceRoot}/build/sample",
-            "args": [
-                "-f",
-                "iris"
-            ],
-            "stopAtEntry": false,
-            "cwd": "${workspaceRoot}/build/",
-            "environment": [],
-            "externalConsole": false,
-            "MIMode": "lldb"
-        },
-    ]
-}

sample/CMakeLists.txt

@@ -1,5 +1,6 @@
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 17)
 
 set(CMAKE_BUILD_TYPE Debug)
 
-add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)
+add_executable(sample sample.cpp ../tests/ArffFiles.cpp)
+target_link_libraries(sample mdlp "${TORCH_LIBRARIES}")

sample/sample.cpp

@@ -5,13 +5,13 @@
 #include <algorithm>
 #include <cstring>
 #include <getopt.h>
+#include <torch/torch.h>
+#include "../Discretizer.h"
 #include "../CPPFImdlp.h"
+#include "../BinDisc.h"
 #include "../tests/ArffFiles.h"
 
-using namespace std;
-using namespace mdlp;
-
-const string PATH = "../../tests/datasets/";
+const string PATH = "tests/datasets/";
 
 /* print a description of all supported options */
 void usage(const char* path)
@@ -20,17 +20,17 @@ void usage(const char* 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
+    std::cout << "usage: " << basename << "[OPTION]" << std::endl;
+    std::cout << "  -h, --help\t\t Print this help and exit." << std::endl;
+    std::cout
         << "  -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
+        << std::endl;
+    std::cout << "  -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << std::endl;
+    std::cout << "  -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << std::endl;
+    std::cout
         << "  -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;
+        << std::endl;
+    std::cout << "  -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << std::endl;
 }
 
 tuple<string, string, int, int, float> parse_arguments(int argc, char** argv)
@@ -96,56 +96,79 @@ void process_file(const string& path, const string& file_name, bool class_last,
     file.load(path + file_name + ".arff", class_last);
     const auto attributes = file.getAttributes();
     const auto items = file.getSize();
-    cout << "Number of lines: " << items << endl;
-    cout << "Attributes: " << endl;
+    std::cout << "Number of lines: " << items << std::endl;
+    std::cout << "Attributes: " << std::endl;
     for (auto attribute : attributes) {
-        cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
+        std::cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << std::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();
+    std::cout << "Class name: " << file.getClassName() << std::endl;
+    std::cout << "Class type: " << file.getClassType() << std::endl;
+    std::cout << "Data: " << std::endl;
+    std::vector<mdlp::samples_t>& X = file.getX();
+    mdlp::labels_t& y = file.getY();
     for (int i = 0; i < 5; i++) {
         for (auto feature : X) {
-            cout << fixed << setprecision(1) << feature[i] << " ";
+            std::cout << fixed << setprecision(1) << feature[i] << " ";
         }
-        cout << y[i] << endl;
+        std::cout << y[i] << std::endl;
     }
     auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
     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 feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
+        std::cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
         test.fit(X[i], y);
         auto cut_points = test.getCutPoints();
         for (auto item : cut_points) {
-            cout << item;
+            std::cout << item;
             if (item != cut_points.back())
-                cout << ", ";
+                std::cout << ", ";
         }
         total += test.getCutPoints().size();
-        cout << "]" << endl;
-        cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
-        cout << "--------------------------" << endl;
+        std::cout << "]" << std::endl;
+        std::cout << "Min: " << *min_max.first << " Max: " << *min_max.second << std::endl;
+        std::cout << "--------------------------" << std::endl;
+    }
+    std::cout << "Total cut points ...: " << total << std::endl;
+    std::cout << "Total feature states: " << total + attributes.size() << std::endl;
+    std::cout << "Version ............: " << test.version() << std::endl;
+    std::cout << "Transformed data (vector)..: " << std::endl;
+    test.fit(X[0], y);
+    auto data = test.transform(X[0]);
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << X[0][i] << " " << data[i] << std::endl;
+    }
+    auto Xt = torch::tensor(X[0], torch::kFloat32);
+    auto yt = torch::tensor(y, torch::kInt32);
+    //test.fit_t(Xt, yt);
+    auto result = test.fit_transform_t(Xt, yt);
+    std::cout << "Transformed data (torch)...: " << std::endl;
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << result[i].item<int>() << std::endl;
+    }
+    auto disc = mdlp::BinDisc(3);
+    auto res_v = disc.fit_transform(X[0], y);
+    disc.fit_t(Xt, yt);
+    auto res_t = disc.transform_t(Xt);
+    std::cout << "Transformed data (BinDisc)...: " << std::endl;
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::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)
 {
-    cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
-        << max_cutpoints << endl << endl;
+    std::cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
+        << max_cutpoints << std::endl << std::endl;
     printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
     printf("==================== ==== ==== ========\n");
     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();
+        std::vector<mdlp::samples_t>& X = file.getX();
+        mdlp::labels_t& y = file.getY();
         size_t timing = 0;
         size_t cut_points = 0;
         for (auto i = 0; i < attributes.size(); i++) {
@@ -163,7 +186,7 @@ void process_all_files(const map<string, bool>& datasets, const string& path, in
 
 int main(int argc, char** argv)
 {
-    map<string, bool> datasets = {
+    std::map<std::string, bool> datasets = {
             {"diabetes",           true},
             {"glass",              true},
             {"iris",               true},
@@ -173,14 +196,14 @@ int main(int argc, char** argv)
             {"mfeat-factors",      true},
             {"test",               true}
     };
-    string file_name;
-    string path;
+    std::string file_name;
+    std::string path;
     int max_depth;
     int min_length;
     float max_cutpoints;
     tie(file_name, path, max_depth, min_length, max_cutpoints) = parse_arguments(argc, argv);
     if (datasets.find(file_name) == datasets.end() && file_name != "all") {
-        cout << "Invalid file name: " << file_name << endl;
+        std::cout << "Invalid file name: " << file_name << std::endl;
         usage(argv[0]);
         exit(1);
     }
@@ -188,10 +211,10 @@ int main(int argc, char** argv)
         process_all_files(datasets, path, max_depth, min_length, max_cutpoints);
     else {
         process_file(path, file_name, datasets[file_name], max_depth, min_length, max_cutpoints);
-        cout << "File name ....: " << file_name << endl;
-        cout << "Max depth ....: " << max_depth << endl;
-        cout << "Min length ...: " << min_length << endl;
-        cout << "Max cutpoints : " << max_cutpoints << endl;
+        std::cout << "File name ....: " << file_name << std::endl;
+        std::cout << "Max depth ....: " << max_depth << std::endl;
+        std::cout << "Min length ...: " << min_length << std::endl;
+        std::cout << "Max cutpoints : " << max_cutpoints << std::endl;
     }
     return 0;
 }

sonar-project.properties

@@ -3,7 +3,7 @@ sonar.organization=rmontanana
 
 # This is the name and version displayed in the SonarCloud UI.
 sonar.projectName=mdlp
-sonar.projectVersion=1.1.3
+sonar.projectVersion=1.2.1
 # sonar.test.exclusions=tests/**
 # sonar.tests=tests/
 # sonar.coverage.exclusions=tests/**,sample/**
@@ -11,4 +11,4 @@ sonar.projectVersion=1.1.3
 #sonar.sources=.
 
 # Encoding of the source code. Default is default system encoding
-sonar.sourceEncoding=UTF-8
+sonar.sourceEncoding=UTF-8

tests/BinDisc_unittest.cpp

@@ -4,6 +4,7 @@
 #include "gtest/gtest.h"
 #include "ArffFiles.h"
 #include "../BinDisc.h"
+#include "Experiments.hpp"
 
 namespace mdlp {
     const float margin = 1e-4;
@@ -40,10 +41,11 @@ namespace mdlp {
         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));
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(3.66667, cuts.at(1), margin);
+        EXPECT_NEAR(6.33333, cuts.at(2), margin);
+        EXPECT_NEAR(9.0, cuts.at(3), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -53,10 +55,11 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts[0], margin);
+        EXPECT_NEAR(3.666667, cuts[1], margin);
+        EXPECT_NEAR(6.333333, cuts[2], margin);
+        EXPECT_NEAR(9, cuts[3], margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -66,12 +69,13 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(4.0, cuts.at(1), margin);
+        EXPECT_NEAR(7.0, cuts.at(2), margin);
+        EXPECT_NEAR(10.0, cuts.at(3), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc3Q, X10BinsQuantile)
@@ -79,12 +83,13 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(4.0, cuts.at(1), margin);
+        EXPECT_NEAR(7.0, cuts.at(2), margin);
+        EXPECT_NEAR(10.0, cuts.at(3), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc3U, X11BinsUniform)
@@ -92,10 +97,11 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(4.33333, cuts.at(1), margin);
+        EXPECT_NEAR(7.66667, cuts.at(2), margin);
+        EXPECT_NEAR(11.0, cuts.at(3), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -105,10 +111,11 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(4.33333, cuts.at(1), margin);
+        EXPECT_NEAR(7.66667, cuts.at(2), margin);
+        EXPECT_NEAR(11.0, cuts.at(3), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
         EXPECT_EQ(expected, labels);
@@ -118,8 +125,9 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(2, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(1, cuts.at(1), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -129,8 +137,9 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(2, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(1, cuts.at(1), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 0, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -140,16 +149,18 @@ namespace mdlp {
         samples_t X = {};
         fit(X);
         auto cuts = getCutPoints();
-        EXPECT_EQ(1, cuts.size());
-        EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
+        ASSERT_EQ(2, cuts.size());
+        EXPECT_NEAR(0, cuts.at(0), margin);
+        EXPECT_NEAR(0, cuts.at(1), margin);
     }
     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]);
+        ASSERT_EQ(2, cuts.size());
+        EXPECT_NEAR(0, cuts.at(0), margin);
+        EXPECT_NEAR(0, cuts.at(1), margin);
     }
     TEST(TestBinDisc3, ExceptionNumberBins)
     {
@@ -160,10 +171,11 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(4, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(1.66667, cuts.at(1), margin);
+        EXPECT_NEAR(2.33333, cuts.at(2), margin);
+        EXPECT_NEAR(3.0, cuts.at(3), margin);
         auto labels = transform(X);
         labels_t expected = { 2, 0, 0, 2, 0, 0, 2, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -174,9 +186,10 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(3, cuts.size());
+        EXPECT_NEAR(1, cuts.at(0), margin);
+        EXPECT_NEAR(1.66667, cuts.at(1), margin);
+        EXPECT_NEAR(3.0, cuts.at(2), margin);
         auto labels = transform(X);
         labels_t expected = { 1, 0, 0, 1, 0, 0, 1, 0, 0 };
         EXPECT_EQ(expected, labels);
@@ -187,11 +200,12 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(3.75, cuts.at(1), margin);
+        EXPECT_NEAR(6.5, cuts.at(2), margin);
+        EXPECT_NEAR(9.25, cuts.at(3), margin);
+        EXPECT_NEAR(12.0, cuts.at(4), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -201,11 +215,12 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(3.75, cuts.at(1), margin);
+        EXPECT_NEAR(6.5, cuts.at(2), margin);
+        EXPECT_NEAR(9.25, cuts.at(3), margin);
+        EXPECT_NEAR(12.0, cuts.at(4), margin);
         auto labels = transform(X);
         labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
@@ -215,13 +230,14 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.0, cuts.at(1), margin);
+        EXPECT_NEAR(7.0, cuts.at(2), margin);
+        EXPECT_NEAR(10.0, cuts.at(3), margin);
+        EXPECT_NEAR(13.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, X13BinsQuantile)
@@ -229,13 +245,14 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.0, cuts.at(1), margin);
+        EXPECT_NEAR(7.0, cuts.at(2), margin);
+        EXPECT_NEAR(10.0, cuts.at(3), margin);
+        EXPECT_NEAR(13.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4U, X14BinsUniform)
@@ -243,11 +260,12 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.25, cuts.at(1), margin);
+        EXPECT_NEAR(7.5, cuts.at(2), margin);
+        EXPECT_NEAR(10.75, cuts.at(3), margin);
+        EXPECT_NEAR(14.0, cuts.at(4), margin);
         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);
@@ -257,11 +275,12 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.25, cuts.at(1), margin);
+        EXPECT_NEAR(7.5, cuts.at(2), margin);
+        EXPECT_NEAR(10.75, cuts.at(3), margin);
+        EXPECT_NEAR(14.0, cuts.at(4), margin);
         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);
@@ -271,13 +290,14 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.5, cuts.at(1), margin);
+        EXPECT_NEAR(8, cuts.at(2), margin);
+        EXPECT_NEAR(11.5, cuts.at(3), margin);
+        EXPECT_NEAR(15.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 3, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
+        labels_t expected = { 3, 1, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, X15BinsQuantile)
@@ -285,13 +305,14 @@ namespace mdlp {
         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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(1.0, cuts.at(0), margin);
+        EXPECT_NEAR(4.5, cuts.at(1), margin);
+        EXPECT_NEAR(8, cuts.at(2), margin);
+        EXPECT_NEAR(11.5, cuts.at(3), margin);
+        EXPECT_NEAR(15.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 3, 3, 3, 3, 1, 0, 2, 2, 2, 2, 1, 0, 0, 1, 0 };
+        labels_t expected = { 3, 3, 3, 3, 1, 0, 1, 2, 2, 2, 1, 0, 0, 1, 0 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4U, RepeatedValuesUniform)
@@ -300,13 +321,14 @@ namespace mdlp {
         //               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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(0.0, cuts.at(0), margin);
+        EXPECT_NEAR(1.0, cuts.at(1), margin);
+        EXPECT_NEAR(2.0, cuts.at(2), margin);
+        EXPECT_NEAR(3.0, cuts.at(3), margin);
+        EXPECT_NEAR(4.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
@@ -315,12 +337,14 @@ namespace mdlp {
         //               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]);
+        ASSERT_EQ(5, cuts.size());
+        EXPECT_NEAR(0.0, cuts.at(0), margin);
+        EXPECT_NEAR(1.0, cuts.at(1), margin);
+        EXPECT_NEAR(2.0, cuts.at(2), margin);
+        EXPECT_NEAR(3.0, cuts.at(3), margin);
+        EXPECT_NEAR(4.0, cuts.at(4), margin);
         auto labels = transform(X);
-        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 2 };
+        labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
         EXPECT_EQ(expected, labels);
     }
     TEST_F(TestBinDisc4U, irisUniform)
@@ -332,6 +356,13 @@ namespace mdlp {
         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);
+        auto Xtt = fit_transform(X[0], file.getY());
+        EXPECT_EQ(expected, Xtt);
+        auto Xt_t = torch::tensor(X[0], torch::kFloat32);
+        auto y_t = torch::tensor(file.getY(), torch::kInt32);
+        auto Xtt_t = fit_transform_t(Xt_t, y_t);
+        for (int i = 0; i < expected.size(); i++)
+            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
     }
     TEST_F(TestBinDisc4Q, irisQuantile)
     {
@@ -342,5 +373,44 @@ namespace mdlp {
         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);
+        auto Xtt = fit_transform(X[0], file.getY());
+        EXPECT_EQ(expected, Xtt);
+        auto Xt_t = torch::tensor(X[0], torch::kFloat32);
+        auto y_t = torch::tensor(file.getY(), torch::kInt32);
+        auto Xtt_t = fit_transform_t(Xt_t, y_t);
+        for (int i = 0; i < expected.size(); i++)
+            EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
+        fit_t(Xt_t, y_t);
+        auto Xt_t2 = transform_t(Xt_t);
+        for (int i = 0; i < expected.size(); i++)
+            EXPECT_EQ(expected[i], Xt_t2[i].item<int>());
+    }
+    TEST(TestBinDiscGeneric, Fileset)
+    {
+        Experiments exps(data_path + "tests.txt");
+        int num = 0;
+        while (exps.is_next()) {
+            Experiment exp = exps.next();
+            std::cout << "Exp #: " << ++num << " From: " << exp.from_ << " To: " << exp.to_ << " Step: " << exp.step_ << " Bins: " << exp.n_bins_ << " Strategy: " << exp.strategy_ << std::endl;
+            BinDisc disc(exp.n_bins_, exp.strategy_ == "Q" ? strategy_t::QUANTILE : strategy_t::UNIFORM);
+            std::vector<float> test;
+            for (float i = exp.from_; i < exp.to_; i += exp.step_) {
+                test.push_back(i);
+            }
+            // show_vector(test, "Test");
+            auto empty = std::vector<int>();
+            auto Xt = disc.fit_transform(test, empty);
+            auto cuts = disc.getCutPoints();
+            EXPECT_EQ(exp.discretized_data_.size(), Xt.size());
+            for (int i = 0; i < exp.discretized_data_.size(); ++i) {
+                if (exp.discretized_data_.at(i) != Xt.at(i)) {
+                    std::cout << "Error at " << i << " Expected: " << exp.discretized_data_.at(i) << " Got: " << Xt.at(i) << std::endl;
+                }
+            }
+            EXPECT_EQ(exp.cutpoints_.size(), cuts.size());
+            for (int i = 0; i < exp.cutpoints_.size(); ++i) {
+                EXPECT_NEAR(exp.cutpoints_.at(i), cuts.at(i), margin);
+            }
+        }
     }
 }

tests/CMakeLists.txt

@@ -1,10 +1,8 @@
 cmake_minimum_required(VERSION 3.20)
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 17)
+cmake_policy(SET CMP0135 NEW)
 include(FetchContent)
-
 include_directories(${GTEST_INCLUDE_DIRS})
-
-
 FetchContent_Declare(
         googletest
         URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
@@ -13,25 +11,29 @@ FetchContent_Declare(
 set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
 FetchContent_MakeAvailable(googletest)
 
+find_package(Torch REQUIRED)
+
 enable_testing()
 
+include_directories(${TORCH_INCLUDE_DIRS})
+
 add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
 target_link_libraries(Metrics_unittest GTest::gtest_main)
 target_compile_options(Metrics_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)
+add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp ../Discretizer.cpp)
+target_link_libraries(FImdlp_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 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)
+add_executable(BinDisc_unittest ../BinDisc.cpp ArffFiles.cpp BinDisc_unittest.cpp ../Discretizer.cpp)
+target_link_libraries(BinDisc_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 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)
+add_executable(Discretizer_unittest ../BinDisc.cpp ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp ../Discretizer.cpp Discretizer_unittest.cpp)
+target_link_libraries(Discretizer_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
 target_compile_options(Discretizer_unittest PRIVATE --coverage)
 target_link_options(Discretizer_unittest PRIVATE --coverage)
 

tests/Discretizer_unittest.cpp

@@ -21,6 +21,15 @@ namespace mdlp {
     }
     const std::string data_path = set_data_path();
 
+    TEST(Discretizer, Version)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        auto version = disc->version();
+        delete disc;
+        std::cout << "Version computed: " << version;
+        EXPECT_EQ("1.2.3", version);
+    }
+
     TEST(Discretizer, BinIrisUniform)
     {
         ArffFiles file;

tests/Experiments.hpp

@@ -0,0 +1,108 @@
+#ifndef EXPERIMENTS_HPP
+#define EXPERIMENTS_HPP
+#include<sstream>
+#include<iostream>
+#include<string>
+#include<fstream>
+#include<vector>
+#include<tuple>
+#include "../typesFImdlp.h"
+class Experiment {
+public:
+    Experiment(float from_, float to_, float step_, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<float> cutpoints) :
+        from_{ from_ }, to_{ to_ }, step_{ step_ }, n_bins_{ n_bins }, strategy_{ strategy }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }
+    {
+        if (strategy != "Q" && strategy != "U") {
+            throw std::invalid_argument("Invalid strategy " + strategy);
+        }
+    }
+    float from_;
+    float to_;
+    float step_;
+    int n_bins_;
+    std::string strategy_;
+    std::vector<int> discretized_data_;
+    std::vector<float> cutpoints_;
+};
+class Experiments {
+public:
+    Experiments(const std::string filename) : filename{ filename }
+    {
+        test_file.open(filename);
+        if (!test_file.is_open()) {
+            throw std::runtime_error("File " + filename + " not found");
+        }
+        exp_end = false;
+    }
+    ~Experiments()
+    {
+        test_file.close();
+    }
+    bool end() const
+    {
+        return exp_end;
+    }
+    bool is_next()
+    {
+        while (std::getline(test_file, line) && line[0] == '#');
+        if (test_file.eof()) {
+            exp_end = true;
+            return false;
+        }
+        return true;
+    }
+    Experiment next()
+    {
+        return parse_experiment(line);
+    }
+private:
+    std::tuple<float, float, float, int, std::string> parse_header(const std::string& line)
+    {
+        std::istringstream iss(line);
+        std::string from_, to_, step_, n_bins, strategy;
+        iss >> from_ >> to_ >> step_ >> n_bins >> strategy;
+        return { std::stof(from_), std::stof(to_), std::stof(step_), std::stoi(n_bins), strategy };
+    }
+    template <typename T>
+    std::vector<T> parse_vector(const std::string& line)
+    {
+        std::istringstream iss(line);
+        std::vector<T> data;
+        std::string d;
+        while (iss >> d) {
+            data.push_back(std::is_same<T, float>::value ? std::stof(d) : std::stoi(d));
+        }
+        return data;
+    }
+    Experiment parse_experiment(std::string& line)
+    {
+        if (line == "RANGE") {
+            std::getline(test_file, line);
+            auto [from_, to_, step_, n_bins, strategy] = parse_header(line);
+        } else {
+            std::getline(test_file, line);
+
+        }
+        std::getline(test_file, line);
+        auto data_discretized = parse_vector<int>(line);
+        std::getline(test_file, line);
+        auto cutpoints = parse_vector<float>(line);
+        return Experiment{ from_, to_, step_, n_bins, strategy, data_discretized, cutpoints };
+    }
+    std::ifstream test_file;
+    std::string filename;
+    std::string line;
+    bool exp_end;
+};
+template <typename T>
+void show_vector(const std::vector<T>& data, std::string title)
+{
+    std::cout << title << ": ";
+    std::string sep = "";
+    for (const auto& d : data) {
+        std::cout << sep << d;
+        sep = ", ";
+    }
+    std::cout << std::endl;
+}
+#endif

tests/FImdlp_unittest.cpp

@@ -124,7 +124,7 @@ namespace mdlp {
     {
         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 };
+        cutPoints_t expected = { 1.0, 1.5f, 2.5f, 4.0 };
         fit(X_, y_);
         auto computed = getCutPoints();
         EXPECT_EQ(computed.size(), expected.size());
@@ -167,29 +167,31 @@ namespace mdlp {
         y = { 1 };
         fit(X, y);
         computed = getCutPoints();
-        EXPECT_EQ(computed.size(), 0);
+        EXPECT_EQ(computed.size(), 2);
         X = { 1, 3 };
         y = { 1, 2 };
         fit(X, y);
         computed = getCutPoints();
-        EXPECT_EQ(computed.size(), 0);
+        EXPECT_EQ(computed.size(), 2);
         X = { 2, 4 };
         y = { 1, 2 };
         fit(X, y);
         computed = getCutPoints();
-        EXPECT_EQ(computed.size(), 0);
+        EXPECT_EQ(computed.size(), 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);
+        EXPECT_EQ(computed.size(), 3);
+        EXPECT_NEAR(computed[0], 1, precision);
+        EXPECT_NEAR(computed[1], 1.5, precision);
+        EXPECT_NEAR(computed[2], 3, precision);
     }
 
     TEST_F(TestFImdlp, TestArtificialDataset)
     {
         fit(X, y);
-        cutPoints_t expected = { 5.05f };
+        cutPoints_t expected = { 4.7, 5.05, 6.0 };
         vector<precision_t> computed = getCutPoints();
         EXPECT_EQ(computed.size(), expected.size());
         for (unsigned long i = 0; i < computed.size(); i++) {
@@ -200,10 +202,10 @@ namespace mdlp {
     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}
+                {4.3, 5.45f, 5.75f, 7.9},
+                {2, 2.75f, 2.85f, 2.95f, 3.05f, 3.35f, 4.4},
+                {1, 2.45f, 4.75f, 5.05f, 6.9},
+                {0.1, 0.8f,  1.75f, 2.5}
         };
         vector<int> depths = { 3, 5, 4, 3 };
         auto test = CPPFImdlp();
@@ -213,7 +215,7 @@ namespace mdlp {
     TEST_F(TestFImdlp, ComputeCutPointsGCase)
     {
         cutPoints_t expected;
-        expected = { 1.5 };
+        expected = { 0, 1.5, 2 };
         samples_t X_ = { 0, 1, 2, 2, 2 };
         labels_t y_ = { 1, 1, 1, 2, 2 };
         fit(X_, y_);
@@ -247,10 +249,10 @@ namespace mdlp {
         // Set max_depth to 1
         auto test = CPPFImdlp(3, 1, 0);
         vector<cutPoints_t> expected = {
-                {5.45f},
-                {3.35f},
-                {2.45f},
-                {0.8f}
+                {4.3, 5.45f, 7.9},
+                {2, 3.35f, 4.4},
+                {1, 2.45f, 6.9},
+                {0.1, 0.8f, 2.5}
         };
         vector<int> depths = { 1, 1, 1, 1 };
         test_dataset(test, "iris", expected, depths);
@@ -261,10 +263,10 @@ namespace mdlp {
         auto test = CPPFImdlp(75, 100, 0);
         // Set min_length to 75
         vector<cutPoints_t> expected = {
-                {5.45f, 5.75f},
-                {2.85f, 3.35f},
-                {2.45f, 4.75f},
-                {0.8f,  1.75f}
+                {4.3, 5.45f, 5.75f, 7.9},
+                {2, 2.85f, 3.35f, 4.4},
+                {1, 2.45f, 4.75f, 6.9},
+                {0.1, 0.8f,  1.75f, 2.5}
         };
         vector<int> depths = { 3, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
@@ -275,10 +277,10 @@ namespace mdlp {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 0);
         vector<cutPoints_t> expected = {
-                {5.45f, 5.75f},
-                {2.85f, 3.35f},
-                {2.45f, 4.75f},
-                {0.8f,  1.75f}
+                {4.3, 5.45f, 5.75f, 7.9},
+                {2, 2.85f, 3.35f, 4.4},
+                {1, 2.45f, 4.75f, 6.9},
+                {0.1, 0.8f,  1.75f, 2.5}
         };
         vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
@@ -289,10 +291,10 @@ namespace mdlp {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 1);
         vector<cutPoints_t> expected = {
-                {5.45f},
-                {2.85f},
-                {2.45f},
-                {0.8f}
+                {4.3, 5.45f, 7.9},
+                {2, 2.85f, 4.4},
+                {1, 2.45f, 6.9},
+                {0.1, 0.8f, 2.5}
         };
         vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
@@ -304,10 +306,10 @@ namespace mdlp {
         // Set min_length to 75
         auto test = CPPFImdlp(75, 2, 0.2f);
         vector<cutPoints_t> expected = {
-                {5.45f, 5.75f},
-                {2.85f, 3.35f},
-                {2.45f, 4.75f},
-                {0.8f,  1.75f}
+                {4.3, 5.45f, 5.75f, 7.9},
+                {2, 2.85f, 3.35f, 4.4},
+                {1, 2.45f, 4.75f, 6.9},
+                {0.1, 0.8f,  1.75f, 2.5}
         };
         vector<int> depths = { 2, 2, 2, 2 };
         test_dataset(test, "iris", expected, depths);
@@ -327,7 +329,6 @@ namespace mdlp {
             computed = compute_max_num_cut_points();
             ASSERT_EQ(expected, computed);
         }
-
     }
     TEST_F(TestFImdlp, TransformTest)
     {
@@ -350,5 +351,10 @@ namespace mdlp {
         for (unsigned long i = 0; i < computed.size(); i++) {
             EXPECT_EQ(computed[i], expected[i]);
         }
+        auto computed_ft = fit_transform(X[1], y);
+        EXPECT_EQ(computed_ft.size(), expected.size());
+        for (unsigned long i = 0; i < computed_ft.size(); i++) {
+            EXPECT_EQ(computed_ft[i], expected[i]);
+        }
     }
 }

tests/Metrics_unittest.cpp

@@ -2,13 +2,13 @@
 #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 };
-        precision_t precision = 0.000001f;
+        precision_t precision = 1e-6;
 
-        TestMetrics(): Metrics(y_, indices_) {};
+        TestMetrics() : Metrics(y_, indices_) {};
 
         void SetUp() override
         {

tests/datasets/tests.txt

@@ -0,0 +1,149 @@
+#
+# from, to, step, #bins, Q/U
+# discretized data
+# cut points
+#
+RANGE
+0, 100, 1, 4, Q
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.0, 24.75, 49.5, 74.25, 99.0
+RANGE
+0, 50, 1, 4, Q
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.0, 12.25, 24.5, 36.75, 49.0
+RANGE
+0, 100, 1, 3, Q
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.0, 33.0, 66.0, 99.0
+RANGE
+0, 50, 1, 3, Q
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.0, 16.33333, 32.66667, 49.0
+RANGE
+0, 10, 1, 3, Q
+0, 0, 0, 0, 1, 1, 1, 2, 2, 2
+0.0, 3.0, 6.0, 9.0
+RANGE
+0, 100, 1, 4, U
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.0, 24.75, 49.5, 74.25, 99.0
+RANGE
+0, 50, 1, 4, U
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.0, 12.25, 24.5, 36.75, 49.0
+RANGE
+0, 100, 1, 3, U
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.0, 33.0, 66.0, 99.0
+RANGE
+0, 50, 1, 3, U
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.0, 16.33333, 32.66667, 49.0
+RANGE
+0, 10, 1, 3, U
+0, 0, 0, 1, 1, 1, 2, 2, 2, 2
+0.0, 3.0, 6.0, 9.0
+RANGE
+1, 10, 1, 3, Q
+0, 0, 0, 1, 1, 1, 2, 2, 2
+1.0, 3.66667, 6.33333, 9.0
+RANGE
+1, 10, 1, 3, U
+0, 0, 0, 1, 1, 1, 2, 2, 2
+1.0, 3.66667, 6.33333, 9.0
+RANGE
+1, 11, 1, 3, Q
+0, 0, 0, 1, 1, 1, 1, 2, 2, 2
+1.0, 4.0, 7.0, 10.0
+RANGE
+1, 11, 1, 3, U
+0, 0, 0, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.0, 7.0, 10.0
+RANGE
+1, 12, 1, 3, Q
+0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.33333, 7.66667, 11.0
+RANGE
+1, 12, 1, 3, U
+0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.33333, 7.66667, 11.0
+RANGE
+1, 13, 1, 3, Q
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.66667, 8.33333, 12.0
+RANGE
+1, 13, 1, 3, U
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.66667, 8.33333, 12.0
+RANGE
+1, 14, 1, 3, Q
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.0, 9.0, 13.0
+RANGE
+1, 14, 1, 3, U
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.0, 9.0, 13.0
+RANGE
+1, 15, 1, 3, Q
+0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.33333, 9.66667, 14.0
+RANGE
+1, 15, 1, 3, U
+0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.33333, 9.66667, 14.0
+VECTOR
+Q3[3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]
+1, 0, 0, 1, 0, 0, 1, 0, 0
+1.0, 1.66667, 3.0
+VECTOR
+U3[3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]
+2, 0, 0, 2, 0, 0, 2, 0, 0
+1.0, 1.66667, 2.33333, 3.0
+VECTOR
+Q3[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.66667, 8.33333, 12.0
+VECTOR
+U3[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
+1.0, 4.66667, 8.33333, 12.0
+VECTOR
+Q3[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]
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.0, 9.0, 13.0
+VECTOR
+U3[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]
+0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.0, 9.0, 13.0
+VECTOR
+Q3[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]
+0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.33333, 9.66667, 14.0
+VECTOR
+U3[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]
+0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.33333, 9.66667, 14.0
+VECTOR
+Q3[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, 15.0]
+0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.66667, 10.33333, 15.0
+VECTOR
+U3[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, 15.0]
+0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2
+1.0, 5.66667, 10.33333, 15.0
+VECTOR
+Q3[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]
+2, 1, 2, 2, 1, 0, 2, 2, 1, 1, 1, 0, 0, 0, 0
+1.0, 5.66667, 10.33333, 15.0
+VECTOR
+U3[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]
+2, 1, 2, 2, 1, 0, 2, 2, 1, 1, 1, 0, 0, 0, 0
+1.0, 5.66667, 10.33333, 15.0
+VECTOR
+Q3[0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
+0, 0, 0, 0, 1, 1, 2, 2, 2, 2
+0.0, 1.0, 3.0, 4.0
+VECTOR
+U3[0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0]
+0, 0, 0, 0, 1, 1, 2, 2, 2, 2
+0.0, 1.33333, 2.66667, 4.0

tests/k.cpp

@@ -0,0 +1,32 @@
+#include <iostream>
+#include <vector>
+#include <algorithm> // For std::lower_bound
+
+std::vector<int> searchsorted(const std::vector<float>& cuts, const std::vector<float>& data) {
+    std::vector<int> indices;
+    indices.reserve(data.size());
+
+    for (const float& value : data) {
+        // Find the first position in 'a' where 'value' could be inserted to maintain order
+        auto it = std::lower_bound(cuts.begin(), cuts.end(), value);
+        // Calculate the index
+        int index = it - cuts.begin();
+        indices.push_back(index);
+    }
+
+    return indices;
+}
+
+int main() {
+    std::vector<float> cuts = { 10.0 };
+    std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+
+    std::vector<int> result = searchsorted(cuts, data);
+
+    for (int idx : result) {
+        std::cout << idx << " ";
+    }
+
+    return 0;
+}
+

tests/t.cpp

@@ -0,0 +1,102 @@
+#include <iostream>
+#include <algorithm>
+#include <cmath>
+#include <vector>
+#include <string>
+
+typedef float precision_t;
+
+std::vector<int> transform(const std::vector<float> cutPoints, const std::vector<float>& data)
+{
+    std::vector<int> discretizedData;
+    discretizedData.reserve(data.size());
+    for (const float& item : data) {
+        auto upper = std::lower_bound(cutPoints.begin(), cutPoints.end(), item);
+        discretizedData.push_back(upper - cutPoints.begin());
+    }
+    return discretizedData;
+}
+template <typename T>
+void show_vector(const std::vector<T>& data, std::string title)
+{
+    std::cout << title << ": ";
+    std::string sep = "";
+    for (const auto& d : data) {
+        std::cout << sep << d;
+        sep = ", ";
+    }
+    std::cout << std::endl;
+}
+std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
+{
+    if (start == end) {
+        return { start, end };
+    }
+    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(std::vector<precision_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() - 2);
+        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;
+}
+int main()
+{
+    // std::vector<float> test;
+    // std::vector<float> cuts = { 0, 24.75, 49.5, 74.25, 10000 };
+    // for (int i = 0; i < 100; ++i) {
+    //     test.push_back(i);
+    // }
+    // auto Xt = transform(cuts, test);
+    // show_vector(Xt, "Discretized data:");
+    // std::vector<float> test2 = { 0,1,2,3,4,5,6,7,8,9,10,11 };
+    // std::vector<float> cuts2 = { 0,1,2,3,4,5,6,7,8,9 };
+    // auto Xt2 = transform(cuts2, test2);
+    // show_vector(Xt2, "discretized data2: ");
+    auto quantiles = linspace(0.0, 100.0, 3 + 1);
+    std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
+    std::vector<float> cutPoints;
+    std::sort(data.begin(), data.end());
+    cutPoints = percentile(data, quantiles);
+    cutPoints.push_back(std::numeric_limits<precision_t>::max());
+    data.push_back(15);
+    data.push_back(0);
+    cutPoints.pop_back();
+    cutPoints.erase(cutPoints.begin());
+    cutPoints.clear();
+    cutPoints.push_back(9.0);
+    auto Xt = transform(cutPoints, data);
+    show_vector(data, "Original data");
+    show_vector(Xt, "Discretized data");
+    show_vector(cutPoints, "Cutpoints");
+    return 0;
+}
+/*
+n_bins = 3
+data = [1,2,3,4,5,6,7,8,9,10]
+quantiles = np.linspace(0, 100, n_bins + 1)
+bin_edges = np.percentile(data, quantiles)
+
+*/

tests/test

@@ -1,5 +1,5 @@
 #!/bin/bash
-if [ -d build ] ; then
+if [ -d build ] && [ "$1" != "run" ]; then
 	rm -fr build
 fi
 if [ -d gcovr-report ] ; then
@@ -11,8 +11,5 @@ cd build
 ctest --output-on-failure
 cd ..
 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" --gcov-filter "BinDisc.cpp" --gcov-filter "Discretizer.h" --txt --sonarqube=tests/gcovr-report/coverage.xml --exclude-noncode-lines
+gcovr  --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --gcov-filter "BinDisc.cpp" --gcov-filter "Discretizer.cpp" --txt --sonarqube=tests/gcovr-report/coverage.xml --exclude-noncode-lines

tests/tests_do.py

@@ -0,0 +1,50 @@
+import json
+from sklearn.preprocessing import KBinsDiscretizer
+
+with open("datasets/tests.txt") as f:
+    data = f.readlines()
+
+data = [x.strip() for x in data if x[0] != "#"]
+
+for i in range(0, len(data), 4):
+    experiment_type = data[i]
+    print("Experiment:", data[i + 1])
+    if experiment_type == "RANGE":
+        range_data = data[i + 1]
+        from_, to_, step_, n_bins_, strategy_ = range_data.split(",")
+        X = [[float(x)] for x in range(int(from_), int(to_), int(step_))]
+    else:
+        strategy_ = data[i + 1][0]
+        n_bins_ = data[i + 1][1]
+        vector = data[i + 1][2:]
+        X = [[float(x)] for x in json.loads(vector)]
+
+    strategy = "quantile" if strategy_.strip() == "Q" else "uniform"
+    disc = KBinsDiscretizer(
+        n_bins=int(n_bins_),
+        encode="ordinal",
+        strategy=strategy,
+    )
+    expected_data = data[i + 2]
+    cuts_data = data[i + 3]
+    disc.fit(X)
+    result = disc.transform(X)
+    result = [int(x) for x in result.flatten()]
+    expected = [int(x) for x in expected_data.split(",")]
+    assert len(result) == len(expected)
+    for j in range(len(result)):
+        if result[j] != expected[j]:
+            print("Error at", j, "Expected=", expected[j], "Result=", result[j])
+    expected_cuts = disc.bin_edges_[0]
+    computed_cuts = [float(x) for x in cuts_data.split(",")]
+    assert len(expected_cuts) == len(computed_cuts)
+    for j in range(len(expected_cuts)):
+        if round(expected_cuts[j], 5) != computed_cuts[j]:
+            print(
+                "Error at",
+                j,
+                "Expected=",
+                expected_cuts[j],
+                "Result=",
+                computed_cuts[j],
+            )

tests/tests_generate.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import KBinsDiscretizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiments_range = [\n",
+    "    [0, 100, 1, 4, \"Q\"],\n",
+    "    [0, 50, 1, 4, \"Q\"],\n",
+    "    [0, 100, 1, 3, \"Q\"],\n",
+    "    [0, 50, 1, 3, \"Q\"],\n",
+    "    [0, 10, 1, 3, \"Q\"],\n",
+    "    [0, 100, 1, 4, \"U\"],\n",
+    "    [0, 50, 1, 4, \"U\"],\n",
+    "    [0, 100, 1, 3, \"U\"],\n",
+    "    [0, 50, 1, 3, \"U\"],\n",
+    "# \n",
+    "    [0, 10, 1, 3, \"U\"],\n",
+    "    [1, 10, 1, 3, \"Q\"],\n",
+    "    [1, 10, 1, 3, \"U\"],\n",
+    "    [1, 11, 1, 3, \"Q\"],\n",
+    "    [1, 11, 1, 3, \"U\"],\n",
+    "    [1, 12, 1, 3, \"Q\"],\n",
+    "    [1, 12, 1, 3, \"U\"],\n",
+    "    [1, 13, 1, 3, \"Q\"],\n",
+    "    [1, 13, 1, 3, \"U\"],\n",
+    "    [1, 14, 1, 3, \"Q\"],\n",
+    "    [1, 14, 1, 3, \"U\"],\n",
+    "    [1, 15, 1, 3, \"Q\"],\n",
+    "    [1, 15, 1, 3, \"U\"]\n",
+    "]\n",
+    "experiments_vectors = [\n",
+    "    (3, [3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0]),\n",
+    "    (3, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]),\n",
+    "    (3, [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]),\n",
+    "    (3, [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]),\n",
+    "    (3, [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, 15.0]),\n",
+    "    (3, [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]),\n",
+    "    (3, [0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0])\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rmontanana/miniconda3/lib/python3.11/site-packages/sklearn/preprocessing/_discretization.py:307: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in feature 0 are removed. Consider decreasing the number of bins.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "def write_lists(file, data, cuts):\n",
+    "    sep = \"\"\n",
+    "    for res in data:\n",
+    "        file.write(f\"{sep}{int(res):d}\")\n",
+    "        sep= \", \"\n",
+    "    file.write(\"\\n\")\n",
+    "    sep = \"\"\n",
+    "    for res in cuts:\n",
+    "        file.write(sep + str(round(res,5)))\n",
+    "        sep = \", \"\n",
+    "    file.write(\"\\n\")\n",
+    "\n",
+    "with open(\"datasets/tests.txt\", \"w\") as file:\n",
+    "    file.write(\"#\\n\")\n",
+    "    file.write(\"# from, to, step, #bins, Q/U\\n\")\n",
+    "    file.write(\"# discretized data\\n\")\n",
+    "    file.write(\"# cut points\\n\")\n",
+    "    file.write(\"#\\n\")\n",
+    "    for experiment in experiments_range:\n",
+    "        file.write(\"RANGE\\n\")\n",
+    "        (from_, to_, step_, bins_, strategy) = experiment\n",
+    "        disc = KBinsDiscretizer(n_bins=bins_, encode='ordinal', strategy='quantile' if strategy.strip() == \"Q\" else 'uniform')\n",
+    "        data = [[x] for x in range(from_, to_, step_)]\n",
+    "        disc.fit(data)\n",
+    "        result = disc.transform(data)\n",
+    "        file.write(f\"{from_}, {to_}, {step_}, {bins_}, {strategy}\\n\")\n",
+    "        write_lists(file, result, disc.bin_edges_[0])\n",
+    "    for n_bins, experiment in experiments_vectors:\n",
+    "        for strategy in [\"Q\", \"U\"]:\n",
+    "            file.write(\"VECTOR\\n\")\n",
+    "            file.write(f\"{strategy}{n_bins}{experiment}\\n\")\n",
+    "            disc = KBinsDiscretizer(\n",
+    "                n_bins=n_bins,\n",
+    "                encode=\"ordinal\",\n",
+    "                \n",
+    "                strategy=\"quantile\" if strategy.strip() == \"Q\" else \"uniform\",\n",
+    "            )\n",
+    "            data = [[x] for x in experiment]\n",
+    "            result = disc.fit_transform(data)\n",
+    "            write_lists(file, result, disc.bin_edges_[0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}