Fix sonar github action

Fix coverage report
Add coverage badge to README

.github/workflows/build.yml

@@ -13,7 +13,7 @@ jobs:
     env:
       BUILD_WRAPPER_OUT_DIR: build_wrapper_output_directory # Directory where build-wrapper output will be placed
     steps:
-      - uses: actions/checkout@v4.1.6
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis
       - name: Install sonar-scanner and build-wrapper
@@ -28,17 +28,16 @@ jobs:
           unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
       - name: Tests & build-wrapper
         run: |
-          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DENABLE_TESTING=ON
-          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
+          cmake -S . -B build -Wno-dev -DCMAKE_PREFIX_PATH=$(pwd)/libtorch -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
+          build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Debug
+          cmake --build build -j 4
           cd build
-          make
-          ctest -C Release --output-on-failure --test-dir tests
-          cd ..
-          gcovr -f CPPFImdlp.cpp -f Metrics.cpp -f BinDisc.cpp -f Discretizer.cpp --txt --sonarqube=coverage.xml
+          ctest -C Debug --output-on-failure -j 4
+          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 }}
           SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
         run: |
           sonar-scanner --define sonar.cfamily.build-wrapper-output="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
-                        --define sonar.coverageReportPaths=coverage.xml
+                        --define sonar.coverageReportPaths=build/coverage.xml

.vscode/launch.json

@@ -8,7 +8,7 @@
             "name": "C++ Launch config",
             "type": "cppdbg",
             "request": "launch",
-            "program": "${workspaceFolder}/tests/build/Metrics_unittest",
+            "program": "${workspaceFolder}/tests/build/BinDisc_unittest",
             "cwd": "${workspaceFolder}/tests/build",
             "args": [],
             "launchCompleteCommand": "exec-run",

BinDisc.cpp

@@ -1,5 +1,10 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <algorithm>
-#include <limits>
 #include <cmath>
 #include "BinDisc.h"
 #include <iostream>
@@ -20,12 +25,15 @@ 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) {
+            direction = bound_dir_t::RIGHT;
             fit_quantile(X);
         } else if (strategy == strategy_t::UNIFORM) {
+            direction = bound_dir_t::RIGHT;
             fit_uniform(X);
         }
     }
@@ -35,13 +43,12 @@ 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);
         }
@@ -55,6 +62,7 @@ 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.));
@@ -64,8 +72,9 @@ namespace mdlp {
                 (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 +84,16 @@ 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

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef BINDISC_H
 #define BINDISC_H
 
@@ -20,7 +26,6 @@ namespace mdlp {
     private:
         void fit_uniform(samples_t&);
         void fit_quantile(samples_t&);
-        void normalizeCutPoints();
         int n_bins;
         strategy_t strategy;
     };

CMakeLists.txt

@@ -6,6 +6,15 @@ 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)
+set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}  -fno-elide-constructors")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
 if (ENABLE_TESTING)
+    MESSAGE("Debug mode")
+    enable_testing()
+    set(CODE_COVERAGE ON)
+    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
     add_subdirectory(tests)
 endif(ENABLE_TESTING)

CPPFImdlp.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <numeric>
 #include <algorithm>
 #include <set>
@@ -12,6 +18,7 @@ namespace mdlp {
         max_depth(max_depth_),
         proposed_cuts(proposed)
     {
+        direction = bound_dir_t::RIGHT;
     }
 
     size_t CPPFImdlp::compute_max_num_cut_points() const
@@ -20,12 +27,12 @@ namespace mdlp {
         if (proposed_cuts == 0) {
             return numeric_limits<size_t>::max();
         }
-        if (proposed_cuts < 0 || proposed_cuts > static_cast<float>(X.size())) {
+        if (proposed_cuts < 0 || proposed_cuts > static_cast<precision_t>(X.size())) {
             throw invalid_argument("wrong proposed num_cuts value");
         }
         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>(round(static_cast<precision_t>(X.size()) * 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 +65,10 @@ 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)

CPPFImdlp.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef CPPFIMDLP_H
 #define CPPFIMDLP_H
 

Discretizer.cpp

@@ -1,13 +1,26 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "Discretizer.h"
 
 namespace mdlp {
+
     labels_t& Discretizer::transform(const samples_t& data)
     {
         discretizedData.clear();
         discretizedData.reserve(data.size());
+        // CutPoints always have at least two items
+        // Have to ignore first and last cut points provided
+        auto first = cutPoints.begin() + 1;
+        auto last = cutPoints.end() - 1;
+        auto bound = direction == bound_dir_t::LEFT ? std::lower_bound<std::vector<precision_t>::iterator, precision_t> : std::upper_bound<std::vector<precision_t>::iterator, precision_t>;
         for (const precision_t& item : data) {
-            auto upper = std::upper_bound(cutPoints.begin(), cutPoints.end(), item);
-            discretizedData.push_back(upper - cutPoints.begin());
+            auto pos = bound(first, last, item);
+            int number = pos - first;
+            discretizedData.push_back(number);
         }
         return discretizedData;
     }
@@ -26,7 +39,7 @@ namespace mdlp {
     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);
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
         auto result = transform(X);
         return torch::tensor(result, torch::kInt32);
     }

Discretizer.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef DISCRETIZER_H
 #define DISCRETIZER_H
 
@@ -7,6 +13,10 @@
 #include "typesFImdlp.h"
 
 namespace mdlp {
+    enum class bound_dir_t {
+        LEFT,
+        RIGHT
+    };
     class Discretizer {
     public:
         Discretizer() = default;
@@ -18,10 +28,11 @@ namespace mdlp {
         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 "1.2.2"; };
+        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
+        bound_dir_t direction; // used in transform
     };
 }
 #endif

Makefile

@@ -1,13 +1,32 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := build
 .PHONY: build test
+lcov := lcov
 
 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
+	@cmake --build build_release -j 8
 
 test:
-	@echo "Testing..."
-	@cd tests && ./test
+	@if [ -d build_debug ]; then rm -fr build_debug; fi
+	@mkdir build_debug
+	@cmake -B build_debug -S . -DCMAKE_BUILD_TYPE=Debug -DENABLE_TESTING=ON
+	@cmake --build build_debug -j 8
+	@cd build_debug/tests && ctest --output-on-failure -j 8
+	@cd build_debug/tests && $(lcov) --capture --directory ../ --demangle-cpp --ignore-errors source,source --ignore-errors mismatch --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'gtest/*' --output-file coverage.info >/dev/null 2>&1;
+	@genhtml build_debug/tests/coverage.info --demangle-cpp --output-directory build_debug/tests/coverage --title "Discretizer mdlp Coverage Report" -s -k -f --legend
+	@echo "* Coverage report is generated at build_debug/tests/coverage/index.html"
+	@which python || (echo ">>> Please install python"; exit 1)
+	@if [ ! -f build_debug/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found!"; \
+		exit 1; \
+	fi
+	@echo ">>> Updating coverage badge..."
+	@env python update_coverage.py build_debug/tests

Metrics.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "Metrics.h"
 #include <set>
 #include <cmath>

Metrics.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef CCMETRICS_H
 #define CCMETRICS_H
 

README.md

@@ -1,6 +1,7 @@
 [![Build](https://github.com/rmontanana/mdlp/actions/workflows/build.yml/badge.svg)](https://github.com/rmontanana/mdlp/actions/workflows/build.yml)
 [![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)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-100,0%25-green)](html/index.html)
 
 # <img src="logo.png" alt="logo" width="50"/> mdlp
 
@@ -31,15 +32,14 @@ Other features:
 To run the sample, just execute the following commands:
 
 ```bash
-cmake -B build -S .
-cmake --build build
-build/sample/sample -f iris -m 2
-build/sample/sample -h
+make build
+build_release/sample/sample -f iris -m 2
+build_release/sample/sample -h
 ```
 
 ## Test
 
-To run the tests and see coverage (llvm & gcovr have to be installed), execute the following commands:
+To run the tests and see coverage (llvm with lcov and genhtml have to be installed), execute the following commands:
 
 ```bash
 make test

sample/sample.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <iostream>
 #include <vector>
 #include <iomanip>
@@ -144,7 +150,7 @@ void process_file(const string& path, const string& file_name, bool class_last,
     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;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << result[i].item<int>() << std::endl;
     }
     auto disc = mdlp::BinDisc(3);
     auto res_v = disc.fit_transform(X[0], y);
@@ -152,7 +158,7 @@ void process_file(const string& path, const string& file_name, bool class_last,
     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;
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::endl;
     }
 }
 

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=1.2.1
+sonar.projectVersion=2.0.0
 # sonar.test.exclusions=tests/**
 # sonar.tests=tests/
 # sonar.coverage.exclusions=tests/**,sample/**
@@ -12,3 +12,4 @@ sonar.projectVersion=1.2.1
 
 # Encoding of the source code. Default is default system encoding
 sonar.sourceEncoding=UTF-8
+sonar.cfamily.compile-commands

tests/ArffFiles.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "ArffFiles.h"
 #include <fstream>
 #include <sstream>

tests/ArffFiles.h

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #ifndef ARFFFILES_H
 #define ARFFFILES_H
 

tests/BinDisc_unittest.cpp

@@ -1,9 +1,16 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <fstream>
 #include <string>
 #include <iostream>
 #include "gtest/gtest.h"
 #include "ArffFiles.h"
 #include "../BinDisc.h"
+#include "Experiments.hpp"
 
 namespace mdlp {
     const float margin = 1e-4;
@@ -40,10 +47,11 @@ 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 +61,11 @@ 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);
@@ -66,10 +75,11 @@ 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 };
         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 };
         EXPECT_EQ(expected, labels);
@@ -79,10 +89,11 @@ 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 };
         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 };
         EXPECT_EQ(expected, labels);
@@ -92,10 +103,11 @@ 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 +117,11 @@ 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 +131,9 @@ 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 +143,9 @@ 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 +155,18 @@ 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 +177,11 @@ 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 +192,10 @@ 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 +206,12 @@ 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 +221,12 @@ 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);
@@ -215,11 +236,12 @@ 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 };
         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 };
         EXPECT_EQ(expected, labels);
@@ -229,11 +251,12 @@ 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 };
         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 };
         EXPECT_EQ(expected, labels);
@@ -243,11 +266,12 @@ 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 +281,12 @@ 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);
@@ -271,11 +296,12 @@ 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 };
         EXPECT_EQ(expected, labels);
@@ -285,11 +311,12 @@ 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 };
         EXPECT_EQ(expected, labels);
@@ -300,11 +327,12 @@ 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 };
         EXPECT_EQ(expected, labels);
@@ -315,50 +343,69 @@ 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, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
         EXPECT_EQ(expected, labels);
     }
-    TEST_F(TestBinDisc4U, irisUniform)
+    TEST(TestBinDiscGeneric, Fileset)
     {
-        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);
-        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)
-    {
-        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);
-        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>());
+        Experiments exps(data_path + "tests.txt");
+        int num = 0;
+        while (exps.is_next()) {
+            ++num;
+            Experiment exp = exps.next();
+            BinDisc disc(exp.n_bins_, exp.strategy_[0] == 'Q' ? strategy_t::QUANTILE : strategy_t::UNIFORM);
+            std::vector<precision_t> test;
+            if (exp.type_ == experiment_t::RANGE) {
+                for (float i = exp.from_; i < exp.to_; i += exp.step_) {
+                    test.push_back(i);
+                }
+            } else {
+                test = exp.dataset_;
+            }
+            // 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());
+            auto flag = false;
+            size_t n_errors = 0;
+            if (num < 40) {
+                //
+                // Check discretization of only the first 40 tests as after we cannot ensure the same codification due to precision problems
+                //
+                for (int i = 0; i < exp.discretized_data_.size(); ++i) {
+                    if (exp.discretized_data_.at(i) != Xt.at(i)) {
+                        if (!flag) {
+                            if (exp.type_ == experiment_t::RANGE)
+                                std::cout << "+Exp #: " << num << " From: " << exp.from_ << " To: " << exp.to_ << " Step: " << exp.step_ << " Bins: " << exp.n_bins_ << " Strategy: " << exp.strategy_ << std::endl;
+                            else {
+                                std::cout << "+Exp #: " << num << " strategy: " << exp.strategy_ << " " << " n_bins: " << exp.n_bins_ << " ";
+                                show_vector(exp.dataset_, "Dataset");
+                            }
+                            show_vector(cuts, "Cuts");
+                            std::cout << "Error at " << i << " test[i]=" << test.at(i) << " Expected: " << exp.discretized_data_.at(i) << " Got: " << Xt.at(i) << std::endl;
+                            flag = true;
+                            EXPECT_EQ(exp.discretized_data_.at(i), Xt.at(i));
+                        }
+                        n_errors++;
+                    }
+                }
+                if (flag) {
+                    std::cout << "*** Found " << n_errors << " mistakes in this experiment dataset" << 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);
+            }
+        }
+        std::cout << "* Number of experiments tested: " << num << std::endl;
     }
 }

tests/Discretizer_unittest.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <fstream>
 #include <string>
 #include <iostream>
@@ -20,7 +26,15 @@ namespace mdlp {
         return "../../tests/datasets/";
     }
     const std::string data_path = set_data_path();
-
+    const labels_t iris_quantile = { 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(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;
@@ -43,12 +57,198 @@ namespace mdlp {
         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);
+        EXPECT_EQ(iris_quantile, Xt);
     }
+
+    TEST(Discretizer, BinIrisQuantileTorch)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        auto X = file.getX();
+        auto y = file.getY();
+        auto X_torch = torch::tensor(X[0], torch::kFloat32);
+        auto yt = torch::tensor(y, torch::kInt32);
+        disc->fit_t(X_torch, yt);
+        torch::Tensor Xt = disc->transform_t(X_torch);
+        delete disc;
+        EXPECT_EQ(iris_quantile.size(), Xt.size(0));
+        for (int i = 0; i < iris_quantile.size(); ++i) {
+            EXPECT_EQ(iris_quantile.at(i), Xt[i].item<int>());
+        }
+    }
+    TEST(Discretizer, BinIrisQuantileTorchFit_transform)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        auto X = file.getX();
+        auto y = file.getY();
+        auto X_torch = torch::tensor(X[0], torch::kFloat32);
+        auto yt = torch::tensor(y, torch::kInt32);
+        torch::Tensor Xt = disc->fit_transform_t(X_torch, yt);
+        delete disc;
+        EXPECT_EQ(iris_quantile.size(), Xt.size(0));
+        for (int i = 0; i < iris_quantile.size(); ++i) {
+            EXPECT_EQ(iris_quantile.at(i), Xt[i].item<int>());
+        }
+    }
+
     TEST(Discretizer, FImdlpIris)
     {
+        auto 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,
+        };
         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,

tests/Experiments.hpp

@@ -0,0 +1,139 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef EXPERIMENTS_HPP
+#define EXPERIMENTS_HPP
+#include<sstream>
+#include<iostream>
+#include<string>
+#include<fstream>
+#include<vector>
+#include<tuple>
+#include "../typesFImdlp.h"
+
+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;
+}
+enum class experiment_t {
+    RANGE,
+    VECTOR
+};
+class Experiment {
+public:
+    Experiment(float from_, float to_, float step_, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<mdlp::precision_t> cutpoints) :
+        from_{ from_ }, to_{ to_ }, step_{ step_ }, n_bins_{ n_bins }, strategy_{ strategy }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }, type_{ experiment_t::RANGE }
+    {
+        validate_strategy();
+
+    }
+    Experiment(std::vector<mdlp::precision_t> dataset, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<mdlp::precision_t> cutpoints) :
+        n_bins_{ n_bins }, strategy_{ strategy }, dataset_{ dataset }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }, type_{ experiment_t::VECTOR }
+    {
+        validate_strategy();
+    }
+    void validate_strategy()
+    {
+        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<mdlp::precision_t> dataset_;
+    std::vector<int> discretized_data_;
+    std::vector<mdlp::precision_t> cutpoints_;
+    experiment_t type_;
+};
+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)
+    {
+        // Read experiment lines
+        std::string experiment, data, cuts, strategy;
+        std::getline(test_file, experiment);
+        std::getline(test_file, data);
+        std::getline(test_file, cuts);
+        // split data into variables
+        float from_, to_, step_;
+        int n_bins;
+        std::vector<mdlp::precision_t> dataset;
+        auto data_discretized = parse_vector<int>(data);
+        auto cutpoints = parse_vector<mdlp::precision_t>(cuts);
+        if (line == "RANGE") {
+            tie(from_, to_, step_, n_bins, strategy) = parse_header(experiment);
+            return Experiment{ from_, to_, step_, n_bins, strategy, data_discretized, cutpoints };
+        }
+        strategy = experiment.substr(0, 1);
+        n_bins = std::stoi(experiment.substr(1, 1));
+        data = experiment.substr(3, experiment.size() - 4);
+        dataset = parse_vector<mdlp::precision_t>(data);
+        return Experiment(dataset, n_bins, strategy, data_discretized, cutpoints);
+    }
+    std::ifstream test_file;
+    std::string filename;
+    std::string line;
+    bool exp_end;
+};
+#endif

tests/FImdlp_unittest.cpp

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "gtest/gtest.h"
 #include "../Metrics.h"
 #include "../CPPFImdlp.h"
@@ -124,7 +130,7 @@ 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 +173,31 @@ 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 +208,10 @@ 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 +221,7 @@ 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 +255,10 @@ 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 +269,10 @@ 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 +283,10 @@ 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 +297,10 @@ 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 +312,10 @@ 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 +335,6 @@ namespace mdlp {
             computed = compute_max_num_cut_points();
             ASSERT_EQ(expected, computed);
         }
-
     }
     TEST_F(TestFImdlp, TransformTest)
     {
@@ -345,15 +352,15 @@ namespace mdlp {
         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]);
-        // }
-        // 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]);
-        // }
+        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]);
+        }
+        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

@@ -1,3 +1,9 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "gtest/gtest.h"
 #include "../Metrics.h"
 

tests/datasets/tests.txt

@@ -0,0 +1,222 @@
+#
+# from, to, step, #bins, Q/U
+# discretized data
+# cut points
+#
+#
+# Range experiments
+#
+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, 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, 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, 1, 1, 1, 2, 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, 2, 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 experiments
+#
+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, 1, 1, 1, 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
+#
+# Vector experiments with iris
+#
+VECTOR
+Q3[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+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, 0, 0, 2, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 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, 1, 0, 1, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1
+4.3, 5.4, 6.3, 7.9
+VECTOR
+U3[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 1, 2, 0, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1
+4.3, 5.5, 6.7, 7.9
+VECTOR
+Q4[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+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
+4.3, 5.1, 5.8, 6.4, 7.9
+VECTOR
+U4[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
+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, 1, 1, 0, 0, 1, 1, 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, 1, 0, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 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, 2, 2, 3, 3, 3, 2, 2, 2, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1
+4.3, 5.2, 6.1, 7.0, 7.9
+VECTOR
+Q3[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]
+2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 1, 0, 2, 2, 0, 1, 0, 0, 2, 1, 2, 0, 0, 2, 0, 0, 0, 2, 2, 0, 1, 0, 1, 0, 2, 0, 0, 0, 1, 2, 1, 1, 1, 1, 1, 0, 2, 2, 1, 0, 1, 2, 1
+2.0, 2.9, 3.2, 4.4
+VECTOR
+U3[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]
+1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 2, 1, 0, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1
+2.0, 2.8, 3.6, 4.4
+VECTOR
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+VECTOR
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+VECTOR
+Q4[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 3, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
+0.1, 0.3, 1.3, 1.8, 2.5
+VECTOR
+U4[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]
+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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 3, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2
+0.1, 0.7, 1.3, 1.9, 2.5

tests/test

@@ -1,15 +0,0 @@
-#!/bin/bash
-if [ -d build ] && [ "$1" != "run" ]; then
-	rm -fr build
-fi
-if [ -d gcovr-report ] ; then
-	rm -fr gcovr-report
-fi
-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 ..
-mkdir gcovr-report
-cd ..
-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/testKbins.py

@@ -1,412 +0,0 @@
-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}")

tests/tests_do.py

@@ -0,0 +1,71 @@
+# ***************************************************************
+# SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+# SPDX-FileType: SOURCE
+# SPDX-License-Identifier: MIT
+# ***************************************************************
+
+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] != "#"]
+
+errors = False
+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)
+    #
+    # Normalize the cutpoints to remove numerical errors such as 33.0000000001
+    # instead of 33
+    #
+    for j in range(len(disc.bin_edges_[0])):
+        disc.bin_edges_[0][j] = round(disc.bin_edges_[0][j], 5)
+    result = disc.transform(X)
+    result = [int(x) for x in result.flatten()]
+    expected = [int(x) for x in expected_data.split(",")]
+    #
+    # Check the Results
+    #
+    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])
+            errors = True
+    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],
+            )
+            errors = True
+if errors:
+    raise Exception("There were errors!")
+print("*** All tests run succesfully! ***")

tests/tests_generate.ipynb

@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import KBinsDiscretizer\n",
+    "from sklearn.datasets import load_iris"
+   ]
+  },
+  {
+   "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",
+    "def normalize_cuts(cuts):\n",
+    "    #\n",
+    "    # Normalize the cutpoints to remove numerical errors such as 33.0000000001\n",
+    "    # instead of 33\n",
+    "    #\n",
+    "    for k in range(cuts.shape[0]):\n",
+    "        for i in range(len(cuts[k])):\n",
+    "            cuts[k][i] = round(cuts[k][i], 5)\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",
+    "    #\n",
+    "    # Range experiments\n",
+    "    #\n",
+    "    file.write(\"#\\n\")\n",
+    "    file.write(\"# Range experiments\\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",
+    "        normalize_cuts(disc.bin_edges_)\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",
+    "    #\n",
+    "    # Vector experiments\n",
+    "    #\n",
+    "    file.write(\"#\\n\")\n",
+    "    file.write(\"# Vector experiments\\n\")\n",
+    "    file.write(\"#\\n\")\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",
+    "            disc.fit(data)\n",
+    "            normalize_cuts(disc.bin_edges_)\n",
+    "            result = disc.transform(data)\n",
+    "            write_lists(file, result, disc.bin_edges_[0])\n",
+    "    #\n",
+    "    # Vector experiments iris\n",
+    "    #\n",
+    "    file.write(\"#\\n\");\n",
+    "    file.write(\"# Vector experiments with iris\\n\");\n",
+    "    file.write(\"#\\n\");\n",
+    "    X, y = load_iris(return_X_y=True)\n",
+    "    for i in range(X.shape[1]):\n",
+    "        for n_bins in [3, 4]:\n",
+    "            for strategy in [\"Q\", \"U\"]:\n",
+    "                file.write(\"VECTOR\\n\")\n",
+    "                experiment = X[:, i]\n",
+    "                file.write(f\"{strategy}{n_bins}{experiment.tolist()}\\n\")\n",
+    "                disc = KBinsDiscretizer(\n",
+    "                    n_bins=n_bins,\n",
+    "                    encode=\"ordinal\",\n",
+    "                    strategy=\"quantile\" if strategy.strip() == \"Q\" else \"uniform\")\n",
+    "                data = [[x] for x in experiment]\n",
+    "                disc.fit(data)\n",
+    "                normalize_cuts(disc.bin_edges_)\n",
+    "                result = disc.transform(data)\n",
+    "                write_lists(file, result, disc.bin_edges_[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cut points:  [array([ 0., 33., 66., 99.])]\n",
+      "Mistaken transformed data disc.transform([[33]]) = [[0.]]\n",
+      "Reason of the mistake the cutpoint has decimals (double):  33.00000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "#\n",
+    "# Proving the mistakes due to floating point precision\n",
+    "#\n",
+    "from sklearn.preprocessing import KBinsDiscretizer\n",
+    "\n",
+    "data = [[x] for x in range(100)]\n",
+    "disc = KBinsDiscretizer(n_bins=3, encode=\"ordinal\", strategy=\"quantile\")\n",
+    "disc.fit(data)\n",
+    "print(\"Cut points: \", disc.bin_edges_)\n",
+    "print(\"Mistaken transformed data disc.transform([[33]]) =\", disc.transform([[33]]))\n",
+    "print(\"Reason of the mistake the cutpoint has decimals (double): \", disc.bin_edges_[0][1])"
+   ]
+  }
+ ],
+ "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
+}

update_coverage.py

@@ -0,0 +1,38 @@
+# ***************************************************************
+# SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+# SPDX-FileType: SOURCE
+# SPDX-License-Identifier: MIT
+# ***************************************************************
+
+import subprocess
+import sys
+
+readme_file = "README.md"
+print("Updating coverage...")
+# Generate badge line
+output = subprocess.check_output(
+    "lcov --summary " + sys.argv[1] + "/coverage.info",
+    shell=True,
+)
+value = output.decode("utf-8").strip()
+percentage = 0
+for line in value.splitlines():
+    if "lines" in line:
+        percentage = float(line.split(":")[1].split("%")[0])
+        break
+print(f"Coverage: {percentage}%")
+if percentage < 90:
+    print("⛔Coverage is less than 90%. I won't update the badge.")
+    sys.exit(1)
+percentage_label = str(percentage).replace(".", ",")
+coverage_line = f"[![Coverage Badge](https://img.shields.io/badge/Coverage-{percentage_label}%25-green)](html/index.html)"
+# Update README.md
+with open(readme_file, "r") as f:
+    lines = f.readlines()
+with open(readme_file, "w") as f:
+    for line in lines:
+        if "img.shields.io/badge/Coverage" in line:
+            f.write(coverage_line + "\n")
+        else:
+            f.write(line)
+print(f"✅Coverage updated with value: {percentage}")