Add install cmake/make target

Add flag to build sample in Makefile
Fix version number in tests
2025-08-16 07:55:58 +00:00 · 2024-07-22 22:01:33 +02:00 · 2024-07-22 19:38:12 +02:00 · 2024-07-22 12:23:21 +02:00 · 2024-07-22 11:26:16 +02:00 · 2024-07-04 18:21:26 +02:00
47 changed files with 4156 additions and 617 deletions
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,16 @@
+FROM mcr.microsoft.com/devcontainers/cpp:0-ubuntu-22.04
+
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -y install --no-install-recommends \
+    python3 \
+    python3-pip \
+    lcov \
+    cmake \
+    && apt-get autoremove -y && apt-get clean -y && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install --no-cache-dir \
+    cpplint \
+    cmake-format\
+    gcovr
+# [Optional] Uncomment this section to install additional vcpkg ports.
+# RUN su vscode -c "${VCPKG_ROOT}/vcpkg install <your-port-name-here>"
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,32 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/cpp
+{
+	"name": "C++",
+	"build": {
+		"dockerfile": "Dockerfile"
+	},
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+	// Use 'postCreateCommand' to run commands after the container is created.
+	// "postCreateCommand": "gcc -v",
+	// Configure tool-specific properties.
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"ms-vscode.cpptools",
+				"ms-vscode.cpptools-extension-pack",
+				"ms-vscode.cpptools-themes",
+				"jbenden.c-cpp-flylint",
+				"matepek.vscode-catch2-test-adapter",
+				"ms-vscode.cmake-tools",
+				"GitHub.copilot"
+			]
+		}
+	}
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}
--- a/.devcontainer/reinstall-cmake.sh
+++ b/.devcontainer/reinstall-cmake.sh
@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+#-------------------------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See https://go.microsoft.com/fwlink/?linkid=2090316 for license information.
+#-------------------------------------------------------------------------------------------------------------
+#
+set -e
+
+CMAKE_VERSION=${1:-"none"}
+
+if [ "${CMAKE_VERSION}" = "none" ]; then
+    echo "No CMake version specified, skipping CMake reinstallation"
+    exit 0
+fi
+
+# Cleanup temporary directory and associated files when exiting the script.
+cleanup() {
+    EXIT_CODE=$?
+    set +e
+    if [[ -n "${TMP_DIR}" ]]; then
+        echo "Executing cleanup of tmp files"
+        rm -Rf "${TMP_DIR}"
+    fi
+    exit $EXIT_CODE
+}
+trap cleanup EXIT
+
+
+echo "Installing CMake..."
+apt-get -y purge --auto-remove cmake
+mkdir -p /opt/cmake
+
+architecture=$(dpkg --print-architecture)
+case "${architecture}" in
+    arm64)
+        ARCH=aarch64 ;;
+    amd64)
+        ARCH=x86_64 ;;
+    *)
+        echo "Unsupported architecture ${architecture}."
+        exit 1
+        ;;
+esac
+
+CMAKE_BINARY_NAME="cmake-${CMAKE_VERSION}-linux-${ARCH}.sh"
+CMAKE_CHECKSUM_NAME="cmake-${CMAKE_VERSION}-SHA-256.txt"
+TMP_DIR=$(mktemp -d -t cmake-XXXXXXXXXX)
+
+echo "${TMP_DIR}"
+cd "${TMP_DIR}"
+
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_BINARY_NAME}" -O
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_CHECKSUM_NAME}" -O
+
+sha256sum -c --ignore-missing "${CMAKE_CHECKSUM_NAME}"
+sh "${TMP_DIR}/${CMAKE_BINARY_NAME}" --prefix=/opt/cmake --skip-license
+
+ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
+ln -s /opt/cmake/bin/ctest /usr/local/bin/ctest
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1,26 +1,44 @@
 name: Build
-
 on:
  push:
    branches:
      - main
-
-
+      - "*"
+  pull_request:
+    types: [ opened, synchronize, reopened ]
 jobs:
  build:
    name: Build
    runs-on: ubuntu-latest
+    env:
+      BUILD_WRAPPER_OUT_DIR: build_wrapper_output_directory # Directory where build-wrapper output will be placed
    steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
        with:
-          fetch-depth: 0  # Shallow clones should be disabled for a better relevancy of analysis
-      - uses: sonarsource/sonarqube-scan-action@master
+          fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis
+          submodules: recursive
+      - name: Install sonar-scanner and build-wrapper
+        uses: SonarSource/sonarcloud-github-c-cpp@v2
+      - name: Install lcov & gcovr
+        run: |
+          sudo apt-get -y install lcov
+          sudo apt-get -y install gcovr
+      - name: Install Libtorch
+        run: |
+          wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcpu.zip
+          unzip libtorch-cxx11-abi-shared-with-deps-2.3.1+cpu.zip
+      - name: Tests & build-wrapper
+        run: |
+          cmake -S . -B build -Wno-dev -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
+          ctest -C Debug --output-on-failure -j 4
+          gcovr -f ../src/CPPFImdlp.cpp -f ../src/Metrics.cpp -f ../src/BinDisc.cpp -f ../src/Discretizer.cpp --txt --sonarqube=coverage.xml
+      - name: Run sonar-scanner
        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
-          SONAR_HOST_URL: ${{ secrets.SONAR_HOST_URL }}
-      # If you wish to fail your job when the Quality Gate is red, uncomment the
-      # following lines. This would typically be used to fail a deployment.
-      # - uses: sonarsource/sonarqube-quality-gate-action@master
-      #   timeout-minutes: 5
-      #   env:
-      #     SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
+        run: |
+          sonar-scanner --define sonar.cfamily.compile-commands="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
+                        --define sonar.coverageReportPaths=build/coverage.xml
--- a/.gitignore
+++ b/.gitignore
@@ -31,8 +31,12 @@
 *.out
 *.app
 **/build
+build_Debug
+build_Release
+build_debug
+build_release
 **/lcoverage
 .idea
 cmake-*
 **/CMakeFiles
-sonar-project.properties
+**/gcovr-report
--- a/.gitmodules
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "tests/lib/Files"]
+	path = tests/lib/Files
+	url = https://github.com/rmontanana/ArffFiles.git
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -5,18 +5,38 @@
    "version": "0.2.0",
    "configurations": [
        {
-            "name": "(lldb) Launch",
+            "name": "C++ Launch config",
            "type": "cppdbg",
            "request": "launch",
-            "program": "${workspaceRoot}/sample/build/sample",
-            "args": [
-                "mfeat-factors"
-            ],
+            "program": "${workspaceFolder}/tests/build/BinDisc_unittest",
+            "cwd": "${workspaceFolder}/tests/build",
+            "args": [],
+            "launchCompleteCommand": "exec-run",
            "stopAtEntry": false,
-            "cwd": "${workspaceRoot}/sample/build/",
-            "environment": [],
-            "externalConsole": false,
-            "MIMode": "lldb"
-        }
+            "linux": {
+                "MIMode": "gdb",
+                "miDebuggerPath": "/usr/bin/gdb",
+                "setupCommands": [
+                    {
+                        "description": "Enable pretty-printing for gdb",
+                        "text": "-enable-pretty-printing",
+                        "ignoreFailures": true
+                    },
+                    {
+                        "description": "Auto load symbols when loading an .so file",
+                        "text": "set auto-solib-add",
+                        "ignoreFailures": false
+                    }
+                ]
+            },
+            "osx": {
+                "type": "lldb",
+                "MIMode": "lldb"
+            },
+            "windows": {
+                "MIMode": "gdb",
+                "miDebuggerPath": "C:\\MinGw\\bin\\gdb.exe"
+            }
+        },
    ]
 }
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,5 +1,109 @@
 {
    "sonarlint.connectedMode.project": {
-        "projectKey": "rmontanana_mdlp_AYZkjILJHyjW-meBaElG"
+        "connectionId": "rmontanana",
+        "projectKey": "rmontanana_mdlp"
+    },
+    "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools",
+    "cmake.configureOnOpen": true,
+    "sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json",
+    "files.associations": {
+        "*.rmd": "markdown",
+        "*.py": "python",
+        "vector": "cpp",
+        "__bit_reference": "cpp",
+        "__bits": "cpp",
+        "__config": "cpp",
+        "__debug": "cpp",
+        "__errc": "cpp",
+        "__hash_table": "cpp",
+        "__locale": "cpp",
+        "__mutex_base": "cpp",
+        "__node_handle": "cpp",
+        "__nullptr": "cpp",
+        "__split_buffer": "cpp",
+        "__string": "cpp",
+        "__threading_support": "cpp",
+        "__tuple": "cpp",
+        "array": "cpp",
+        "atomic": "cpp",
+        "bitset": "cpp",
+        "cctype": "cpp",
+        "chrono": "cpp",
+        "clocale": "cpp",
+        "cmath": "cpp",
+        "compare": "cpp",
+        "complex": "cpp",
+        "concepts": "cpp",
+        "cstdarg": "cpp",
+        "cstddef": "cpp",
+        "cstdint": "cpp",
+        "cstdio": "cpp",
+        "cstdlib": "cpp",
+        "cstring": "cpp",
+        "ctime": "cpp",
+        "cwchar": "cpp",
+        "cwctype": "cpp",
+        "exception": "cpp",
+        "initializer_list": "cpp",
+        "ios": "cpp",
+        "iosfwd": "cpp",
+        "istream": "cpp",
+        "limits": "cpp",
+        "locale": "cpp",
+        "memory": "cpp",
+        "mutex": "cpp",
+        "new": "cpp",
+        "optional": "cpp",
+        "ostream": "cpp",
+        "ratio": "cpp",
+        "sstream": "cpp",
+        "stdexcept": "cpp",
+        "streambuf": "cpp",
+        "string": "cpp",
+        "string_view": "cpp",
+        "system_error": "cpp",
+        "tuple": "cpp",
+        "type_traits": "cpp",
+        "typeinfo": "cpp",
+        "unordered_map": "cpp",
+        "variant": "cpp",
+        "algorithm": "cpp",
+        "iostream": "cpp",
+        "iomanip": "cpp",
+        "numeric": "cpp",
+        "set": "cpp",
+        "__tree": "cpp",
+        "deque": "cpp",
+        "list": "cpp",
+        "map": "cpp",
+        "unordered_set": "cpp",
+        "any": "cpp",
+        "condition_variable": "cpp",
+        "forward_list": "cpp",
+        "fstream": "cpp",
+        "stack": "cpp",
+        "thread": "cpp",
+        "__memory": "cpp",
+        "filesystem": "cpp",
+        "*.toml": "toml",
+        "utility": "cpp",
+        "span": "cpp",
+        "*.tcc": "cpp",
+        "bit": "cpp",
+        "charconv": "cpp",
+        "cinttypes": "cpp",
+        "codecvt": "cpp",
+        "functional": "cpp",
+        "iterator": "cpp",
+        "memory_resource": "cpp",
+        "random": "cpp",
+        "source_location": "cpp",
+        "format": "cpp",
+        "numbers": "cpp",
+        "semaphore": "cpp",
+        "stop_token": "cpp",
+        "text_encoding": "cpp",
+        "typeindex": "cpp",
+        "valarray": "cpp"
    }
 }
--- a/.vscode/tasks.json
+++ b/.vscode/tasks.json
@@ -1,29 +1,26 @@
 {
-    "tasks": [
-        {
-            "type": "cppbuild",
-            "label": "C/C++: clang++ build active file",
-            "command": "/usr/bin/clang++",
-            "args": [
-                "-fcolor-diagnostics",
-                "-fansi-escape-codes",
-                "-g",
-                "${file}",
-                "-o",
-                "${fileDirname}/${fileBasenameNoExtension}"
-            ],
-            "options": {
-                "cwd": "${fileDirname}"
-            },
-            "problemMatcher": [
-                "$gcc"
-            ],
-            "group": {
-                "kind": "build",
-                "isDefault": true
-            },
-            "detail": "Task generated by Debugger."
-        }
-    ],
-    "version": "2.0.0"
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"type": "cmake",
+			"label": "CMake: build",
+			"command": "build",
+			"targets": [
+				"all"
+			],
+			"group": {
+				"kind": "build",
+				"isDefault": true
+			},
+			"problemMatcher": [],
+			"detail": "CMake template build task"
+		},
+		{
+			"type": "cmake",
+			"label": "CMake: configure",
+			"command": "configure",
+			"problemMatcher": [],
+			"detail": "CMake template configure task"
+		}
+	]
 }
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,7 +1,44 @@
 cmake_minimum_required(VERSION 3.20)
-project(mdlp)

-set(CMAKE_CXX_STANDARD 11)
+project(fimdlp)
+set(CMAKE_CXX_STANDARD 17)
+cmake_policy(SET CMP0135 NEW)

-add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
+find_package(Torch CONFIG REQUIRED)

+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)
+else(ENABLE_TESTING)
+    MESSAGE("Release mode")
+endif(ENABLE_TESTING)
+
+if (ENABLE_SAMPLE)
+    message("Building sample")
+    add_subdirectory(sample)
+endif(ENABLE_SAMPLE)
+
+include_directories(
+    ${TORCH_INCLUDE_DIRS}
+    ${fimdlp_SOURCE_DIR}/src
+)
+
+add_library(fimdlp src/CPPFImdlp.cpp src/Metrics.cpp src/BinDisc.cpp src/Discretizer.cpp)
+target_link_libraries(fimdlp "${TORCH_LIBRARIES}")
+
+# Installation
+# ------------
+install(TARGETS fimdlp
+        ARCHIVE DESTINATION lib
+        LIBRARY DESTINATION lib
+        CONFIGURATIONS Release)
+install(DIRECTORY src/ DESTINATION include/fimdlp FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
--- a/CPPFImdlp.cpp
+++ b/CPPFImdlp.cpp
@@ -1,162 +0,0 @@
-#include <numeric>
-#include <algorithm>
-#include <set>
-#include <cmath>
-#include "CPPFImdlp.h"
-#include "Metrics.h"
-
-namespace mdlp {
-
-    CPPFImdlp::CPPFImdlp(): indices(indices_t()), X(samples_t()), y(labels_t()),
-        metrics(Metrics(y, indices))
-    {
-    }
-    CPPFImdlp::~CPPFImdlp() = default;
-
-    CPPFImdlp& CPPFImdlp::fit(samples_t& X_, labels_t& y_)
-    {
-        X = X_;
-        y = y_;
-        cutPoints.clear();
-        if (X.size() != y.size()) {
-            throw invalid_argument("X and y must have the same size");
-        }
-        if (X.empty() || y.empty()) {
-            throw invalid_argument("X and y must have at least one element");
-        }
-        indices = sortIndices(X_, y_);
-        metrics.setData(y, indices);
-        computeCutPoints(0, X.size());
-        return *this;
-    }
-
-    pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)
-    {
-        size_t n, m, idxPrev = cut - 1 >= start ? cut - 1 : cut;
-        size_t idxNext = cut + 1 < end ? cut + 1 : cut;
-        bool backWall; // true if duplicates reach begining of the interval
-        precision_t previous, actual, next;
-        if (cut - 1 < start || cut + 1 >= end)
-            throw logic_error("Invalid cutpoint index");
-        previous = X[indices[idxPrev]];
-        actual = X[indices[cut]];
-        next = X[indices[idxNext]];
-        // definition 2 of the paper => X[t-1] < X[t]
-        // get the first equal value of X in the interval
-        while (idxPrev > start && actual == previous) {
-            previous = X[indices[--idxPrev]];
-        }
-        backWall = idxPrev == start && actual == previous;
-        // get the last equal value of X in the interval
-        while (idxNext < end - 1 && actual == next) {
-            next = X[indices[++idxNext]];
-        }
-        // # of duplicates before cutpoint
-        n = cut - 1 - idxPrev;
-        // # of duplicates after cutpoint
-        m = idxNext - cut - 1;
-        // Decide which values to use
-        cut = cut + (backWall ? m + 1 : -n);
-        actual = X[indices[cut]];
-        return { (actual + previous) / 2, cut };
-    }
-
-    void CPPFImdlp::computeCutPoints(size_t start, size_t end)
-    {
-        size_t cut;
-        pair<precision_t, size_t> result;
-        if (end - start < 3)
-            return;
-        cut = getCandidate(start, end);
-        if (cut == numeric_limits<size_t>::max())
-            return;
-        if (mdlp(start, cut, end)) {
-            result = valueCutPoint(start, cut, end);
-            cut = result.second;
-            cutPoints.push_back(result.first);
-            computeCutPoints(start, cut);
-            computeCutPoints(cut, end);
-        }
-    }
-
-    size_t CPPFImdlp::getCandidate(size_t start, size_t end)
-    {
-        /* Definition 1: A binary discretization for A is determined by selecting the cut point TA for which
-        E(A, TA; S) is minimal amongst all the candidate cut points. */
-        size_t candidate = numeric_limits<size_t>::max(), elements = end - start;
-        bool sameValues = true;
-        precision_t entropy_left, entropy_right, minEntropy;
-        // Check if all the values of the variable in the interval are the same
-        for (size_t idx = start + 1; idx < end; idx++) {
-            if (X[indices[idx]] != X[indices[start]]) {
-                sameValues = false;
-                break;
-            }
-        }
-        if (sameValues)
-            return candidate;
-        minEntropy = metrics.entropy(start, end);
-        for (size_t idx = start + 1; idx < end; idx++) {
-            // Cutpoints are always on boundaries (definition 2)
-            if (y[indices[idx]] == y[indices[idx - 1]])
-                continue;
-            entropy_left = precision_t(idx - start) / elements * metrics.entropy(start, idx);
-            entropy_right = precision_t(end - idx) / elements * metrics.entropy(idx, end);
-            if (entropy_left + entropy_right < minEntropy) {
-                minEntropy = entropy_left + entropy_right;
-                candidate = idx;
-            }
-        }
-        return candidate;
-    }
-
-    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
-    {
-        int k, k1, k2;
-        precision_t ig, delta;
-        precision_t ent, ent1, ent2;
-        auto N = precision_t(end - start);
-        if (N < 2) {
-            return false;
-        }
-        k = metrics.computeNumClasses(start, end);
-        k1 = metrics.computeNumClasses(start, cut);
-        k2 = metrics.computeNumClasses(cut, end);
-        ent = metrics.entropy(start, end);
-        ent1 = metrics.entropy(start, cut);
-        ent2 = metrics.entropy(cut, end);
-        ig = metrics.informationGain(start, cut, end);
-        delta = log2(pow(3, precision_t(k)) - 2) -
-            (precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2);
-        precision_t term = 1 / N * (log2(N - 1) + delta);
-        return ig > term;
-    }
-
-    // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
-    indices_t CPPFImdlp::sortIndices(samples_t& X_, labels_t& y_)
-    {
-        indices_t idx(X_.size());
-        iota(idx.begin(), idx.end(), 0);
-        for (size_t i = 0; i < X_.size(); i++)
-            stable_sort(idx.begin(), idx.end(), [&X_, &y_](size_t i1, size_t i2) {
-            if (X_[i1] == X_[i2])
-                return y_[i1] < y_[i2];
-            else
-                return X_[i1] < X_[i2];
-                });
-        return idx;
-    }
-
-    cutPoints_t CPPFImdlp::getCutPoints()
-    {
-        // Remove duplicates and sort
-        cutPoints_t output(cutPoints.size());
-        set<precision_t> s;
-        unsigned size = cutPoints.size();
-        for (unsigned i = 0; i < size; i++)
-            s.insert(cutPoints[i]);
-        output.assign(s.begin(), s.end());
-        sort(output.begin(), output.end());
-        return output;
-    }
-}
--- a/CPPFImdlp.h
+++ b/CPPFImdlp.h
@@ -1,29 +0,0 @@
-#ifndef CPPFIMDLP_H
-#define CPPFIMDLP_H
-#include "typesFImdlp.h"
-#include "Metrics.h"
-#include <utility>
-#include <string>
-namespace mdlp {
-    class CPPFImdlp {
-    protected:
-        indices_t indices;
-        samples_t X;
-        labels_t y;
-        Metrics metrics;
-        cutPoints_t cutPoints;
-
-        static indices_t sortIndices(samples_t&, labels_t&);
-        void computeCutPoints(size_t, size_t);
-        bool mdlp(size_t, size_t, size_t);
-        size_t getCandidate(size_t, size_t);
-        pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
-    public:
-        CPPFImdlp();
-        ~CPPFImdlp();
-        CPPFImdlp& fit(samples_t&, labels_t&);
-        samples_t getCutPoints();
-        inline string version() { return "1.1.0"; };
-    };
-}
-#endif
--- a/35
+++ b/35
@@ -0,0 +1,35 @@
+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 -DENABLE_SAMPLE=ON
+	@cmake --build build_release -j 8
+
+install:
+	@cmake --build build_release --target install -j 8		
+
+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 -DENABLE_SAMPLE=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
--- a/Metrics.h
+++ b/Metrics.h
@@ -1,20 +0,0 @@
-#ifndef CCMETRICS_H
-#define CCMETRICS_H
-#include "typesFImdlp.h"
-namespace mdlp {
-    class Metrics {
-    protected:
-        labels_t& y;
-        indices_t& indices;
-        int numClasses;
-        cacheEnt_t entropyCache;
-        cacheIg_t igCache;
-    public:
-        Metrics(labels_t&, indices_t&);
-        void setData(labels_t&, indices_t&);
-        int computeNumClasses(size_t, size_t);
-        precision_t entropy(size_t, size_t);
-        precision_t informationGain(size_t, size_t, size_t);
-    };
-}
-#endif
--- a/README.md
+++ b/README.md
@@ -1,4 +1,9 @@
-# mdlp
+[![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

 Discretization algorithm based on the paper by Fayyad &amp; Irani [Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning](https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf)

@@ -7,26 +12,35 @@ The implementation tries to mitigate the problem of different label values with
 - Sorts the values of the variable using the label values as a tie-breaker
 - Once found a valid candidate for the split, it checks if the previous value is the same as actual one, and tries to get previous one, or next if the former is not possible.

-The algorithm returns the cut points for the variable.
+Other features:
+
+- Intervals with the same value of the variable are not taken into account for cutpoints.
+- Intervals have to have more than two examples to be evaluated (mdlp).
+
+- The algorithm returns the cut points for the variable.
+
+- The transform method uses the cut points returning its index in the following way:
+
+        cut[i - 1] <= x < cut[i]
+
+    using the [std::upper_bound](https://en.cppreference.com/w/cpp/algorithm/upper_bound) method
+
+- K-Bins discretization is also implemented, and "quantile" and "uniform" strategies are available.

 ## Sample

 To run the sample, just execute the following commands:

 ```bash
-cd sample
-mkdir build
-cd build
-cmake ..
-make
-./sample iris
+make build
+build_release/sample/sample -f iris -m 2
+build_release/sample/sample -h
 ```

 ## Test

-To run the tests, 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
-cd tests
-./test
+make test
 ```
--- a/logo.png
+++ b/logo.png
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -1,6 +1,11 @@
-cmake_minimum_required(VERSION 3.20)
-project(main)
+set(CMAKE_CXX_STANDARD 17)

-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Debug)

-add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)
+include_directories(
+    ${fimdlp_SOURCE_DIR}/src
+    ${fimdlp_SOURCE_DIR}/tests/lib/Files
+)
+
+add_executable(sample sample.cpp )
+target_link_libraries(sample fimdlp "${TORCH_LIBRARIES}")
--- a/sample/sample.cpp
+++ b/sample/sample.cpp
@@ -1,59 +1,226 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include <iostream>
 #include <vector>
 #include <iomanip>
-#include "../CPPFImdlp.h"
-#include "../tests/ArffFiles.h"
+#include <chrono>
+#include <algorithm>
+#include <cstring>
+#include <getopt.h>
+#include <torch/torch.h>
+#include <ArffFiles.hpp>
+#include "Discretizer.h"
+#include "CPPFImdlp.h"
+#include "BinDisc.h"

-using namespace std;
-using namespace mdlp;
+const string PATH = "tests/datasets/";
+
+/* print a description of all supported options */
+void usage(const char* path)
+{
+    /* take only the last portion of the path */
+    const char* basename = strrchr(path, '/');
+    basename = basename ? basename + 1 : path;
+
+    std::cout << "usage: " << basename << "[OPTION]" << std::endl;
+    std::cout << "  -h, --help\t\t Print this help and exit." << std::endl;
+    std::cout
+        << "  -f, --file[=FILENAME]\t {all, diabetes, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
+        << std::endl;
+    std::cout << "  -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << std::endl;
+    std::cout << "  -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << std::endl;
+    std::cout
+        << "  -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 -> any"
+        << std::endl;
+    std::cout << "  -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << std::endl;
+}
+
+tuple<string, string, int, int, float> parse_arguments(int argc, char** argv)
+{
+    string file_name;
+    string path = PATH;
+    int max_depth = numeric_limits<int>::max();
+    int min_length = 3;
+    float max_cutpoints = 0;
+    const vector<struct option> long_options = {
+            {"help",          no_argument,       nullptr, 'h'},
+            {"file",          required_argument, nullptr, 'f'},
+            {"path",          required_argument, nullptr, 'p'},
+            {"max_depth",     required_argument, nullptr, 'm'},
+            {"max_cutpoints", required_argument, nullptr, 'c'},
+            {"min_length",    required_argument, nullptr, 'n'},
+            {nullptr,         no_argument,       nullptr, 0}
+    };
+    while (true) {
+        const auto c = getopt_long(argc, argv, "hf:p:m:c:n:", long_options.data(), nullptr);
+        if (c == -1)
+            break;
+        switch (c) {
+            case 'h':
+                usage(argv[0]);
+                exit(0);
+            case 'f':
+                file_name = string(optarg);
+                break;
+            case 'm':
+                max_depth = stoi(optarg);
+                break;
+            case 'n':
+                min_length = stoi(optarg);
+                break;
+            case 'c':
+                max_cutpoints = stof(optarg);
+                break;
+            case 'p':
+                path = optarg;
+                if (path.back() != '/')
+                    path += '/';
+                break;
+            case '?':
+                usage(argv[0]);
+                exit(1);
+            default:
+                abort();
+        }
+    }
+    if (file_name.empty()) {
+        usage(argv[0]);
+        exit(1);
+    }
+    return make_tuple(file_name, path, max_depth, min_length, max_cutpoints);
+}
+
+void process_file(const string& path, const string& file_name, bool class_last, int max_depth, int min_length,
+    float max_cutpoints)
+{
+    ArffFiles file;
+
+    file.load(path + file_name + ".arff", class_last);
+    const auto attributes = file.getAttributes();
+    const auto items = file.getSize();
+    std::cout << "Number of lines: " << items << std::endl;
+    std::cout << "Attributes: " << std::endl;
+    for (auto attribute : attributes) {
+        std::cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << std::endl;
+    }
+    std::cout << "Class name: " << file.getClassName() << std::endl;
+    std::cout << "Class type: " << file.getClassType() << std::endl;
+    std::cout << "Data: " << std::endl;
+    std::vector<mdlp::samples_t>& X = file.getX();
+    mdlp::labels_t& y = file.getY();
+    for (int i = 0; i < 5; i++) {
+        for (auto feature : X) {
+            std::cout << fixed << setprecision(1) << feature[i] << " ";
+        }
+        std::cout << y[i] << std::endl;
+    }
+    auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
+    size_t total = 0;
+    for (auto i = 0; i < attributes.size(); i++) {
+        auto min_max = minmax_element(X[i].begin(), X[i].end());
+        std::cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
+        test.fit(X[i], y);
+        auto cut_points = test.getCutPoints();
+        for (auto item : cut_points) {
+            std::cout << item;
+            if (item != cut_points.back())
+                std::cout << ", ";
+        }
+        total += test.getCutPoints().size();
+        std::cout << "]" << std::endl;
+        std::cout << "Min: " << *min_max.first << " Max: " << *min_max.second << std::endl;
+        std::cout << "--------------------------" << std::endl;
+    }
+    std::cout << "Total cut points ...: " << total << std::endl;
+    std::cout << "Total feature states: " << total + attributes.size() << std::endl;
+    std::cout << "Version ............: " << test.version() << std::endl;
+    std::cout << "Transformed data (vector)..: " << std::endl;
+    test.fit(X[0], y);
+    auto data = test.transform(X[0]);
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << X[0][i] << " " << data[i] << std::endl;
+    }
+    auto Xt = torch::tensor(X[0], torch::kFloat32);
+    auto yt = torch::tensor(y, torch::kInt32);
+    //test.fit_t(Xt, yt);
+    auto result = test.fit_transform_t(Xt, yt);
+    std::cout << "Transformed data (torch)...: " << std::endl;
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << result[i].item<int>() << std::endl;
+    }
+    auto disc = mdlp::BinDisc(3);
+    auto res_v = disc.fit_transform(X[0], y);
+    disc.fit_t(Xt, yt);
+    auto res_t = disc.transform_t(Xt);
+    std::cout << "Transformed data (BinDisc)...: " << std::endl;
+    for (int i = 130; i < 135; i++) {
+        std::cout << std::fixed << std::setprecision(1) << Xt[i].item<mdlp::precision_t>() << " " << res_v[i] << " " << res_t[i].item<int>() << std::endl;
+    }
+}
+
+void process_all_files(const map<string, bool>& datasets, const string& path, int max_depth, int min_length,
+    float max_cutpoints)
+{
+    std::cout << "Results: " << "Max_depth: " << max_depth << "  Min_length: " << min_length << "  Max_cutpoints: "
+        << max_cutpoints << std::endl << std::endl;
+    printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
+    printf("==================== ==== ==== ========\n");
+    for (const auto& dataset : datasets) {
+        ArffFiles file;
+        file.load(path + dataset.first + ".arff", dataset.second);
+        auto attributes = file.getAttributes();
+        std::vector<mdlp::samples_t>& X = file.getX();
+        mdlp::labels_t& y = file.getY();
+        size_t timing = 0;
+        size_t cut_points = 0;
+        for (auto i = 0; i < attributes.size(); i++) {
+            auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
+            std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
+            test.fit(X[i], y);
+            std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
+            timing += std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count();
+            cut_points += test.getCutPoints().size();
+        }
+        printf("%-20s %4lu %4zu %8zu\n", dataset.first.c_str(), attributes.size(), cut_points, timing);
+    }
+}


 int main(int argc, char** argv)
 {
-    ArffFiles file;
-    string path = "../../tests/datasets/";
-    map<string, bool> datasets = {
-            {"mfeat-factors",      true},
-            {"iris",               true},
-            {"letter",             true},
+    std::map<std::string, bool> datasets = {
+            {"diabetes",           true},
            {"glass",              true},
+            {"iris",               true},
            {"kdd_JapaneseVowels", false},
+            {"letter",             true},
+            {"liver-disorders",    true},
+            {"mfeat-factors",      true},
            {"test",               true}
    };
-    if (argc != 2 || datasets.find(argv[1]) == datasets.end()) {
-        cout << "Usage: " << argv[0] << " {mfeat-factors, glass, iris, letter, kdd_JapaneseVowels, test}" << endl;
-        return 1;
+    std::string file_name;
+    std::string path;
+    int max_depth;
+    int min_length;
+    float max_cutpoints;
+    tie(file_name, path, max_depth, min_length, max_cutpoints) = parse_arguments(argc, argv);
+    if (datasets.find(file_name) == datasets.end() && file_name != "all") {
+        std::cout << "Invalid file name: " << file_name << std::endl;
+        usage(argv[0]);
+        exit(1);
    }
-
-    file.load(path + argv[1] + ".arff", datasets[argv[1]]);
-    auto attributes = file.getAttributes();
-    int items = file.getSize();
-    cout << "Number of lines: " << items << endl;
-    cout << "Attributes: " << endl;
-    for (auto attribute : attributes) {
-        cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
-    }
-    cout << "Class name: " << file.getClassName() << endl;
-    cout << "Class type: " << file.getClassType() << endl;
-    cout << "Data: " << endl;
-    vector<samples_t>& X = file.getX();
-    labels_t& y = file.getY();
-    for (int i = 0; i < 50; i++) {
-        for (auto feature : X) {
-            cout << fixed << setprecision(1) << feature[i] << " ";
-        }
-        cout << y[i] << endl;
-    }
-    mdlp::CPPFImdlp test = mdlp::CPPFImdlp();
-    for (auto i = 0; i < attributes.size(); i++) {
-        auto min_max = minmax_element(X[i].begin(), X[i].end());
-        cout << "Cut points for " << get<0>(attributes[i]) << endl;
-        cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
-        cout << "--------------------------" << setprecision(3) << endl;
-        test.fit(X[i], y);
-        for (auto item : test.getCutPoints()) {
-            cout << item << endl;
-        }
+    if (file_name == "all")
+        process_all_files(datasets, path, max_depth, min_length, max_cutpoints);
+    else {
+        process_file(path, file_name, datasets[file_name], max_depth, min_length, max_cutpoints);
+        std::cout << "File name ....: " << file_name << std::endl;
+        std::cout << "Max depth ....: " << max_depth << std::endl;
+        std::cout << "Min length ...: " << min_length << std::endl;
+        std::cout << "Max cutpoints : " << max_cutpoints << std::endl;
    }
    return 0;
-}
+}
--- a/sonar-project.properties
+++ b/sonar-project.properties
@@ -0,0 +1,14 @@
+sonar.projectKey=rmontanana_mdlp
+sonar.organization=rmontanana
+
+# This is the name and version displayed in the SonarCloud UI.
+sonar.projectName=mdlp
+sonar.projectVersion=2.0.1
+# sonar.test.exclusions=tests/**
+# sonar.tests=tests/
+# sonar.coverage.exclusions=tests/**,sample/**
+# Path is relative to the sonar-project.properties file. Replace "\" by "/" on Windows.
+#sonar.sources=.
+
+# Encoding of the source code. Default is default system encoding
+sonar.sourceEncoding=UTF-8
--- a/src/BinDisc.cpp
+++ b/src/BinDisc.cpp
@@ -0,0 +1,98 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#include <algorithm>
+#include <cmath>
+#include "BinDisc.h"
+#include <iostream>
+#include <string>
+
+namespace mdlp {
+
+    BinDisc::BinDisc(int n_bins, strategy_t strategy) :
+        Discretizer(), n_bins{ n_bins }, strategy{ strategy }
+    {
+        if (n_bins < 3) {
+            throw std::invalid_argument("n_bins must be greater than 2");
+        }
+    }
+    BinDisc::~BinDisc() = default;
+    void BinDisc::fit(samples_t& X)
+    {
+        // y is included for compatibility with the Discretizer interface
+        cutPoints.clear();
+        if (X.empty()) {
+            cutPoints.push_back(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);
+        }
+    }
+    void BinDisc::fit(samples_t& X, labels_t& y)
+    {
+        fit(X);
+    }
+    std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
+    {
+        if (start == end) {
+            return { start, end };
+        }
+        precision_t delta = (end - start) / static_cast<precision_t>(num - 1);
+        std::vector<precision_t> linspc;
+        for (size_t i = 0; i < num; ++i) {
+            precision_t val = start + delta * static_cast<precision_t>(i);
+            linspc.push_back(val);
+        }
+        return linspc;
+    }
+    size_t clip(const size_t n, const size_t lower, const size_t upper)
+    {
+        return std::max(lower, std::min(n, upper));
+    }
+    std::vector<precision_t> percentile(samples_t& data, const std::vector<precision_t>& percentiles)
+    {
+        // 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 auto i = static_cast<size_t>(std::floor(static_cast<precision_t>(data.size() - 1) * percentile / 100.));
+            const auto indexLower = clip(i, 0, data.size() - 2);
+            const precision_t percentI = static_cast<precision_t>(indexLower) / static_cast<precision_t>(data.size() - 1);
+            const precision_t fraction =
+                (percentile / 100.0 - percentI) /
+                (static_cast<precision_t>(indexLower + 1) / static_cast<precision_t>(data.size() - 1) - percentI);
+            if (const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction; value != results.back() || first) // first needed as results.back() return is undefined for empty vectors
+                results.push_back(value);
+            first = false;
+        }
+        return results;
+    }
+    void BinDisc::fit_quantile(const samples_t& X)
+    {
+        auto quantiles = linspace(0.0, 100.0, n_bins + 1);
+        auto data = X;
+        std::sort(data.begin(), data.end());
+        if (data.front() == data.back() || data.size() == 1) {
+            // if X is constant, 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);
+    }
+    void BinDisc::fit_uniform(const samples_t& X)
+    {
+        auto [vmin, vmax] = std::minmax_element(X.begin(), X.end());
+        cutPoints = linspace(*vmin, *vmax, n_bins + 1);
+    }
+}
--- a/src/BinDisc.h
+++ b/src/BinDisc.h
@@ -0,0 +1,33 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef BINDISC_H
+#define BINDISC_H
+
+#include "typesFImdlp.h"
+#include "Discretizer.h"
+#include <string>
+
+namespace mdlp {
+    enum class strategy_t {
+        UNIFORM,
+        QUANTILE
+    };
+    class BinDisc : public Discretizer {
+    public:
+        BinDisc(int n_bins = 3, strategy_t strategy = strategy_t::UNIFORM);
+        ~BinDisc();
+        // y is included for compatibility with the Discretizer interface
+        void fit(samples_t& X_, labels_t& y) override;
+        void fit(samples_t& X);
+    private:
+        void fit_uniform(const samples_t&);
+        void fit_quantile(const samples_t&);
+        int n_bins;
+        strategy_t strategy;
+    };
+}
+#endif
--- a/src/CPPFImdlp.cpp
+++ b/src/CPPFImdlp.cpp
@@ -0,0 +1,221 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#include <numeric>
+#include <algorithm>
+#include <set>
+#include <cmath>
+#include "CPPFImdlp.h"
+
+namespace mdlp {
+
+    CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) :
+        Discretizer(),
+        min_length(min_length_),
+        max_depth(max_depth_),
+        proposed_cuts(proposed)
+    {
+        direction = bound_dir_t::RIGHT;
+    }
+
+    size_t CPPFImdlp::compute_max_num_cut_points() const
+    {
+        // Set the actual maximum number of cut points as a number or as a percentage of the number of samples
+        if (proposed_cuts == 0) {
+            return numeric_limits<size_t>::max();
+        }
+        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<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_)
+    {
+        X = X_;
+        y = y_;
+        num_cut_points = compute_max_num_cut_points();
+        depth = 0;
+        discretizedData.clear();
+        cutPoints.clear();
+        if (X.size() != y.size()) {
+            throw invalid_argument("X and y must have the same size");
+        }
+        if (X.empty() || y.empty()) {
+            throw invalid_argument("X and y must have at least one element");
+        }
+        if (min_length < 3) {
+            throw invalid_argument("min_length must be greater than 2");
+        }
+        if (max_depth < 1) {
+            throw invalid_argument("max_depth must be greater than 0");
+        }
+        indices = sortIndices(X_, y_);
+        metrics.setData(y, indices);
+        computeCutPoints(0, X.size(), 1);
+        sort(cutPoints.begin(), cutPoints.end());
+        if (num_cut_points > 0) {
+            // Select the best (with lower entropy) cut points
+            while (cutPoints.size() > num_cut_points) {
+                resizeCutPoints();
+            }
+        }
+        // Insert first & last X value to the cutpoints as them shall be ignored in transform
+        auto [vmin, vmax] = std::minmax_element(X.begin(), X.end());
+        cutPoints.push_back(*vmax);
+        cutPoints.insert(cutPoints.begin(), *vmin);
+    }
+
+    pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)
+    {
+        size_t n;
+        size_t m;
+        size_t idxPrev = cut - 1 >= start ? cut - 1 : cut;
+        size_t idxNext = cut + 1 < end ? cut + 1 : cut;
+        bool backWall; // true if duplicates reach beginning of the interval
+        precision_t previous;
+        precision_t actual;
+        precision_t next;
+        previous = X[indices[idxPrev]];
+        actual = X[indices[cut]];
+        next = X[indices[idxNext]];
+        // definition 2 of the paper => X[t-1] < X[t]
+        // get the first equal value of X in the interval
+        while (idxPrev > start && actual == previous) {
+            previous = X[indices[--idxPrev]];
+        }
+        backWall = idxPrev == start && actual == previous;
+        // get the last equal value of X in the interval
+        while (idxNext < end - 1 && actual == next) {
+            next = X[indices[++idxNext]];
+        }
+        // # of duplicates before cutpoint
+        n = cut - 1 - idxPrev;
+        // # of duplicates after cutpoint
+        m = idxNext - cut - 1;
+        // Decide which values to use
+        cut = cut + (backWall ? m + 1 : -n);
+        actual = X[indices[cut]];
+        return { (actual + previous) / 2, cut };
+    }
+
+    void CPPFImdlp::computeCutPoints(size_t start, size_t end, int depth_)
+    {
+        size_t cut;
+        pair<precision_t, size_t> result;
+        // Check if the interval length and the depth are Ok
+        if (end - start < min_length || depth_ > max_depth)
+            return;
+        depth = depth_ > depth ? depth_ : depth;
+        cut = getCandidate(start, end);
+        if (cut == numeric_limits<size_t>::max())
+            return;
+        if (mdlp(start, cut, end)) {
+            result = valueCutPoint(start, cut, end);
+            cut = result.second;
+            cutPoints.push_back(result.first);
+            computeCutPoints(start, cut, depth_ + 1);
+            computeCutPoints(cut, end, depth_ + 1);
+        }
+    }
+
+    size_t CPPFImdlp::getCandidate(size_t start, size_t end)
+    {
+        /* Definition 1: A binary discretization for A is determined by selecting the cut point TA for which
+        E(A, TA; S) is minimal amongst all the candidate cut points. */
+        size_t candidate = numeric_limits<size_t>::max();
+        size_t elements = end - start;
+        bool sameValues = true;
+        precision_t entropy_left;
+        precision_t entropy_right;
+        precision_t minEntropy;
+        // Check if all the values of the variable in the interval are the same
+        for (size_t idx = start + 1; idx < end; idx++) {
+            if (X[indices[idx]] != X[indices[start]]) {
+                sameValues = false;
+                break;
+            }
+        }
+        if (sameValues)
+            return candidate;
+        minEntropy = metrics.entropy(start, end);
+        for (size_t idx = start + 1; idx < end; idx++) {
+            // Cutpoints are always on boundaries (definition 2)
+            if (y[indices[idx]] == y[indices[idx - 1]])
+                continue;
+            entropy_left = precision_t(idx - start) / static_cast<precision_t>(elements) * metrics.entropy(start, idx);
+            entropy_right = precision_t(end - idx) / static_cast<precision_t>(elements) * metrics.entropy(idx, end);
+            if (entropy_left + entropy_right < minEntropy) {
+                minEntropy = entropy_left + entropy_right;
+                candidate = idx;
+            }
+        }
+        return candidate;
+    }
+
+    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
+    {
+        int k;
+        int k1;
+        int k2;
+        precision_t ig;
+        precision_t delta;
+        precision_t ent;
+        precision_t ent1;
+        precision_t ent2;
+        auto N = precision_t(end - start);
+        k = metrics.computeNumClasses(start, end);
+        k1 = metrics.computeNumClasses(start, cut);
+        k2 = metrics.computeNumClasses(cut, end);
+        ent = metrics.entropy(start, end);
+        ent1 = metrics.entropy(start, cut);
+        ent2 = metrics.entropy(cut, end);
+        ig = metrics.informationGain(start, cut, end);
+        delta = static_cast<precision_t>(log2(pow(3, precision_t(k)) - 2) -
+            (precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2));
+        precision_t term = 1 / N * (log2(N - 1) + delta);
+        return ig > term;
+    }
+
+    // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
+    indices_t CPPFImdlp::sortIndices(samples_t& X_, labels_t& y_)
+    {
+        indices_t idx(X_.size());
+        std::iota(idx.begin(), idx.end(), 0);
+        stable_sort(idx.begin(), idx.end(), [&X_, &y_](size_t i1, size_t i2) {
+            if (X_[i1] == X_[i2])
+                return y_[i1] < y_[i2];
+            else
+                return X_[i1] < X_[i2];
+            });
+        return idx;
+    }
+
+    void CPPFImdlp::resizeCutPoints()
+    {
+        //Compute entropy of each of the whole cutpoint set and discards the biggest value
+        precision_t maxEntropy = 0;
+        precision_t entropy;
+        size_t maxEntropyIdx = 0;
+        size_t begin = 0;
+        size_t end;
+        for (size_t idx = 0; idx < cutPoints.size(); idx++) {
+            end = begin;
+            while (X[indices[end]] < cutPoints[idx] && end < X.size())
+                end++;
+            entropy = metrics.entropy(begin, end);
+            if (entropy > maxEntropy) {
+                maxEntropy = entropy;
+                maxEntropyIdx = idx;
+            }
+            begin = end;
+        }
+        cutPoints.erase(cutPoints.begin() + static_cast<long>(maxEntropyIdx));
+    }
+
+}
--- a/src/CPPFImdlp.h
+++ b/src/CPPFImdlp.h
@@ -0,0 +1,44 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef CPPFIMDLP_H
+#define CPPFIMDLP_H
+
+#include "typesFImdlp.h"
+#include <limits>
+#include <utility>
+#include <string>
+#include "Metrics.h"
+#include "Discretizer.h"
+
+namespace mdlp {
+    class CPPFImdlp : public Discretizer {
+    public:
+        CPPFImdlp() = default;
+        CPPFImdlp(size_t min_length_, int max_depth_, float proposed);
+        virtual ~CPPFImdlp() = default;
+        void fit(samples_t& X_, labels_t& y_) override;
+        inline int get_depth() const { return depth; };
+    protected:
+        size_t min_length = 3;
+        int depth = 0;
+        int max_depth = numeric_limits<int>::max();
+        float proposed_cuts = 0;
+        indices_t indices = indices_t();
+        samples_t X = samples_t();
+        labels_t y = labels_t();
+        Metrics metrics = Metrics(y, indices);
+        size_t num_cut_points = numeric_limits<size_t>::max();
+        static indices_t sortIndices(samples_t&, labels_t&);
+        void computeCutPoints(size_t, size_t, int);
+        void resizeCutPoints();
+        bool mdlp(size_t, size_t, size_t);
+        size_t getCandidate(size_t, size_t);
+        size_t compute_max_num_cut_points() const;
+        pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
+    };
+}
+#endif
--- a/src/Discretizer.cpp
+++ b/src/Discretizer.cpp
@@ -0,0 +1,54 @@
+// ****************************************************************
+// 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 pos = bound(first, last, item);
+            auto number = pos - first;
+            discretizedData.push_back(static_cast<label_t>(number));
+        }
+        return discretizedData;
+    }
+    labels_t& Discretizer::fit_transform(samples_t& X_, labels_t& y_)
+    {
+        fit(X_, y_);
+        return transform(X_);
+    }
+    void Discretizer::fit_t(const torch::Tensor& X_, const torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        fit(X, y);
+    }
+    torch::Tensor Discretizer::transform_t(const torch::Tensor& X_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        auto result = transform(X);
+        return torch::tensor(result, torch_label_t);
+    }
+    torch::Tensor Discretizer::fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        auto result = fit_transform(X, y);
+        return torch::tensor(result, torch_label_t);
+    }
+}
--- a/src/Discretizer.h
+++ b/src/Discretizer.h
@@ -0,0 +1,39 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef DISCRETIZER_H
+#define DISCRETIZER_H
+
+#include <string>
+#include <algorithm>
+#include "typesFImdlp.h"
+#include <torch/torch.h>
+
+namespace mdlp {
+    enum class bound_dir_t {
+        LEFT,
+        RIGHT
+    };
+    const auto torch_label_t = torch::kInt32;
+    class Discretizer {
+    public:
+        Discretizer() = default;
+        virtual ~Discretizer() = default;
+        inline cutPoints_t getCutPoints() const { return cutPoints; };
+        virtual void fit(samples_t& X_, labels_t& y_) = 0;
+        labels_t& transform(const samples_t& data);
+        labels_t& fit_transform(samples_t& X_, labels_t& y_);
+        void fit_t(const torch::Tensor& X_, const torch::Tensor& y_);
+        torch::Tensor transform_t(const torch::Tensor& X_);
+        torch::Tensor fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_);
+        static inline std::string version() { return "2.0.1"; };
+    protected:
+        labels_t discretizedData = labels_t();
+        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
--- a/src/Metrics.cpp
+++ b/src/Metrics.cpp
@@ -1,20 +1,30 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
 #include "Metrics.h"
 #include <set>
 #include <cmath>
+
 using namespace std;
 namespace mdlp {
-    Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_), numClasses(computeNumClasses(0, indices.size())), entropyCache(cacheEnt_t()), igCache(cacheIg_t())
+    Metrics::Metrics(labels_t& y_, indices_t& indices_) : y(y_), indices(indices_),
+        numClasses(computeNumClasses(0, indices_.size()))
    {
    }
+
    int Metrics::computeNumClasses(size_t start, size_t end)
    {
        set<int> nClasses;
        for (auto i = start; i < end; ++i) {
            nClasses.insert(y[indices[i]]);
        }
-        return nClasses.size();
+        return static_cast<int>(nClasses.size());
    }
-    void Metrics::setData(labels_t& y_, indices_t& indices_)
+
+    void Metrics::setData(const labels_t& y_, const indices_t& indices_)
    {
        indices = indices_;
        y = y_;
@@ -22,9 +32,11 @@ namespace mdlp {
        entropyCache.clear();
        igCache.clear();
    }
+
    precision_t Metrics::entropy(size_t start, size_t end)
    {
-        precision_t p, ventropy = 0;
+        precision_t p;
+        precision_t ventropy = 0;
        int nElements = 0;
        labels_t counts(numClasses + 1, 0);
        if (end - start < 2)
@@ -38,26 +50,33 @@ namespace mdlp {
        }
        for (auto count : counts) {
            if (count > 0) {
-                p = (precision_t)count / nElements;
+                p = static_cast<precision_t>(count) / static_cast<precision_t>(nElements);
                ventropy -= p * log2(p);
            }
        }
        entropyCache[{start, end}] = ventropy;
        return ventropy;
    }
+
    precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)
    {
        precision_t iGain;
-        precision_t entropyInterval, entropyLeft, entropyRight;
-        int nElementsLeft = cut - start, nElementsRight = end - cut;
-        int nElements = end - start;
+        precision_t entropyInterval;
+        precision_t entropyLeft;
+        precision_t entropyRight;
+        size_t nElementsLeft = cut - start;
+        size_t nElementsRight = end - cut;
+        size_t nElements = end - start;
        if (igCache.find(make_tuple(start, cut, end)) != igCache.end()) {
            return igCache[make_tuple(start, cut, end)];
        }
        entropyInterval = entropy(start, end);
        entropyLeft = entropy(start, cut);
        entropyRight = entropy(cut, end);
-        iGain = entropyInterval - ((precision_t)nElementsLeft * entropyLeft + (precision_t)nElementsRight * entropyRight) / nElements;
+        iGain = entropyInterval -
+            (static_cast<precision_t>(nElementsLeft) * entropyLeft +
+                static_cast<precision_t>(nElementsRight) * entropyRight) /
+            static_cast<precision_t>(nElements);
        igCache[make_tuple(start, cut, end)] = iGain;
        return iGain;
    }
--- a/src/Metrics.h
+++ b/src/Metrics.h
@@ -0,0 +1,28 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef CCMETRICS_H
+#define CCMETRICS_H
+
+#include "typesFImdlp.h"
+
+namespace mdlp {
+    class Metrics {
+    protected:
+        labels_t& y;
+        indices_t& indices;
+        int numClasses;
+        cacheEnt_t entropyCache = cacheEnt_t();
+        cacheIg_t igCache = cacheIg_t();
+    public:
+        Metrics(labels_t&, indices_t&);
+        void setData(const labels_t&, const indices_t&);
+        int computeNumClasses(size_t, size_t);
+        precision_t entropy(size_t, size_t);
+        precision_t informationGain(size_t, size_t, size_t);
+    };
+}
+#endif
--- a/src/typesFImdlp.h
+++ b/src/typesFImdlp.h
@@ -0,0 +1,25 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#ifndef TYPES_H
+#define TYPES_H
+
+#include <vector>
+#include <map>
+#include <stdexcept>
+
+using namespace std;
+namespace mdlp {
+    typedef float precision_t;
+    typedef int label_t;
+    typedef std::vector<precision_t> samples_t;
+    typedef std::vector<label_t> labels_t;
+    typedef std::vector<size_t> indices_t;
+    typedef std::vector<precision_t> cutPoints_t;
+    typedef std::map<std::pair<int, int>, precision_t> cacheEnt_t;
+    typedef std::map<std::tuple<int, int, int>, precision_t> cacheIg_t;
+}
+#endif
--- a/tests/ArffFiles.cpp
+++ b/tests/ArffFiles.cpp
@@ -1,116 +0,0 @@
-#include "ArffFiles.h"
-#include <fstream>
-#include <sstream>
-#include <map>
-#include <iostream>
-
-using namespace std;
-
-ArffFiles::ArffFiles()
-{
-}
-vector<string> ArffFiles::getLines()
-{
-    return lines;
-}
-unsigned long int ArffFiles::getSize()
-{
-    return lines.size();
-}
-vector<pair<string, string>> ArffFiles::getAttributes()
-{
-    return attributes;
-}
-string ArffFiles::getClassName()
-{
-    return className;
-}
-string ArffFiles::getClassType()
-{
-    return classType;
-}
-vector<vector<float>>& ArffFiles::getX()
-{
-    return X;
-}
-vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-void ArffFiles::load(string fileName, bool classLast)
-{
-    ifstream file(fileName);
-    string keyword, attribute, type;
-    if (file.is_open()) {
-        string line;
-        while (getline(file, line)) {
-            if (line[0] == '%' || line.empty() || line == "\r" || line == " ") {
-                continue;
-            }
-            if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
-                stringstream ss(line);
-                ss >> keyword >> attribute >> type;
-                attributes.push_back({ attribute, type });
-                continue;
-            }
-            if (line[0] == '@') {
-                continue;
-            }
-            lines.push_back(line);
-        }
-        file.close();
-        if (attributes.empty())
-            throw invalid_argument("No attributes found");
-        if (classLast) {
-            className = get<0>(attributes.back());
-            classType = get<1>(attributes.back());
-            attributes.pop_back();
-        } else {
-            className = get<0>(attributes.front());
-            classType = get<1>(attributes.front());
-            attributes.erase(attributes.begin());
-        }
-        generateDataset(classLast);
-    } else
-        throw invalid_argument("Unable to open file");
-}
-void ArffFiles::generateDataset(bool classLast)
-{
-    X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
-    vector<string> yy = vector<string>(lines.size(), "");
-    int labelIndex = classLast ? attributes.size() : 0;
-    for (int i = 0; i < lines.size(); i++) {
-        stringstream ss(lines[i]);
-        string value;
-        int pos = 0, xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    y = factorize(yy);
-}
-string ArffFiles::trim(const string& source)
-{
-    string s(source);
-    s.erase(0, s.find_first_not_of(" \n\r\t"));
-    s.erase(s.find_last_not_of(" \n\r\t") + 1);
-    return s;
-}
-vector<int> ArffFiles::factorize(const vector<string>& labels_t)
-{
-    vector<int> yy;
-    yy.reserve(labels_t.size());
-    map<string, int> labelMap;
-    int i = 0;
-    for (string label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}
--- a/tests/ArffFiles.h
+++ b/tests/ArffFiles.h
@@ -1,27 +0,0 @@
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-#include <string>
-#include <vector>
-using namespace std;
-class ArffFiles {
-private:
-    vector<string> lines;
-    vector<pair<string, string>> attributes;
-    string className, classType;
-    vector<vector<float>> X;
-    vector<int> y;
-    void generateDataset(bool);
-public:
-    ArffFiles();
-    void load(string, bool = true);
-    vector<string> getLines();
-    unsigned long int getSize();
-    string getClassName();
-    string getClassType();
-    string trim(const string&);
-    vector<vector<float>>& getX();
-    vector<int>& getY();
-    vector<pair<string, string>> getAttributes();
-    vector<int> factorize(const vector<string>& labels_t);
-};
-#endif
--- a/tests/BinDisc_unittest.cpp
+++ b/tests/BinDisc_unittest.cpp
@@ -0,0 +1,411 @@
+// ****************************************************************
+// 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.hpp>
+#include "BinDisc.h"
+#include "Experiments.hpp"
+
+namespace mdlp {
+    const float margin = 1e-4;
+    static std::string set_data_path()
+    {
+        std::string path = "../datasets/";
+        std::ifstream file(path + "iris.arff");
+        if (file.is_open()) {
+            file.close();
+            return path;
+        }
+        return "../../tests/datasets/";
+    }
+    const std::string data_path = set_data_path();
+    class TestBinDisc3U : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc3U(int n_bins = 3) : BinDisc(n_bins, strategy_t::UNIFORM) {};
+    };
+    class TestBinDisc3Q : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc3Q(int n_bins = 3) : BinDisc(n_bins, strategy_t::QUANTILE) {};
+    };
+    class TestBinDisc4U : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc4U(int n_bins = 4) : BinDisc(n_bins, strategy_t::UNIFORM) {};
+    };
+    class TestBinDisc4Q : public BinDisc, public testing::Test {
+    public:
+        TestBinDisc4Q(int n_bins = 4) : BinDisc(n_bins, strategy_t::QUANTILE) {};
+    };
+    TEST_F(TestBinDisc3U, Easy3BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
+        auto y = labels_t();
+        fit(X, y);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3Q, Easy3BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3U, X10BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3Q, X10BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3U, X11BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3U, X11BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3U, ConstantUniform)
+    {
+        samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST_F(TestBinDisc3Q, ConstantQuantile)
+    {
+        samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc3U, EmptyUniform)
+    {
+        samples_t X = {};
+        fit(X);
+        auto cuts = getCutPoints();
+        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();
+        ASSERT_EQ(2, cuts.size());
+        EXPECT_NEAR(0, cuts.at(0), margin);
+        EXPECT_NEAR(0, cuts.at(1), margin);
+    }
+    TEST(TestBinDisc3, ExceptionNumberBins)
+    {
+        EXPECT_THROW(BinDisc(2), std::invalid_argument);
+    }
+    TEST_F(TestBinDisc3U, EasyRepeated)
+    {
+        samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+        ASSERT_EQ(3.0, X[0]); // X is not modified
+    }
+    TEST_F(TestBinDisc3Q, EasyRepeated)
+    {
+        samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+        ASSERT_EQ(3.0, X[0]); // X is not modified
+    }
+    TEST_F(TestBinDisc4U, Easy4BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4Q, Easy4BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4U, X13BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4Q, X13BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4U, X14BinsUniform)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4Q, X14BinsQuantile)
+    {
+        samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4U, X15BinsUniform)
+    {
+        samples_t X = { 15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4Q, X15BinsQuantile)
+    {
+        samples_t X = { 15.0, 13.0, 12.0, 14.0, 6.0, 1.0, 8.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4U, RepeatedValuesUniform)
+    {
+        samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
+        //               0    1     2   3    4    5    6    7    8    9
+        fit(X);
+        auto cuts = getCutPoints();
+        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);
+    }
+    TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
+    {
+        samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
+        //               0    1     2   3    4    5    6    7    8    9
+        fit(X);
+        auto cuts = getCutPoints();
+        ASSERT_EQ(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);
+    }
+    TEST(TestBinDiscGeneric, Fileset)
+    {
+        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;
+    }
+}
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,32 +1,44 @@
-cmake_minimum_required(VERSION 3.14)
-project(FImdlp)
-
-# GoogleTest requires at least C++14
-set(CMAKE_CXX_STANDARD 14)
 include(FetchContent)
-
 include_directories(${GTEST_INCLUDE_DIRS})
-
 FetchContent_Declare(
-  googletest
-  URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
+        googletest
+        URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
 )
 # For Windows: Prevent overriding the parent project's compiler/linker settings
 set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
 FetchContent_MakeAvailable(googletest)

-enable_testing()
+include_directories(
+        ${TORCH_INCLUDE_DIRS}
+        ${fimdlp_SOURCE_DIR}/src
+        ${fimdlp_SOURCE_DIR}/tests/lib/Files
+)

-add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
-add_executable(FImdlp_unittest ../CPPFImdlp.cpp ../ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp)
+add_executable(Metrics_unittest ${fimdlp_SOURCE_DIR}/src/Metrics.cpp Metrics_unittest.cpp)
 target_link_libraries(Metrics_unittest GTest::gtest_main)
-target_link_libraries(FImdlp_unittest GTest::gtest_main)
 target_compile_options(Metrics_unittest PRIVATE --coverage)
-target_compile_options(FImdlp_unittest PRIVATE --coverage)
 target_link_options(Metrics_unittest PRIVATE --coverage)
+
+add_executable(FImdlp_unittest FImdlp_unittest.cpp
+${fimdlp_SOURCE_DIR}/src/CPPFImdlp.cpp ${fimdlp_SOURCE_DIR}/src/Metrics.cpp  ${fimdlp_SOURCE_DIR}/src/Discretizer.cpp)
+target_link_libraries(FImdlp_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
+target_compile_options(FImdlp_unittest PRIVATE --coverage)
 target_link_options(FImdlp_unittest PRIVATE --coverage)

+add_executable(BinDisc_unittest BinDisc_unittest.cpp ${fimdlp_SOURCE_DIR}/src/BinDisc.cpp  ${fimdlp_SOURCE_DIR}/src/Discretizer.cpp)
+target_link_libraries(BinDisc_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
+target_compile_options(BinDisc_unittest PRIVATE --coverage)
+target_link_options(BinDisc_unittest PRIVATE --coverage)
+
+add_executable(Discretizer_unittest Discretizer_unittest.cpp
+${fimdlp_SOURCE_DIR}/src/BinDisc.cpp ${fimdlp_SOURCE_DIR}/src/CPPFImdlp.cpp ${fimdlp_SOURCE_DIR}/src/Metrics.cpp ${fimdlp_SOURCE_DIR}/src/Discretizer.cpp )
+target_link_libraries(Discretizer_unittest GTest::gtest_main "${TORCH_LIBRARIES}")
+target_compile_options(Discretizer_unittest PRIVATE --coverage)
+target_link_options(Discretizer_unittest PRIVATE --coverage)
+
 include(GoogleTest)
+
 gtest_discover_tests(Metrics_unittest)
 gtest_discover_tests(FImdlp_unittest)
-
+gtest_discover_tests(BinDisc_unittest)
+gtest_discover_tests(Discretizer_unittest)
--- a/tests/Discretizer_unittest.cpp
+++ b/tests/Discretizer_unittest.cpp
@@ -0,0 +1,274 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#include <fstream>
+#include <string>
+#include <iostream>
+#include <ArffFiles.hpp>
+#include "gtest/gtest.h"
+#include "Discretizer.h"
+#include "BinDisc.h"
+#include "CPPFImdlp.h"
+
+namespace mdlp {
+    const float margin = 1e-4;
+    static std::string set_data_path()
+    {
+        std::string path = "../datasets/";
+        std::ifstream file(path + "iris.arff");
+        if (file.is_open()) {
+            file.close();
+            return path;
+        }
+        return "../../tests/datasets/";
+    }
+    const std::string data_path = set_data_path();
+    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("2.0.1", version);
+    }
+    TEST(Discretizer, BinIrisUniform)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        auto y = labels_t();
+        disc->fit(X[0], y);
+        auto Xt = disc->transform(X[0]);
+        labels_t expected = { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 1, 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1 };
+        delete disc;
+        EXPECT_EQ(expected, Xt);
+    }
+    TEST(Discretizer, BinIrisQuantile)
+    {
+        ArffFiles file;
+        Discretizer* disc = new BinDisc(4, strategy_t::QUANTILE);
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        auto y = labels_t();
+        disc->fit(X[0], y);
+        auto Xt = disc->transform(X[0]);
+        delete disc;
+        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,
+            5, 3, 5, 4, 5, 4, 4, 4, 4, 0, 1, 1, 4, 0, 2, 0, 0, 3, 0, 2, 2, 4,
+            3, 0, 0, 0, 4, 1, 0, 1, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 3, 5, 4, 0,
+            3, 0, 0, 3, 0, 0, 0, 3, 2, 2, 0, 1, 4, 0, 3, 2, 3, 3, 0, 2, 0, 5,
+            4, 0, 3, 0, 1, 4, 3, 5, 0, 0, 4, 1, 1, 0, 4, 4, 1, 3, 1, 3, 1, 5,
+            1, 1, 0, 3, 5, 4, 3, 4, 4, 4, 0, 4, 4, 3, 0, 3, 5, 3
+        };
+        ArffFiles file;
+        Discretizer* disc = new CPPFImdlp();
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
+        disc->fit(X[1], y);
+        auto computed = disc->transform(X[1]);
+        delete disc;
+        EXPECT_EQ(computed.size(), expected.size());
+        for (unsigned long i = 0; i < computed.size(); i++) {
+            EXPECT_EQ(computed[i], expected[i]);
+        }
+    }
+}
--- a/tests/Experiments.hpp
+++ b/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
--- a/tests/FImdlp_unittest.cpp
+++ b/tests/FImdlp_unittest.cpp
@@ -1,20 +1,54 @@
-#include "gtest/gtest.h"
-#include "../Metrics.h"
-#include "../CPPFImdlp.h"
-#include "ArffFiles.h"
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#include <fstream>
 #include <iostream>
+#include <ArffFiles.hpp>
+#include "gtest/gtest.h"
+#include "Metrics.h"
+#include "CPPFImdlp.h"
+
+#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
+try { \
+stmt; \
+} catch (const etype& ex) { \
+EXPECT_EQ(whatstring, std::string(ex.what())); \
+throw; \
+} \
+, etype)

 namespace mdlp {
-    class TestFImdlp: public CPPFImdlp, public testing::Test {
+    class TestFImdlp : public CPPFImdlp, public testing::Test {
    public:
-        precision_t precision = 0.000001;
-        TestFImdlp(): CPPFImdlp() {}
-        void SetUp()
+        precision_t precision = 0.000001f;
+
+        TestFImdlp() : CPPFImdlp() {}
+
+        string data_path;
+
+        void SetUp() override
        {
-            X = { 4.7, 4.7, 4.7, 4.7, 4.8, 4.8, 4.8, 4.8, 4.9, 4.95, 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
+            X = { 4.7f, 4.7f, 4.7f, 4.7f, 4.8f, 4.8f, 4.8f, 4.8f, 4.9f, 4.95f, 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f,
+                 6.0f, 5.1f, 5.9f };
            y = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
            fit(X, y);
+            data_path = set_data_path();
        }
+
+        static string set_data_path()
+        {
+            string path = "../datasets/";
+            ifstream file(path + "iris.arff");
+            if (file.is_open()) {
+                file.close();
+                return path;
+            }
+            return "../../tests/datasets/";
+        }
+
        void checkSortedVector()
        {
            indices_t testSortedIndices = sortIndices(X, y);
@@ -25,24 +59,17 @@ namespace mdlp {
                prev = X[testSortedIndices[i]];
            }
        }
-        void checkCutPoints(cutPoints_t& expected)
+
+        void checkCutPoints(cutPoints_t& computed, cutPoints_t& expected) const
        {
-            int expectedSize = expected.size();
-            EXPECT_EQ(cutPoints.size(), expectedSize);
-            for (unsigned long i = 0; i < cutPoints.size(); i++) {
-                EXPECT_NEAR(cutPoints[i], expected[i], precision);
+            EXPECT_EQ(computed.size(), expected.size());
+            for (unsigned long i = 0; i < computed.size(); i++) {
+                cout << "(" << computed[i] << ", " << expected[i] << ") ";
+                EXPECT_NEAR(computed[i], expected[i], precision);
            }
        }
-        template<typename T, typename A>
-        void checkVectors(std::vector<T, A> const& expected, std::vector<T, A> const& computed)
-        {
-            EXPECT_EQ(expected.size(), computed.size());
-            ASSERT_EQ(expected.size(), computed.size());
-            for (auto i = 0; i < expected.size(); i++) {
-                EXPECT_NEAR(expected[i], computed[i], precision);
-            }
-        }
-        bool test_result(samples_t& X_, size_t cut, float midPoint, size_t limit, string title)
+
+        bool test_result(const samples_t& X_, size_t cut, float midPoint, size_t limit, const string& title)
        {
            pair<precision_t, size_t> result;
            labels_t y_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
@@ -55,37 +82,90 @@ namespace mdlp {
            EXPECT_EQ(result.second, limit);
            return true;
        }
+
+        void test_dataset(CPPFImdlp& test, const string& filename, vector<cutPoints_t>& expected,
+            vector<int>& depths) const
+        {
+            ArffFiles file;
+            file.load(data_path + filename + ".arff", true);
+            vector<samples_t>& X = file.getX();
+            labels_t& y = file.getY();
+            auto attributes = file.getAttributes();
+            for (auto feature = 0; feature < attributes.size(); feature++) {
+                test.fit(X[feature], y);
+                EXPECT_EQ(test.get_depth(), depths[feature]);
+                auto computed = test.getCutPoints();
+                cout << "Feature " << feature << ": ";
+                checkCutPoints(computed, expected[feature]);
+                cout << endl;
+            }
+        }
    };
+
    TEST_F(TestFImdlp, FitErrorEmptyDataset)
    {
        X = samples_t();
        y = labels_t();
-        EXPECT_THROW(fit(X, y), std::invalid_argument);
+        EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have at least one element");
    }
+
    TEST_F(TestFImdlp, FitErrorDifferentSize)
    {
        X = { 1, 2, 3 };
        y = { 1, 2 };
-        EXPECT_THROW(fit(X, y), std::invalid_argument);
+        EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have the same size");
    }
+
+    TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth)
+    {
+        auto testLength = CPPFImdlp(2, 10, 0);
+        auto testDepth = CPPFImdlp(3, 0, 0);
+        X = { 1, 2, 3 };
+        y = { 1, 2, 3 };
+        EXPECT_THROW_WITH_MESSAGE(testLength.fit(X, y), invalid_argument, "min_length must be greater than 2");
+        EXPECT_THROW_WITH_MESSAGE(testDepth.fit(X, y), invalid_argument, "max_depth must be greater than 0");
+    }
+
+    TEST_F(TestFImdlp, JoinFit)
+    {
+        samples_t X_ = { 1, 2, 2, 3, 4, 2, 3 };
+        labels_t y_ = { 0, 0, 1, 2, 3, 4, 5 };
+        cutPoints_t expected = { 1.0, 1.5f, 2.5f, 4.0 };
+        fit(X_, y_);
+        auto computed = getCutPoints();
+        EXPECT_EQ(computed.size(), expected.size());
+        checkCutPoints(computed, expected);
+    }
+
+    TEST_F(TestFImdlp, FitErrorMaxCutPoints)
+    {
+        auto testmin = CPPFImdlp(2, 10, -1);
+        auto testmax = CPPFImdlp(3, 0, 200);
+        X = { 1, 2, 3 };
+        y = { 1, 2, 3 };
+        EXPECT_THROW_WITH_MESSAGE(testmin.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
+        EXPECT_THROW_WITH_MESSAGE(testmax.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
+    }
+
    TEST_F(TestFImdlp, SortIndices)
    {
-        X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
+        X = { 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f, 6.0f, 5.1f, 5.9f };
        y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
        indices = { 4, 3, 6, 8, 2, 1, 5, 0, 9, 7 };
        checkSortedVector();
-        X = { 5.77, 5.88, 5.99 };
+        X = { 5.77f, 5.88f, 5.99f };
        y = { 1, 2, 1 };
        indices = { 0, 1, 2 };
        checkSortedVector();
-        X = { 5.33, 5.22, 5.11 };
+        X = { 5.33f, 5.22f, 5.11f };
        y = { 1, 2, 1 };
        indices = { 2, 1, 0 };
        checkSortedVector();
-        X = { 5.33, 5.22, 5.33 };
+        X = { 5.33f, 5.22f, 5.33f };
        y = { 2, 2, 1 };
        indices = { 1, 2, 0 };
    }
+
    TEST_F(TestFImdlp, TestShortDatasets)
    {
        vector<precision_t> computed;
@@ -93,89 +173,194 @@ 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);
-        computeCutPoints(0, 20);
-        cutPoints_t expected = { 5.05 };
+        cutPoints_t expected = { 4.7, 5.05, 6.0 };
        vector<precision_t> computed = getCutPoints();
-        computed = getCutPoints();
-        int expectedSize = expected.size();
        EXPECT_EQ(computed.size(), expected.size());
        for (unsigned long i = 0; i < computed.size(); i++) {
            EXPECT_NEAR(computed[i], expected[i], precision);
        }
    }
+
    TEST_F(TestFImdlp, TestIris)
    {
-        ArffFiles file;
-        string path = "../datasets/";
-
-        file.load(path + "iris.arff", true);
-        int items = file.getSize();
-        vector<samples_t>& X = file.getX();
        vector<cutPoints_t> expected = {
-            { 5.4499998092651367, 5.75 },
-            { 2.75, 2.85, 2.95, 3.05, 3.35 },
-            { 2.4500000476837158, 4.75, 5.0500001907348633 },
-            { 0.80000001192092896, 1.75 }
+                {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}
        };
-        labels_t& y = file.getY();
-        auto attributes = file.getAttributes();
-        for (auto feature = 0; feature < attributes.size(); feature++) {
-            fit(X[feature], y);
-            vector<precision_t> computed = getCutPoints();
-            EXPECT_EQ(computed.size(), expected[feature].size());
-            for (auto i = 0; i < computed.size(); i++) {
-                EXPECT_NEAR(computed[i], expected[feature][i], precision);
-            }
-        }
+        vector<int> depths = { 3, 5, 4, 3 };
+        auto test = CPPFImdlp();
+        test_dataset(test, "iris", expected, depths);
    }
+
    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_);
-        checkCutPoints(expected);
+        auto computed = getCutPoints();
+        checkCutPoints(computed, expected);
    }
+
    TEST_F(TestFImdlp, ValueCutPoint)
    {
        // Case titles as stated in the doc
-        samples_t X1a{ 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0 };
-        test_result(X1a, 6, 7.3 / 2, 6, "1a");
-        samples_t X2a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
-        test_result(X2a, 6, 7.1 / 2, 4, "2a");
-        samples_t X2b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
-        test_result(X2b, 6, 7.5 / 2, 7, "2b");
-        samples_t X3a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
-        test_result(X3a, 4, 7.1 / 2, 4, "3a");
-        samples_t X3b = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
-        test_result(X3b, 4, 7.1 / 2, 4, "3b");
-        samples_t X4a = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.9, 4.0 };
-        test_result(X4a, 4, 6.9 / 2, 2, "4a");
-        samples_t X4b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
-        test_result(X4b, 4, 7.5 / 2, 7, "4b");
-        samples_t X4c = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
-        test_result(X4c, 4, 6.9 / 2, 2, "4c");
+        samples_t X1a{ 3.1f, 3.2f, 3.3f, 3.4f, 3.5f, 3.6f, 3.7f, 3.8f, 3.9f, 4.0f };
+        test_result(X1a, 6, 7.3f / 2, 6, "1a");
+        samples_t X2a = { 3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
+        test_result(X2a, 6, 7.1f / 2, 4, "2a");
+        samples_t X2b = { 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
+        test_result(X2b, 6, 7.5f / 2, 7, "2b");
+        samples_t X3a = { 3.f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
+        test_result(X3a, 4, 7.1f / 2, 4, "3a");
+        samples_t X3b = { 3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f };
+        test_result(X3b, 4, 7.1f / 2, 4, "3b");
+        samples_t X4a = { 3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.9f, 4.0f };
+        test_result(X4a, 4, 6.9f / 2, 2, "4a");
+        samples_t X4b = { 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f };
+        test_result(X4b, 4, 7.5f / 2, 7, "4b");
+        samples_t X4c = { 3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f };
+        test_result(X4c, 4, 6.9f / 2, 2, "4c");
+    }
+
+    TEST_F(TestFImdlp, MaxDepth)
+    {
+        // Set max_depth to 1
+        auto test = CPPFImdlp(3, 1, 0);
+        vector<cutPoints_t> expected = {
+                {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);
+    }
+
+    TEST_F(TestFImdlp, MinLength)
+    {
+        auto test = CPPFImdlp(75, 100, 0);
+        // Set min_length to 75
+        vector<cutPoints_t> expected = {
+                {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);
+    }
+
+    TEST_F(TestFImdlp, MinLengthMaxDepth)
+    {
+        // Set min_length to 75
+        auto test = CPPFImdlp(75, 2, 0);
+        vector<cutPoints_t> expected = {
+                {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);
+    }
+
+    TEST_F(TestFImdlp, MaxCutPointsInteger)
+    {
+        // Set min_length to 75
+        auto test = CPPFImdlp(75, 2, 1);
+        vector<cutPoints_t> expected = {
+                {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);
+
+    }
+
+    TEST_F(TestFImdlp, MaxCutPointsFloat)
+    {
+        // Set min_length to 75
+        auto test = CPPFImdlp(75, 2, 0.2f);
+        vector<cutPoints_t> expected = {
+                {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);
+    }
+
+    TEST_F(TestFImdlp, ProposedCuts)
+    {
+        vector<pair<float, size_t>> proposed_list = { {0.1f,  2},
+                                                     {0.5f,  10},
+                                                     {0.07f, 1},
+                                                     {1.0f,  1},
+                                                     {2.0f,  2} };
+        size_t expected;
+        size_t computed;
+        for (auto proposed_item : proposed_list) {
+            tie(proposed_cuts, expected) = proposed_item;
+            computed = compute_max_num_cut_points();
+            ASSERT_EQ(expected, computed);
+        }
+    }
+    TEST_F(TestFImdlp, TransformTest)
+    {
+        labels_t expected = {
+            5, 3, 4, 4, 5, 5, 5, 5, 2, 4, 5, 5, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5,
+            5, 4, 5, 3, 5, 5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 4, 3, 5, 5, 0, 4, 5,
+            5, 3, 5, 4, 5, 4, 4, 4, 4, 0, 1, 1, 4, 0, 2, 0, 0, 3, 0, 2, 2, 4,
+            3, 0, 0, 0, 4, 1, 0, 1, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 3, 5, 4, 0,
+            3, 0, 0, 3, 0, 0, 0, 3, 2, 2, 0, 1, 4, 0, 3, 2, 3, 3, 0, 2, 0, 5,
+            4, 0, 3, 0, 1, 4, 3, 5, 0, 0, 4, 1, 1, 0, 4, 4, 1, 3, 1, 3, 1, 5,
+            1, 1, 0, 3, 5, 4, 3, 4, 4, 4, 0, 4, 4, 3, 0, 3, 5, 3
+        };
+        ArffFiles file;
+        file.load(data_path + "iris.arff", true);
+        vector<samples_t>& X = file.getX();
+        labels_t& y = file.getY();
+        fit(X[1], y);
+        auto computed = transform(X[1]);
+        EXPECT_EQ(computed.size(), expected.size());
+        for (unsigned long i = 0; i < computed.size(); i++) {
+            EXPECT_EQ(computed[i], expected[i]);
+        }
+        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]);
+        }
    }
 }
--- a/tests/Metrics_unittest.cpp
+++ b/tests/Metrics_unittest.cpp
@@ -1,23 +1,27 @@
-#include "gtest/gtest.h"
-#include "../Metrics.h"
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************

+#include "gtest/gtest.h"
+#include "Metrics.h"

 namespace mdlp {
-    class TestMetrics: public Metrics, public testing::Test {
+    class TestMetrics : public Metrics, public testing::Test {
    public:
-        labels_t y;
-        samples_t X;
-        indices_t indices;
-        precision_t precision = 0.000001;
+        labels_t y_ = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
+        indices_t indices_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+        precision_t precision = 1e-6;

-        TestMetrics(): Metrics(y, indices) {}
-        void SetUp()
+        TestMetrics() : Metrics(y_, indices_) {};
+
+        void SetUp() override
        {
-            y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
-            indices = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
-            setData(y, indices);
+            setData(y_, indices_);
        }
    };
+
    TEST_F(TestMetrics, NumClasses)
    {
        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
@@ -25,19 +29,31 @@ namespace mdlp {
        EXPECT_EQ(2, computeNumClasses(0, 10));
        EXPECT_EQ(2, computeNumClasses(8, 10));
    }
+
    TEST_F(TestMetrics, Entropy)
    {
        EXPECT_EQ(1, entropy(0, 10));
        EXPECT_EQ(0, entropy(0, 5));
        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
        setData(y, indices);
-        ASSERT_NEAR(0.468996, entropy(0, 10), precision);
+        ASSERT_NEAR(0.468996f, entropy(0, 10), precision);
    }
+
+    TEST_F(TestMetrics, EntropyDouble)
+    {
+        y = { 0, 0, 1, 2, 3 };
+        samples_t expected_entropies = { 0.0, 0.0, 0.91829583, 1.5, 1.4575424759098898 };
+        for (auto idx = 0; idx < y.size(); ++idx) {
+            ASSERT_NEAR(expected_entropies[idx], entropy(0, idx + 1), precision);
+        }
+    }
+
    TEST_F(TestMetrics, InformationGain)
    {
        ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
+        ASSERT_NEAR(1, informationGain(0, 5, 10), precision); // For cache
        y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
        setData(y, indices);
-        ASSERT_NEAR(0.108032, informationGain(0, 5, 10), precision);
+        ASSERT_NEAR(0.108032f, informationGain(0, 5, 10), precision);
    }
 }
--- a/tests/cover
+++ b/tests/cover
@@ -1,4 +0,0 @@
-rm -fr lcoverage/*
-lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
-genhtml lcoverage/main_coverage.info --output-directory lcoverage
-open lcoverage/index.html
--- a/tests/datasets/diabetes.arff
+++ b/tests/datasets/diabetes.arff
@@ -0,0 +1,863 @@
+% 1. Title: Pima Indians Diabetes Database
+% 
+% 2. Sources:
+%    (a) Original owners: National Institute of Diabetes and Digestive and
+%                         Kidney Diseases
+%    (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)
+%                           Research Center, RMI Group Leader
+%                           Applied Physics Laboratory
+%                           The Johns Hopkins University
+%                           Johns Hopkins Road
+%                           Laurel, MD 20707
+%                           (301) 953-6231
+%    (c) Date received: 9 May 1990
+% 
+% 3. Past Usage:
+%     1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
+%        Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
+%        the onset of diabetes mellitus.  In {\it Proceedings of the Symposium
+%        on Computer Applications and Medical Care} (pp. 261--265).  IEEE
+%        Computer Society Press.
+% 
+%        The diagnostic, binary-valued variable investigated is whether the
+%        patient shows signs of diabetes according to World Health Organization
+%        criteria (i.e., if the 2 hour post-load plasma glucose was at least 
+%        200 mg/dl at any survey  examination or if found during routine medical
+%        care).   The population lives near Phoenix, Arizona, USA.
+% 
+%        Results: Their ADAP algorithm makes a real-valued prediction between
+%        0 and 1.  This was transformed into a binary decision using a cutoff of 
+%        0.448.  Using 576 training instances, the sensitivity and specificity
+%        of their algorithm was 76% on the remaining 192 instances.
+% 
+% 4. Relevant Information:
+%       Several constraints were placed on the selection of these instances from
+%       a larger database.  In particular, all patients here are females at
+%       least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning
+%       routine that generates and executes digital analogs of perceptron-like
+%       devices.  It is a unique algorithm; see the paper for details.
+% 
+% 5. Number of Instances: 768
+% 
+% 6. Number of Attributes: 8 plus class 
+% 
+% 7. For Each Attribute: (all numeric-valued)
+%    1. Number of times pregnant
+%    2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
+%    3. Diastolic blood pressure (mm Hg)
+%    4. Triceps skin fold thickness (mm)
+%    5. 2-Hour serum insulin (mu U/ml)
+%    6. Body mass index (weight in kg/(height in m)^2)
+%    7. Diabetes pedigree function
+%    8. Age (years)
+%    9. Class variable (0 or 1)
+% 
+% 8. Missing Attribute Values: None
+% 
+% 9. Class Distribution: (class value 1 is interpreted as "tested positive for
+%    diabetes")
+% 
+%    Class Value  Number of instances
+%    0            500
+%    1            268
+% 
+% 10. Brief statistical analysis:
+% 
+%     Attribute number:    Mean:   Standard Deviation:
+%     1.                     3.8     3.4
+%     2.                   120.9    32.0
+%     3.                    69.1    19.4
+%     4.                    20.5    16.0
+%     5.                    79.8   115.2
+%     6.                    32.0     7.9
+%     7.                     0.5     0.3
+%     8.                    33.2    11.8
+% 
+% 
+%
+%
+%
+%
+% Relabeled values in attribute 'class'
+%    From: 0                       To: tested_negative     
+%    From: 1                       To: tested_positive     
+%
+@relation pima_diabetes
+@attribute 'preg' real
+@attribute 'plas' real
+@attribute 'pres' real
+@attribute 'skin' real
+@attribute 'insu' real
+@attribute 'mass' real
+@attribute 'pedi' real
+@attribute 'age' real
+@attribute 'class' { tested_negative, tested_positive}
+@data
+6,148,72,35,0,33.6,0.627,50,tested_positive
+1,85,66,29,0,26.6,0.351,31,tested_negative
+8,183,64,0,0,23.3,0.672,32,tested_positive
+1,89,66,23,94,28.1,0.167,21,tested_negative
+0,137,40,35,168,43.1,2.288,33,tested_positive
+5,116,74,0,0,25.6,0.201,30,tested_negative
+3,78,50,32,88,31,0.248,26,tested_positive
+10,115,0,0,0,35.3,0.134,29,tested_negative
+2,197,70,45,543,30.5,0.158,53,tested_positive
+8,125,96,0,0,0,0.232,54,tested_positive
+4,110,92,0,0,37.6,0.191,30,tested_negative
+10,168,74,0,0,38,0.537,34,tested_positive
+10,139,80,0,0,27.1,1.441,57,tested_negative
+1,189,60,23,846,30.1,0.398,59,tested_positive
+5,166,72,19,175,25.8,0.587,51,tested_positive
+7,100,0,0,0,30,0.484,32,tested_positive
+0,118,84,47,230,45.8,0.551,31,tested_positive
+7,107,74,0,0,29.6,0.254,31,tested_positive
+1,103,30,38,83,43.3,0.183,33,tested_negative
+1,115,70,30,96,34.6,0.529,32,tested_positive
+3,126,88,41,235,39.3,0.704,27,tested_negative
+8,99,84,0,0,35.4,0.388,50,tested_negative
+7,196,90,0,0,39.8,0.451,41,tested_positive
+9,119,80,35,0,29,0.263,29,tested_positive
+11,143,94,33,146,36.6,0.254,51,tested_positive
+10,125,70,26,115,31.1,0.205,41,tested_positive
+7,147,76,0,0,39.4,0.257,43,tested_positive
+1,97,66,15,140,23.2,0.487,22,tested_negative
+13,145,82,19,110,22.2,0.245,57,tested_negative
+5,117,92,0,0,34.1,0.337,38,tested_negative
+5,109,75,26,0,36,0.546,60,tested_negative
+3,158,76,36,245,31.6,0.851,28,tested_positive
+3,88,58,11,54,24.8,0.267,22,tested_negative
+6,92,92,0,0,19.9,0.188,28,tested_negative
+10,122,78,31,0,27.6,0.512,45,tested_negative
+4,103,60,33,192,24,0.966,33,tested_negative
+11,138,76,0,0,33.2,0.42,35,tested_negative
+9,102,76,37,0,32.9,0.665,46,tested_positive
+2,90,68,42,0,38.2,0.503,27,tested_positive
+4,111,72,47,207,37.1,1.39,56,tested_positive
+3,180,64,25,70,34,0.271,26,tested_negative
+7,133,84,0,0,40.2,0.696,37,tested_negative
+7,106,92,18,0,22.7,0.235,48,tested_negative
+9,171,110,24,240,45.4,0.721,54,tested_positive
+7,159,64,0,0,27.4,0.294,40,tested_negative
+0,180,66,39,0,42,1.893,25,tested_positive
+1,146,56,0,0,29.7,0.564,29,tested_negative
+2,71,70,27,0,28,0.586,22,tested_negative
+7,103,66,32,0,39.1,0.344,31,tested_positive
+7,105,0,0,0,0,0.305,24,tested_negative
+1,103,80,11,82,19.4,0.491,22,tested_negative
+1,101,50,15,36,24.2,0.526,26,tested_negative
+5,88,66,21,23,24.4,0.342,30,tested_negative
+8,176,90,34,300,33.7,0.467,58,tested_positive
+7,150,66,42,342,34.7,0.718,42,tested_negative
+1,73,50,10,0,23,0.248,21,tested_negative
+7,187,68,39,304,37.7,0.254,41,tested_positive
+0,100,88,60,110,46.8,0.962,31,tested_negative
+0,146,82,0,0,40.5,1.781,44,tested_negative
+0,105,64,41,142,41.5,0.173,22,tested_negative
+2,84,0,0,0,0,0.304,21,tested_negative
+8,133,72,0,0,32.9,0.27,39,tested_positive
+5,44,62,0,0,25,0.587,36,tested_negative
+2,141,58,34,128,25.4,0.699,24,tested_negative
+7,114,66,0,0,32.8,0.258,42,tested_positive
+5,99,74,27,0,29,0.203,32,tested_negative
+0,109,88,30,0,32.5,0.855,38,tested_positive
+2,109,92,0,0,42.7,0.845,54,tested_negative
+1,95,66,13,38,19.6,0.334,25,tested_negative
+4,146,85,27,100,28.9,0.189,27,tested_negative
+2,100,66,20,90,32.9,0.867,28,tested_positive
+5,139,64,35,140,28.6,0.411,26,tested_negative
+13,126,90,0,0,43.4,0.583,42,tested_positive
+4,129,86,20,270,35.1,0.231,23,tested_negative
+1,79,75,30,0,32,0.396,22,tested_negative
+1,0,48,20,0,24.7,0.14,22,tested_negative
+7,62,78,0,0,32.6,0.391,41,tested_negative
+5,95,72,33,0,37.7,0.37,27,tested_negative
+0,131,0,0,0,43.2,0.27,26,tested_positive
+2,112,66,22,0,25,0.307,24,tested_negative
+3,113,44,13,0,22.4,0.14,22,tested_negative
+2,74,0,0,0,0,0.102,22,tested_negative
+7,83,78,26,71,29.3,0.767,36,tested_negative
+0,101,65,28,0,24.6,0.237,22,tested_negative
+5,137,108,0,0,48.8,0.227,37,tested_positive
+2,110,74,29,125,32.4,0.698,27,tested_negative
+13,106,72,54,0,36.6,0.178,45,tested_negative
+2,100,68,25,71,38.5,0.324,26,tested_negative
+15,136,70,32,110,37.1,0.153,43,tested_positive
+1,107,68,19,0,26.5,0.165,24,tested_negative
+1,80,55,0,0,19.1,0.258,21,tested_negative
+4,123,80,15,176,32,0.443,34,tested_negative
+7,81,78,40,48,46.7,0.261,42,tested_negative
+4,134,72,0,0,23.8,0.277,60,tested_positive
+2,142,82,18,64,24.7,0.761,21,tested_negative
+6,144,72,27,228,33.9,0.255,40,tested_negative
+2,92,62,28,0,31.6,0.13,24,tested_negative
+1,71,48,18,76,20.4,0.323,22,tested_negative
+6,93,50,30,64,28.7,0.356,23,tested_negative
+1,122,90,51,220,49.7,0.325,31,tested_positive
+1,163,72,0,0,39,1.222,33,tested_positive
+1,151,60,0,0,26.1,0.179,22,tested_negative
+0,125,96,0,0,22.5,0.262,21,tested_negative
+1,81,72,18,40,26.6,0.283,24,tested_negative
+2,85,65,0,0,39.6,0.93,27,tested_negative
+1,126,56,29,152,28.7,0.801,21,tested_negative
+1,96,122,0,0,22.4,0.207,27,tested_negative
+4,144,58,28,140,29.5,0.287,37,tested_negative
+3,83,58,31,18,34.3,0.336,25,tested_negative
+0,95,85,25,36,37.4,0.247,24,tested_positive
+3,171,72,33,135,33.3,0.199,24,tested_positive
+8,155,62,26,495,34,0.543,46,tested_positive
+1,89,76,34,37,31.2,0.192,23,tested_negative
+4,76,62,0,0,34,0.391,25,tested_negative
+7,160,54,32,175,30.5,0.588,39,tested_positive
+4,146,92,0,0,31.2,0.539,61,tested_positive
+5,124,74,0,0,34,0.22,38,tested_positive
+5,78,48,0,0,33.7,0.654,25,tested_negative
+4,97,60,23,0,28.2,0.443,22,tested_negative
+4,99,76,15,51,23.2,0.223,21,tested_negative
+0,162,76,56,100,53.2,0.759,25,tested_positive
+6,111,64,39,0,34.2,0.26,24,tested_negative
+2,107,74,30,100,33.6,0.404,23,tested_negative
+5,132,80,0,0,26.8,0.186,69,tested_negative
+0,113,76,0,0,33.3,0.278,23,tested_positive
+1,88,30,42,99,55,0.496,26,tested_positive
+3,120,70,30,135,42.9,0.452,30,tested_negative
+1,118,58,36,94,33.3,0.261,23,tested_negative
+1,117,88,24,145,34.5,0.403,40,tested_positive
+0,105,84,0,0,27.9,0.741,62,tested_positive
+4,173,70,14,168,29.7,0.361,33,tested_positive
+9,122,56,0,0,33.3,1.114,33,tested_positive
+3,170,64,37,225,34.5,0.356,30,tested_positive
+8,84,74,31,0,38.3,0.457,39,tested_negative
+2,96,68,13,49,21.1,0.647,26,tested_negative
+2,125,60,20,140,33.8,0.088,31,tested_negative
+0,100,70,26,50,30.8,0.597,21,tested_negative
+0,93,60,25,92,28.7,0.532,22,tested_negative
+0,129,80,0,0,31.2,0.703,29,tested_negative
+5,105,72,29,325,36.9,0.159,28,tested_negative
+3,128,78,0,0,21.1,0.268,55,tested_negative
+5,106,82,30,0,39.5,0.286,38,tested_negative
+2,108,52,26,63,32.5,0.318,22,tested_negative
+10,108,66,0,0,32.4,0.272,42,tested_positive
+4,154,62,31,284,32.8,0.237,23,tested_negative
+0,102,75,23,0,0,0.572,21,tested_negative
+9,57,80,37,0,32.8,0.096,41,tested_negative
+2,106,64,35,119,30.5,1.4,34,tested_negative
+5,147,78,0,0,33.7,0.218,65,tested_negative
+2,90,70,17,0,27.3,0.085,22,tested_negative
+1,136,74,50,204,37.4,0.399,24,tested_negative
+4,114,65,0,0,21.9,0.432,37,tested_negative
+9,156,86,28,155,34.3,1.189,42,tested_positive
+1,153,82,42,485,40.6,0.687,23,tested_negative
+8,188,78,0,0,47.9,0.137,43,tested_positive
+7,152,88,44,0,50,0.337,36,tested_positive
+2,99,52,15,94,24.6,0.637,21,tested_negative
+1,109,56,21,135,25.2,0.833,23,tested_negative
+2,88,74,19,53,29,0.229,22,tested_negative
+17,163,72,41,114,40.9,0.817,47,tested_positive
+4,151,90,38,0,29.7,0.294,36,tested_negative
+7,102,74,40,105,37.2,0.204,45,tested_negative
+0,114,80,34,285,44.2,0.167,27,tested_negative
+2,100,64,23,0,29.7,0.368,21,tested_negative
+0,131,88,0,0,31.6,0.743,32,tested_positive
+6,104,74,18,156,29.9,0.722,41,tested_positive
+3,148,66,25,0,32.5,0.256,22,tested_negative
+4,120,68,0,0,29.6,0.709,34,tested_negative
+4,110,66,0,0,31.9,0.471,29,tested_negative
+3,111,90,12,78,28.4,0.495,29,tested_negative
+6,102,82,0,0,30.8,0.18,36,tested_positive
+6,134,70,23,130,35.4,0.542,29,tested_positive
+2,87,0,23,0,28.9,0.773,25,tested_negative
+1,79,60,42,48,43.5,0.678,23,tested_negative
+2,75,64,24,55,29.7,0.37,33,tested_negative
+8,179,72,42,130,32.7,0.719,36,tested_positive
+6,85,78,0,0,31.2,0.382,42,tested_negative
+0,129,110,46,130,67.1,0.319,26,tested_positive
+5,143,78,0,0,45,0.19,47,tested_negative
+5,130,82,0,0,39.1,0.956,37,tested_positive
+6,87,80,0,0,23.2,0.084,32,tested_negative
+0,119,64,18,92,34.9,0.725,23,tested_negative
+1,0,74,20,23,27.7,0.299,21,tested_negative
+5,73,60,0,0,26.8,0.268,27,tested_negative
+4,141,74,0,0,27.6,0.244,40,tested_negative
+7,194,68,28,0,35.9,0.745,41,tested_positive
+8,181,68,36,495,30.1,0.615,60,tested_positive
+1,128,98,41,58,32,1.321,33,tested_positive
+8,109,76,39,114,27.9,0.64,31,tested_positive
+5,139,80,35,160,31.6,0.361,25,tested_positive
+3,111,62,0,0,22.6,0.142,21,tested_negative
+9,123,70,44,94,33.1,0.374,40,tested_negative
+7,159,66,0,0,30.4,0.383,36,tested_positive
+11,135,0,0,0,52.3,0.578,40,tested_positive
+8,85,55,20,0,24.4,0.136,42,tested_negative
+5,158,84,41,210,39.4,0.395,29,tested_positive
+1,105,58,0,0,24.3,0.187,21,tested_negative
+3,107,62,13,48,22.9,0.678,23,tested_positive
+4,109,64,44,99,34.8,0.905,26,tested_positive
+4,148,60,27,318,30.9,0.15,29,tested_positive
+0,113,80,16,0,31,0.874,21,tested_negative
+1,138,82,0,0,40.1,0.236,28,tested_negative
+0,108,68,20,0,27.3,0.787,32,tested_negative
+2,99,70,16,44,20.4,0.235,27,tested_negative
+6,103,72,32,190,37.7,0.324,55,tested_negative
+5,111,72,28,0,23.9,0.407,27,tested_negative
+8,196,76,29,280,37.5,0.605,57,tested_positive
+5,162,104,0,0,37.7,0.151,52,tested_positive
+1,96,64,27,87,33.2,0.289,21,tested_negative
+7,184,84,33,0,35.5,0.355,41,tested_positive
+2,81,60,22,0,27.7,0.29,25,tested_negative
+0,147,85,54,0,42.8,0.375,24,tested_negative
+7,179,95,31,0,34.2,0.164,60,tested_negative
+0,140,65,26,130,42.6,0.431,24,tested_positive
+9,112,82,32,175,34.2,0.26,36,tested_positive
+12,151,70,40,271,41.8,0.742,38,tested_positive
+5,109,62,41,129,35.8,0.514,25,tested_positive
+6,125,68,30,120,30,0.464,32,tested_negative
+5,85,74,22,0,29,1.224,32,tested_positive
+5,112,66,0,0,37.8,0.261,41,tested_positive
+0,177,60,29,478,34.6,1.072,21,tested_positive
+2,158,90,0,0,31.6,0.805,66,tested_positive
+7,119,0,0,0,25.2,0.209,37,tested_negative
+7,142,60,33,190,28.8,0.687,61,tested_negative
+1,100,66,15,56,23.6,0.666,26,tested_negative
+1,87,78,27,32,34.6,0.101,22,tested_negative
+0,101,76,0,0,35.7,0.198,26,tested_negative
+3,162,52,38,0,37.2,0.652,24,tested_positive
+4,197,70,39,744,36.7,2.329,31,tested_negative
+0,117,80,31,53,45.2,0.089,24,tested_negative
+4,142,86,0,0,44,0.645,22,tested_positive
+6,134,80,37,370,46.2,0.238,46,tested_positive
+1,79,80,25,37,25.4,0.583,22,tested_negative
+4,122,68,0,0,35,0.394,29,tested_negative
+3,74,68,28,45,29.7,0.293,23,tested_negative
+4,171,72,0,0,43.6,0.479,26,tested_positive
+7,181,84,21,192,35.9,0.586,51,tested_positive
+0,179,90,27,0,44.1,0.686,23,tested_positive
+9,164,84,21,0,30.8,0.831,32,tested_positive
+0,104,76,0,0,18.4,0.582,27,tested_negative
+1,91,64,24,0,29.2,0.192,21,tested_negative
+4,91,70,32,88,33.1,0.446,22,tested_negative
+3,139,54,0,0,25.6,0.402,22,tested_positive
+6,119,50,22,176,27.1,1.318,33,tested_positive
+2,146,76,35,194,38.2,0.329,29,tested_negative
+9,184,85,15,0,30,1.213,49,tested_positive
+10,122,68,0,0,31.2,0.258,41,tested_negative
+0,165,90,33,680,52.3,0.427,23,tested_negative
+9,124,70,33,402,35.4,0.282,34,tested_negative
+1,111,86,19,0,30.1,0.143,23,tested_negative
+9,106,52,0,0,31.2,0.38,42,tested_negative
+2,129,84,0,0,28,0.284,27,tested_negative
+2,90,80,14,55,24.4,0.249,24,tested_negative
+0,86,68,32,0,35.8,0.238,25,tested_negative
+12,92,62,7,258,27.6,0.926,44,tested_positive
+1,113,64,35,0,33.6,0.543,21,tested_positive
+3,111,56,39,0,30.1,0.557,30,tested_negative
+2,114,68,22,0,28.7,0.092,25,tested_negative
+1,193,50,16,375,25.9,0.655,24,tested_negative
+11,155,76,28,150,33.3,1.353,51,tested_positive
+3,191,68,15,130,30.9,0.299,34,tested_negative
+3,141,0,0,0,30,0.761,27,tested_positive
+4,95,70,32,0,32.1,0.612,24,tested_negative
+3,142,80,15,0,32.4,0.2,63,tested_negative
+4,123,62,0,0,32,0.226,35,tested_positive
+5,96,74,18,67,33.6,0.997,43,tested_negative
+0,138,0,0,0,36.3,0.933,25,tested_positive
+2,128,64,42,0,40,1.101,24,tested_negative
+0,102,52,0,0,25.1,0.078,21,tested_negative
+2,146,0,0,0,27.5,0.24,28,tested_positive
+10,101,86,37,0,45.6,1.136,38,tested_positive
+2,108,62,32,56,25.2,0.128,21,tested_negative
+3,122,78,0,0,23,0.254,40,tested_negative
+1,71,78,50,45,33.2,0.422,21,tested_negative
+13,106,70,0,0,34.2,0.251,52,tested_negative
+2,100,70,52,57,40.5,0.677,25,tested_negative
+7,106,60,24,0,26.5,0.296,29,tested_positive
+0,104,64,23,116,27.8,0.454,23,tested_negative
+5,114,74,0,0,24.9,0.744,57,tested_negative
+2,108,62,10,278,25.3,0.881,22,tested_negative
+0,146,70,0,0,37.9,0.334,28,tested_positive
+10,129,76,28,122,35.9,0.28,39,tested_negative
+7,133,88,15,155,32.4,0.262,37,tested_negative
+7,161,86,0,0,30.4,0.165,47,tested_positive
+2,108,80,0,0,27,0.259,52,tested_positive
+7,136,74,26,135,26,0.647,51,tested_negative
+5,155,84,44,545,38.7,0.619,34,tested_negative
+1,119,86,39,220,45.6,0.808,29,tested_positive
+4,96,56,17,49,20.8,0.34,26,tested_negative
+5,108,72,43,75,36.1,0.263,33,tested_negative
+0,78,88,29,40,36.9,0.434,21,tested_negative
+0,107,62,30,74,36.6,0.757,25,tested_positive
+2,128,78,37,182,43.3,1.224,31,tested_positive
+1,128,48,45,194,40.5,0.613,24,tested_positive
+0,161,50,0,0,21.9,0.254,65,tested_negative
+6,151,62,31,120,35.5,0.692,28,tested_negative
+2,146,70,38,360,28,0.337,29,tested_positive
+0,126,84,29,215,30.7,0.52,24,tested_negative
+14,100,78,25,184,36.6,0.412,46,tested_positive
+8,112,72,0,0,23.6,0.84,58,tested_negative
+0,167,0,0,0,32.3,0.839,30,tested_positive
+2,144,58,33,135,31.6,0.422,25,tested_positive
+5,77,82,41,42,35.8,0.156,35,tested_negative
+5,115,98,0,0,52.9,0.209,28,tested_positive
+3,150,76,0,0,21,0.207,37,tested_negative
+2,120,76,37,105,39.7,0.215,29,tested_negative
+10,161,68,23,132,25.5,0.326,47,tested_positive
+0,137,68,14,148,24.8,0.143,21,tested_negative
+0,128,68,19,180,30.5,1.391,25,tested_positive
+2,124,68,28,205,32.9,0.875,30,tested_positive
+6,80,66,30,0,26.2,0.313,41,tested_negative
+0,106,70,37,148,39.4,0.605,22,tested_negative
+2,155,74,17,96,26.6,0.433,27,tested_positive
+3,113,50,10,85,29.5,0.626,25,tested_negative
+7,109,80,31,0,35.9,1.127,43,tested_positive
+2,112,68,22,94,34.1,0.315,26,tested_negative
+3,99,80,11,64,19.3,0.284,30,tested_negative
+3,182,74,0,0,30.5,0.345,29,tested_positive
+3,115,66,39,140,38.1,0.15,28,tested_negative
+6,194,78,0,0,23.5,0.129,59,tested_positive
+4,129,60,12,231,27.5,0.527,31,tested_negative
+3,112,74,30,0,31.6,0.197,25,tested_positive
+0,124,70,20,0,27.4,0.254,36,tested_positive
+13,152,90,33,29,26.8,0.731,43,tested_positive
+2,112,75,32,0,35.7,0.148,21,tested_negative
+1,157,72,21,168,25.6,0.123,24,tested_negative
+1,122,64,32,156,35.1,0.692,30,tested_positive
+10,179,70,0,0,35.1,0.2,37,tested_negative
+2,102,86,36,120,45.5,0.127,23,tested_positive
+6,105,70,32,68,30.8,0.122,37,tested_negative
+8,118,72,19,0,23.1,1.476,46,tested_negative
+2,87,58,16,52,32.7,0.166,25,tested_negative
+1,180,0,0,0,43.3,0.282,41,tested_positive
+12,106,80,0,0,23.6,0.137,44,tested_negative
+1,95,60,18,58,23.9,0.26,22,tested_negative
+0,165,76,43,255,47.9,0.259,26,tested_negative
+0,117,0,0,0,33.8,0.932,44,tested_negative
+5,115,76,0,0,31.2,0.343,44,tested_positive
+9,152,78,34,171,34.2,0.893,33,tested_positive
+7,178,84,0,0,39.9,0.331,41,tested_positive
+1,130,70,13,105,25.9,0.472,22,tested_negative
+1,95,74,21,73,25.9,0.673,36,tested_negative
+1,0,68,35,0,32,0.389,22,tested_negative
+5,122,86,0,0,34.7,0.29,33,tested_negative
+8,95,72,0,0,36.8,0.485,57,tested_negative
+8,126,88,36,108,38.5,0.349,49,tested_negative
+1,139,46,19,83,28.7,0.654,22,tested_negative
+3,116,0,0,0,23.5,0.187,23,tested_negative
+3,99,62,19,74,21.8,0.279,26,tested_negative
+5,0,80,32,0,41,0.346,37,tested_positive
+4,92,80,0,0,42.2,0.237,29,tested_negative
+4,137,84,0,0,31.2,0.252,30,tested_negative
+3,61,82,28,0,34.4,0.243,46,tested_negative
+1,90,62,12,43,27.2,0.58,24,tested_negative
+3,90,78,0,0,42.7,0.559,21,tested_negative
+9,165,88,0,0,30.4,0.302,49,tested_positive
+1,125,50,40,167,33.3,0.962,28,tested_positive
+13,129,0,30,0,39.9,0.569,44,tested_positive
+12,88,74,40,54,35.3,0.378,48,tested_negative
+1,196,76,36,249,36.5,0.875,29,tested_positive
+5,189,64,33,325,31.2,0.583,29,tested_positive
+5,158,70,0,0,29.8,0.207,63,tested_negative
+5,103,108,37,0,39.2,0.305,65,tested_negative
+4,146,78,0,0,38.5,0.52,67,tested_positive
+4,147,74,25,293,34.9,0.385,30,tested_negative
+5,99,54,28,83,34,0.499,30,tested_negative
+6,124,72,0,0,27.6,0.368,29,tested_positive
+0,101,64,17,0,21,0.252,21,tested_negative
+3,81,86,16,66,27.5,0.306,22,tested_negative
+1,133,102,28,140,32.8,0.234,45,tested_positive
+3,173,82,48,465,38.4,2.137,25,tested_positive
+0,118,64,23,89,0,1.731,21,tested_negative
+0,84,64,22,66,35.8,0.545,21,tested_negative
+2,105,58,40,94,34.9,0.225,25,tested_negative
+2,122,52,43,158,36.2,0.816,28,tested_negative
+12,140,82,43,325,39.2,0.528,58,tested_positive
+0,98,82,15,84,25.2,0.299,22,tested_negative
+1,87,60,37,75,37.2,0.509,22,tested_negative
+4,156,75,0,0,48.3,0.238,32,tested_positive
+0,93,100,39,72,43.4,1.021,35,tested_negative
+1,107,72,30,82,30.8,0.821,24,tested_negative
+0,105,68,22,0,20,0.236,22,tested_negative
+1,109,60,8,182,25.4,0.947,21,tested_negative
+1,90,62,18,59,25.1,1.268,25,tested_negative
+1,125,70,24,110,24.3,0.221,25,tested_negative
+1,119,54,13,50,22.3,0.205,24,tested_negative
+5,116,74,29,0,32.3,0.66,35,tested_positive
+8,105,100,36,0,43.3,0.239,45,tested_positive
+5,144,82,26,285,32,0.452,58,tested_positive
+3,100,68,23,81,31.6,0.949,28,tested_negative
+1,100,66,29,196,32,0.444,42,tested_negative
+5,166,76,0,0,45.7,0.34,27,tested_positive
+1,131,64,14,415,23.7,0.389,21,tested_negative
+4,116,72,12,87,22.1,0.463,37,tested_negative
+4,158,78,0,0,32.9,0.803,31,tested_positive
+2,127,58,24,275,27.7,1.6,25,tested_negative
+3,96,56,34,115,24.7,0.944,39,tested_negative
+0,131,66,40,0,34.3,0.196,22,tested_positive
+3,82,70,0,0,21.1,0.389,25,tested_negative
+3,193,70,31,0,34.9,0.241,25,tested_positive
+4,95,64,0,0,32,0.161,31,tested_positive
+6,137,61,0,0,24.2,0.151,55,tested_negative
+5,136,84,41,88,35,0.286,35,tested_positive
+9,72,78,25,0,31.6,0.28,38,tested_negative
+5,168,64,0,0,32.9,0.135,41,tested_positive
+2,123,48,32,165,42.1,0.52,26,tested_negative
+4,115,72,0,0,28.9,0.376,46,tested_positive
+0,101,62,0,0,21.9,0.336,25,tested_negative
+8,197,74,0,0,25.9,1.191,39,tested_positive
+1,172,68,49,579,42.4,0.702,28,tested_positive
+6,102,90,39,0,35.7,0.674,28,tested_negative
+1,112,72,30,176,34.4,0.528,25,tested_negative
+1,143,84,23,310,42.4,1.076,22,tested_negative
+1,143,74,22,61,26.2,0.256,21,tested_negative
+0,138,60,35,167,34.6,0.534,21,tested_positive
+3,173,84,33,474,35.7,0.258,22,tested_positive
+1,97,68,21,0,27.2,1.095,22,tested_negative
+4,144,82,32,0,38.5,0.554,37,tested_positive
+1,83,68,0,0,18.2,0.624,27,tested_negative
+3,129,64,29,115,26.4,0.219,28,tested_positive
+1,119,88,41,170,45.3,0.507,26,tested_negative
+2,94,68,18,76,26,0.561,21,tested_negative
+0,102,64,46,78,40.6,0.496,21,tested_negative
+2,115,64,22,0,30.8,0.421,21,tested_negative
+8,151,78,32,210,42.9,0.516,36,tested_positive
+4,184,78,39,277,37,0.264,31,tested_positive
+0,94,0,0,0,0,0.256,25,tested_negative
+1,181,64,30,180,34.1,0.328,38,tested_positive
+0,135,94,46,145,40.6,0.284,26,tested_negative
+1,95,82,25,180,35,0.233,43,tested_positive
+2,99,0,0,0,22.2,0.108,23,tested_negative
+3,89,74,16,85,30.4,0.551,38,tested_negative
+1,80,74,11,60,30,0.527,22,tested_negative
+2,139,75,0,0,25.6,0.167,29,tested_negative
+1,90,68,8,0,24.5,1.138,36,tested_negative
+0,141,0,0,0,42.4,0.205,29,tested_positive
+12,140,85,33,0,37.4,0.244,41,tested_negative
+5,147,75,0,0,29.9,0.434,28,tested_negative
+1,97,70,15,0,18.2,0.147,21,tested_negative
+6,107,88,0,0,36.8,0.727,31,tested_negative
+0,189,104,25,0,34.3,0.435,41,tested_positive
+2,83,66,23,50,32.2,0.497,22,tested_negative
+4,117,64,27,120,33.2,0.23,24,tested_negative
+8,108,70,0,0,30.5,0.955,33,tested_positive
+4,117,62,12,0,29.7,0.38,30,tested_positive
+0,180,78,63,14,59.4,2.42,25,tested_positive
+1,100,72,12,70,25.3,0.658,28,tested_negative
+0,95,80,45,92,36.5,0.33,26,tested_negative
+0,104,64,37,64,33.6,0.51,22,tested_positive
+0,120,74,18,63,30.5,0.285,26,tested_negative
+1,82,64,13,95,21.2,0.415,23,tested_negative
+2,134,70,0,0,28.9,0.542,23,tested_positive
+0,91,68,32,210,39.9,0.381,25,tested_negative
+2,119,0,0,0,19.6,0.832,72,tested_negative
+2,100,54,28,105,37.8,0.498,24,tested_negative
+14,175,62,30,0,33.6,0.212,38,tested_positive
+1,135,54,0,0,26.7,0.687,62,tested_negative
+5,86,68,28,71,30.2,0.364,24,tested_negative
+10,148,84,48,237,37.6,1.001,51,tested_positive
+9,134,74,33,60,25.9,0.46,81,tested_negative
+9,120,72,22,56,20.8,0.733,48,tested_negative
+1,71,62,0,0,21.8,0.416,26,tested_negative
+8,74,70,40,49,35.3,0.705,39,tested_negative
+5,88,78,30,0,27.6,0.258,37,tested_negative
+10,115,98,0,0,24,1.022,34,tested_negative
+0,124,56,13,105,21.8,0.452,21,tested_negative
+0,74,52,10,36,27.8,0.269,22,tested_negative
+0,97,64,36,100,36.8,0.6,25,tested_negative
+8,120,0,0,0,30,0.183,38,tested_positive
+6,154,78,41,140,46.1,0.571,27,tested_negative
+1,144,82,40,0,41.3,0.607,28,tested_negative
+0,137,70,38,0,33.2,0.17,22,tested_negative
+0,119,66,27,0,38.8,0.259,22,tested_negative
+7,136,90,0,0,29.9,0.21,50,tested_negative
+4,114,64,0,0,28.9,0.126,24,tested_negative
+0,137,84,27,0,27.3,0.231,59,tested_negative
+2,105,80,45,191,33.7,0.711,29,tested_positive
+7,114,76,17,110,23.8,0.466,31,tested_negative
+8,126,74,38,75,25.9,0.162,39,tested_negative
+4,132,86,31,0,28,0.419,63,tested_negative
+3,158,70,30,328,35.5,0.344,35,tested_positive
+0,123,88,37,0,35.2,0.197,29,tested_negative
+4,85,58,22,49,27.8,0.306,28,tested_negative
+0,84,82,31,125,38.2,0.233,23,tested_negative
+0,145,0,0,0,44.2,0.63,31,tested_positive
+0,135,68,42,250,42.3,0.365,24,tested_positive
+1,139,62,41,480,40.7,0.536,21,tested_negative
+0,173,78,32,265,46.5,1.159,58,tested_negative
+4,99,72,17,0,25.6,0.294,28,tested_negative
+8,194,80,0,0,26.1,0.551,67,tested_negative
+2,83,65,28,66,36.8,0.629,24,tested_negative
+2,89,90,30,0,33.5,0.292,42,tested_negative
+4,99,68,38,0,32.8,0.145,33,tested_negative
+4,125,70,18,122,28.9,1.144,45,tested_positive
+3,80,0,0,0,0,0.174,22,tested_negative
+6,166,74,0,0,26.6,0.304,66,tested_negative
+5,110,68,0,0,26,0.292,30,tested_negative
+2,81,72,15,76,30.1,0.547,25,tested_negative
+7,195,70,33,145,25.1,0.163,55,tested_positive
+6,154,74,32,193,29.3,0.839,39,tested_negative
+2,117,90,19,71,25.2,0.313,21,tested_negative
+3,84,72,32,0,37.2,0.267,28,tested_negative
+6,0,68,41,0,39,0.727,41,tested_positive
+7,94,64,25,79,33.3,0.738,41,tested_negative
+3,96,78,39,0,37.3,0.238,40,tested_negative
+10,75,82,0,0,33.3,0.263,38,tested_negative
+0,180,90,26,90,36.5,0.314,35,tested_positive
+1,130,60,23,170,28.6,0.692,21,tested_negative
+2,84,50,23,76,30.4,0.968,21,tested_negative
+8,120,78,0,0,25,0.409,64,tested_negative
+12,84,72,31,0,29.7,0.297,46,tested_positive
+0,139,62,17,210,22.1,0.207,21,tested_negative
+9,91,68,0,0,24.2,0.2,58,tested_negative
+2,91,62,0,0,27.3,0.525,22,tested_negative
+3,99,54,19,86,25.6,0.154,24,tested_negative
+3,163,70,18,105,31.6,0.268,28,tested_positive
+9,145,88,34,165,30.3,0.771,53,tested_positive
+7,125,86,0,0,37.6,0.304,51,tested_negative
+13,76,60,0,0,32.8,0.18,41,tested_negative
+6,129,90,7,326,19.6,0.582,60,tested_negative
+2,68,70,32,66,25,0.187,25,tested_negative
+3,124,80,33,130,33.2,0.305,26,tested_negative
+6,114,0,0,0,0,0.189,26,tested_negative
+9,130,70,0,0,34.2,0.652,45,tested_positive
+3,125,58,0,0,31.6,0.151,24,tested_negative
+3,87,60,18,0,21.8,0.444,21,tested_negative
+1,97,64,19,82,18.2,0.299,21,tested_negative
+3,116,74,15,105,26.3,0.107,24,tested_negative
+0,117,66,31,188,30.8,0.493,22,tested_negative
+0,111,65,0,0,24.6,0.66,31,tested_negative
+2,122,60,18,106,29.8,0.717,22,tested_negative
+0,107,76,0,0,45.3,0.686,24,tested_negative
+1,86,66,52,65,41.3,0.917,29,tested_negative
+6,91,0,0,0,29.8,0.501,31,tested_negative
+1,77,56,30,56,33.3,1.251,24,tested_negative
+4,132,0,0,0,32.9,0.302,23,tested_positive
+0,105,90,0,0,29.6,0.197,46,tested_negative
+0,57,60,0,0,21.7,0.735,67,tested_negative
+0,127,80,37,210,36.3,0.804,23,tested_negative
+3,129,92,49,155,36.4,0.968,32,tested_positive
+8,100,74,40,215,39.4,0.661,43,tested_positive
+3,128,72,25,190,32.4,0.549,27,tested_positive
+10,90,85,32,0,34.9,0.825,56,tested_positive
+4,84,90,23,56,39.5,0.159,25,tested_negative
+1,88,78,29,76,32,0.365,29,tested_negative
+8,186,90,35,225,34.5,0.423,37,tested_positive
+5,187,76,27,207,43.6,1.034,53,tested_positive
+4,131,68,21,166,33.1,0.16,28,tested_negative
+1,164,82,43,67,32.8,0.341,50,tested_negative
+4,189,110,31,0,28.5,0.68,37,tested_negative
+1,116,70,28,0,27.4,0.204,21,tested_negative
+3,84,68,30,106,31.9,0.591,25,tested_negative
+6,114,88,0,0,27.8,0.247,66,tested_negative
+1,88,62,24,44,29.9,0.422,23,tested_negative
+1,84,64,23,115,36.9,0.471,28,tested_negative
+7,124,70,33,215,25.5,0.161,37,tested_negative
+1,97,70,40,0,38.1,0.218,30,tested_negative
+8,110,76,0,0,27.8,0.237,58,tested_negative
+11,103,68,40,0,46.2,0.126,42,tested_negative
+11,85,74,0,0,30.1,0.3,35,tested_negative
+6,125,76,0,0,33.8,0.121,54,tested_positive
+0,198,66,32,274,41.3,0.502,28,tested_positive
+1,87,68,34,77,37.6,0.401,24,tested_negative
+6,99,60,19,54,26.9,0.497,32,tested_negative
+0,91,80,0,0,32.4,0.601,27,tested_negative
+2,95,54,14,88,26.1,0.748,22,tested_negative
+1,99,72,30,18,38.6,0.412,21,tested_negative
+6,92,62,32,126,32,0.085,46,tested_negative
+4,154,72,29,126,31.3,0.338,37,tested_negative
+0,121,66,30,165,34.3,0.203,33,tested_positive
+3,78,70,0,0,32.5,0.27,39,tested_negative
+2,130,96,0,0,22.6,0.268,21,tested_negative
+3,111,58,31,44,29.5,0.43,22,tested_negative
+2,98,60,17,120,34.7,0.198,22,tested_negative
+1,143,86,30,330,30.1,0.892,23,tested_negative
+1,119,44,47,63,35.5,0.28,25,tested_negative
+6,108,44,20,130,24,0.813,35,tested_negative
+2,118,80,0,0,42.9,0.693,21,tested_positive
+10,133,68,0,0,27,0.245,36,tested_negative
+2,197,70,99,0,34.7,0.575,62,tested_positive
+0,151,90,46,0,42.1,0.371,21,tested_positive
+6,109,60,27,0,25,0.206,27,tested_negative
+12,121,78,17,0,26.5,0.259,62,tested_negative
+8,100,76,0,0,38.7,0.19,42,tested_negative
+8,124,76,24,600,28.7,0.687,52,tested_positive
+1,93,56,11,0,22.5,0.417,22,tested_negative
+8,143,66,0,0,34.9,0.129,41,tested_positive
+6,103,66,0,0,24.3,0.249,29,tested_negative
+3,176,86,27,156,33.3,1.154,52,tested_positive
+0,73,0,0,0,21.1,0.342,25,tested_negative
+11,111,84,40,0,46.8,0.925,45,tested_positive
+2,112,78,50,140,39.4,0.175,24,tested_negative
+3,132,80,0,0,34.4,0.402,44,tested_positive
+2,82,52,22,115,28.5,1.699,25,tested_negative
+6,123,72,45,230,33.6,0.733,34,tested_negative
+0,188,82,14,185,32,0.682,22,tested_positive
+0,67,76,0,0,45.3,0.194,46,tested_negative
+1,89,24,19,25,27.8,0.559,21,tested_negative
+1,173,74,0,0,36.8,0.088,38,tested_positive
+1,109,38,18,120,23.1,0.407,26,tested_negative
+1,108,88,19,0,27.1,0.4,24,tested_negative
+6,96,0,0,0,23.7,0.19,28,tested_negative
+1,124,74,36,0,27.8,0.1,30,tested_negative
+7,150,78,29,126,35.2,0.692,54,tested_positive
+4,183,0,0,0,28.4,0.212,36,tested_positive
+1,124,60,32,0,35.8,0.514,21,tested_negative
+1,181,78,42,293,40,1.258,22,tested_positive
+1,92,62,25,41,19.5,0.482,25,tested_negative
+0,152,82,39,272,41.5,0.27,27,tested_negative
+1,111,62,13,182,24,0.138,23,tested_negative
+3,106,54,21,158,30.9,0.292,24,tested_negative
+3,174,58,22,194,32.9,0.593,36,tested_positive
+7,168,88,42,321,38.2,0.787,40,tested_positive
+6,105,80,28,0,32.5,0.878,26,tested_negative
+11,138,74,26,144,36.1,0.557,50,tested_positive
+3,106,72,0,0,25.8,0.207,27,tested_negative
+6,117,96,0,0,28.7,0.157,30,tested_negative
+2,68,62,13,15,20.1,0.257,23,tested_negative
+9,112,82,24,0,28.2,1.282,50,tested_positive
+0,119,0,0,0,32.4,0.141,24,tested_positive
+2,112,86,42,160,38.4,0.246,28,tested_negative
+2,92,76,20,0,24.2,1.698,28,tested_negative
+6,183,94,0,0,40.8,1.461,45,tested_negative
+0,94,70,27,115,43.5,0.347,21,tested_negative
+2,108,64,0,0,30.8,0.158,21,tested_negative
+4,90,88,47,54,37.7,0.362,29,tested_negative
+0,125,68,0,0,24.7,0.206,21,tested_negative
+0,132,78,0,0,32.4,0.393,21,tested_negative
+5,128,80,0,0,34.6,0.144,45,tested_negative
+4,94,65,22,0,24.7,0.148,21,tested_negative
+7,114,64,0,0,27.4,0.732,34,tested_positive
+0,102,78,40,90,34.5,0.238,24,tested_negative
+2,111,60,0,0,26.2,0.343,23,tested_negative
+1,128,82,17,183,27.5,0.115,22,tested_negative
+10,92,62,0,0,25.9,0.167,31,tested_negative
+13,104,72,0,0,31.2,0.465,38,tested_positive
+5,104,74,0,0,28.8,0.153,48,tested_negative
+2,94,76,18,66,31.6,0.649,23,tested_negative
+7,97,76,32,91,40.9,0.871,32,tested_positive
+1,100,74,12,46,19.5,0.149,28,tested_negative
+0,102,86,17,105,29.3,0.695,27,tested_negative
+4,128,70,0,0,34.3,0.303,24,tested_negative
+6,147,80,0,0,29.5,0.178,50,tested_positive
+4,90,0,0,0,28,0.61,31,tested_negative
+3,103,72,30,152,27.6,0.73,27,tested_negative
+2,157,74,35,440,39.4,0.134,30,tested_negative
+1,167,74,17,144,23.4,0.447,33,tested_positive
+0,179,50,36,159,37.8,0.455,22,tested_positive
+11,136,84,35,130,28.3,0.26,42,tested_positive
+0,107,60,25,0,26.4,0.133,23,tested_negative
+1,91,54,25,100,25.2,0.234,23,tested_negative
+1,117,60,23,106,33.8,0.466,27,tested_negative
+5,123,74,40,77,34.1,0.269,28,tested_negative
+2,120,54,0,0,26.8,0.455,27,tested_negative
+1,106,70,28,135,34.2,0.142,22,tested_negative
+2,155,52,27,540,38.7,0.24,25,tested_positive
+2,101,58,35,90,21.8,0.155,22,tested_negative
+1,120,80,48,200,38.9,1.162,41,tested_negative
+11,127,106,0,0,39,0.19,51,tested_negative
+3,80,82,31,70,34.2,1.292,27,tested_positive
+10,162,84,0,0,27.7,0.182,54,tested_negative
+1,199,76,43,0,42.9,1.394,22,tested_positive
+8,167,106,46,231,37.6,0.165,43,tested_positive
+9,145,80,46,130,37.9,0.637,40,tested_positive
+6,115,60,39,0,33.7,0.245,40,tested_positive
+1,112,80,45,132,34.8,0.217,24,tested_negative
+4,145,82,18,0,32.5,0.235,70,tested_positive
+10,111,70,27,0,27.5,0.141,40,tested_positive
+6,98,58,33,190,34,0.43,43,tested_negative
+9,154,78,30,100,30.9,0.164,45,tested_negative
+6,165,68,26,168,33.6,0.631,49,tested_negative
+1,99,58,10,0,25.4,0.551,21,tested_negative
+10,68,106,23,49,35.5,0.285,47,tested_negative
+3,123,100,35,240,57.3,0.88,22,tested_negative
+8,91,82,0,0,35.6,0.587,68,tested_negative
+6,195,70,0,0,30.9,0.328,31,tested_positive
+9,156,86,0,0,24.8,0.23,53,tested_positive
+0,93,60,0,0,35.3,0.263,25,tested_negative
+3,121,52,0,0,36,0.127,25,tested_positive
+2,101,58,17,265,24.2,0.614,23,tested_negative
+2,56,56,28,45,24.2,0.332,22,tested_negative
+0,162,76,36,0,49.6,0.364,26,tested_positive
+0,95,64,39,105,44.6,0.366,22,tested_negative
+4,125,80,0,0,32.3,0.536,27,tested_positive
+5,136,82,0,0,0,0.64,69,tested_negative
+2,129,74,26,205,33.2,0.591,25,tested_negative
+3,130,64,0,0,23.1,0.314,22,tested_negative
+1,107,50,19,0,28.3,0.181,29,tested_negative
+1,140,74,26,180,24.1,0.828,23,tested_negative
+1,144,82,46,180,46.1,0.335,46,tested_positive
+8,107,80,0,0,24.6,0.856,34,tested_negative
+13,158,114,0,0,42.3,0.257,44,tested_positive
+2,121,70,32,95,39.1,0.886,23,tested_negative
+7,129,68,49,125,38.5,0.439,43,tested_positive
+2,90,60,0,0,23.5,0.191,25,tested_negative
+7,142,90,24,480,30.4,0.128,43,tested_positive
+3,169,74,19,125,29.9,0.268,31,tested_positive
+0,99,0,0,0,25,0.253,22,tested_negative
+4,127,88,11,155,34.5,0.598,28,tested_negative
+4,118,70,0,0,44.5,0.904,26,tested_negative
+2,122,76,27,200,35.9,0.483,26,tested_negative
+6,125,78,31,0,27.6,0.565,49,tested_positive
+1,168,88,29,0,35,0.905,52,tested_positive
+2,129,0,0,0,38.5,0.304,41,tested_negative
+4,110,76,20,100,28.4,0.118,27,tested_negative
+6,80,80,36,0,39.8,0.177,28,tested_negative
+10,115,0,0,0,0,0.261,30,tested_positive
+2,127,46,21,335,34.4,0.176,22,tested_negative
+9,164,78,0,0,32.8,0.148,45,tested_positive
+2,93,64,32,160,38,0.674,23,tested_positive
+3,158,64,13,387,31.2,0.295,24,tested_negative
+5,126,78,27,22,29.6,0.439,40,tested_negative
+10,129,62,36,0,41.2,0.441,38,tested_positive
+0,134,58,20,291,26.4,0.352,21,tested_negative
+3,102,74,0,0,29.5,0.121,32,tested_negative
+7,187,50,33,392,33.9,0.826,34,tested_positive
+3,173,78,39,185,33.8,0.97,31,tested_positive
+10,94,72,18,0,23.1,0.595,56,tested_negative
+1,108,60,46,178,35.5,0.415,24,tested_negative
+5,97,76,27,0,35.6,0.378,52,tested_positive
+4,83,86,19,0,29.3,0.317,34,tested_negative
+1,114,66,36,200,38.1,0.289,21,tested_negative
+1,149,68,29,127,29.3,0.349,42,tested_positive
+5,117,86,30,105,39.1,0.251,42,tested_negative
+1,111,94,0,0,32.8,0.265,45,tested_negative
+4,112,78,40,0,39.4,0.236,38,tested_negative
+1,116,78,29,180,36.1,0.496,25,tested_negative
+0,141,84,26,0,32.4,0.433,22,tested_negative
+2,175,88,0,0,22.9,0.326,22,tested_negative
+2,92,52,0,0,30.1,0.141,22,tested_negative
+3,130,78,23,79,28.4,0.323,34,tested_positive
+8,120,86,0,0,28.4,0.259,22,tested_positive
+2,174,88,37,120,44.5,0.646,24,tested_positive
+2,106,56,27,165,29,0.426,22,tested_negative
+2,105,75,0,0,23.3,0.56,53,tested_negative
+4,95,60,32,0,35.4,0.284,28,tested_negative
+0,126,86,27,120,27.4,0.515,21,tested_negative
+8,65,72,23,0,32,0.6,42,tested_negative
+2,99,60,17,160,36.6,0.453,21,tested_negative
+1,102,74,0,0,39.5,0.293,42,tested_positive
+11,120,80,37,150,42.3,0.785,48,tested_positive
+3,102,44,20,94,30.8,0.4,26,tested_negative
+1,109,58,18,116,28.5,0.219,22,tested_negative
+9,140,94,0,0,32.7,0.734,45,tested_positive
+13,153,88,37,140,40.6,1.174,39,tested_negative
+12,100,84,33,105,30,0.488,46,tested_negative
+1,147,94,41,0,49.3,0.358,27,tested_positive
+1,81,74,41,57,46.3,1.096,32,tested_negative
+3,187,70,22,200,36.4,0.408,36,tested_positive
+6,162,62,0,0,24.3,0.178,50,tested_positive
+4,136,70,0,0,31.2,1.182,22,tested_positive
+1,121,78,39,74,39,0.261,28,tested_negative
+3,108,62,24,0,26,0.223,25,tested_negative
+0,181,88,44,510,43.3,0.222,26,tested_positive
+8,154,78,32,0,32.4,0.443,45,tested_positive
+1,128,88,39,110,36.5,1.057,37,tested_positive
+7,137,90,41,0,32,0.391,39,tested_negative
+0,123,72,0,0,36.3,0.258,52,tested_positive
+1,106,76,0,0,37.5,0.197,26,tested_negative
+6,190,92,0,0,35.5,0.278,66,tested_positive
+2,88,58,26,16,28.4,0.766,22,tested_negative
+9,170,74,31,0,44,0.403,43,tested_positive
+9,89,62,0,0,22.5,0.142,33,tested_negative
+10,101,76,48,180,32.9,0.171,63,tested_negative
+2,122,70,27,0,36.8,0.34,27,tested_negative
+5,121,72,23,112,26.2,0.245,30,tested_negative
+1,126,60,0,0,30.1,0.349,47,tested_positive
+1,93,70,31,0,30.4,0.315,23,tested_negative
--- a/tests/datasets/glass.arff
+++ b/tests/datasets/glass.arff
@@ -114,7 +114,7 @@
@attribute 'Ca' real
@attribute 'Ba' real
@attribute 'Fe' real
-@attribute 'Type' { 'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
+@attribute 'Type' {'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@data
 1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'
 1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float'
--- a/tests/datasets/liver-disorders.arff
+++ b/tests/datasets/liver-disorders.arff
@@ -0,0 +1,399 @@
+% 1. Title: BUPA liver disorders
+% 
+% 2. Source information:
+%    -- Creators: BUPA Medical Research Ltd.
+%    -- Donor: Richard S. Forsyth
+%              8 Grosvenor Avenue
+%              Mapperley Park
+%              Nottingham NG3 5DX
+%              0602-621676
+%    -- Date: 5/15/1990
+% 
+% 3. Past usage: 
+%    -- None known other than what is shown in the PC/BEAGLE User's Guide
+%       (written by Richard S. Forsyth).
+% 
+% 4. Relevant information:
+%    -- The first 5 variables are all blood tests which are thought
+%       to be sensitive to liver disorders that might arise from
+%       excessive alcohol consumption.  Each line in the bupa.data file
+%       constitutes the record of a single male individual.
+%    -- It appears that drinks>5 is some sort of a selector on this database.
+%       See the PC/BEAGLE User's Guide for more information.
+% 
+% 5. Number of instances: 345
+% 
+% 6. Number of attributes: 7 overall
+% 
+% 7. Attribute information:
+%    1. mcv	mean corpuscular volume
+%    2. alkphos	alkaline phosphotase
+%    3. sgpt	alamine aminotransferase
+%    4. sgot 	aspartate aminotransferase
+%    5. gammagt	gamma-glutamyl transpeptidase
+%    6. drinks	number of half-pint equivalents of alcoholic beverages
+%                 drunk per day
+%    7. selector  field used to split data into two sets
+% 
+% 8. Missing values: none%
+% Information about the dataset
+% CLASSTYPE: nominal
+% CLASSINDEX: last
+%
+
+@relation liver-disorders
+
+@attribute mcv INTEGER
+@attribute alkphos INTEGER
+@attribute sgpt INTEGER
+@attribute sgot INTEGER
+@attribute gammagt INTEGER
+@attribute drinks REAL
+@attribute selector {1,2}
+
+@data
+85,92,45,27,31,0.0,1
+85,64,59,32,23,0.0,2
+86,54,33,16,54,0.0,2
+91,78,34,24,36,0.0,2
+87,70,12,28,10,0.0,2
+98,55,13,17,17,0.0,2
+88,62,20,17,9,0.5,1
+88,67,21,11,11,0.5,1
+92,54,22,20,7,0.5,1
+90,60,25,19,5,0.5,1
+89,52,13,24,15,0.5,1
+82,62,17,17,15,0.5,1
+90,64,61,32,13,0.5,1
+86,77,25,19,18,0.5,1
+96,67,29,20,11,0.5,1
+91,78,20,31,18,0.5,1
+89,67,23,16,10,0.5,1
+89,79,17,17,16,0.5,1
+91,107,20,20,56,0.5,1
+94,116,11,33,11,0.5,1
+92,59,35,13,19,0.5,1
+93,23,35,20,20,0.5,1
+90,60,23,27,5,0.5,1
+96,68,18,19,19,0.5,1
+84,80,47,33,97,0.5,1
+92,70,24,13,26,0.5,1
+90,47,28,15,18,0.5,1
+88,66,20,21,10,0.5,1
+91,102,17,13,19,0.5,1
+87,41,31,19,16,0.5,1
+86,79,28,16,17,0.5,1
+91,57,31,23,42,0.5,1
+93,77,32,18,29,0.5,1
+88,96,28,21,40,0.5,1
+94,65,22,18,11,0.5,1
+91,72,155,68,82,0.5,2
+85,54,47,33,22,0.5,2
+79,39,14,19,9,0.5,2
+85,85,25,26,30,0.5,2
+89,63,24,20,38,0.5,2
+84,92,68,37,44,0.5,2
+89,68,26,39,42,0.5,2
+89,101,18,25,13,0.5,2
+86,84,18,14,16,0.5,2
+85,65,25,14,18,0.5,2
+88,61,19,21,13,0.5,2
+92,56,14,16,10,0.5,2
+95,50,29,25,50,0.5,2
+91,75,24,22,11,0.5,2
+83,40,29,25,38,0.5,2
+89,74,19,23,16,0.5,2
+85,64,24,22,11,0.5,2
+92,57,64,36,90,0.5,2
+94,48,11,23,43,0.5,2
+87,52,21,19,30,0.5,2
+85,65,23,29,15,0.5,2
+84,82,21,21,19,0.5,2
+88,49,20,22,19,0.5,2
+96,67,26,26,36,0.5,2
+90,63,24,24,24,0.5,2
+90,45,33,34,27,0.5,2
+90,72,14,15,18,0.5,2
+91,55,4,8,13,0.5,2
+91,52,15,22,11,0.5,2
+87,71,32,19,27,1.0,1
+89,77,26,20,19,1.0,1
+89,67,5,17,14,1.0,2
+85,51,26,24,23,1.0,2
+103,75,19,30,13,1.0,2
+90,63,16,21,14,1.0,2
+90,63,29,23,57,2.0,1
+90,67,35,19,35,2.0,1
+87,66,27,22,9,2.0,1
+90,73,34,21,22,2.0,1
+86,54,20,21,16,2.0,1
+90,80,19,14,42,2.0,1
+87,90,43,28,156,2.0,2
+96,72,28,19,30,2.0,2
+91,55,9,25,16,2.0,2
+95,78,27,25,30,2.0,2
+92,101,34,30,64,2.0,2
+89,51,41,22,48,2.0,2
+91,99,42,33,16,2.0,2
+94,58,21,18,26,2.0,2
+92,60,30,27,297,2.0,2
+94,58,21,18,26,2.0,2
+88,47,33,26,29,2.0,2
+92,65,17,25,9,2.0,2
+92,79,22,20,11,3.0,1
+84,83,20,25,7,3.0,1
+88,68,27,21,26,3.0,1
+86,48,20,20,6,3.0,1
+99,69,45,32,30,3.0,1
+88,66,23,12,15,3.0,1
+89,62,42,30,20,3.0,1
+90,51,23,17,27,3.0,1
+81,61,32,37,53,3.0,2
+89,89,23,18,104,3.0,2
+89,65,26,18,36,3.0,2
+92,75,26,26,24,3.0,2
+85,59,25,20,25,3.0,2
+92,61,18,13,81,3.0,2
+89,63,22,27,10,4.0,1
+90,84,18,23,13,4.0,1
+88,95,25,19,14,4.0,1
+89,35,27,29,17,4.0,1
+91,80,37,23,27,4.0,1
+91,109,33,15,18,4.0,1
+91,65,17,5,7,4.0,1
+88,107,29,20,50,4.0,2
+87,76,22,55,9,4.0,2
+87,86,28,23,21,4.0,2
+87,42,26,23,17,4.0,2
+88,80,24,25,17,4.0,2
+90,96,34,49,169,4.0,2
+86,67,11,15,8,4.0,2
+92,40,19,20,21,4.0,2
+85,60,17,21,14,4.0,2
+89,90,15,17,25,4.0,2
+91,57,15,16,16,4.0,2
+96,55,48,39,42,4.0,2
+79,101,17,27,23,4.0,2
+90,134,14,20,14,4.0,2
+89,76,14,21,24,4.0,2
+88,93,29,27,31,4.0,2
+90,67,10,16,16,4.0,2
+92,73,24,21,48,4.0,2
+91,55,28,28,82,4.0,2
+83,45,19,21,13,4.0,2
+90,74,19,14,22,4.0,2
+92,66,21,16,33,5.0,1
+93,63,26,18,18,5.0,1
+86,78,47,39,107,5.0,2
+97,44,113,45,150,5.0,2
+87,59,15,19,12,5.0,2
+86,44,21,11,15,5.0,2
+87,64,16,20,24,5.0,2
+92,57,21,23,22,5.0,2
+90,70,25,23,112,5.0,2
+99,59,17,19,11,5.0,2
+92,80,10,26,20,6.0,1
+95,60,26,22,28,6.0,1
+91,63,25,26,15,6.0,1
+92,62,37,21,36,6.0,1
+95,50,13,14,15,6.0,1
+90,76,37,19,50,6.0,1
+96,70,70,26,36,6.0,1
+95,62,64,42,76,6.0,1
+92,62,20,23,20,6.0,1
+91,63,25,26,15,6.0,1
+82,56,67,38,92,6.0,2
+92,82,27,24,37,6.0,2
+90,63,12,26,21,6.0,2
+88,37,9,15,16,6.0,2
+100,60,29,23,76,6.0,2
+98,43,35,23,69,6.0,2
+91,74,87,50,67,6.0,2
+92,87,57,25,44,6.0,2
+93,99,36,34,48,6.0,2
+90,72,17,19,19,6.0,2
+97,93,21,20,68,6.0,2
+93,50,18,25,17,6.0,2
+90,57,20,26,33,6.0,2
+92,76,31,28,41,6.0,2
+88,55,19,17,14,6.0,2
+89,63,24,29,29,6.0,2
+92,79,70,32,84,7.0,1
+92,93,58,35,120,7.0,1
+93,84,58,47,62,7.0,2
+97,71,29,22,52,8.0,1
+84,99,33,19,26,8.0,1
+96,44,42,23,73,8.0,1
+90,62,22,21,21,8.0,1
+92,94,18,17,6,8.0,1
+90,67,77,39,114,8.0,1
+97,71,29,22,52,8.0,1
+91,69,25,25,66,8.0,2
+93,59,17,20,14,8.0,2
+92,95,85,48,200,8.0,2
+90,50,26,22,53,8.0,2
+91,62,59,47,60,8.0,2
+92,93,22,28,123,9.0,1
+92,77,86,41,31,10.0,1
+86,66,22,24,26,10.0,2
+98,57,31,34,73,10.0,2
+95,80,50,64,55,10.0,2
+92,108,53,33,94,12.0,2
+97,92,22,28,49,12.0,2
+93,77,39,37,108,16.0,1
+94,83,81,34,201,20.0,1
+87,75,25,21,14,0.0,1
+88,56,23,18,12,0.0,1
+84,97,41,20,32,0.0,2
+94,91,27,20,15,0.5,1
+97,62,17,13,5,0.5,1
+92,85,25,20,12,0.5,1
+82,48,27,15,12,0.5,1
+88,74,31,25,15,0.5,1
+95,77,30,14,21,0.5,1
+88,94,26,18,8,0.5,1
+91,70,19,19,22,0.5,1
+83,54,27,15,12,0.5,1
+91,105,40,26,56,0.5,1
+86,79,37,28,14,0.5,1
+91,96,35,22,135,0.5,1
+89,82,23,14,35,0.5,1
+90,73,24,23,11,0.5,1
+90,87,19,25,19,0.5,1
+89,82,33,32,18,0.5,1
+85,79,17,8,9,0.5,1
+85,119,30,26,17,0.5,1
+78,69,24,18,31,0.5,1
+88,107,34,21,27,0.5,1
+89,115,17,27,7,0.5,1
+92,67,23,15,12,0.5,1
+89,101,27,34,14,0.5,1
+91,84,11,12,10,0.5,1
+94,101,41,20,53,0.5,2
+88,46,29,22,18,0.5,2
+88,122,35,29,42,0.5,2
+84,88,28,25,35,0.5,2
+90,79,18,15,24,0.5,2
+87,69,22,26,11,0.5,2
+65,63,19,20,14,0.5,2
+90,64,12,17,14,0.5,2
+85,58,18,24,16,0.5,2
+88,81,41,27,36,0.5,2
+86,78,52,29,62,0.5,2
+82,74,38,28,48,0.5,2
+86,58,36,27,59,0.5,2
+94,56,30,18,27,0.5,2
+87,57,30,30,22,0.5,2
+98,74,148,75,159,0.5,2
+94,75,20,25,38,0.5,2
+83,68,17,20,71,0.5,2
+93,56,25,21,33,0.5,2
+101,65,18,21,22,0.5,2
+92,65,25,20,31,0.5,2
+92,58,14,16,13,0.5,2
+86,58,16,23,23,0.5,2
+85,62,15,13,22,0.5,2
+86,57,13,20,13,0.5,2
+86,54,26,30,13,0.5,2
+81,41,33,27,34,1.0,1
+91,67,32,26,13,1.0,1
+91,80,21,19,14,1.0,1
+92,60,23,15,19,1.0,1
+91,60,32,14,8,1.0,1
+93,65,28,22,10,1.0,1
+90,63,45,24,85,1.0,2
+87,92,21,22,37,1.0,2
+83,78,31,19,115,1.0,2
+95,62,24,23,14,1.0,2
+93,59,41,30,48,1.0,2
+84,82,43,32,38,2.0,1
+87,71,33,20,22,2.0,1
+86,44,24,15,18,2.0,1
+86,66,28,24,21,2.0,1
+88,58,31,17,17,2.0,1
+90,61,28,29,31,2.0,1
+88,69,70,24,64,2.0,1
+93,87,18,17,26,2.0,1
+98,58,33,21,28,2.0,1
+91,44,18,18,23,2.0,2
+87,75,37,19,70,2.0,2
+94,91,30,26,25,2.0,2
+88,85,14,15,10,2.0,2
+89,109,26,25,27,2.0,2
+87,59,37,27,34,2.0,2
+93,58,20,23,18,2.0,2
+88,57,9,15,16,2.0,2
+94,65,38,27,17,3.0,1
+91,71,12,22,11,3.0,1
+90,55,20,20,16,3.0,1
+91,64,21,17,26,3.0,2
+88,47,35,26,33,3.0,2
+82,72,31,20,84,3.0,2
+85,58,83,49,51,3.0,2
+91,54,25,22,35,4.0,1
+98,50,27,25,53,4.0,2
+86,62,29,21,26,4.0,2
+89,48,32,22,14,4.0,2
+82,68,20,22,9,4.0,2
+83,70,17,19,23,4.0,2
+96,70,21,26,21,4.0,2
+94,117,77,56,52,4.0,2
+93,45,11,14,21,4.0,2
+93,49,27,21,29,4.0,2
+84,73,46,32,39,4.0,2
+91,63,17,17,46,4.0,2
+90,57,31,18,37,4.0,2
+87,45,19,13,16,4.0,2
+91,68,14,20,19,4.0,2
+86,55,29,35,108,4.0,2
+91,86,52,47,52,4.0,2
+88,46,15,33,55,4.0,2
+85,52,22,23,34,4.0,2
+89,72,33,27,55,4.0,2
+95,59,23,18,19,4.0,2
+94,43,154,82,121,4.0,2
+96,56,38,26,23,5.0,2
+90,52,10,17,12,5.0,2
+94,45,20,16,12,5.0,2
+99,42,14,21,49,5.0,2
+93,102,47,23,37,5.0,2
+94,71,25,26,31,5.0,2
+92,73,33,34,115,5.0,2
+87,54,41,29,23,6.0,1
+92,67,15,14,14,6.0,1
+98,101,31,26,32,6.0,1
+92,53,51,33,92,6.0,1
+97,94,43,43,82,6.0,1
+93,43,11,16,54,6.0,1
+93,68,24,18,19,6.0,1
+95,36,38,19,15,6.0,1
+99,86,58,42,203,6.0,1
+98,66,103,57,114,6.0,1
+92,80,10,26,20,6.0,1
+96,74,27,25,43,6.0,2
+95,93,21,27,47,6.0,2
+86,109,16,22,28,6.0,2
+91,46,30,24,39,7.0,2
+102,82,34,78,203,7.0,2
+85,50,12,18,14,7.0,2
+91,57,33,23,12,8.0,1
+91,52,76,32,24,8.0,1
+93,70,46,30,33,8.0,1
+87,55,36,19,25,8.0,1
+98,123,28,24,31,8.0,1
+82,55,18,23,44,8.0,2
+95,73,20,25,225,8.0,2
+97,80,17,20,53,8.0,2
+100,83,25,24,28,8.0,2
+88,91,56,35,126,9.0,2
+91,138,45,21,48,10.0,1
+92,41,37,22,37,10.0,1
+86,123,20,25,23,10.0,2
+91,93,35,34,37,10.0,2
+87,87,15,23,11,10.0,2
+87,56,52,43,55,10.0,2
+99,75,26,24,41,12.0,1
+96,69,53,43,203,12.0,2
+98,77,55,35,89,15.0,1
+91,68,27,26,14,16.0,1
+98,99,57,45,65,20.0,1
--- a/tests/datasets/tests.txt
+++ b/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
+Q4[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]
+3, 2, 2, 2, 3, 3, 3, 3, 1, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 0, 2, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 0, 1, 1, 3, 0, 1, 0, 0, 2, 0, 1, 1, 2, 2, 0, 0, 0, 2, 1, 0, 1, 1, 2, 1, 2, 1, 0, 0, 0, 0, 0, 2, 3, 2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 1, 1, 0, 1, 3, 0, 2, 1, 2, 2, 0, 1, 0, 3, 2, 0, 2, 0, 1, 2, 2, 3, 0, 0, 2, 1, 1, 0, 3, 2, 1, 2, 1, 2, 1, 3, 1, 1, 0, 2, 3, 2, 2, 2, 2, 2, 0, 2, 3, 2, 0, 2, 3, 2
+2.0, 2.8, 3.0, 3.3, 4.4
+VECTOR
+U4[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, 3, 2, 2, 1, 1, 2, 2, 1, 1, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 3, 3, 1, 2, 2, 2, 1, 2, 2, 0, 2, 2, 3, 1, 3, 2, 2, 2, 2, 2, 1, 0, 1, 1, 2, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 2, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 2, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 2, 2, 1, 1, 0, 1, 2, 1, 3, 1, 0, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 0, 1, 2, 1
+2.0, 2.6, 3.2, 3.8, 4.4
+VECTOR
+Q3[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]
+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, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+1.0, 2.63333, 4.9, 6.9
+VECTOR
+U3[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]
+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, 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, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+1.0, 2.96667, 4.93333, 6.9
+VECTOR
+Q4[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]
+0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 3, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3
+1.0, 1.6, 4.35, 5.1, 6.9
+VECTOR
+U4[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]
+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, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2
+1.0, 2.475, 3.95, 5.425, 6.9
+VECTOR
+Q3[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, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 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, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.1, 0.86667, 1.6, 2.5
+VECTOR
+U3[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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 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, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+0.1, 0.9, 1.7, 2.5
+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
--- a/tests/lib/Files
+++ b/tests/lib/Files
--- a/tests/test
+++ b/tests/test
@@ -1,12 +0,0 @@
-cmake -S . -B build -Wno-dev 
-if test $? -ne 0; then
-   echo "Error in creating build commands."
-   exit 1
-fi
-cmake --build build
-if test $? -ne 0; then
-   echo "Error in build command."
-   exit 1
-fi
-cd build
-ctest --output-on-failure
--- a/tests/tests_do.py
+++ b/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! ***")
--- a/tests/tests_generate.ipynb
+++ b/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": "3.1.undefined"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/typesFImdlp.h
+++ b/typesFImdlp.h
@@ -1,17 +0,0 @@
-#ifndef TYPES_H
-#define TYPES_H
-#include <vector>
-#include <map>
-#include <stdexcept>
-
-using namespace std;
-namespace mdlp {
-    typedef float precision_t;
-    typedef vector<precision_t> samples_t;
-    typedef vector<int> labels_t;
-    typedef vector<size_t> indices_t;
-    typedef vector<precision_t> cutPoints_t;
-    typedef map<pair<int, int>, precision_t> cacheEnt_t;
-    typedef map<tuple<int, int, int>, precision_t> cacheIg_t;
-}
-#endif
--- a/update_coverage.py
+++ b/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}")
Author	SHA1	Message	Date
Ricardo Montañana Gómez	ab12622009	Add install cmake/make target	2024-07-22 22:01:33 +02:00
Ricardo Montañana Gómez	248a511972	Add flag to build sample in Makefile	2024-07-22 19:38:12 +02:00
Ricardo Montañana Gómez	d9bd0126f9	Fix version number in tests	2024-07-22 12:23:21 +02:00
Ricardo Montañana Gómez	210af46a88	Change library name to fimdlp	2024-07-22 11:26:16 +02:00
Ricardo Montañana Gómez	2db60e007d	Update version in test	2024-07-04 18:21:26 +02:00
Ricardo Montañana Gómez	1cf245fa49	Update version number	2024-07-04 18:19:05 +02:00
Ricardo Montañana Gómez	e36d9af8f9	Fix BinDisc quantile mistakes (#9 ) * Fix BinDisc quantile mistakes * Fix FImdlp tests * Fix tests, samples and remove uneeded support files * Add coypright header to sources Fix coverage report Add coverage badge to README * Update sonar github action * Move sources to a folder and change ArffFiles files to library * Add recursive submodules to github action	2024-07-04 17:27:39 +02:00
Ricardo Montañana Gómez	7b0673fd4b	Update README	2024-06-24 11:47:03 +02:00
Ricardo Montañana Gómez	a1346e1943	Fix Error in percentile method	2024-06-24 10:55:26 +02:00
Ricardo Montañana Gómez	b3fc598c29	Update build.yml	2024-06-14 22:04:29 +02:00
Ricardo Montañana Gómez	cc1efa0b4e	Update README	2024-06-14 22:01:11 +02:00
Ricardo Montañana Gómez	90965877eb	Add Makefile with build & test actions	2024-06-14 21:17:30 +02:00
Ricardo Montañana Gómez	c4e6c041fe	Fix int type	2024-06-09 00:29:55 +02:00
Ricardo Montañana Gómez	7938df7f0f	Update sonar mdlp version	2024-06-08 13:25:28 +02:00
Ricardo Montañana Gómez	7ee9896734	Fix mistake in github action	2024-06-08 12:36:56 +02:00
Ricardo Montañana Gómez	8f7f605670	Fix mistake in github action	2024-06-08 12:32:18 +02:00
Ricardo Montañana Gómez	2f55b27691	Fix mistake in github action	2024-06-08 12:28:23 +02:00
Ricardo Montañana Gómez	378fbd51ef	Fix mistake in github action	2024-06-08 12:25:17 +02:00
Ricardo Montañana Gómez	402d0da878	Fix mistake in github action	2024-06-08 12:23:28 +02:00
Ricardo Montañana Gómez	f34bcc2ed7	Add libtorch to github action	2024-06-08 12:20:51 +02:00
Ricardo Montañana Gómez	c9ba35fb58	update test script	2024-06-08 12:02:16 +02:00
Ricardo Montañana Gómez	e205668906	Add torch methods to discretize Add fit_transform methods	2024-06-07 23:54:42 +02:00
Ricardo Montañana Gómez	633aa52849	Refactor sample build	2024-06-06 12:04:55 +02:00
Ricardo Montañana Gómez	61de687476	Fix library creation problem	2024-06-06 11:13:50 +02:00
Ricardo Montañana Gómez	7ff88c8e4b	Update Discretizer version	2024-06-05 17:55:45 +02:00
Ricardo Montañana Gómez	638bb2a59e	Discretizer (#8 ) * Add better check in testKBins.py * Add Discretizer base class for Both discretizers * Refactor order of constructors init	2024-06-05 17:53:08 +02:00
Ricardo Montañana Gómez	f258fc220f	Merge pull request #7 from rmontanana/BinDisc Implement BinDisc and tests	2024-06-05 11:08:56 +02:00
Ricardo Montañana Gómez	0beeda320d	Update workflow build	2024-06-05 10:56:49 +02:00
Ricardo Montañana Gómez	6b68a41c42	Implement BinDisc and tests	2024-06-05 10:45:11 +02:00
Ricardo Montañana	236d1b2f8b	Update sonarcloud github action	2024-05-02 12:51:40 +02:00
Ricardo Montañana	52ee93178f	Update dockerfile	2024-05-02 10:46:29 +00:00
Ricardo Montañana	eeda4347e9	Add logo to README	2024-05-02 11:56:18 +02:00
Ricardo Montañana	5708dc3de9	Fix initialization mistake in transform	2023-08-01 17:30:37 +02:00
Ricardo Montañana	fbffc3a9c4	Remove sample from library binary file	2023-07-20 18:42:46 +02:00
Ricardo Montañana	ab3786e2a2	Add transform test	2023-07-06 16:40:58 +02:00
Ricardo Montañana	be1917d05b	Fix attribute name extraction in ArffFiles	2023-07-06 16:15:23 +02:00
Ricardo Montañana	5679d607e5	Add transform method to discretize values using CutPoints	2023-07-06 16:06:52 +02:00
Ricardo Montañana	e8559faf1f	Add diabetes dataset to sample	2023-06-12 12:34:04 +02:00
Ricardo Montañana	b21e85f5e8	Add devcontainer and tasks config for docker dev	2023-05-02 14:33:22 +02:00
Ricardo Montañana Gómez	db76afc4e2	Merge pull request #6 from rmontanana/max_cut_points_entropy Max cut points entropy	2023-04-25 10:52:00 +02:00
Ricardo Montañana	a1f26a257c	Reformat code and update version	2023-04-25 10:48:59 +02:00
Ricardo Montañana	22997f5d69	Add debug option in CMakeLists sample	2023-04-25 10:20:12 +02:00
Ricardo Montañana	ef16488ffa	Remove vscode config files	2023-04-14 11:55:26 +02:00
Ricardo Montañana	449bf3a67e	refactor sample	2023-04-14 11:53:25 +02:00
Ricardo Montañana	e689d1f69c	refactor computing max_cuts	2023-04-14 11:53:16 +02:00
Ricardo Montañana	d77d27459b	refactor system types in library Add new test taken from join_fit in FImdlp python Update instructions in README	2023-04-11 19:24:31 +02:00
Ricardo Montañana	49c08bfe12	Change some types in sample	2023-04-02 18:52:25 +02:00
Ricardo Montañana	62e9276fbf	Refactor sonar properties	2023-04-02 14:24:24 +02:00
Ricardo Montañana	c52c7d0828	Refactor sonar properties	2023-04-02 11:59:34 +02:00
Ricardo Montañana Gómez	0b35a15d62	Merge pull request #5 from rmontanana/hiperparameters -Fix a big mistake in sortIndices method (removed unneeded loop) -Add three hyperparameters to algorithm: * max_depth: maximum level of recursion when looking for cut point candidates. * min_length: minimum length of the interval of samples to be searched for candidates. * max_cut: Maximum number of cutpoints. This could be achieved in two ways: a natural number meaning the maximum number of outpoints in each feature of the dataset, or this number could be a number int the range (0, 1) meaning a proportion of the number of samples.	2023-04-01 19:05:12 +02:00
Ricardo Montañana	c662a96da8	Refactor github build action	2023-04-01 17:59:46 +02:00
Ricardo Montañana	0ead15be7c	Refactor github build action	2023-04-01 17:53:37 +02:00
Ricardo Montañana	da41a9317d	Refactor github build action	2023-04-01 17:53:00 +02:00
Ricardo Montañana	42e83b3d26	move limits include to CPPFImdlp header	2023-03-22 18:17:11 +01:00
Ricardo Montañana	77135739cf	Reformat some test files	2023-03-21 09:55:40 +01:00
Ricardo Montañana	27ea3bf338	Refactor tests	2023-03-21 00:53:18 +01:00
Ricardo Montañana	12222f7903	Remove trailing space in attribute type of Arff	2023-03-20 20:24:32 +01:00
Ricardo Montañana	cfade7a556	Remove unneeded loop in sortIndices Add some static casts	2023-03-19 19:13:37 +01:00
Ricardo Montañana	f0845c5bd1	Fix mistake in class type of ArffFiles Add some type casting to CPPFImdlp Add additional path to datasets in tests Fix some smells in sample Join CMakeLists	2023-03-18 18:40:10 +01:00
Ricardo Montañana	1f4abade2c	Add launch.json for debugging sample in vscode	2023-03-17 00:14:28 +01:00
Ricardo Montañana	770502c8e5	Update sample	2023-03-14 11:36:38 +01:00
Ricardo Montañana	ed7433672d	Add checked strings in exceptions	2023-03-13 17:45:06 +01:00
Ricardo Montañana	14860ea0b9	Fix smell and add new test	2023-03-13 17:17:31 +01:00
Ricardo Montañana	d9a6f528f6	Fix 2 code smell	2023-03-13 16:56:09 +01:00
Ricardo Montañana	7551b0d669	Refactor constructor	2023-03-13 01:36:29 +01:00
Ricardo Montañana	ffb8df4d1c	Add max_cutpoints Hyperparameter	2023-03-13 01:17:04 +01:00
Ricardo Montañana	ed784736ca	update build	2023-03-12 11:39:35 +01:00
Ricardo Montañana	49e9dd3e12	Update build	2023-03-12 11:30:43 +01:00
Ricardo Montañana	083a56b311	Change seconds for milliseconds in sample change path of coverage report in build	2023-03-12 11:27:02 +01:00
Ricardo Montañana	4492252729	Add headers needed in sample.cpp	2023-03-11 22:45:34 +01:00
Ricardo Montañana	c00b7a613c	Add path argument to command line	2023-02-28 10:52:26 +01:00
Ricardo Montañana	200015000c	Add all datasets to sample	2023-02-28 10:28:23 +01:00
Ricardo Montañana	ce9ddb3be3	Cosmetic refactor in unittest	2023-02-28 00:50:12 +01:00
Ricardo Montañana	90428218c2	Add dataset to test and add hyperparameters to sample	2023-02-28 00:43:37 +01:00
Ricardo Montañana	0b63d9ace0	Update build	2023-02-27 01:18:46 +01:00
Ricardo Montañana	6875127394	Update Test coverage and build	2023-02-27 01:01:24 +01:00
Ricardo Montañana	747f610ce9	Remove unneeded code in CPPFImdlp	2023-02-27 00:53:00 +01:00
Ricardo Montañana	a7d13f602d	set min_length as protected	2023-02-26 12:07:52 +01:00
Ricardo Montañana	552b03afc9	make public min_length for tests	2023-02-26 11:33:10 +01:00
Ricardo Montañana	4a9664c4aa	Fix depth init in fit	2023-02-26 11:26:37 +01:00
Ricardo Montañana	964555de20	Add echo total of cut points in sample	2023-02-25 18:31:57 +01:00
Ricardo Montañana	d6cece1006	Add max_depth and min_length as hyperparams	2023-02-25 18:16:20 +01:00
Ricardo Montañana Gómez	e25ca378f0	Merge pull request #4 from rmontanana/test Add tests to GH action	2023-02-24 11:45:14 +01:00
Ricardo Montañana	71c1dc2928	Build project and tests in action	2023-02-24 11:41:50 +01:00
Ricardo Montañana	ebea31afd1	Build tests in action	2023-02-24 11:39:38 +01:00
Ricardo Montañana	89d675eb1f	Action to execute tests	2023-02-24 11:36:48 +01:00
Ricardo Montañana	e8fcc20a32	Fix mistake in build action	2023-02-24 11:33:26 +01:00
Ricardo Montañana	848ee7ba24	Try tests in build action	2023-02-24 11:32:10 +01:00
Ricardo Montañana Gómez	32a6fd9ba0	Update version number	2023-02-22 12:05:47 +01:00
Ricardo Montañana	cd04f97fd0	Remove exception of valuecutpoint it's not needed	2023-02-22 11:55:31 +01:00
Ricardo Montañana	458a313aee	Add limits header	2023-02-22 11:32:37 +01:00
Ricardo Montañana	e97aea2a4d	try new build	2023-02-22 01:33:49 +01:00
Ricardo Montañana	4707bc0b7f	update sonar project key	2023-02-22 01:16:39 +01:00
Ricardo Montañana	8c868981e8	New build.yml version	2023-02-22 00:46:54 +01:00
Ricardo Montañana	e812e91540	Update gha to sonarcloud	2023-02-22 00:30:14 +01:00
Ricardo Montañana	dddeea4024	fix token name mistake in github action	2023-02-21 20:38:10 +01:00
Ricardo Montañana	5b7d66d922	Update sonar configuration	2023-02-21 20:27:54 +01:00