Remove alternative and Classic

Refactor ValueCutPoint
Reefactor sameValues in getCandidate

.github/workflows/build.yml

@@ -0,0 +1,26 @@
+name: Build
+
+on:
+  push:
+    branches:
+      - main
+
+
+jobs:
+  build:
+    name: Build
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0  # Shallow clones should be disabled for a better relevancy of analysis
+      - uses: sonarsource/sonarqube-scan-action@master
+        env:
+          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 }}

.gitignore

@@ -35,3 +35,4 @@
 .idea
 cmake-*
 **/CMakeFiles
+sonar-project.properties

.vscode/launch.json

@@ -0,0 +1,22 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "(lldb) Launch",
+            "type": "cppdbg",
+            "request": "launch",
+            "program": "${workspaceRoot}/sample/build/sample",
+            "args": [
+                "test"
+            ],
+            "stopAtEntry": false,
+            "cwd": "${workspaceRoot}/sample/build/",
+            "environment": [],
+            "externalConsole": false,
+            "MIMode": "lldb"
+        }
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "sonarlint.connectedMode.project": {
+        "projectKey": "rmontanana_mdlp_AYZkjILJHyjW-meBaElG"
+    }
+}

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 project(mdlp)
 
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 11)
 
 add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
 

CPPFImdlp.cpp

@@ -6,13 +6,15 @@
 #include "Metrics.h"
 
 namespace mdlp {
-    CPPFImdlp::CPPFImdlp(int algorithm) : algorithm(algorithm), indices(indices_t()), X(samples_t()), y(labels_t()),
-                                          metrics(Metrics(y, indices)) {
-    }
 
+    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_) {
+    CPPFImdlp& CPPFImdlp::fit(samples_t& X_, labels_t& y_)
+    {
         X = X_;
         y = y_;
         cutPoints.clear();
@@ -24,117 +26,75 @@ namespace mdlp {
         }
         indices = sortIndices(X_, y_);
         metrics.setData(y, indices);
-        switch (algorithm) {
-            case 0:
         computeCutPoints(0, X.size());
-                break;
-            case 1:
-                computeCutPointsAlternative(0, X.size());
-                break;
-            case 2:
-                indices = sortIndices1(X_);
-                metrics.setData(y, indices);
-                computeCutPointsClassic(0, X.size());
-                break;
-            default:
-                throw invalid_argument("algorithm must be 0, 1 or 2");
-        }
         return *this;
     }
 
-    precision_t CPPFImdlp::halfWayValueCutPoint(size_t start, size_t idx) {
-        size_t idxPrev = idx - 1;
-        precision_t previous = X[indices[idxPrev]], actual = X[indices[idx]];
-        // definition 2 of the paper => X[t-1] < X[t]
-        while (idxPrev-- > start && actual == previous) {
-            previous = X[indices[idxPrev]];
-        }
-        return (previous + actual) / 2;
-    }
-
-    tuple<precision_t, size_t> CPPFImdlp::completeValueCutPoint(size_t start, size_t cut, size_t end) {
-        size_t idxPrev = cut - 1;
-        bool fforward = false;
-        precision_t previous, actual;
+    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;
         previous = X[indices[idxPrev]];
         actual = X[indices[cut]];
+        next = X[indices[idxNext]];
         // definition 2 of the paper => X[t-1] < X[t]
-        while (idxPrev-- > start && actual == previous) {
-            previous = X[indices[idxPrev]];
+        // 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 (actual == X[indices[cut]] && cut + 1 < end) {
-            cut++;
-            fforward = true;
+        while (idxNext < end - 1 && actual == next) {
+            next = X[indices[++idxNext]];
         }
-        if (fforward)
-            cut--;
-        // try to get the next value if it can't be found backwards
-        if (previous == actual && cut + 1 < end)
-            actual = X[indices[cut + 1]];
-        return make_tuple((previous + actual) / 2, cut);
+        // # 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) {
+    void CPPFImdlp::computeCutPoints(size_t start, size_t end)
+    {
         size_t cut;
-        tuple<precision_t, size_t> result;
-        if (end - start < 2)
+        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 = completeValueCutPoint(start, cut, end);
-            cut = get<1>(result);
-            cutPoints.push_back(get<0>(result));
+            result = valueCutPoint(start, cut, end);
+            cut = result.second;
+            cutPoints.push_back(result.first);
             computeCutPoints(start, cut);
             computeCutPoints(cut, end);
         }
     }
 
-    void CPPFImdlp::computeCutPointsAlternative(size_t start, size_t end) {
-        size_t cut;
-        if (end - start < 2)
-            return;
-        cut = getCandidate(start, end);
-        if (cut == numeric_limits<size_t>::max())
-            return;
-        if (mdlp(start, cut, end)) {
-            cutPoints.push_back(halfWayValueCutPoint(start, cut));
-            computeCutPointsAlternative(start, cut);
-            computeCutPointsAlternative(cut, end);
-        }
-    }
-
-    void CPPFImdlp::computeCutPointsClassic(size_t start, size_t end) {
-        size_t cut;
-        cut = getCandidate(start, end);
-        if (cut == numeric_limits<size_t>::max() || !mdlp(start, cut, end)) {
-            // cut.value == -1 means that there is no candidate in the interval
-            // No boundary found, so we add both ends of the interval as cutpoints
-            // because they were selected by the algorithm before
-            if (start == end)
-                return;
-            if (start != 0)
-                cutPoints.push_back((X[indices[start]] + X[indices[start - 1]]) / 2);
-            if (end != X.size())
-                cutPoints.push_back((X[indices[end]] + X[indices[end - 1]]) / 2);
-            return;
-        }
-        computeCutPoints(start, cut);
-        computeCutPoints(cut, end);
-    }
-
-    size_t CPPFImdlp::getCandidate(size_t start, size_t end) {
+    size_t CPPFImdlp::getCandidate(size_t start, size_t end)
+    {
         /* Definition 1: A binary discretization for A is determined by selecting the cut point TA for which
         E(A, TA; S) is minimal amongst all the candidate cut points. */
         size_t candidate = numeric_limits<size_t>::max(), elements = end - start;
-        bool same_values = true;
+        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 (auto idx = start + 1; idx < end; idx++) {
-            if (X[indices[idx]] != X[indices[idx - 1]])
-                same_values = false;
+        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;
@@ -145,13 +105,11 @@ namespace mdlp {
                 candidate = idx;
             }
         }
-        // If all the values of the variable in the interval are the same, it doesn't consider the cut point
-        if (same_values)
-            candidate = numeric_limits<size_t>::max();
         return candidate;
     }
 
-    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end) {
+    bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
+    {
         int k, k1, k2;
         precision_t ig, delta;
         precision_t ent, ent1, ent2;
@@ -173,7 +131,8 @@ namespace mdlp {
     }
 
     // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
-    indices_t CPPFImdlp::sortIndices(samples_t &X_, labels_t &y_) {
+    indices_t CPPFImdlp::sortIndices(samples_t& X_, labels_t& y_)
+    {
         indices_t idx(X_.size());
         iota(idx.begin(), idx.end(), 0);
         for (size_t i = 0; i < X_.size(); i++)
@@ -186,18 +145,8 @@ namespace mdlp {
         return idx;
     }
 
-    // Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
-    indices_t CPPFImdlp::sortIndices1(samples_t &X_) {
-        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_](size_t i1, size_t i2) {
-                return X_[i1] < X_[i2];
-            });
-        return idx;
-    }
-
-    cutPoints_t CPPFImdlp::getCutPoints() {
+    cutPoints_t CPPFImdlp::getCutPoints()
+    {
         // Remove duplicates and sort
         cutPoints_t output(cutPoints.size());
         set<precision_t> s;

CPPFImdlp.h

@@ -3,12 +3,10 @@
 #include "typesFImdlp.h"
 #include "Metrics.h"
 #include <utility>
-#include <tuple>
 #include <string>
 namespace mdlp {
     class CPPFImdlp {
     protected:
-        int algorithm;
         indices_t indices;
         samples_t X;
         labels_t y;
@@ -16,20 +14,16 @@ namespace mdlp {
         cutPoints_t cutPoints;
 
         static indices_t sortIndices(samples_t&, labels_t&);
-        static indices_t sortIndices1(samples_t&);
         void computeCutPoints(size_t, size_t);
-        void computeCutPointsAlternative(size_t, size_t);
-        void computeCutPointsClassic(size_t, size_t);
         bool mdlp(size_t, size_t, size_t);
         size_t getCandidate(size_t, size_t);
-        precision_t halfWayValueCutPoint(size_t, size_t);
-        tuple<precision_t, size_t> completeValueCutPoint(size_t, size_t, size_t);
+        pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
     public:
-        CPPFImdlp(int algorithm = 0);
+        CPPFImdlp();
         ~CPPFImdlp();
         CPPFImdlp& fit(samples_t&, labels_t&);
         samples_t getCutPoints();
-        inline string version() { return "1.0.0"; };
+        inline string version() { return "1.1.0"; };
     };
 }
 #endif

Metrics.cpp

@@ -29,8 +29,8 @@ namespace mdlp {
         labels_t counts(numClasses + 1, 0);
         if (end - start < 2)
             return 0;
-        if (entropyCache.find(make_tuple(start, end)) != entropyCache.end()) {
-            return entropyCache[make_tuple(start, end)];
+        if (entropyCache.find({ start, end }) != entropyCache.end()) {
+            return entropyCache[{start, end}];
         }
         for (auto i = &indices[start]; i != &indices[end]; ++i) {
             counts[y[*i]]++;
@@ -42,7 +42,7 @@ namespace mdlp {
                 ventropy -= p * log2(p);
             }
         }
-        entropyCache[make_tuple(start, end)] = ventropy;
+        entropyCache[{start, end}] = ventropy;
         return ventropy;
     }
     precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)

sample/CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.20)
 project(main)
 
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 11)
 
 add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)

sample/sample.cpp

@@ -7,17 +7,19 @@
 using namespace std;
 using namespace mdlp;
 
+
 int main(int argc, char** argv)
 {
     ArffFiles file;
     vector<string> lines;
     string path = "../../tests/datasets/";
-    map<string, bool > datasets = {
+    map<string, bool> datasets = {
             {"mfeat-factors",      true},
             {"iris",               true},
             {"letter",             true},
             {"glass",              true},
             {"kdd_JapaneseVowels", false},
+            {"mfeat-factors",      true},
             {"test",               true}
     };
     if (argc != 2 || datasets.find(argv[1]) == datasets.end()) {
@@ -44,9 +46,11 @@ int main(int argc, char** argv)
         }
         cout << y[i] << endl;
     }
-    mdlp::CPPFImdlp test = mdlp::CPPFImdlp(0);
+    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()) {

tests/ArffFiles.cpp

@@ -17,7 +17,7 @@ unsigned long int ArffFiles::getSize()
 {
     return lines.size();
 }
-vector<tuple<string, string>> ArffFiles::getAttributes()
+vector<pair<string, string>> ArffFiles::getAttributes()
 {
     return attributes;
 }
@@ -50,7 +50,7 @@ void ArffFiles::load(string fileName, bool classLast)
             if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
                 stringstream ss(line);
                 ss >> keyword >> attribute >> type;
-                attributes.push_back(make_tuple(attribute, type));
+                attributes.push_back({ attribute, type });
                 continue;
             }
             if (line[0] == '@') {

tests/ArffFiles.h

@@ -2,12 +2,11 @@
 #define ARFFFILES_H
 #include <string>
 #include <vector>
-#include <tuple>
 using namespace std;
 class ArffFiles {
 private:
     vector<string> lines;
-    vector<tuple<string, string>> attributes;
+    vector<pair<string, string>> attributes;
     string className, classType;
     vector<vector<float>> X;
     vector<int> y;
@@ -22,7 +21,7 @@ public:
     string trim(const string&);
     vector<vector<float>>& getX();
     vector<int>& getY();
-    vector<tuple<string, string>> getAttributes();
+    vector<pair<string, string>> getAttributes();
     vector<int> factorize(const vector<string>& labels_t);
 };
 #endif

tests/FImdlp_unittest.cpp

@@ -13,18 +13,13 @@ namespace mdlp {
         {
             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 };
             y = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
-            algorithm = false;
             fit(X, y);
         }
-        void setalgorithm(bool value)
-        {
-            algorithm = value;
-        }
         void checkSortedVector()
         {
             indices_t testSortedIndices = sortIndices(X, y);
             precision_t prev = X[testSortedIndices[0]];
-            for (auto i = 0; i < X.size(); ++i) {
+            for (unsigned long i = 0; i < X.size(); ++i) {
                 EXPECT_EQ(testSortedIndices[i], indices[i]);
                 EXPECT_LE(prev, X[testSortedIndices[i]]);
                 prev = X[testSortedIndices[i]];
@@ -34,7 +29,7 @@ namespace mdlp {
         {
             int expectedSize = expected.size();
             EXPECT_EQ(cutPoints.size(), expectedSize);
-            for (auto i = 0; i < cutPoints.size(); i++) {
+            for (unsigned long i = 0; i < cutPoints.size(); i++) {
                 EXPECT_NEAR(cutPoints[i], expected[i], precision);
             }
         }
@@ -47,6 +42,19 @@ namespace mdlp {
                 EXPECT_NEAR(expected[i], computed[i], precision);
             }
         }
+        bool test_result(samples_t& X_, size_t cut, float midPoint, size_t limit, string title)
+        {
+            pair<precision_t, size_t> result;
+            labels_t y_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+            X = X_;
+            y = y_;
+            indices = sortIndices(X, y);
+            cout << "* " << title << endl;
+            result = valueCutPoint(0, cut, 10);
+            EXPECT_NEAR(result.first, midPoint, precision);
+            EXPECT_EQ(result.second, limit);
+            return true;
+        }
     };
     TEST_F(TestFImdlp, FitErrorEmptyDataset)
     {
@@ -54,11 +62,6 @@ namespace mdlp {
         y = labels_t();
         EXPECT_THROW(fit(X, y), std::invalid_argument);
     }
-    TEST_F(TestFImdlp, FitErrorIncorrectAlgorithm)
-    {
-        algorithm = 2;
-        EXPECT_THROW(fit(X, y), std::invalid_argument);
-    }
     TEST_F(TestFImdlp, FitErrorDifferentSize)
     {
         X = { 1, 2, 3 };
@@ -83,31 +86,41 @@ namespace mdlp {
         y = { 2, 2, 1 };
         indices = { 1, 2, 0 };
     }
-    TEST_F(TestFImdlp, TestArtificialDatasetAlternative)
+    TEST_F(TestFImdlp, TestShortDatasets)
     {
-        algorithm = 1;
+        vector<precision_t> computed;
+        X = { 1 };
+        y = { 1 };
         fit(X, y);
-        computeCutPoints(0, 20);
-        cutPoints_t expected = { 5.0500001907348633 };
-        vector<precision_t> computed = getCutPoints();
         computed = getCutPoints();
-        int expectedSize = expected.size();
-        EXPECT_EQ(computed.size(), expected.size());
-        for (auto i = 0; i < computed.size(); i++) {
-            EXPECT_NEAR(computed[i], expected[i], precision);
-        }
+        EXPECT_EQ(computed.size(), 0);
+        X = { 1, 3 };
+        y = { 1, 2 };
+        fit(X, y);
+        computed = getCutPoints();
+        EXPECT_EQ(computed.size(), 0);
+        X = { 2, 4 };
+        y = { 1, 2 };
+        fit(X, y);
+        computed = getCutPoints();
+        EXPECT_EQ(computed.size(), 0);
+        X = { 1, 2, 3 };
+        y = { 1, 2, 2 };
+        fit(X, y);
+        computed = getCutPoints();
+        EXPECT_EQ(computed.size(), 1);
+        EXPECT_NEAR(computed[0], 1.5, precision);
     }
     TEST_F(TestFImdlp, TestArtificialDataset)
     {
-        algorithm = 0;
         fit(X, y);
         computeCutPoints(0, 20);
-        cutPoints_t expected = { 5.0500001907348633 };
+        cutPoints_t expected = { 5.05 };
         vector<precision_t> computed = getCutPoints();
         computed = getCutPoints();
         int expectedSize = expected.size();
         EXPECT_EQ(computed.size(), expected.size());
-        for (auto i = 0; i < computed.size(); i++) {
+        for (unsigned long i = 0; i < computed.size(); i++) {
             EXPECT_NEAR(computed[i], expected[i], precision);
         }
     }
@@ -116,44 +129,17 @@ namespace mdlp {
         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, 6.25 },
-            { 2.8499999046325684, 3, 3.0499999523162842, 3.3499999046325684 },
-            { 2.4500000476837158, 4.75, 5.0500001907348633 },
-            { 0.80000001192092896, 1.4500000476837158, 1.75 }
-        };
-        labels_t& y = file.getY();
-        auto attributes = file.getAttributes();
-        algorithm = 0;
-        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);
-            }
-        }
-    }
-    TEST_F(TestFImdlp, TestIrisAlternative)
-    {
-        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 },
-            { 2.8499999046325684, 3.3499999046325684 },
-            { 2.4500000476837158, 4.75 },
+            { 2.75, 2.85, 2.95, 3.05, 3.35 },
+            { 2.4500000476837158, 4.75, 5.0500001907348633 },
             { 0.80000001192092896, 1.75 }
         };
         labels_t& y = file.getY();
         auto attributes = file.getAttributes();
-        algorithm = 1;
         for (auto feature = 0; feature < attributes.size(); feature++) {
             fit(X[feature], y);
             vector<precision_t> computed = getCutPoints();
@@ -166,21 +152,30 @@ namespace mdlp {
     TEST_F(TestFImdlp, ComputeCutPointsGCase)
     {
         cutPoints_t expected;
-        algorithm = 0;
         expected = { 1.5 };
-        samples_t X_ = { 0, 1, 2, 2 };
-        labels_t y_ = { 1, 1, 1, 2 };
+        samples_t X_ = { 0, 1, 2, 2, 2 };
+        labels_t y_ = { 1, 1, 1, 2, 2 };
         fit(X_, y_);
         checkCutPoints(expected);
     }
-    TEST_F(TestFImdlp, ComputeCutPointsAlternativeGCase)
+    TEST_F(TestFImdlp, CompleteValueCutPoint)
     {
-        cutPoints_t expected;
-        expected = { 1.5 };
-        algorithm = true;
-        samples_t X_ = { 0, 1, 2, 2 };
-        labels_t y_ = { 1, 1, 1, 2 };
-        fit(X_, y_);
-        checkCutPoints(expected);
+        // 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");
     }
 }

typesFImdlp.h

@@ -11,7 +11,7 @@ namespace mdlp {
     typedef vector<int> labels_t;
     typedef vector<size_t> indices_t;
     typedef vector<precision_t> cutPoints_t;
-    typedef map<tuple<int, int>, precision_t> cacheEnt_t;
+    typedef map<pair<int, int>, precision_t> cacheEnt_t;
     typedef map<tuple<int, int, int>, precision_t> cacheIg_t;
 }
 #endif