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refactor system types in library
Add new test taken from join_fit in FImdlp python Update instructions in README
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@@ -27,7 +27,7 @@ string ArffFiles::getClassType() const {
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return classType;
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}
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vector<vector<float>> &ArffFiles::getX() {
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vector<mdlp::samples_t> &ArffFiles::getX() {
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return X;
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}
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@@ -80,7 +80,7 @@ void ArffFiles::load(const string &fileName, bool classLast) {
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}
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void ArffFiles::generateDataset(bool classLast) {
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X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
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X = vector<mdlp::samples_t>(attributes.size(), mdlp::samples_t(lines.size()));
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auto yy = vector<string>(lines.size(), "");
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int labelIndex = classLast ? static_cast<int>(attributes.size()) : 0;
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for (size_t i = 0; i < lines.size(); i++) {
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@@ -3,6 +3,7 @@
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#include <string>
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#include <vector>
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#include "../typesFImdlp.h"
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using namespace std;
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@@ -12,7 +13,7 @@ private:
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vector<pair<string, string>> attributes;
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string className;
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string classType;
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vector<vector<float>> X;
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vector<mdlp::samples_t> X;
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vector<int> y;
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void generateDataset(bool);
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@@ -32,7 +33,7 @@ public:
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static string trim(const string &);
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vector<vector<float>> &getX();
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vector<mdlp::samples_t> &getX();
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vector<int> &getY();
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@@ -111,6 +111,16 @@ namespace mdlp {
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EXPECT_THROW_WITH_MESSAGE(testDepth.fit(X, y), invalid_argument, "max_depth must be greater than 0");
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}
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TEST_F(TestFImdlp, JoinFit) {
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samples_t X_ = {1, 2, 2, 3, 4, 2, 3};
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labels_t y_ = {0, 0, 1, 2, 3, 4, 5};
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cutPoints_t expected = {1.5f, 2.5f};
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fit(X_, y_);
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auto computed = getCutPoints();
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EXPECT_EQ(computed.size(), expected.size());
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checkCutPoints(computed, expected);
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}
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TEST_F(TestFImdlp, FitErrorMaxCutPoints) {
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auto testmin = CPPFImdlp(2, 10, -1);
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auto testmax = CPPFImdlp(3, 0, 200);
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@@ -30,6 +30,14 @@ namespace mdlp {
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ASSERT_NEAR(0.468996f, entropy(0, 10), precision);
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}
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TEST_F(TestMetrics, EntropyDouble) {
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y = {0, 0, 1, 2, 3};
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samples_t expected_entropies = {0.0, 0.0, 0.91829583, 1.5, 1.4575424759098898};
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for (auto idx = 0; idx < y.size(); ++idx) {
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ASSERT_NEAR(expected_entropies[idx], entropy(0, idx + 1), precision);
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}
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}
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TEST_F(TestMetrics, InformationGain) {
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ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
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ASSERT_NEAR(1, informationGain(0, 5, 10), precision); // For cache
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