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https://github.com/rmontanana/mdlp.git
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Refactor Algorithm
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@@ -4,31 +4,26 @@
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#include <iostream>
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namespace mdlp {
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class TestFImdlp : public CPPFImdlp, public testing::Test {
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class TestFImdlp: public CPPFImdlp, public testing::Test {
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public:
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precision_t precision = 0.000001;
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TestFImdlp() : CPPFImdlp(false) {}
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void SetUp() {
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TestFImdlp(): CPPFImdlp() {}
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void SetUp()
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{
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// 5.0, 5.1, 5.1, 5.1, 5.2, 5.3, 5.6, 5.7, 5.9, 6.0]
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//(5.0, 1) (5.1, 1) (5.1, 2) (5.1, 2) (5.2, 1) (5.3, 1) (5.6, 2) (5.7, 1) (5.9, 2) (6.0, 2)
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X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
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y = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
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X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
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y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
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algorithm = false;
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fit(X, y);
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}
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void setalgorithm(bool value) {
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void setalgorithm(bool value)
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{
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algorithm = value;
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}
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// void initIndices()
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// {
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// indices = indices_t();
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// }
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void checkSortedVector() {
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indices_t testSortedIndices = sortIndices(X);
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void checkSortedVector()
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{
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indices_t testSortedIndices = sortIndices(X, y);
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precision_t prev = X[testSortedIndices[0]];
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for (auto i = 0; i < X.size(); ++i) {
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EXPECT_EQ(testSortedIndices[i], indices[i]);
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@@ -36,54 +31,55 @@ namespace mdlp {
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prev = X[testSortedIndices[i]];
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}
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}
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void checkCutPoints(cutPoints_t &expected) {
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void checkCutPoints(cutPoints_t& expected)
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{
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int expectedSize = expected.size();
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EXPECT_EQ(cutPoints.size(), expectedSize);
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for (auto i = 0; i < cutPoints.size(); i++) {
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EXPECT_NEAR(cutPoints[i], expected[i], precision);
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}
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}
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template<typename T, typename A>
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void checkVectors(std::vector<T, A> const &expected, std::vector<T, A> const &computed) {
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void checkVectors(std::vector<T, A> const& expected, std::vector<T, A> const& computed)
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{
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EXPECT_EQ(expected.size(), computed.size());
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ASSERT_EQ(expected.size(), computed.size());
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for (auto i = 0; i < expected.size(); i++) {
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EXPECT_NEAR(expected[i], computed[i],precision);
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EXPECT_NEAR(expected[i], computed[i], precision);
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}
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}
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};
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TEST_F(TestFImdlp, FitErrorEmptyDataset) {
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TEST_F(TestFImdlp, FitErrorEmptyDataset)
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{
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X = samples_t();
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y = labels_t();
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EXPECT_THROW(fit(X, y), std::invalid_argument);
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}
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TEST_F(TestFImdlp, FitErrorDifferentSize) {
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X = {1, 2, 3};
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y = {1, 2};
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TEST_F(TestFImdlp, FitErrorDifferentSize)
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{
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X = { 1, 2, 3 };
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y = { 1, 2 };
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EXPECT_THROW(fit(X, y), std::invalid_argument);
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}
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TEST_F(TestFImdlp, SortIndices) {
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X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
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indices = {4, 3, 6, 8, 2, 1, 5, 0, 9, 7};
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TEST_F(TestFImdlp, SortIndices)
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{
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X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
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indices = { 4, 3, 6, 8, 2, 1, 5, 0, 9, 7 };
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checkSortedVector();
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X = {5.77, 5.88, 5.99};
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indices = {0, 1, 2};
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X = { 5.77, 5.88, 5.99 };
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indices = { 0, 1, 2 };
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checkSortedVector();
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X = {5.33, 5.22, 5.11};
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indices = {2, 1, 0};
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X = { 5.33, 5.22, 5.11 };
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indices = { 2, 1, 0 };
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checkSortedVector();
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}
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TEST_F(TestFImdlp, TestDataset) {
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algorithm = false;
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TEST_F(TestFImdlp, TestDataset)
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{
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algorithm = 0;
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fit(X, y);
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computeCutPointsOriginal(0, 10);
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cutPoints_t expected = {5.6499996185302734};
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computeCutPoints(0, 10);
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cutPoints_t expected = { 5.6499996185302734 };
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vector<precision_t> computed = getCutPoints();
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computed = getCutPoints();
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int expectedSize = expected.size();
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@@ -92,49 +88,49 @@ namespace mdlp {
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EXPECT_NEAR(computed[i], expected[i], precision);
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}
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}
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TEST_F(TestFImdlp, ComputeCutPointsOriginal) {
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cutPoints_t expected = {5.65};
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TEST_F(TestFImdlp, ComputeCutPoints)
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{
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cutPoints_t expected = { 5.65 };
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algorithm = false;
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computeCutPointsOriginal(0, 10);
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computeCutPoints(0, 10);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsOriginalGCase) {
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TEST_F(TestFImdlp, ComputeCutPointsGCase)
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{
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cutPoints_t expected;
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algorithm = false;
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expected = {2};
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samples_t X_ = {0, 1, 2, 2};
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labels_t y_ = {1, 1, 1, 2};
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expected = { 2 };
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samples_t X_ = { 0, 1, 2, 2 };
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labels_t y_ = { 1, 1, 1, 2 };
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fit(X_, y_);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsalgorithm) {
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TEST_F(TestFImdlp, ComputeCutPointsalAlternative)
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{
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algorithm = true;
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cutPoints_t expected;
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expected = {};
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fit(X, y);
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computeCutPointsalgorithm();
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computeCutPointsAlternative(0, 10);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsalgorithmGCase) {
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TEST_F(TestFImdlp, ComputeCutPointsAlternativeGCase)
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{
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cutPoints_t expected;
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expected = {1.5};
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expected = { 1.5 };
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algorithm = true;
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samples_t X_ = {0, 1, 2, 2};
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labels_t y_ = {1, 1, 1, 2};
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samples_t X_ = { 0, 1, 2, 2 };
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labels_t y_ = { 1, 1, 1, 2 };
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fit(X_, y_);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, GetCutPoints) {
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samples_t computed, expected = {5.65};
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TEST_F(TestFImdlp, GetCutPoints)
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{
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samples_t computed, expected = { 5.65 };
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algorithm = false;
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computeCutPointsOriginal(0, 10);
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computeCutPoints(0, 10);
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computed = getCutPoints();
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for (auto item: cutPoints)
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for (auto item : cutPoints)
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cout << setprecision(6) << item << endl;
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checkVectors(expected, computed);
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}
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