1 files changed, 143 insertions, 0 deletions
diff --git a/ml/kmeans/Tests.cc b/ml/kmeans/Tests.cc
new file mode 100644
index 000000000..0cb595945
--- /dev/null
+++ b/ml/kmeans/Tests.cc
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+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#include "ml/ml-private.h"
+#include <gtest/gtest.h>
+
+/*
+ * The SamplesBuffer class implements the functionality of the following python
+ * code:
+ *      >> df = pd.DataFrame(data=samples)
+ *      >> df = df.diff(diff_n).dropna()
+ *      >> df = df.rolling(smooth_n).mean().dropna()
+ *      >> df = pd.concat([df.shift(n) for n in range(lag_n + 1)], axis=1).dropna()
+ *
+ * Its correctness has been verified by automatically generating random
+ * data frames in Python and comparing them with the correspondent preprocessed
+ * SampleBuffers.
+ *
+ * The following tests are meant to catch unintended changes in the SamplesBuffer
+ * implementation. For development purposes, one should compare changes against
+ * the aforementioned python code.
+*/
+
+TEST(SamplesBufferTest, NS_8_NDPS_1_DN_1_SN_3_LN_1) {
+    size_t NumSamples = 8, NumDimsPerSample = 1;
+    size_t DiffN = 1, SmoothN = 3, LagN = 3;
+
+    size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
+    CalculatedNumber *CNs = new CalculatedNumber[N]();
+
+    CNs[0] = 0.7568336679490107;
+    CNs[1] = 0.4814406581763254;
+    CNs[2] = 0.40073555156221874;
+    CNs[3] = 0.5973257298194408;
+    CNs[4] = 0.5334727814345868;
+    CNs[5] = 0.2632477193454843;
+    CNs[6] = 0.2684839023122384;
+    CNs[7] = 0.851332948637479;
+
+    SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
+    SB.preprocess();
+
+    std::vector<Sample> Samples = SB.getPreprocessedSamples();
+    EXPECT_EQ(Samples.size(), 2);
+
+    Sample S0 = Samples[0];
+    const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
+    Sample S1 = Samples[1];
+    const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
+
+    EXPECT_NEAR(S0_CNs[0], -0.109614, 0.001);
+    EXPECT_NEAR(S0_CNs[1], -0.0458293, 0.001);
+    EXPECT_NEAR(S0_CNs[2], 0.017344, 0.001);
+    EXPECT_NEAR(S0_CNs[3], -0.0531693, 0.001);
+
+    EXPECT_NEAR(S1_CNs[0], 0.105953, 0.001);
+    EXPECT_NEAR(S1_CNs[1], -0.109614, 0.001);
+    EXPECT_NEAR(S1_CNs[2], -0.0458293, 0.001);
+    EXPECT_NEAR(S1_CNs[3], 0.017344, 0.001);
+
+    delete[] CNs;
+}
+
+TEST(SamplesBufferTest, NS_8_NDPS_1_DN_2_SN_3_LN_2) {
+    size_t NumSamples = 8, NumDimsPerSample = 1;
+    size_t DiffN = 2, SmoothN = 3, LagN = 2;
+
+    size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
+    CalculatedNumber *CNs = new CalculatedNumber[N]();
+
+    CNs[0] = 0.20511885291342846;
+    CNs[1] = 0.13151717360306558;
+    CNs[2] = 0.6017085062423134;
+    CNs[3] = 0.46256882933941545;
+    CNs[4] = 0.7887758447877941;
+    CNs[5] = 0.9237989080034406;
+    CNs[6] = 0.15552559051428083;
+    CNs[7] = 0.6309750314597955;
+
+    SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
+    SB.preprocess();
+
+    std::vector<Sample> Samples = SB.getPreprocessedSamples();
+    EXPECT_EQ(Samples.size(), 2);
+
+    Sample S0 = Samples[0];
+    const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
+    Sample S1 = Samples[1];
+    const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
+
+    EXPECT_NEAR(S0_CNs[0], 0.005016, 0.001);
+    EXPECT_NEAR(S0_CNs[1], 0.326450, 0.001);
+    EXPECT_NEAR(S0_CNs[2], 0.304903, 0.001);
+
+    EXPECT_NEAR(S1_CNs[0], -0.154948, 0.001);
+    EXPECT_NEAR(S1_CNs[1], 0.005016, 0.001);
+    EXPECT_NEAR(S1_CNs[2], 0.326450, 0.001);
+
+    delete[] CNs;
+}
+
+TEST(SamplesBufferTest, NS_8_NDPS_3_DN_2_SN_4_LN_1) {
+    size_t NumSamples = 8, NumDimsPerSample = 3;
+    size_t DiffN = 2, SmoothN = 4, LagN = 1;
+
+    size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
+    CalculatedNumber *CNs = new CalculatedNumber[N]();
+
+    CNs[0] = 0.34310900399667765; CNs[1] = 0.14694315994488194; CNs[2] = 0.8246677800938796;
+    CNs[3] = 0.48249504592307835; CNs[4] = 0.23241087965531182; CNs[5] = 0.9595348555892567;
+    CNs[6] = 0.44281094035598334; CNs[7] = 0.5143142171362715; CNs[8] = 0.06391303014242555;
+    CNs[9] = 0.7460491027783901; CNs[10] = 0.43887217459032923; CNs[11] = 0.2814395025355999;
+    CNs[12] = 0.9231114281214198; CNs[13] = 0.326882401786898; CNs[14] = 0.26747939220376216;
+    CNs[15] = 0.7787571209969636; CNs[16] =0.5851700001235088; CNs[17] = 0.34410728945321567;
+    CNs[18] = 0.9394494507088997; CNs[19] =0.17567223681734334; CNs[20] = 0.42732886195446984;
+    CNs[21] = 0.9460522396152958; CNs[22] =0.23462747016780894; CNs[23] = 0.35983249900892145;
+
+    SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
+    SB.preprocess();
+
+    std::vector<Sample> Samples = SB.getPreprocessedSamples();
+    EXPECT_EQ(Samples.size(), 2);
+
+    Sample S0 = Samples[0];
+    const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
+    Sample S1 = Samples[1];
+    const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
+
+    EXPECT_NEAR(S0_CNs[0], 0.198225, 0.001);
+    EXPECT_NEAR(S0_CNs[1], 0.003529, 0.001);
+    EXPECT_NEAR(S0_CNs[2], -0.063003, 0.001);
+    EXPECT_NEAR(S0_CNs[3], 0.219066, 0.001);
+    EXPECT_NEAR(S0_CNs[4], 0.133175, 0.001);
+    EXPECT_NEAR(S0_CNs[5], -0.293154, 0.001);
+
+    EXPECT_NEAR(S1_CNs[0], 0.174160, 0.001);
+    EXPECT_NEAR(S1_CNs[1], -0.135722, 0.001);
+    EXPECT_NEAR(S1_CNs[2], 0.110452, 0.001);
+    EXPECT_NEAR(S1_CNs[3], 0.198225, 0.001);
+    EXPECT_NEAR(S1_CNs[4], 0.003529, 0.001);
+    EXPECT_NEAR(S1_CNs[5], -0.063003, 0.001);
+
+    delete[] CNs;
+}