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Diffstat (limited to 'ml/SamplesBufferTests.cc')
| -rw-r--r-- | ml/SamplesBufferTests.cc | 146 |
1 files changed, 146 insertions, 0 deletions
diff --git a/ml/SamplesBufferTests.cc b/ml/SamplesBufferTests.cc new file mode 100644 index 00000000..5997a2a1 --- /dev/null +++ b/ml/SamplesBufferTests.cc @@ -0,0 +1,146 @@ +// 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; + + std::vector<uint32_t> RandNums(NumSamples, std::numeric_limits<uint32_t>::max()); + SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN, 1.0, RandNums); + 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; + + std::vector<uint32_t> RandNums(NumSamples, std::numeric_limits<uint32_t>::max()); + SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN, 1.0, RandNums); + 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; + + std::vector<uint32_t> RandNums(NumSamples, std::numeric_limits<uint32_t>::max()); + SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN, 1.0, RandNums); + 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; +} |