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authoryazevnul <yazevnul@yandex-team.ru>2022-02-10 16:46:46 +0300
committerDaniil Cherednik <dcherednik@yandex-team.ru>2022-02-10 16:46:46 +0300
commit8cbc307de0221f84c80c42dcbe07d40727537e2c (patch)
tree625d5a673015d1df891e051033e9fcde5c7be4e5 /library/cpp/linear_regression
parent30d1ef3941e0dc835be7609de5ebee66958f215a (diff)
downloadydb-8cbc307de0221f84c80c42dcbe07d40727537e2c.tar.gz
Restoring authorship annotation for <yazevnul@yandex-team.ru>. Commit 1 of 2.
Diffstat (limited to 'library/cpp/linear_regression')
-rw-r--r--library/cpp/linear_regression/linear_regression.h10
-rw-r--r--library/cpp/linear_regression/linear_regression_ut.cpp30
-rw-r--r--library/cpp/linear_regression/welford.h6
3 files changed, 23 insertions, 23 deletions
diff --git a/library/cpp/linear_regression/linear_regression.h b/library/cpp/linear_regression/linear_regression.h
index e57de5ff6c..20b3ae1e53 100644
--- a/library/cpp/linear_regression/linear_regression.h
+++ b/library/cpp/linear_regression/linear_regression.h
@@ -146,12 +146,12 @@ public:
bool Add(const double* featuresBegin, const double* featuresEnd, const double* goalsBegin, const double* weightsBegin);
bool Add(const TVector<double>& features, const TVector<double>& goals) {
- Y_ASSERT(features.size() == goals.size());
+ Y_ASSERT(features.size() == goals.size());
return Add(features.data(), features.data() + features.size(), goals.data());
}
bool Add(const TVector<double>& features, const TVector<double>& goals, const TVector<double>& weights) {
- Y_ASSERT(features.size() == goals.size() && features.size() == weights.size());
+ Y_ASSERT(features.size() == goals.size() && features.size() == weights.size());
return Add(features.data(), features.data() + features.size(), goals.data(), weights.data());
}
@@ -239,7 +239,7 @@ struct TTransformationParameters {
double FeatureOffset = 0.;
double FeatureNormalizer = 1.;
- Y_SAVELOAD_DEFINE(RegressionFactor,
+ Y_SAVELOAD_DEFINE(RegressionFactor,
RegressionIntercept,
FeatureOffset,
FeatureNormalizer);
@@ -251,7 +251,7 @@ private:
TTransformationParameters TransformationParameters;
public:
- Y_SAVELOAD_DEFINE(TransformationType, TransformationParameters);
+ Y_SAVELOAD_DEFINE(TransformationType, TransformationParameters);
TFeaturesTransformer() = default;
@@ -273,7 +273,7 @@ public:
return TransformationParameters.RegressionIntercept + TransformationParameters.RegressionFactor * transformedValue;
}
}
- Y_ASSERT(0);
+ Y_ASSERT(0);
return 0.;
}
};
diff --git a/library/cpp/linear_regression/linear_regression_ut.cpp b/library/cpp/linear_regression/linear_regression_ut.cpp
index e71a16b67a..991a95c0d0 100644
--- a/library/cpp/linear_regression/linear_regression_ut.cpp
+++ b/library/cpp/linear_regression/linear_regression_ut.cpp
@@ -13,8 +13,8 @@ namespace {
}
}
-Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
- Y_UNIT_TEST(MeanAndDeviationTest) {
+Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
+ Y_UNIT_TEST(MeanAndDeviationTest) {
TVector<double> arguments;
TVector<double> weights;
@@ -77,7 +77,7 @@ Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
ValueIsCorrect(deviationCalculator.GetDeviation(), checkRemovingDeviationCalculator.GetDeviation(), 1e-5);
}
- Y_UNIT_TEST(CovariationTest) {
+ Y_UNIT_TEST(CovariationTest) {
TVector<double> firstValues;
TVector<double> secondValues;
TVector<double> weights;
@@ -176,15 +176,15 @@ Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
}
}
- Y_UNIT_TEST(FastSLRTest) {
+ Y_UNIT_TEST(FastSLRTest) {
SLRTest<TFastSLRSolver>();
}
- Y_UNIT_TEST(KahanSLRTest) {
+ Y_UNIT_TEST(KahanSLRTest) {
SLRTest<TKahanSLRSolver>();
}
- Y_UNIT_TEST(SLRTest) {
+ Y_UNIT_TEST(SLRTest) {
SLRTest<TSLRSolver>();
}
@@ -231,11 +231,11 @@ Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
UNIT_ASSERT_DOUBLES_EQUAL(lrSolver.SumSquaredErrors(), expectedSumSquaredErrors, expectedSumSquaredErrors * 0.01);
}
- Y_UNIT_TEST(FastLRTest) {
+ Y_UNIT_TEST(FastLRTest) {
LinearRegressionTest<TFastLinearRegressionSolver>();
}
- Y_UNIT_TEST(LRTest) {
+ Y_UNIT_TEST(LRTest) {
LinearRegressionTest<TLinearRegressionSolver>();
}
@@ -275,31 +275,31 @@ Y_UNIT_TEST_SUITE(TLinearRegressionTest) {
UNIT_ASSERT_DOUBLES_EQUAL(rmse, 0., 1e-3);
}
- Y_UNIT_TEST(SigmaTest100) {
+ Y_UNIT_TEST(SigmaTest100) {
TransformationTest(ETransformationType::TT_SIGMA, 100);
}
- Y_UNIT_TEST(SigmaTest1000) {
+ Y_UNIT_TEST(SigmaTest1000) {
TransformationTest(ETransformationType::TT_SIGMA, 1000);
}
- Y_UNIT_TEST(SigmaTest10000) {
+ Y_UNIT_TEST(SigmaTest10000) {
TransformationTest(ETransformationType::TT_SIGMA, 10000);
}
- Y_UNIT_TEST(SigmaTest100000) {
+ Y_UNIT_TEST(SigmaTest100000) {
TransformationTest(ETransformationType::TT_SIGMA, 100000);
}
- Y_UNIT_TEST(SigmaTest1000000) {
+ Y_UNIT_TEST(SigmaTest1000000) {
TransformationTest(ETransformationType::TT_SIGMA, 1000000);
}
- Y_UNIT_TEST(SigmaTest10000000) {
+ Y_UNIT_TEST(SigmaTest10000000) {
TransformationTest(ETransformationType::TT_SIGMA, 10000000);
}
- Y_UNIT_TEST(ResetCalculatorTest) {
+ Y_UNIT_TEST(ResetCalculatorTest) {
TVector<double> arguments;
TVector<double> weights;
const double eps = 1e-10;
diff --git a/library/cpp/linear_regression/welford.h b/library/cpp/linear_regression/welford.h
index ee865d6693..a39800974c 100644
--- a/library/cpp/linear_regression/welford.h
+++ b/library/cpp/linear_regression/welford.h
@@ -11,7 +11,7 @@ private:
TKahanAccumulator<double> SumWeights;
public:
- Y_SAVELOAD_DEFINE(Mean, SumWeights);
+ Y_SAVELOAD_DEFINE(Mean, SumWeights);
void Multiply(const double value);
void Add(const double value, const double weight = 1.);
@@ -40,7 +40,7 @@ private:
TKahanAccumulator<double> SumWeights;
public:
- Y_SAVELOAD_DEFINE(Covariation, FirstValueMean, SecondValueMean, SumWeights);
+ Y_SAVELOAD_DEFINE(Covariation, FirstValueMean, SecondValueMean, SumWeights);
void Add(const double firstValue, const double secondValue, const double weight = 1.);
void Remove(const double firstValue, const double secondValue, const double weight = 1.);
@@ -62,7 +62,7 @@ private:
TMeanCalculator MeanCalculator;
public:
- Y_SAVELOAD_DEFINE(Deviation, MeanCalculator);
+ Y_SAVELOAD_DEFINE(Deviation, MeanCalculator);
void Add(const double value, const double weight = 1.);
void Remove(const double value, const double weight = 1.);