16 references to VectorUtils
Microsoft.ML.FastTree (16)
Training\EnsembleCompression\LassoBasedEnsembleCompressor.cs (10)
195double mean = VectorUtils.GetMean(_observations[j]); 207featureStds[j] = Math.Sqrt(VectorUtils.GetDotProduct(_observations[j], _observations[j])); 209VectorUtils.DivideInPlace(_observations[j], (float)featureStds[j]); 212float targetMean = (float)VectorUtils.GetMean(_targets); 223float targetStd = (float)Math.Sqrt(VectorUtils.GetDotProduct(_targets, _targets)); 224VectorUtils.DivideInPlace(_targets, targetStd); 228feature2residualCorrelations[j] = VectorUtils.GetDotProduct(_targets, _observations[j]); 231double[][] feature2featureCorrelations = VectorUtils.AllocateDoubleMatrix(_numFeatures, maxAllowedFeaturesAlongPath); 328feature2featureCorrelations[j][numberOfInputs - 1] = VectorUtils.GetDotProduct(_observations[j], _observations[k]); 390pFeature2residualCorrelations[j] -= VectorUtils.GetDotProduct(denseActiveSet, feature2featureCorrelations[j], numberOfInputs);
Training\EnsembleCompression\LassoFit.cs (1)
41CompressedWeights = VectorUtils.AllocateDoubleMatrix(numberOfLambdas, maxAllowedFeaturesAlongPath);
Training\OptimizationAlgorithms\ConjugateGradientDescent.cs (5)
38double beta = VectorUtils.GetDotProduct(_currentGradient, VectorUtils.Subtract(_currentGradient, _previousGradient)) / VectorUtils.GetDotProduct(_previousGradient, _previousGradient); 47VectorUtils.MutiplyInPlace(previousDk, beta); 48VectorUtils.AddInPlace(previousDk, _currentGradient);