60 references to WindowSize
Microsoft.ML.TimeSeries (60)
AdaptiveSingularSpectrumSequenceModeler.cs (60)
526Contracts.Assert(1 <= rank && rank <= tMat.WindowSize); 527Contracts.Assert(Utils.Size(singularVectors) >= tMat.WindowSize * rank); 530var k = tMat.SeriesLength - tMat.WindowSize + 1; 542tMat.MultiplyTranspose(singularVectors, v, false, tMat.WindowSize * i, 0); 543tMat.RankOneHankelization(singularVectors, v, 1, output, true, tMat.WindowSize * i, 0, 0); 550Contracts.Assert(1 <= rank && rank <= tMat.WindowSize); 551Contracts.Assert(Utils.Size(singularVectors) >= tMat.WindowSize * rank); 553int len = 2 * tMat.WindowSize - 1; 564int offset1 = tMat.SeriesLength - 2 * tMat.WindowSize + 1; 565int offset2 = tMat.SeriesLength - tMat.WindowSize; 574for (j = offset1; j < offset1 + tMat.WindowSize; ++j) 575v += series[j] * singularVectors[tMat.WindowSize * i - offset1 + j]; 577for (j = 0; j < tMat.WindowSize - 1; ++j) 578output[j] += v * singularVectors[tMat.WindowSize * i + j]; 580temp = v * singularVectors[tMat.WindowSize * (i + 1) - 1]; 583for (j = offset2; j < offset2 + tMat.WindowSize; ++j) 584v += series[j] * singularVectors[tMat.WindowSize * i - offset2 + j]; 586for (j = tMat.WindowSize; j < 2 * tMat.WindowSize - 1; ++j) 587output[j] += v * singularVectors[tMat.WindowSize * (i - 1) + j + 1]; 589temp += v * singularVectors[tMat.WindowSize * i]; 590output[tMat.WindowSize - 1] += (temp / 2); 648Contracts.Assert(Utils.Size(singularVectors) >= tMat.WindowSize * tMat.WindowSize); 649Contracts.Assert(Utils.Size(singularValues) >= tMat.WindowSize); 650Contracts.Assert(1 <= maxRank && maxRank <= tMat.WindowSize - 1); 653var k = inputSeriesLength - tMat.WindowSize + 1; 658var alpha = new Single[tMat.WindowSize - 1]; 664int evaluationLength = Math.Min(Math.Max(tMat.WindowSize, 200), k); 666TrajectoryMatrix xTM = new TrajectoryMatrix(null, x, tMat.WindowSize - 1, inputSeriesLength - 1); 677FixedSizeQueue<Single> window = new FixedSizeQueue<float>(tMat.WindowSize - 1); 679for (i = 0; i < tMat.WindowSize; ++i) 685lambda = singularVectors[tMat.WindowSize * i + tMat.WindowSize - 1]; 686for (j = 0; j < tMat.WindowSize - 1; ++j) 687alpha[j] += lambda * singularVectors[tMat.WindowSize * i + j]; 693tMat.MultiplyTranspose(singularVectors, v, false, tMat.WindowSize * i, 0); 694tMat.RankOneHankelization(singularVectors, v, 1, x, true, tMat.WindowSize * i, 0, 0); 701for (j = inputSeriesLength - evaluationLength - tMat.WindowSize + 1; j < inputSeriesLength - evaluationLength; ++j) 708for (n = 0; n < tMat.WindowSize - 1; ++n) 742Contracts.Assert(Utils.Size(singularVectors) >= tMat.WindowSize * tMat.WindowSize); 743Contracts.Assert(Utils.Size(singularValues) >= tMat.WindowSize); 744Contracts.Assert(1 <= maxRank && maxRank <= tMat.WindowSize - 1); 747var k = inputSeriesLength - tMat.WindowSize + 1; 750var x = new Single[tMat.WindowSize - 1]; 751var alpha = new Single[tMat.WindowSize - 1]; 757int evaluationLength = Math.Min(Math.Max(tMat.WindowSize, 200), k); 768FixedSizeQueue<Single> window = new FixedSizeQueue<float>(tMat.WindowSize - 1); 773lambda = singularVectors[tMat.WindowSize * i + tMat.WindowSize - 1]; 774for (j = 0; j < tMat.WindowSize - 1; ++j) 775alpha[j] += lambda * singularVectors[tMat.WindowSize * i + j]; 782offset = inputSeriesLength - evaluationLength - tMat.WindowSize + 1; 785v += series[j] * singularVectors[tMat.WindowSize * i - offset + j]; 787for (j = 0; j < tMat.WindowSize - 1; ++j) 788x[j] += v * singularVectors[tMat.WindowSize * i + j]; 794observationNoiseMean /= (tMat.WindowSize - 1); 796for (j = 0; j < tMat.WindowSize - 1; ++j) 803for (n = 0; n < tMat.WindowSize - 1; ++n)