71 references to Exp
Microsoft.ML.AutoML (1)
Sweepers\SweeperProbabilityUtils.cs (1)
16
return 1 / Math.Sqrt(2 * Math.PI) * Math.
Exp
(-Math.Pow(x, 2) / 2);
Microsoft.ML.AutoML.Tests (3)
TunerTests.cs (3)
433
return Math.Log(Math.
Exp
(x) + Math.
Exp
(y) + Math.
Exp
(z));
Microsoft.ML.Core (8)
Utilities\MathUtils.cs (6)
230
intermediate += Math.
Exp
(inputs[i] - max);
261
return (float)(max + Math.Log(1.0 + Math.
Exp
(negDiff)));
431
return Math.
Exp
(LogGamma(a) + LogGamma(b) - LogGamma(a + b));
532
return Math.
Exp
(x).ToFloat();
810
soFar += Math.
Exp
(term - max);
813
soFar = Math.
Exp
(max - term) * soFar + 1;
Utilities\Stats.cs (2)
147
double expLam = Math.
Exp
(-lambda);
194
double p = sqrtLam * Math.
Exp
(-lambda + k * logLam - logkFac);
Microsoft.ML.Core.Tests (6)
UnitTests\TestLoss.cs (6)
76
TestHelper(loss, 1, 3, Math.
Exp
(-3), Math.
Exp
(-3));
77
TestHelper(loss, 0, 3, Math.
Exp
(3), -Math.
Exp
(3));
78
TestHelper(loss, 0, -3, Math.
Exp
(-3), -Math.
Exp
(-3));
Microsoft.ML.CpuMath (2)
ProbabilityFunctions.cs (2)
33
double ev = ((((((((a5 * t) + a4) * t) + a3) * t) + a2) * t + a1) * t) * Math.
Exp
(-(x * x));
54
double ev = 1.0 - ((((((((a5 * t) + a4) * t) + a3) * t) + a2) * t + a1) * t) * Math.
Exp
(-(x * x));
Microsoft.ML.Data (5)
Prediction\Calibrator.cs (1)
1780
return (float)(1 / (1 + Math.
Exp
(a * output + b)));
Transforms\NormalizeColumnDbl.cs (1)
1489
return (TFloat)(0.5 + 0.5 * Math.Sign(x) * Math.Sqrt(1 - Math.
Exp
(-x2 * (4 / Math.PI + ax2) / (1 + ax2))));
Transforms\NormalizeColumnSng.cs (1)
1652
return (TFloat)(0.5 + 0.5 * Math.Sign(x) * Math.Sqrt(1 - Math.
Exp
(-x2 * (4 / Math.PI + ax2) / (1 + ax2))));
Utils\LossFunctions.cs (2)
547
return Math.
Exp
(output) - label * output;
552
return (float)Math.
Exp
(output) - label;
Microsoft.ML.FastTree (13)
FastTreeClassification.cs (1)
389
double response = label * _sigmoidParameter / (1.0 + Math.
Exp
(label * _sigmoidParameter * pScores[i]));
FastTreeRanking.cs (2)
674
_sigmoidTable[i] = 2.0 - 2.0 / (1.0 + Math.
Exp
(-2.0 * sigmoidParam * score));
676
_sigmoidTable[i] = 2.0 / (1.0 + Math.
Exp
(2.0 * sigmoidParam * score));
FastTreeTweedie.cs (6)
421
denom += w * Math.
Exp
(s);
431
num += w * _labels[i] * Math.
Exp
(_index1 * s);
432
denom += w * Math.
Exp
(_index2 * s);
467
Gradient[i] = Math.
Exp
(Scores[i]) - _labels[i];
476
Gradient[i] = Math.
Exp
(_index2 * Scores[i]) - _labels[i] * Math.
Exp
(_index1 * Scores[i]);
Training\EnsembleCompression\LassoBasedEnsembleCompressor.cs (1)
453
fit.Lambdas[0] = Math.
Exp
(2 * Math.Log(fit.Lambdas[1]) - Math.Log(fit.Lambdas[2]));
Training\Test.cs (1)
680
double loss = Math.Log(1.0 + Math.
Exp
(-1.0 * _sigmoidParameter * (label ? 1 : -1) * scores[i]));
Utils\LinqExtensions.cs (2)
270
double total = values.Sum(value => Math.
Exp
(value - max));
282
r -= Math.
Exp
(value - max);
Microsoft.ML.Predictor.Tests (1)
TestPredictors.cs (1)
741
Assert.Equal(prob, 1 / (1 + Math.
Exp
(-score)), 0.000001);
Microsoft.ML.Samples (7)
Dynamic\Trainers\BinaryClassification\Gam.cs (1)
174
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Trainers\BinaryClassification\GamWithOptions.cs (1)
181
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Trainers\BinaryClassification\PermutationFeatureImportance.cs (1)
118
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Trainers\BinaryClassification\PermutationFeatureImportanceLoadFromDisk.cs (1)
104
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Trainers\Regression\GamAdvanced.cs (1)
172
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Trainers\Regression\GamWithOptionsAdvanced.cs (1)
180
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Dynamic\Transforms\CalculateFeatureContributionCalibrated.cs (1)
143
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.
Exp
(-1 * x));
Microsoft.ML.StandardTrainers (2)
Optimizer\LineSearch.cs (1)
457
double e = Math.
Exp
(x);
Standard\MulticlassClassification\OneVersusAllTrainer.cs (1)
901
score[i] = Math.
Exp
(scores[i]);
Microsoft.ML.Sweeper (2)
Algorithms\SweeperProbabilityUtils.cs (2)
33
return 1 / Math.Sqrt(2 * Math.PI * variance) * Math.
Exp
(-Math.Pow(x - mean, 2) / (2 * variance));
46
return 1 / Math.Sqrt(2 * Math.PI) * Math.
Exp
(-Math.Pow(x, 2) / 2);
Microsoft.ML.Tests (4)
TrainerEstimators\TreeEstimators.cs (4)
672
sum += Math.
Exp
((float)nativeResult1[j + i * _classNumber]);
676
double prob = Math.
Exp
(nativeResult1[j + i * _classNumber]);
701
sum += Math.
Exp
((float)nativeResult1[j + i * _classNumber]);
705
double prob = Math.
Exp
(nativeResult1[j + i * _classNumber]);
Microsoft.ML.TimeSeries (7)
IidChangePointDetector.cs (2)
127
InternalTransform.AlertThreshold = Math.
Exp
(InternalTransform.WindowSize * InternalTransform.LogPowerMartigaleBettingFunc(1 - options.Confidence / 100, InternalTransform.PowerMartingaleEpsilon));
130
InternalTransform.AlertThreshold = Math.
Exp
(InternalTransform.WindowSize * InternalTransform.LogMixtureMartigaleBettingFunc(1 - options.Confidence / 100));
SequentialAnomalyDetectionTransformBase.cs (2)
443
protected Double LatestMartingaleScore => Math.
Exp
(_logMartingaleValue);
614
result.Values[3] = Math.
Exp
(_logMartingaleValue);
SrCnnEntireAnomalyDetector.cs (1)
582
_spectralList[i] = Math.
Exp
(_magLogList[i] - _cumSumList[i]);
SsaChangePointDetector.cs (2)
137
InternalTransform.AlertThreshold = Math.
Exp
(InternalTransform.WindowSize * InternalTransform.LogPowerMartigaleBettingFunc(1 - options.Confidence / 100, InternalTransform.PowerMartingaleEpsilon));
140
InternalTransform.AlertThreshold = Math.
Exp
(InternalTransform.WindowSize * InternalTransform.LogMixtureMartigaleBettingFunc(1 - options.Confidence / 100));
Microsoft.ML.Transforms (2)
Expression\BuiltinFunctions.cs (2)
176
FunctionProviderUtils.Fn<R8, R8>(Math.
Exp
));
515
return (R4)Math.
Exp
(a);
PresentationCore (4)
System\Windows\Media\Animation\ElasticEase.cs (2)
81
expo = (Math.
Exp
(springiness * normalizedTime) - 1.0) / (Math.
Exp
(springiness) - 1.0);
System\Windows\Media\Animation\ExponentialEase.cs (2)
54
return (Math.
Exp
(factor * normalizedTime) - 1.0) / (Math.
Exp
(factor) - 1.0);
System.Private.CoreLib (2)
src\libraries\System.Private.CoreLib\src\System\Double.cs (2)
633
public static double Exp(double x) => Math.
Exp
(x);
636
public static double ExpM1(double x) => Math.
Exp
(x) - 1;
System.Runtime.Numerics (2)
System\Numerics\Complex.cs (2)
630
double expReal = Math.
Exp
(value.m_real);
757
double t = Math.Pow(rho, powerReal) * Math.
Exp
(-powerImaginary * theta);