25 references to NormSquared
Microsoft.ML.Core.Tests (1)
UnitTests\TestVBuffer.cs (1)
105
Assert.True(CompareNumbersWithTolerance(l2Squared, VectorUtils.
NormSquared
(in a), digitsOfPrecision: tol));
Microsoft.ML.Data (1)
Deprecated\Vector\VBufferMathUtils.cs (1)
41
return MathUtils.Sqrt(
NormSquared
(in a));
Microsoft.ML.KMeansClustering (11)
KMeansModelParameters.cs (2)
169
float instanceL2 = VectorUtils.
NormSquared
(in src);
270
_centroidL2s[i] = VectorUtils.
NormSquared
(_centroids[i]);
KMeansPlusPlusTrainer.cs (9)
394
l2 = VectorUtils.
NormSquared
(cursor.Features);
441
centroidL2s[i] = cachedCandidateL2 ?? VectorUtils.
NormSquared
(candidate);
699
float pointNorm = VectorUtils.
NormSquared
(in point);
860
clustersL2s[clusterCount] = VectorUtils.
NormSquared
(clusters[clusterCount]);
896
clustersL2s[clusterCount] = VectorUtils.
NormSquared
(clusters[clusterCount]);
1225
centroidL2s[i] = VectorUtils.
NormSquared
(Centroids[i]);
1305
float instanceNormSquared = VectorUtils.
NormSquared
(in features);
1363
centroidL2s[i] = VectorUtils.
NormSquared
(in centroids[i]);
1827
float l2 = VectorUtils.
NormSquared
(in features);
Microsoft.ML.PCA (3)
PcaTrainer.cs (3)
470
_norm2Mean = VectorUtils.
NormSquared
(mean);
497
_norm2Mean = VectorUtils.
NormSquared
(_mean);
637
float norm2X = VectorUtils.
NormSquared
(in src) -
Microsoft.ML.StandardTrainers (9)
Optimizer\OptimizationMonitor.cs (1)
323
float gradientNormSquared = VectorUtils.
NormSquared
(gradient);
Optimizer\SgdOptimizer.cs (2)
341
float newByNew = VectorUtils.
NormSquared
(_newGrad);
343
float oldByOld = VectorUtils.
NormSquared
(_grad);
Standard\SdcaBinary.cs (4)
583
var normSquared = VectorUtils.
NormSquared
(features);
824
var featuresNormSquared = VectorUtils.
NormSquared
(features);
997
var l2Regularizer = l2Const * (VectorUtils.
NormSquared
(weights[0]) + biasReg[0] * biasReg[0]) * 0.5;
2094
var newLoss = lossSum.Sum / count + l2Weight * VectorUtils.
NormSquared
(weights) * 0.5;
Standard\SdcaMulticlass.cs (2)
218
normSquared = VectorUtils.
NormSquared
(in features);
422
weightsL2NormSquared += VectorUtils.
NormSquared
(weights[iClass]) + biasReg[iClass] * biasReg[iClass];