Skip to main content
torch.js has not been released yet.
torch.js logotorch.js logotorch.js
PlaygroundContact
Login
Documentation
IntroductionType SafetyTensor ExpressionsTensor IndexingEinsumEinopsAutogradTraining a ModelProfiling & MemoryPyTorch MigrationBest PracticesRuntimesPerformancePyTorch CompatibilityBenchmarksDType Coverage
IntroductionRenderer GalleryRenderersAnalysis
computeQuantilescomputePercentilescomputeCorrelationcomputeCovariancedetectOutliersdetectOutliersZScorecomputeExtendedStatsdescribeExtendedStatsQuantileResultCorrelationResultOutlierResultExtendedStatscreateDBSCANdbscanDBSCANDBSCAN.fitDBSCAN.destroyDBSCANOptionsDBSCANResultcreateHierarchicalClusteringhierarchicalClusteringHierarchicalClusteringHierarchicalClustering.fitHierarchicalClustering.destroyLinkageMethodDistanceMetricHierarchicalOptionsDendrogramNodeHierarchicalResultcomputeHistogramgetHistogramCentersnormalizeHistogramHistogramResultcreateKDEkdeKDEKDE.fitKDE.destroyKernelTypeKDEOptionsKDEResultcreateKMeanskmeansKMeansKMeans.fitKMeans.destroyKMeansOptionsKMeansResultcreatePCApcaPCAPCA.fitPCA.destroyPCAOptionsPCAResultthrottleProgressconsoleProgressProgressInfoProgressCallbackcomputeStatscomputeMinMaxdescribeStatsTensorStatscreateTSNEtsneTSNETSNE.fitTSNE.destroyTSNEOptionsTSNEResultcreateUMAPumapUMAPUMAP.fitUMAP.destroyUMAPOptionsUMAPResult
torch.js· 2026
LegalTerms of UsePrivacy Policy
/
/
  1. docs
  2. viz
  3. viz
  4. analysis
  5. computeCovariance

viz.analysis.computeCovariance

function computeCovariance(tensor: Tensor, unbiased = true): Promise<{ matrix: Float32Array; numVariables: number }>

Compute covariance matrix for multiple variables.

Parameters

tensorTensor
2D tensor of shape [num_variables, num_observations]
unbiasedunknownoptional
If true (default), use Bessel's correction (divide by n-1)

Returns

Promise<{ matrix: Float32Array; numVariables: number }>– Covariance matrix as Float32Array
Previous
computeCorrelation
Next
computeExtendedStats