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. PCAResult

viz.analysis.PCAResult

export interface PCAResult {
  /** Projected data [num_samples, num_components] */
  projected: Float32Array;
  /** Principal component vectors [num_components, num_features] */
  components: Float32Array;
  /** Eigenvalues (explained variance) for each component */
  eigenvalues: Float32Array;
  /** Original data shape */
  originalShape: readonly [number, number];
  /** Number of components extracted */
  numComponents: number;
}
projected(Float32Array)
– Projected data [num_samples, num_components]
components(Float32Array)
– Principal component vectors [num_components, num_features]
eigenvalues(Float32Array)
– Eigenvalues (explained variance) for each component
originalShape(readonly [number, number])
– Original data shape
numComponents(number)
– Number of components extracted
Previous
PCAOptions
Next
ProgressCallback