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Introduction Type Safety Tensor Expressions Tensor Indexing Einsum Einops Autograd Training a Model Profiling & Memory PyTorch Migration Best Practices Runtimes Performance PyTorch Compatibility Benchmarks DType Coverage

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computeQuantiles computePercentiles computeCorrelation computeCovariance detectOutliers detectOutliersZScore computeExtendedStats describeExtendedStats QuantileResult CorrelationResult OutlierResult ExtendedStats createDBSCAN dbscan DBSCAN DBSCAN.fit DBSCAN.destroy DBSCANOptions DBSCANResult createHierarchicalClustering hierarchicalClustering HierarchicalClustering HierarchicalClustering.fit HierarchicalClustering.destroy LinkageMethod DistanceMetric HierarchicalOptions DendrogramNode HierarchicalResult computeHistogram getHistogramCenters normalizeHistogram HistogramResult createKDE kde KDE KDE.fit KDE.destroy KernelType KDEOptions KDEResult createKMeans kmeans KMeans KMeans.fit KMeans.destroy KMeansOptions KMeansResult createPCA pca PCA PCA.fit PCA.destroy PCAOptions PCAResult throttleProgress consoleProgress ProgressInfo ProgressCallback computeStats computeMinMax describeStats TensorStats createTSNE tsne TSNE TSNE.fit TSNE.destroy TSNEOptions TSNEResult createUMAP umap UMAP UMAP.fit UMAP.destroy UMAPOptions UMAPResult

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analysis
getHistogramCenters

viz.analysis.getHistogramCenters

function getHistogramCenters(hist: HistogramResult): Float32Array

Generate bin centers from histogram result.

Parameters

histHistogramResult

Returns

Float32Array

hierarchicalClustering