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torch.histogramdd#

torch.histogramdd(input,bins,*,range=None,weight=None,density=False,out=None)->(Tensor,Tensor[])#

Computes a multi-dimensional histogram of the values in a tensor.

Interprets the elements of an input tensor whose innermost dimension has size Nas a collection of N-dimensional points. Maps each of the points into a set ofN-dimensional bins and returns the number of points (or total weight) in each bin.

input must be a tensor with at least 2 dimensions.If input has shape (M, N), each of its M rows defines a point in N-dimensional space.If input has three or more dimensions, all but the last dimension are flattened.

Each dimension is independently associated with its own strictly increasing sequenceof bin edges. Bin edges may be specified explicitly by passing a sequence of 1Dtensors. Alternatively, bin edges may be constructed automatically by passing asequence of integers specifying the number of equal-width bins in each dimension.

For each N-dimensional point in input:
  • Each of its coordinates is binned independently among the bin edges

    corresponding to its dimension

  • Binning results are combined to identify the N-dimensional bin (if any)

    into which the point falls

  • If the point falls into a bin, the bin’s count (or total weight) is incremented

  • Points which do not fall into any bin do not contribute to the output

bins can be a sequence of N 1D tensors, a sequence of N ints, or a single int.

Ifbins is a sequence of N 1D tensors, it explicitly specifies the N sequencesof bin edges. Each 1D tensor should contain a strictly increasing sequence with atleast one element. A sequence of K bin edges defines K-1 bins, explicitly specifyingthe left and right edges of all bins. Every bin is inclusive of its left edge. Onlythe rightmost bin is inclusive of its right edge.

Ifbins is a sequence of N ints, it specifies the number of equal-width binsin each dimension. By default, the leftmost and rightmost bin edges in each dimensionare determined by the minimum and maximum elements of the input tensor in thecorresponding dimension. Therange argument can be provided to manuallyspecify the leftmost and rightmost bin edges in each dimension.

Ifbins is an int, it specifies the number of equal-width bins for all dimensions.

Note

See alsotorch.histogram(), which specifically computes 1D histograms.Whiletorch.histogramdd() infers the dimensionality of its bins andbinned values from the shape ofinput,torch.histogram()accepts and flattensinput of any shape.

Parameters

  • input (Tensor) – the input tensor.

  • bins – Tensor[], int[], or int.If Tensor[], defines the sequences of bin edges.If int[], defines the number of equal-width bins in each dimension.If int, defines the number of equal-width bins for all dimensions.

Keyword Arguments

  • range (sequence offloat) – Defines the leftmost and rightmost bin edgesin each dimension.

  • weight (Tensor) – By default, each value in the input has weight 1. If a weighttensor is passed, each N-dimensional coordinate in inputcontributes its associated weight towards its bin’s result.The weight tensor should have the same shape as theinputtensor excluding its innermost dimension N.

  • density (bool) – If False (default), the result will contain the count (or total weight)in each bin. If True, each count (weight) is divided by the total count(total weight), then divided by the volume of its associated bin.

Returns

N-dimensional Tensor containing the values of the histogram.bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges.

Return type

hist (Tensor)

Example:

>>>torch.histogramdd(torch.tensor([[0.,1.],[1.,0.],[2.,0.],[2.,2.]]),bins=[3,3],...weight=torch.tensor([1.,2.,4.,8.]))    torch.return_types.histogramdd(        hist=tensor([[0., 1., 0.],                     [2., 0., 0.],                     [4., 0., 8.]]),        bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]),                   tensor([0.0000, 0.6667, 1.3333, 2.0000])))>>>torch.histogramdd(torch.tensor([[0.,0.],[1.,1.],[2.,2.]]),bins=[2,2],...range=[0.,1.,0.,1.],density=True)    torch.return_types.histogramdd(       hist=tensor([[2., 0.],                    [0., 2.]]),       bin_edges=(tensor([0.0000, 0.5000, 1.0000]),                  tensor([0.0000, 0.5000, 1.0000])))