histogramdd
- class deepinv.physics.functional.histogramdd(x: Tensor, bins: int | Sequence[int] = 10, low: float | Sequence[float] | None = None, upp: float | Sequence[float] | None = None, bounded: bool = False, weights: Tensor | None = None, sparse: bool = False, edges: Tensor | Sequence[Tensor] | None = None)[source]
Bases:
Computes the multidimensional histogram of a tensor.
This is a torch implementation of numpy.histogramdd. This function is borrowed from torchist.
Note
Similar to numpy.histogram, all bins are half-open except the last bin which also includes the upper bound.
- Parameters:
x (torch.Tensor) – A tensor, (*, D).
bins (int, sequence[int]) – The number of bins in each dimension, scalar or (D,).
low (float, sequence[float]) – The lower bound in each dimension, scalar or (D,). If low is
None
, the min of x is used instead.upp (float, sequence[float]) – The upper bound in each dimension, scalar or (D,). If upp is
None
, the max of x is used instead.bounded (bool) – Whether x is bounded by low and upp, included. If False, out-of-bounds values are filtered out.
weights (torch.Tensor) – A tensor of weights, (*,). Each sample of x contributes its associated weight towards the bin count (instead of 1).
sparse (bool) – Whether the histogram is returned as a sparse tensor or not.
edges (torch.Tensor, sequence[torch.Tensor]) – The edges of the histogram. Either a vector or a list of vectors. If provided, bins, low and upp are inferred from edges.
- Returns:
(torch.Tensor) : the histogram