Sparse Tensor

Python offers an inbuilt library called numpy to manipulate multi-dimensional arrays. The organization and use of this library is a primary requirement for developing the pytensor library.

Sptensor is a class that represents the sparse tensor. A sparse tensor is a dataset in which most of the entries are zero, one such example would be a large diagonal matrix. (which has many zero elements). It does not store the whole values of the tensor object but stores the non-zero values and the corresponding coordinates of them. Sparse tensor storage formats allow us to only store non-zero values thereby reducing storage requirements and eliminating unnecessary silent computations involving zero values. Here are its main attributes:

vals (numpy.ndarray) A 1-dimensional array of non-zero values of the sparse tensor.
subs (numpy.ndarray) A 2-dimensional array of coordinates of the values in vals.
shape(tuple)

The shape of the sparse tensor.

func(binary operator) This function is used to construct the sparse tensor as an accumulator.

On top of that, its main functions are:

__init__(self, subs, vals, shape = None, func=sum.__call__) Consturctor for the sptensor class. subs and vals (numpy.ndarray) or (list) are coordinates and values of the sptensor.
tondarray(self) This function returns a numpy. ndarray object that has the same values with the sptensor.
permute(self, order) By applying this function it will return the sptensor object that is permuted by the given order (list).
ipermute(self, order) Returns the sptensor object that is permuted by the inverse of the given order (list).
copy(self) Returns the copied sptensor object of the sptensor.
totensor(self) Returns the tensor object that has the same values with the sptensor.
nnz(self) Returns the number of non-zero elements in the sptensor.
ndims(self) Returns the number of dimensions of the tensor.
dimsize(self, ind)

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