srch

scipy.sparse.diags¶ ... Construct a sparse matrix from diagonals. New in version 0.11. ... Sequence of arrays containing the matrix diagonals, corresponding to ...

scipy.sparse.block_diag(mats, format=None, dtype=None)[source]¶ Build a block diagonal sparse matrix from provided matrices. Parameters matssequence of matrices Input matrices. formatstr, optional The sparse format of the result (e.g., “csr”). is returned in “coo” format. dtypedtype specifier, optional The data-type of the output matrix.

11.05.2014 · scipy.sparse.diags(diagonals, offsets, shape=None, format=None, dtype=None) [source] ¶. Construct a sparse matrix from diagonals. New in version 0.11. Parameters: diagonals : sequence of array_like. Sequence of arrays containing the matrix diagonals, corresponding to offsets. offsets : sequence of int. Diagonals to set:

The following are 29 code examples for showing how to use scipy.sparse.block_diag().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

Is your feature request related to a problem? Please describe. I'm writing a numerical code that makes use of scipy.sparse.dia_matrix.

scipy.sparse.diags¶ scipy.sparse.diags(diagonals, offsets, shape=None, format=None, dtype=None) [source] ¶ Construct a sparse matrix from diagonals.

scipy.sparse.csr_matrix.diagonal¶ csr_matrix. diagonal (k = 0) [source] ¶ Returns the kth diagonal of the matrix. Parameters k int, optional. Which diagonal to get, corresponding to …

Python scipy.sparse.block_diag () Examples The following are 29 code examples for showing how to use scipy.sparse.block_diag () . These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

cupyx.scipy.sparse.spmatrix Notes This function differs from spdiags in the way it handles off-diagonals. The result from diags is the sparse equivalent of: cupy.diag(diagonals[0], offsets[0]) + ... + cupy.diag(diagonals[k], offsets[k]) Repeated diagonal offsets are disallowed. cupyx.scipy.sparse.kron cupyx.scipy.sparse.spdiags

Construct a sparse matrix from diagonals. ... This function differs from spdiags in the way it handles off-diagonals. ... Repeated diagonal offsets are disallowed.

scipy.sparse.block_diag¶ scipy.sparse. block_diag (mats, format = None, dtype = None) [source] ¶ Build a block diagonal sparse matrix from provided matrices. Parameters mats sequence of matrices. Input matrices. format str, optional. The sparse format of the result (e.g., “csr”).

scipy.sparse.diags(diagonals, offsets=0, shape=None, format=None, dtype=None) [source] ¶ Construct a sparse matrix from diagonals. Parameters diagonalssequence of array_like Sequence of arrays containing the matrix diagonals, corresponding to offsets. offsetssequence of int or an int, optional Diagonals to set: k = 0 the main diagonal (default)

If you do want to apply a NumPy function to these matrices, first check if SciPy has its own implementation for the given sparse matrix class, or convert the sparse matrix to a NumPy array (e.g., using the toarray() method of the class) first before applying the method.

scipy.sparse.dia_matrix¶ class scipy.sparse. dia_matrix (arg1, shape = None, dtype = None, copy = False) [source] ¶. Sparse matrix with DIAgonal storage. This can be instantiated in several ways: dia_matrix(D) with a dense matrix. dia_matrix(S)

k < 0 the k-th lower diagonal. shape (tuple of int) – Shape of the result. If omitted, a square matrix large enough to contain the diagonals is returned.

Default: 0 (the main diagonal). New in version 1.0. See also. numpy.diagonal. Equivalent numpy function. Examples. >>> from scipy.sparse import csr_matrix ...

Use scipy.sparse.spdiags (which does a lot, and so may be confusing, at first), scipy.sparse.dia_matrix and/or scipy.sparse.lil_diags .

scipy.sparse.diags¶ scipy.sparse. diags (diagonals, offsets = 0, shape = None, format = None, dtype = None) [source] ¶ Construct a sparse matrix from diagonals. Parameters diagonals sequence of array_like. Sequence of arrays containing the matrix diagonals, corresponding to offsets.. offsets sequence of int or an int, optional Diagonals to set:

import numpy as np import scipy as sp import scipy.sparse x = np.arange(10) # "0" here indicates the main diagonal.