Delete csr python

import numpy as np
from scipy.sparse import csr_matrix

def delete_from_csr(mat, row_indices=[], col_indices=[]):
    """
    Remove the rows (denoted by ``row_indices``) and columns (denoted by ``col_indices``) from the CSR sparse matrix ``mat``.
    WARNING: Indices of altered axes are reset in the returned matrix
    """
    if not isinstance(mat, csr_matrix):
        raise ValueError("works only for CSR format -- use .tocsr() first")

    rows = []
    cols = []
    if row_indices:
        rows = list(row_indices)
    if col_indices:
        cols = list(col_indices)

    if len(rows) > 0 and len(cols) > 0:
        row_mask = np.ones(mat.shape[0], dtype=bool)
        row_mask[rows] = False
        col_mask = np.ones(mat.shape[1], dtype=bool)
        col_mask[cols] = False
        return mat[row_mask][:,col_mask]
    elif len(rows) > 0:
        mask = np.ones(mat.shape[0], dtype=bool)
        mask[rows] = False
        return mat[mask]
    elif len(cols) > 0:
        mask = np.ones(mat.shape[1], dtype=bool)
        mask[cols] = False
        return mat[:,mask]
    else:
        return mat

Source: stackoverflow.com

Delete csr python

A0 = J.T * B

Source: stackoverflow.com

Delete csr python

def delete_rows_csr(mat, indices):
    """
    Remove the rows denoted by ``indices`` form the CSR sparse matrix ``mat``.
    """
    if not isinstance(mat, scipy.sparse.csr_matrix):
        raise ValueError("works only for CSR format -- use .tocsr() first")
    indices = list(indices)
    mask = numpy.ones(mat.shape[0], dtype=bool)
    mask[indices] = False
    return mat[mask]

Source: stackoverflow.com

Delete csr python

scipy.sparse.vstack([X[:i, :], X[i:, :]])

Source: stackoverflow.com

Delete csr python

def delete_row_csr(mat, i):
    if not isinstance(mat, scipy.sparse.csr_matrix):
        raise ValueError("works only for CSR format -- use .tocsr() first")
    n = mat.indptr[i+1] - mat.indptr[i]
    if n > 0:
        mat.data[mat.indptr[i]:-n] = mat.data[mat.indptr[i+1]:]
        mat.data = mat.data[:-n]
        mat.indices[mat.indptr[i]:-n] = mat.indices[mat.indptr[i+1]:]
        mat.indices = mat.indices[:-n]
    mat.indptr[i:-1] = mat.indptr[i+1:]
    mat.indptr[i:] -= n
    mat.indptr = mat.indptr[:-1]
    mat._shape = (mat._shape[0]-1, mat._shape[1])

Source: stackoverflow.com

Similar codes

-1 vote242 views1 answer
Pyton for
Python
0 vote256 views1 answer
Factors for negative number python
Python
31 votes276 views6 answers
Python venv create
Python
0 vote231 views1 answer
Logartim normalization python pandas
Python
0 vote212 views1 answer
Python program to demonstrate scoping
Python
0 vote249 views1 answer
Qpushbutton clicked
Python
1 vote225 views1 answer
Pip install django celery results
Python
13 votes225 views1 answer
5.4.7 categories python
Python
36 votes220 views2 answers
Python3 iterate through indexes
Python
18 votes243 views6 answers
Pygame download
Python