inplace_csr_column_scale (original) (raw)

sklearn.utils.sparsefuncs.inplace_csr_column_scale(X, scale)[source]#

Inplace column scaling of a CSR matrix.

Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape.

Parameters:

Xsparse matrix of shape (n_samples, n_features)

Matrix to normalize using the variance of the features. It should be of CSR format.

scalendarray of shape (n_features,), dtype={np.float32, np.float64}

Array of precomputed feature-wise values to use for scaling.

Examples

from sklearn.utils import sparsefuncs from scipy import sparse import numpy as np indptr = np.array([0, 3, 4, 4, 4]) indices = np.array([0, 1, 2, 2]) data = np.array([8, 1, 2, 5]) scale = np.array([2, 3, 2]) csr = sparse.csr_matrix((data, indices, indptr)) csr.todense() matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) sparsefuncs.inplace_csr_column_scale(csr, scale) csr.todense() matrix([[16, 3, 4], [ 0, 0, 10], [ 0, 0, 0], [ 0, 0, 0]])