sklearn.utils.sparsefuncs.mean_variance_axis

sklearn.utils.sparsefuncs.mean_variance_axis(X, axis, weights=None, return_sum_weights=False) [source]

Compute mean and variance along an axis on a CSR or CSC matrix.

Parameters

Xsparse matrix of shape (n_samples, n_features)

Input data. It can be of CSR or CSC format.

axis{0, 1}

Axis along which the axis should be computed.

weightsndarray of shape (n_samples,) or (n_features,), default=None

if axis is set to 0 shape is (n_samples,) or if axis is set to 1 shape is (n_features,). If it is set to None, then samples are equally weighted.

New in version 0.24.

return_sum_weightsbool, default=False

If True, returns the sum of weights seen for each feature if axis=0 or each sample if axis=1.

New in version 0.24.

Returns

meansndarray of shape (n_features,), dtype=floating

Feature-wise means.

variancesndarray of shape (n_features,), dtype=floating

Feature-wise variances.

sum_weightsndarray of shape (n_features,), dtype=floating

Returned if return_sum_weights is True.