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 ifaxis=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
isTrue
.