sklearn.utils.sparsefuncs
.incr_mean_variance_axis¶

sklearn.utils.sparsefuncs.
incr_mean_variance_axis
(X, *, axis, last_mean, last_var, last_n, weights=None)[source]¶ Compute incremental mean and variance along an axis on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this function. Both must be initialized to 0arrays of the proper size, i.e. the number of features in X. last_n is the number of samples encountered until now.
 Parameters
 XCSR or CSC sparse matrix of shape (n_samples, n_features)
Input data.
 axis{0, 1}
Axis along which the axis should be computed.
 last_meanndarray of shape (n_features,) or (n_samples,), dtype=floating
Array of means to update with the new data X. Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.
 last_varndarray of shape (n_features,) or (n_samples,), dtype=floating
Array of variances to update with the new data X. Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.
 last_nfloat or ndarray of shape (n_features,) or (n_samples,), dtype=floating
Sum of the weights seen so far, excluding the current weights If not float, it should be of shape (n_samples,) if axis=0 or (n_features,) if axis=1. If float it corresponds to having same weights for all samples (or features).
 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.
 Returns
 meansndarray of shape (n_features,) or (n_samples,), dtype=floating
Updated featurewise means if axis = 0 or samplewise means if axis = 1.
 variancesndarray of shape (n_features,) or (n_samples,), dtype=floating
Updated featurewise variances if axis = 0 or samplewise variances if axis = 1.
 nndarray of shape (n_features,) or (n_samples,), dtype=integral
Updated number of seen samples per feature if axis=0 or number of seen features per sample if axis=1.
If weights is not None, n is a sum of the weights of the seen samples or features instead of the actual number of seen samples or features.
Notes
NaNs are ignored in the algorithm.