sklearn.utils.sparsefuncs.incr_mean_variance_axis

sklearn.utils.sparsefuncs.incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None)

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 0-arrays 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_features,) if axis=0 or (n_samples,) 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 feature-wise means if axis = 0 or sample-wise means if axis = 1.

variancesndarray of shape (n_features,) or (n_samples,), dtype=floating

Updated feature-wise variances if axis = 0 or sample-wise 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.