sklearn.utils.sparsefuncs.incr_mean_variance_axis

sklearn.utils.sparsefuncs.incr_mean_variance_axis(X, axis, last_mean, last_var, last_n) [source]

Compute incremental mean and variance along an axix 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:

X : CSR or CSC sparse matrix, shape (n_samples, n_features) Input data. axis : int (either 0 or 1) Axis along which the axis should be computed. last_mean : float array with shape (n_features,) Array of feature-wise means to update with the new data X. last_var : float array with shape (n_features,) Array of feature-wise var to update with the new data X. last_n : int with shape (n_features,) Number of samples seen so far, excluded X.

Returns:

means : float array with shape (n_features,) Updated feature-wise means. variances : float array with shape (n_features,) Updated feature-wise variances. n : int with shape (n_features,) Updated number of seen samples.

Notes

NaNs are ignored in the algorithm.