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 initilized 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 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 Updated number of seen samples. |
© 2007–2017 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html