ó ‡ˆ\c@syddlZddlmZddlmZddlmZddlmZddl m Z d eefd „ƒYZ dS( iÿÿÿÿNi(t BaseEstimatori(t SelectorMixin(t check_array(tmean_variance_axis(tcheck_is_fittedtVarianceThresholdcBs/eZdZdd„Zdd„Zd„ZRS(s5Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning. Read more in the :ref:`User Guide `. Parameters ---------- threshold : float, optional Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. Attributes ---------- variances_ : array, shape (n_features,) Variances of individual features. Examples -------- The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:: >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]] >>> selector = VarianceThreshold() >>> selector.fit_transform(X) array([[2, 0], [1, 4], [1, 1]]) gcCs ||_dS(N(t threshold(tselfR((sKlib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.pyt__init__-scCs½t|d dtjƒ}t|dƒrEt|ddƒ\}|_ntj|ddƒ|_tj|j|jkƒr¹d}|j ddkrž|d 7}nt |j |jƒƒ‚n|S( sLearn empirical variances from X. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Sample vectors from which to compute variances. y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns ------- self tcsrtcsctdtypettoarraytaxisis4No feature in X meets the variance threshold {0:.5f}is (X contains only one sample)(R R ( Rtnptfloat64thasattrRt variances_tvartallRtshapet ValueErrortformat(RtXtyt_tmsg((sKlib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.pytfit0s cCst|dƒ|j|jkS(NR(RRR(R((sKlib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.pyt_get_support_maskOs N(t__name__t __module__t__doc__RtNoneRR(((sKlib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.pyR s  ( tnumpyRtbaseRRtutilsRtutils.sparsefuncsRtutils.validationRR(((sKlib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.pyts