"""Univariate features selection."""

# Authors: V. Michel, B. Thirion, G. Varoquaux, A. Gramfort, E. Duchesnay.
#          L. Buitinck, A. Joly
# License: BSD 3 clause


import numpy as np
import warnings

from scipy import special, stats
from scipy.sparse import issparse

from ..base import BaseEstimator
from ..preprocessing import LabelBinarizer
from ..utils import (as_float_array, check_array, check_X_y, safe_sqr,
                     safe_mask)
from ..utils.extmath import norm, safe_sparse_dot
from ..utils.validation import check_is_fitted
from .base import SelectorMixin


def _clean_nans(scores):
    """
    Fixes Issue #1240: NaNs can't be properly compared, so change them to the
    smallest value of scores's dtype. -inf seems to be unreliable.
    """
    # XXX where should this function be called? fit? scoring functions
    # themselves?
    scores = as_float_array(scores, copy=True)
    scores[np.isnan(scores)] = np.finfo(scores.dtype).min
    return scores


######################################################################
# Scoring functions


# The following function is a rewriting of scipy.stats.f_oneway
# Contrary to the scipy.stats.f_oneway implementation it does not
# copy the data while keeping the inputs unchanged.
def f_oneway(*args):
    """Performs a 1-way ANOVA.

    The one-way ANOVA tests the null hypothesis that 2 or more groups have
    the same population mean. The test is applied to samples from two or
    more groups, possibly with differing sizes.

    Parameters
    ----------
    sample1, sample2, ... : array_like, sparse matrices
        The sample measurements should be given as arguments.