from __future__ import absolute_import
import numpy as np
import scipy as sp
from six.moves import range
from six.moves import zip


def to_categorical(y, nb_classes=None):
    '''Convert class vector (integers from 0 to nb_classes)
    to binary class matrix, for use with categorical_crossentropy
    '''
    y = np.asarray(y, dtype='int32')
    if not nb_classes:
        nb_classes = np.max(y)+1
    Y = np.zeros((len(y), nb_classes))
    for i in range(len(y)):
        Y[i, y[i]] = 1.
    return Y


def normalize(a, axis=-1, order=2):
    l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
    l2[l2 == 0] = 1
    return a / np.expand_dims(l2, axis)


def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res


def multiclass_logloss(P, Y):
    npreds = [P[i][Y[i]-1] for i in range(len(Y))]
    score = -(1. / len(Y)) * np.sum(np.log(npreds))
    return score


def accuracy(p, y):
    return np.mean([a == b for a, b in zip(p, y)])


def probas_to_classes(y_pred):
    if len(y_pred.shape) > 1 and y_pred.shape[1] > 1:
        return categorical_probas_to_classes(y_pred)
    return np.array([1 if p > 0.5 else 0 for p in y_pred])


def categorical_probas_to_classes(p):
    return np.argmax(p, axis=1)
