# coding: utf-8
#import cupy as cp
import numpy as cp
import numpy as np


def identity_function(x):
    return x


def step_function(x):
    return np.array(x > 0, dtype=np.int)


def sigmoid(x):
    return 1 / (1 + np.exp(-x))    


def sigmoid_grad(x):
    return (1.0 - sigmoid(x)) * sigmoid(x)
    

def relu(x):
    return np.maximum(0, x)


def relu_grad(x):
    grad = np.zeros(x)
    grad[x>=0] = 1
    return grad
    

def softmax(x):
    if x.ndim == 2:
        x = x.T
        x = x - cp.max(x, axis=0)
        y = cp.exp(x, dtype=np.float32) / cp.sum(cp.exp(x, dtype=np.float32), axis=0, dtype=np.float32)
        return y.T 

    x = x - cp.max(x) # オーバーフロー対策
    return cp.exp(x) / cp.sum(cp.exp(x))


def mean_squared_error(y, t):
    return 0.5 * np.sum((y-t)**2)


def cross_entropy_error(y, t):
    if y.ndim == 1:
        t = t.reshape(1, t.size)
        y = y.reshape(1, y.size)
        
    # 教師データがone-hot-vectorの場合、正解ラベルのインデックスに変換
    if t.size == y.size:
        t = t.argmax(axis=1)
             
    batch_size = y.shape[0]
    return -cp.sum(cp.log(y[cp.arange(batch_size), t])) / batch_size


def softmax_loss(X, t):
    y = softmax(X)
    return cross_entropy_error(y, t)