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def step_function(x):
    y = x > 0
    return y.astype(np.int32)

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-5.0, 5.0, 0.1)
y = step_function(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1)
plt.show()

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

x = np.arange(-5.0, 5.0, 0.1)
y = sigmoid(x)
plt.plot(x, y)
plt.ylim(-0.1, 1.1)
plt.show()

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

X = np.array([1, 0.5])
W1 = np.array([
    [0.1, 0.3, 0.5],
    [0.2, 0.4, 0.6]
])
B1 = np.array([0.1, 0.2, 0.3])

print(W1.shape)
print(X.shape)
print(B1.shape)

A1 = np.dot(X, W1) + B1
print(A1.shape)

Z1 = sigmoid(A1)
print(A1)
print(Z1)

def identity_function(x):
    return x

def init_network():
    network = {}
    network['W1'] = np.array([
        [0.1, 0.3, 0.5],
        [0.2, 0.4, 0.6]
    ])
    network['b1'] = np.array([0.1, 0.2, 0.3])
    network['W2'] = np.array([
        [0.1, 0.4], [0.2, 0.5], [0.3, 0.6]
    ])
    network['b2'] = np.array([0.1, 0.2])
    network['W3'] = np.array([
        [0.1, 0.3], [0.2, 0.4]
    ])
    network['b3'] = np.array([0.1, 0.2])
    return network

def forward(network, x):
    W1, W2, W3 = network['W1'], network['W2'], network['W3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']
    
    a1 = np.dot(x, W1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1,W2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2,W3) + b3
    y = identity_function(a3)
    return y

network = init_network()
x = np.array([1.0, 0.5])
y = forward(network, x)
print(y)

def softmax(a):
    C = np.max(a)
    exp_a = np.exp( a - C)
    sum_exp_a = np.sum(exp_a)
    y = exp_a / sum_exp_a
    return y

def AND(x1, x2):
    w1, w2, theta = 0.5, 0.5, 0.7
    tmp = x1*w1 + x2*w2
    if tmp <= theta:
        return 0
    elif tmp > theta:
        return 1

print(AND(0,0))
print(AND(1,0))
print(AND(0,1))
print(AND(1,1))

import numpy as np
x = np.array([0, 1])
w = np.array([0.5, 0.5])
b = -0.8
print(w*x)
print(np.sum(w*x))
print(np.sum(w*x)+b)

def NAND(x1, x2):
    x = np.array([x1, x2])
    w = np.array([-0.5, -0.5])
    b = 0.7
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1

def OR(x1, x2):
    x = np.array([x1, x2])
    w = np.array([0.5, 0.5])
    b = -0.2
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1

def XOR(x1, x2):
    s1 = NAND(x1, x2)
    s2 = OR(x1, x2)
    y = AND(s1, s2)
    return y

print(XOR(0,0))
print(XOR(1,0))
print(XOR(0,1))
print(XOR(1,1))