import numpy as np
import ActiveFunction as f

# 入力
X = np.array([1.0, 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

Z1 = f.sigmoid(A1)
print("A1", A1)
print("Z1", Z1)
# 入力層(0層)から第1層の変換が完了

W2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
B2 = np.array([0.1, 0.2])
print(Z1.shape)
print(W2.shape)
print(B2.shape)

A2 = np.dot(Z1, W2) + B2
Z2 = f.sigmoid(A2)
print("A2", A2)
print("Z2", Z2)


def identity_function(x):
    return x


W3 = np.array([[0.1, 0.3], [0.2, 0.4]])
B3 = np.array([0.1, 0.2])

A3 = np.dot(Z2, W3) + B3

Y = identity_function(A3)
print(Y)


def init_network():
    network = {};
    #2x3
    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])
    #3x2
    network["W2"] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
    network["b2"] = np.array([0.1, 0.2])
    #2x2
    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 = f.sigmoid(a1)
    a2 = np.dot(z1, W2) + b2
    z2 = f.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:", y)


