import numpy as np
import sys, os
sys.path.append(os.pardir)
from common.activation_functions import sigmoid, identity_function


'''
신경망의 내적
간단한 레이어간의 내적 구현해보기
'''
# X=np.array([1,2])
# print(X.shape)
# W=np.array([[1,3,5],[2,4,6]])
# print(W)
# print(W.shape)
# Y=np.dot(X,W)
# print(Y)

'''
3층 신경망 구현하기
'''
# 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=sigmoid(A1)
# print(A1)
# print(Z1)
#
# W2=np.array([[.1, .4],[.2,.5],[.3,.6]])
# B2=np.array([.1, .2])
# print(Z1.shape)
# print(W2.shape)
# print(B2.shape)
# A2=np.dot(Z1,W2)+B2
# Z2=sigmoid(A2)
# print(A2)
# print(Z2)
#
# W3=np.array([[.1,.3],[.2,.4]])
# B3=np.array([.1,.2])
# print(Z2.shape)
# print(W3.shape)
# print(B3.shape)
# A3=np.dot(Z2,W3)+B3
# Y=identity_function(A3)
# print(A3)
# print(Y)

'''
구현 정리
'''
def init_network():
    network = {}
    network['W1'] = np.array([[.1,.3,.5],[.2,.4,.6]])
    network['b1'] = np.array([.1, .2, .3])
    network['W2'] = np.array([[.1, .4],[.2,.5],[.3,.6]])
    network['b2'] = np.array([.1,.2])
    network['W3'] = np.array([[.1,.3],[.2,.4]])
    network['b3'] = np.array([.1,.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, .5])
y = forward(network, x)
print(y)
