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import numpy as np

# X = (Số giờ học, giờ ngủ), y = điểm số tương ứng
xAll = np.array(([2, 9], [1, 5], [3, 6], [5, 10]), dtype=float)  # input data
y = np.array(([92], [86], [89]), dtype=float)  # output
inp = np.split(xAll,[3])[1]

# units
xAll = xAll / np.amax(xAll, axis=0)
y = y / 100

# split data
X = np.split(xAll, [3])[0]  # Dữ liệu training
xPredicted = np.split(xAll, [3])[1]  # dữ liệu cần dự đoán

y = np.array(([92], [86], [89]), dtype=float)
y = y / 100

class Neural_Network(object):
    def __init__(self):
        # parameters
        self.inputSize = 2
        self.outputSize = 1
        self.hiddenSize = 3

# weights
        self.W1 = np.random.randn(self.inputSize, self.hiddenSize)  # (3x2) weight matrix from input to hidden layer
        self.W2 = np.random.randn(self.hiddenSize, self.outputSize)  # (3x1) weight matrix from hidden to output layer

def forward(self, X):
        # forward propagation through our network
        self.z = np.dot(X, self.W1)  # dot product of X (input) and first set of 3x2 weights
        self.z2 = self.sigmoid(self.z)  # activation function
        self.z3 = np.dot(self.z2, self.W2)  # dot product of hidden layer (z2) and second set of 3x1 weights
        o = self.sigmoid(self.z3)  # final activation function
        return o

def sigmoid(self, s):
        # activation function
        return 1 / (1 + np.exp(-s))

def sigmoidPrime(self, s):
        # derivative of sigmoid
        return s * (1 - s)

def backward(self, X, y, o):
        # backward propagate through the network
        self.o_error = y - o  # error in output
        self.o_delta = self.o_error * self.sigmoidPrime(o)  # applying derivative of sigmoid to error

self.z2_error = self.o_delta.dot(
            self.W2.T)  # z2 error: how much our hidden layer weights contributed to output error
        self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)  # applying derivative of sigmoid to z2 error

self.W1 += X.T.dot(self.z2_delta)  # adjusting first set (input --> hidden) weights
        self.W2 += self.z2.T.dot(self.o_delta)  # adjusting second set (hidden --> output) weights

def train(self, X, y):
        o = self.forward(X)
        self.backward(X, y, o)

def predict(self):
        print("Predicted data based on trained weights: ")
        print("Input : " + str(inp))
        print("Input (scaled): " + str(xPredicted))
        print("Output: " + str(self.forward(xPredicted) * 100))

NN = Neural_Network()
for i in range(10001):  # trains the NN 1,000 times
    print("# " + str(i) + "\n")
    print("Input (scaled): \n" + str(X))
    print("Actual Output: \n" + str(y))
    print("Predicted Output: \n" + str(NN.forward(X)))
    print("Loss: \n" + str(np.mean(np.square(y - NN.forward(X)))))  # mean sum squared loss
    print("\n")
    NN.train(X, y)

NN.predict()

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