import numpy as np

def tanh(x):
    return np.tanh(x)

def tanh_deriv(x):
    return 1.0-np.tanh(x)*np.tanh(x)


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

def logistic_derivative(x):
    return logistic(x)*(1-logistic(x))

class NeuralNetwork:
    def __init__(self,layers,activation='tanh'):
        if activation == 'logistic':
            self.activation=logistic
            self.activation_deriv=logistic_derivative
        elif activation == 'tanh':
            self.activation=tanh
            self.activation_deriv=tanh_deriv
        self.weights=[]
        
        for i in range(1,len(layers) -1 ):
            self.weights.append((2*np.random.random((layers[i-1] + 1,layers[i]+1))-1)*0.25)
            self.weights.append((2*np.random.random((layers[i] + 1,layers[i+1]))-1)*0.25)
    def fit(self,X,y,learning_rate=0.2,epochs=10000):
        X=np.atleast_2d(X)
        #
        temp = np.ones([X.shape[0],X.shape[1]+1])
        temp[:,0:-1]=X
        X=temp
        y = np.array(y)
        
        for k in range(epochs):
            i = np.random.randint(X.shape[0])
            a=[X[i]]
            
            for l in range(len(self.weights)):
                a.append(self.activation(np.dot(a[l],self.weights[l])))
            error=y[i]-a[-1]
            deltas=[error*self.activation_deriv(a[-1])]
            
            for l in range(len(a)-2,0,-1):
                deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
                
            deltas.reverse()
            
            for i in range(len(self.weights)):
                layer = np.atleast_2d(a[i])
                delta=np.atleast_2d(deltas[i])
                self.weights[i]+=learning_rate*layer.T.dot(delta)
    def predict(self,x):
        x = np.array(x)
        temp = np.ones(x.shape[0]+1)
        temp[0:-1]=x
        a=temp
        for l in range(0,len(self.weights)):
            a = self.activation(np.dot(a,self.weights[l]))
        return a
    
    
    
nn=NeuralNetwork([2,2,1],'tanh')

X=np.array([[0,0],[0,1],[1,0],[1,1]])
y=np.array([0.5,1,1,0.5])

nn.fit(X,y)

for i in [[0,0],[0,1],[1,0],[1,1]]:
    print(i,nn.predict(i))