import numpy as np

from util import *


class MLP():

    def __init__(self, dim_in, dim_hid, dim_out):
        self.dim_in     = dim_in
        self.dim_hid    = dim_hid
        self.dim_out    = dim_out

        self.W_hid = np.random.rand(dim_hid,dim_in) # FIXME
        self.W_out = np.random.rand(dim_out,dim_hid) # FIXME

    def f_hid(self, x):
        pass

    def df_hid(self, x):
        pass

    def f_out(self, x):
        pass

    def df_out(self, x):
        pass


    def forward(self, x):
        a = np.dot(self.W_hid,x) # FIXME
        h = self.f_hid(a) # FIXME
        b = np.dot(self.W_out,h) # FIXME
        y = self.f_out(b) # FIXME

        return y, b, h, a


    def backward(self, x, d):
        y, b, h, a = self.forward(x)
        # print(y.shape[0])
        # print(a.shape[0])
        g_out= np.zeros(y.shape[0]);
        g_hid=np.zeros(a.shape[0])
        for i in range(y.shape[0]):
         g_out[i] = (d[i]-y[i])*self.df_out(b[i]); # FIXME
        # g_hid = (np.dot(np.transpose(self.W_out),g_out))*self.df_hid(a) # FIXME
        for k in range(a.shape[0]):
            halpweight=0
            for i in range(g_out.shape[0]):
                halpweight+=self.W_out[i,k]*g_out[i]
            g_hid[k]=halpweight*self.df_hid(a[k])

        dW_out = np.outer(g_out,h.T) # FIXME
        dW_hid = np.outer(g_hid,x.T) # FIXME

        return y, dW_hid, dW_out