import numpy as np
from Network import NeuralNetwork

def read_file(filename):
    with open(filename) as f:
        content = f.readlines()
        X = []
        y = []
        for i in content:
            X.append([float(num) for num in i.strip().split()[:-1]])
            y.append(i.strip().split()[-1:])
        return (X, y)

if __name__ == "__main__":
    (X, _y) = read_file("column_2C.dat")
    y = []
    for i in _y:
        if i[0] == 'NO':
            y.append([0,1])
        if i[0] == 'AB':
            y.append([1,0])

    M = np.amax(X, axis=0)
    N = np.amin(X, axis=0)
    _X = (X - N) / (M - N)

    _y = y

    X = np.array(_X[:298])
    y = np.array(_y[:298])

    nn = NeuralNetwork(X, y)

    for i in range(1500):
        nn.feedforward()
        nn.backprop()

    k = 0
    correct = 0
    nn.input = np.array(_X[298:])
    nn.feedforward()

    for i in nn.output:
        print(i, end=" Resulted: ")
        if i[0] > i[1]:
            print("AB", end=" Expected: ")
            if (_y[298:][k][0] == 1):
                print("AB")
                correct += 1
                print("CORRECT")
            else:
                print("NO")
                print("WRONG")
        else:
            print("NO", end=" Expected: ")
            if (_y[298:][k][0] == 0):
                print("NO")
                correct += 1
                print("CORRECT")
            else:
                print("AB")
                print("WRONG")
        k += 1
    print("ACCURACY: ", end="")
    print(correct/(310-298))