chapter_5.py

import numpy as np

# toes %win #fans
weights = [[0.1, 0.1, -0.3], [0.1, 0.2, 0.0], [0.0, 1.3, 0.1]]  # hurt?  # win?  # sad?


def w_sum(a, b):
    assert len(a) == len(b)
    output = 0

    for i in range(len(a)):
        output += a[i] * b[i]

    return output


def vect_mat_mul(vect, matrix):
    assert len(vect) == len(matrix)
    output = [0, 0, 0]
    for i in range(len(vect)):
        output[i] = w_sum(vect, matrix[i])
    return output


def neural_network(input, weights):
    pred = vect_mat_mul(input, weights)
    return pred


toes = [8.5, 9.5, 9.9, 9.0]
wlrec = [0.65, 0.8, 0.8, 0.9]
nfans = [1.2, 1.3, 0.5, 1.0]

hurt = [0.1, 0.0, 0.0, 0.1]
win = [1, 1, 0, 1]
sad = [0.1, 0.0, 0.1, 0.2]

alpha = 0.01

input = [toes[0], wlrec[0], nfans[0]]
true = [hurt[0], win[0], sad[0]]

pred = neural_network(input, weights)

error = [0, 0, 0]
delta = [0, 0, 0]

for i in range(len(true)):
    error[i] = (pred[i] - true[i]) ** 2
    delta[i] = pred[i] - true[i]


def outer_prod(a, b):

    # just a matrix of zeros
    out = np.zeros((len(a), len(b)))

    for i in range(len(a)):
        for j in range(len(b)):
            out[i][j] = a[i] * b[j]
    return out


weight_deltas = outer_prod(delta, input)

for i in range(len(weights)):
    for j in range(len(weights[0])):
        weights[i][j] -= alpha * weight_deltas[i][j]

print(weights)