import numpy as np

def generate(count):
    X = np.random.random_integers(0, high=255, size=(count, 9))
    Y = X.dot(np.array([1, 1, 1, 0, 0, 0, -1, -1, -1]))
    Y[Y > 0] = 1
    Y[Y < 0] = 0
    return X, Y

def run():
    m = 5000
    c = 9
    epochs = 50000

    # weight vector
    w = np.random.randn(c).reshape(c, 1)

    # training loop
    lr = 1e-3
    print('\n\n{:^8s} | {:^8s} | {:^6s}'.format('epoch', 'loss', 'acc'))
    print('----------------------------')
    for t in range(epochs):
        # get new training data
        X, y = generate(m)
        X = X / 255
        y = y.reshape(m, 1) * 2 - 1

        # model function
        h = X.dot(w)

        # compute loss
        loss = np.square(h - y).mean()

        # compute accuracy
        acc = (np.sign(h) == y).mean()

        if t % 5000 == 0:
            print('{:>8d} | {:>8f} | {:>.4f}'.format(t, loss, acc))

        # no more to do
        if acc >= 1:
            print('\nStopping:\n{:>8d} | {:>8f} | {:>.4f}'.format(t, loss, acc))
            break

        # grad + update
        grad = 2 * (h - y)
        w = w - lr * X.T.dot(grad) / m

    print('\nFinal W = \n\n{}'.format(w))

if __name__ == "__main__":
    run()