lvq.py

import matplotlib.pyplot as plt
import numpy as np


def lvq_fit(train, target, lrate, b, max_epoch):
    label, train_idx = np.unique(target, return_index=True)
    weight = train[train_idx].astype(np.float64)
    train = np.array([e for i, e in enumerate(
        zip(train, target)) if i not in train_idx], dtype=object)
    train, target = train[:, 0], train[:, 1]
    epoch = 0

    while epoch < max_epoch:
        for i, x in enumerate(train):
            distance = [sum((w - x) ** 2) for w in weight]
            min = np.argmin(distance)
            sign = 1 if target[i] == label[min] else -1
            weight[min] += sign * lrate * (x - weight[min])

        lrate *= b
        epoch += 1

    return weight, label


def lvq_predict(X, model):
    center, label = model
    Y = []

    for x in X:
        d = [sum((c - x) ** 2) for c in center]
        Y.append(label[np.argmin(d)])

    return Y


def calc_accuracy(a, b):
    s = [1 if a[i] == b[i] else 0 for i in range(len(a))]

    return sum(s) / len(a)


def class_to_int(category):
    return '+0*#'.index(category)


def class_to_plot(category):
    return '+o*^'[category]


def main():
    data = ('#####0000'
            '#####0000'
            '+++++++00'
            '+++++++00'
            '+++++++**'
            '+++++++**'
            '+++++++**'
            '+++++++**'
            '+++++++**')

    max_epoch = 40

    X_train = np.array([((9 - i//9)/9, (i % 9)/9) for i in range(len(data))])
    y_train = np.array([class_to_int(i) for i in data])

    model = lvq_fit(X_train, y_train, lrate=.01, b=1, max_epoch=max_epoch)
    output = lvq_predict(X_train, model)
    accuracy = calc_accuracy(output, y_train)
    colors = 'rgby'

    for x, label in zip(X_train, output):
        plt.plot(x[0], x[1], colors[label] + class_to_plot(label))

    for center, label in zip(model[0], model[1]):
        plt.plot(center[0], center[1], colors[label] + '.')

    plt.title(f'Epoch = {max_epoch} (Accuracy: {accuracy})')
    plt.show()


if __name__ == '__main__':
    main()