vector.py

from math import sqrt
from random import randrange
from random import seed

# calculate the Euclidean distance between two vectors
def euclidean_distance(row1, row2):
	distance = 0.0
	for i in range(len(row1)-1):
		distance += (row1[i] - row2[i])**2
	return sqrt(distance)

# Locate the best matching unit
def get_best_matching_unit(codebooks, test_row):
	distances = list()
	for codebook in codebooks:
		dist = euclidean_distance(codebook, test_row)
		distances.append((codebook, dist))
	distances.sort(key=lambda tup: tup[1])
	return distances[0][0]

# Create a random codebook vector
def random_codebook(train):
	n_records = len(train)
	n_features = len(train[0])
	codebook = [train[randrange(n_records)][i] for i in range(n_features)]
	return codebook

# Train a set of codebook vectors
def train_codebooks(train, n_codebooks, lrate, epochs):
	codebooks = [random_codebook(train) for i in range(n_codebooks)]
	for epoch in range(epochs):
		rate = lrate * (1.0-(epoch/float(epochs)))
		sum_error = 0.0
		for row in train:
			bmu = get_best_matching_unit(codebooks, row)
			for i in range(len(row)-1):
				error = row[i] - bmu[i]
				sum_error += error**2
				if bmu[-1] == row[-1]:
					bmu[i] += rate * error
				else:
					bmu[i] -= rate * error
		print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, rate, sum_error))
	return codebooks

# Test the training function
seed(1)
dataset = [[2.7810836,2.550537003,0],
	[1.465489372,2.362125076,0],
	[3.396561688,4.400293529,0],
	[1.38807019,1.850220317,0],
	[3.06407232,3.005305973,0],
	[7.627531214,2.759262235,1],
	[5.332441248,2.088626775,1],
	[6.922596716,1.77106367,1],
	[8.675418651,-0.242068655,1],
	[7.673756466,3.508563011,1]]
learn_rate = 0.3
n_epochs = 10
n_codebooks = 2
print('Input : %s' % dataset)
codebooks = train_codebooks(dataset, n_codebooks, learn_rate, n_epochs)
print('Codebooks: %s' % codebooks)