Python kmean implementation

kmean.py

#!/usr/bin/python

# Kmean on a CSV data set
# 2016

import sys
import csv

if len(sys.argv) < 2:
	print "USAGE : %s csv-file" % (sys.argv[0])


def square4DistanceTo(point1, point2):
	return (pow(abs(point1[0] - point2[0]), 2) +
		pow(abs(point1[1] - point2[1]), 2) +
		pow(abs(point1[2] - point2[2]), 2) +
		pow(abs(point1[3] - point2[3]), 2))
	


filename = sys.argv[1]
nbCluster = 3
if len(sys.argv) > 2:
	nbCluster = int(sys.argv[2])

data = []
#  Parse csv
with open(filename, 'rb') as csvfile:
	reader = csv.reader(csvfile, delimiter=',')
	for row in reader:
		# Parse to float
		for i in range(0, 4):
			row[i] = float(row[i])
		data.append(row)


# Chaque cluster contient la liste des index des donnees qui leur sont ratachees
# Pour les centres initiaux, c'est arbitraire
clusters = [[] for i in range(0, nbCluster)]
centre_clusters = [[data[i][0], data[i][1], data[i][2], data[i][3]] for i in range(0, nbCluster)]

modification = True


while modification:
	# Assignation des points dans les clusters
	clusters = [[] for i in range(0, nbCluster)]  # Reset clusters
	
	rowIndex = 0
	for row in data:  # For each point
		# Trouver le centre cluster le plus proche
		nearest = 0
		nearestDistance = square4DistanceTo(centre_clusters[0], row)
		if nearestDistance > 0:
			for i in range(1, nbCluster):
				distance = square4DistanceTo(centre_clusters[i], row)
				if distance < 0:
					print distance
				if distance < nearestDistance :
					nearest = i
					nearestDistance = distance
		# Assign point to cluster
		clusters[nearest].append(rowIndex)
		rowIndex += 1

	modification = False

	# Calcul des moyennes des clusters
	for i in range(0, nbCluster):
		# Calcul moyenne cluster i
		nbRows = len(clusters[i])
		totalSl = 0.0
		totalSw = 0.0
		totalPl = 0.0
		totalPw = 0.0
		for rowIndex in clusters[i]:
			row = data[rowIndex]
			totalSl += row[0]
			totalSw += row[1]
			totalPl += row[2]
			totalPw += row[3]

		centre = [totalSl / nbRows,
			totalSw / nbRows,
			totalPl / nbRows,
			totalPw / nbRows]
			
		if not modification:
			prCentre = centre_clusters[i]
			
			if ((prCentre[0] != centre[0]) or 
				(prCentre[1] != centre[1]) or
				(prCentre[2] != centre[2]) or
				(prCentre[3] != centre[3])):
				modification = True

		centre_clusters[i] = centre


with open("out.csv", "wb") as csvfile:
	writer = csv.writer(csvfile, delimiter = ",")
	for i in range(0, nbCluster):
		for rowIndex in clusters[i]:
			row = data[rowIndex]
			row.insert(0, i + 1)
			writer.writerow(row)