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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Credit: Machine Learning in Action: Chapter 10
#
# Aaron LI
# 2015/06/23
#
"""
k-means clustering algorithm
"""
import numpy as np
def loadDataSet(fileName):
dataMat = []
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
fltLine = list(map(float, curLine))
dataMat.append(fltLine)
return np.array(dataMat)
def distEclud(vecA, vecB):
return np.sqrt(np.sum(np.power(vecA - vecB, 2)))
def randCent(dataSet, k):
n = np.shape(dataSet)[1]
centroids = np.zeros((k, n))
for j in range(n):
minJ = np.min(dataSet[:, j])
rangeJ = float(np.max(dataSet[:, j]) - minJ)
centroids[:, j] = minJ + rangeJ * np.random.rand(k)
return centroids
def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
m = np.shape(dataSet)[0]
clusterAssment = np.zeros((m, 2))
centroids = createCent(dataSet, k)
clusterChanged = True
iterations = 0
while clusterChanged:
clusterChanged = False
iterations += 1
for i in range(m):
minDist = np.inf
minIndex = -1
# to find the nearest centroid
for j in range(k):
distJI = distMeas(centroids[j, :], dataSet[i, :])
if distJI < minDist:
minDist = distJI
minIndex = j
if clusterAssment[i, 0] != minIndex:
clusterChanged = True
clusterAssment[i, :] = minIndex, minDist**2
#print(centroids)
for cent in range(k):
ptsInClust = dataSet[np.nonzero(clusterAssment[:, 0] == cent)]
centroids[cent, :] = np.mean(ptsInClust, axis=0)
result = {
'k': k,
'centroids': centroids,
'labels': clusterAssment[:, 0].astype(int),
'distance2': clusterAssment[:, 1],
'accessment': clusterAssment,
'iterations': iterations
}
return result
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