#!/usr/bin/pythonimport syssys.path.append('..')import modules.similarity as sim # http://hi.baidu.com/bithigher/item/6ca6844ac29d0128fb896026 import randomwords = []blogs = []data = []def getData(file = 'blogdata.txt'): global words, map f = open(file) line = f.readline() words = line.strip().split('\t')[1:] for line in f: p = line.strip().split('\t') blogs.append(p[0]) data.append([float(x) for x in p[1:]]) f.close() def hcluster(similarity = sim.pearson): global data clusters = [bicluster(data[i], id=i) for i in range(len(data))] currentId = -1 # id for merged bicluster dis = {} #record the distance computed before while(len(clusters) > 1): closestIndex = (0, 1) closestKey = (clusters[0].id, clusters[1].id) if closestKey not in dis: dis[closestKey] = 1 - similarity(clusters[0].vec, clusters[1].vec) closest = dis[closestKey] for i in range(len(clusters)): for j in range(i+1, len(clusters)): key = (clusters[i].id, clusters[j].id) if key not in dis: dis[key] = 1 - similarity(clusters[i].vec, clusters[j].vec) if(dis[key] < closest): closest = dis[key] closestKey = key closestIndex = (i, j) # generate a new bicluster and delete the closePair mergedVec = [(clusters[closestIndex[0]].vec[i] + clusters[closestIndex[1]].vec[i])/2.0 for i in range(len(clusters[0].vec))] clusters.append(bicluster(mergedVec, clusters[closestIndex[0]], clusters[closestIndex[1]], closest, currentId)) currentId -= 1 del dis[closestKey] #ATTENTION!!! remove the later one first #ATTENTION!!! because the index will change if remove the former one first del clusters[closestIndex[1]] del clusters[closestIndex[0]] print closestIndex, closestKey return clusters[0] def rotateData(): global data newData = [] for i in range(len(data[0])): newData.append([data[j][i] for j in range(len(data))]) data = newData def printCluster(cluster, labels, n=0): if cluster == None: return for i in range(n): print ' ', if(cluster.id >= 0): print '-', labels[cluster.id] else: print '+', cluster.id printCluster(cluster.left, labels, n+1) printCluster(cluster.right, labels, n+1) class bicluster: def __init__(self, vec, left=None, right=None, distance=0, id=None): self.vec = vec self.left = left self.right = right self.distance = distance self.id = id def kmeans(k=5, iterations=100, similarity=sim.pearson): global data if(len(data) <= k): return data clusters = [[] for i in range(k)] length = len(data[0]) ranges = [(min(row[i] for row in data), max(row[i] for row in data)) for i in range(length)] #random k centers centers = [[random.random()*(ranges[i][1]-ranges[i][0])+ranges[i][0] for i in range(length)] for j in range(k)] # in case of falling into endless loop while(iterations > 0): iterations -= 1 clusters = [[] for i in range(k)] newCenters = [[0 for i in range(length)] for j in range(k)] for i in range(len(data)): minVal = 1 - similarity(data[i], centers[0]) minIndex = 0 for j in range(1, k): curMin = 1 - similarity(data[i], centers[j]) if curMin < minVal: minVal = curMin minIndex = j clusters[minIndex].append(i) newCenters[minIndex] = [newCenters[minIndex][j] + data[i][j] for j in range(length)] for i in range(k): num = float(len(clusters[i])) if(num > 0): newCenters[i] = [newCenters[i][j] / num for j in range(length)] if(newCenters == centers): return clusters else: centers = newCenters
