dbscan实现密度聚类
本次作业为实现密度聚类,原理较为简单。
代码原出处:
http://blog.csdn.net/u013421629/article/details/77100063
改写地方:
只对数据的读取进行了修改,其他未动。并未真正实现聚类效果,大家一块帮忙看下一下。
# -*- coding: utf-8 -*- import scipy.io as sio import numpy as np import matplotlib.pyplot as plt import math import time UNCLASSIFIED = False NOISE = 0 def loadDataSet(fileName): """ 输入:文件名 输出:数据集 描述:从文件读入数据集 """ dataSet = [] file=sio.loadmat(r'D:/P/smile.mat') dataSet = file['smile']#makethe data fit PYTHON from mat dataSet = dataSet.tolist() print('\n%s'%dataSet) return dataSet def dist(a, b): """ 输入:向量A, 向量B 输出:两个向量的欧式距离 """ return math.sqrt(np.power(a - b, 2).sum()) def eps_neighbor(a, b, eps): """ 输入:向量A, 向量B 输出:是否在eps范围内 """ return dist(a, b) < eps def region_query(data, pointId, eps): """ 输入:数据集, 查询点id, 半径大小 输出:在eps范围内的点的id """ nPoints = data.shape[1] seeds = [] for i in range(nPoints): if eps_neighbor(data[:, pointId], data[:, i], eps): seeds.append(i) return seeds def expand_cluster(data, clusterResult, pointId, clusterId, eps, minPts): """ 输入:数据集, 分类结果, 待分类点id, 簇id, 半径大小, 最小点个数 输出:能否成功分类 """ seeds = region_query(data, pointId, eps) if len(seeds) < minPts: # 不满足minPts条件的为噪声点 clusterResult[pointId] = NOISE return False else: clusterResult[pointId] = clusterId # 划分到该簇 for seedId in seeds: clusterResult[seedId] = clusterId while len(seeds) > 0: # 持续扩张 currentPoint = seeds[0] queryResults = region_query(data, currentPoint, eps) if len(queryResults) >= minPts: for i in range(len(queryResults)): resultPoint = queryResults[i] if clusterResult[resultPoint] == UNCLASSIFIED: seeds.append(resultPoint) clusterResult[resultPoint] = clusterId elif clusterResult[resultPoint] == NOISE: clusterResult[resultPoint] = clusterId seeds = seeds[1:] return True def dbscan(data, eps, minPts): """ 输入:数据集, 半径大小, 最小点个数 输出:分类簇id """ clusterId = 1 nPoints = data.shape[1] clusterResult = [UNCLASSIFIED] * nPoints for pointId in range(nPoints): point = data[:, pointId] if clusterResult[pointId] == UNCLASSIFIED: if expand_cluster(data, clusterResult, pointId, clusterId, eps, minPts): clusterId = clusterId + 1 return clusterResult, clusterId - 1 def plotFeature(data, clusters, clusterNum): nPoints = data.shape[1] matClusters = np.mat(clusters).transpose() fig = plt.figure() scatterColors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown'] ax = fig.add_subplot(111) for i in range(clusterNum + 1): colorSytle = scatterColors[i % len(scatterColors)] subCluster = data[:, np.nonzero(matClusters[:, 0].A == i)] ax.scatter(subCluster[0, :].flatten().A[0], subCluster[1, :].flatten().A[0], c=colorSytle, s=50) def main(): dataSet = loadDataSet('D:/P/smile.mat') dataSet = np.mat(dataSet).transpose() # print(dataSet) clusters, clusterNum = dbscan(dataSet, 2, 15) print("cluster Numbers = ", clusterNum) # print(clusters) plotFeature(dataSet, clusters, clusterNum) if __name__ == '__main__': start = time.clock() main() end = time.clock() print('finish all in %s' % str(end - start)) plt.show()