Python实现DBScan，Python,from：https

Python实现DBScan，Python,from：https

from：https://www.cnblogs.com/wsine/p/5180778.html

运行环境

Pyhton3numpy(科学计算包)matplotlib(画图所需，不画图可不必)

计算过程

st=>start: 开始e=>end: 结束op1=>operation: 读入数据cond=>condition: 是否还有未分类数据op2=>operation: 找一未分类点扩散op3=>operation: 输出结果st->op1->op2->condcond(yes)->op2cond(no)->op3->e

输入样例

/* 788points.txt */15.55,28.6514.9,27.5514.45,28.3514.15,28.813.75,28.0513.35,28.4513,29.1513.45,27.513.6,26.512.8,27.3512.4,27.8512.3,28.412.2,28.6513.4,25.112.95,25.95

788points.txt完整文件：下载

代码实现

# -*- coding: utf-8 -*-__author__ = ‘Wsine‘import numpy as npimport matplotlib.pyplot as pltimport mathimport timeUNCLASSIFIED = FalseNOISE = 0def loadDataSet(fileName, splitChar=‘\t‘):    """    输入：文件名    输出：数据集    描述：从文件读入数据集    """    dataSet = []    with open(fileName) as fr:        for line in fr.readlines():            curline = line.strip().split(splitChar)            fltline = list(map(float, curline))            dataSet.append(fltline)    return dataSetdef 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) < epsdef 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 seedsdef 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 Truedef 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 - 1def 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(‘788points.txt‘, splitChar=‘,‘)    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()

输出样例

cluster Numbers =  7finish all in 32.712135628590794

Python实现DBScan