Python取读csv文件做dbscan分析_python_

from sklearn import datasets import numpy as np import random import matplotlib.pyplot as plt import time import copy import pandas as pd # from sklearn.datasets import load_iris   def find_neighbor(j, x, eps):     N = list()     for i in range(x.shape[0]):         temp = np.sqrt(np.sum(np.square(x[j] - x[i])))  # 计算欧式距离         if temp <= eps:             N.append(i)     return set(N)     def DBSCAN(X, eps, min_Pts):     k = -1     neighbor_list = []  # 用来保存每个数据的邻域     omega_list = []  # 核心对象集合     gama = set([x for x in range(len(X))])  # 初始时将所有点标记为未访问     cluster = [-1 for _ in range(len(X))]  # 聚类     for i in range(len(X)):         neighbor_list.append(find_neighbor(i, X, eps))         if len(neighbor_list[-1]) >= min_Pts:             omega_list.append(i)  # 将样本加入核心对象集合     omega_list = set(omega_list)  # 转化为集合便于操作     while len(omega_list) > 0:         gama_old = copy.deepcopy(gama)         j = random.choice(list(omega_list))  # 随机选取一个核心对象         k = k + 1         Q = list()         Q.append(j)         gama.remove(j)         while len(Q) > 0:             q = Q[0]             Q.remove(q)             if len(neighbor_list[q]) >= min_Pts:                 delta = neighbor_list[q] & gama                 deltalist = list(delta)                 for i in range(len(delta)):                     Q.append(deltalist[i])                     gama = gama - delta         Ck = gama_old - gama         Cklist = list(Ck)         for i in range(len(Ck)):             cluster[Cklist[i]] = k         omega_list = omega_list - Ck     return cluster   # X = load_iris().data data = pd.read_csv("testdata.csv") x,y=data['Time (sec)'],data['Height (m HAE)'] print(type(x)) n=len(x) x=np.array(x) x=x.reshape(n,1) y=np.array(y) y=y.reshape(n,1) X = np.hstack((x, y)) cluster_std=[[.1]], random_state=9)   eps = 0.08 min_Pts = 5 begin = time.time() C = DBSCAN(X, eps, min_Pts) end = time.time() plt.figure() plt.scatter(X[:, 0], X[:, 1], c=C) plt.show()

eps = 0.8 min_Pts = 5