Python怎么取读csv文件做dbscan分析

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1.读取csv数据做dbscan分析

读取csv文件中相应的列，然后进行转化，处理为本算法需要的格式，然后进行dbscan运算，目前公开的代码也比较多，本文根据公开代码修改，

具体代码如下：

from sklearn import datasetsimport numpy as npimport randomimport matplotlib.pyplot as pltimport timeimport copyimport 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().datadata = 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 = 0.08min_Pts = 5begin = time.time()C = DBSCAN(X, eps, min_Pts)end = time.time()plt.figure()plt.scatter(X[:, 0], X[:, 1], c=C)plt.show()

2.输出结果显示

修改参数显示：

eps = 0.8min_Pts = 5

3.计算效率

采用少量数据计算的时候效率问题不明显，随着数据量增大，计算效率问题就变得尤为明显，难以满足大量数据的计算需求了，后期将想办法优化计算方法或者收集C++代码进行优化了。

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