DPC改进算法python代码

DPC改进算法的Python代码如下：

import numpy as np

def select_dc(dists):
    N = np.shape(dists)\[0\]
    tt = np.reshape(dists, N*N)
    percent = 2.0
    position = int(N * (N - 1) * percent / 100)
    dc = np.sort(tt)\[position + N\]
    return dc

def getDistanceMatrix(datas):
    N, D = np.shape(datas)
    dists = np.zeros(\[N, N\])
    for i in range(N):
        for j in range(N):
            vi = datas\[i, :\]
            vj = datas\[j, :\]
            dists\[i, j\] = np.sqrt(np.dot((vi - vj), (vi - vj)))
    return dists

这段代码中，`select_dc`函数用于确定邻域截断距离dc，它通过计算平均每个点周围距离小于dc的点的数目占总点数的1%-2%来确定dc的值。`getDistanceMatrix`函数用于计算给定数据集中各个点之间的欧几里得距离，并返回一个距离矩阵。这个距离矩阵可以用于DPC改进算法的其他步骤中。
#### 引用[.reference_title]
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