DPC-KNN-PCA算法python完整代码

以下是DPC-KNN-PCA算法的Python完整代码，包括数据预处理、DPC-KNN-PCA算法实现和结果可视化：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 数据预处理
data = pd.read_csv('data.csv')
X = data.values[:, :-1]
y = data.values[:, -1]

# DPC-KNN-PCA算法实现
def DPC_KNN_PCA(X, k, alpha, beta, gamma):
    n, m = X.shape
    D = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            D[i, j] = np.linalg.norm(X[i] - X[j])
    D_sort = np.sort(D, axis=1)
    idx_sort = np.argsort(D, axis=1)
    K = np.zeros((n, k))
    for i in range(n):
        for j in range(k):
            K[i, j] = idx_sort[i, j+1]
    W = np.zeros((n, n))
    for i in range(n):
        for j in range(k):
            W[int(K[i, j]), i] = 1
    W = np.maximum(W, W.T)
    D_bar = np.diag(np.sum(W, axis=1))
    L = D_bar - W
    M = np.dot(X.T, L).dot(X)
    [U, S, V] = np.linalg.svd(M)
    U_pca = U[:, :2]
    Z = np.dot(X, U_pca)
    L_pca = np.dot(U_pca.T, M).dot(U_pca)
    D_pca = np.diag(np.sum(L_pca, axis=1))
    L_norm = np.linalg.inv(np.sqrt(D_pca)).dot(L_pca).dot(np.linalg.inv(np.sqrt(D_pca)))
    W_norm = np.exp(-alpha*L_norm) - np.eye(n)
    D_norm = np.diag(np.sum(W_norm, axis=1))
    L1_norm = D_norm - W_norm
    L2_norm = np.linalg.inv(np.sqrt(D_norm)).dot(L_norm).dot(np.linalg.inv(np.sqrt(D_norm)))
    W_dpc = np.exp(-beta*L1_norm - gamma*L2_norm) - np.eye(n)
    D_dpc = np.diag(np.sum(W_dpc, axis=1))
    L_dpc = D_dpc - W_dpc
    return Z, L_dpc

# 运行DPC-KNN-PCA算法并可视化结果
Z, L_dpc = DPC_KNN_PCA(X, 10, 0.5, 0.1, 0.1)
plt.scatter(Z[:, 0], Z[:, 1], c=y)
plt.show()