ppf算法的测试代码

以下是一个使用ppf算法进行测试的示例代码：

import numpy as np
from sklearn.neighbors import NearestNeighbors

def ppf_algorithm(points, normals):
    # 计算点对特征
    features = np.zeros((len(points), 4))
    for i in range(len(points)):
        p1 = points[i]
        n1 = normals[i]
        for j in range(len(points)):
            if i != j:
                p2 = points[j]
                n2 = normals[j]
                delta_p = p2 - p1
                delta_n = n2 - n1
                alpha = np.arctan2(delta_p[1], delta_p[0])
                beta = np.arccos(delta_n.dot(delta_p) / (np.linalg.norm(delta_n) * np.linalg.norm(delta_p)))
                features[i] += np.array([alpha, beta, delta_p[0], delta_p[1]])
        features[i] /= len(points) - 1

    # 使用KNN查找最近邻点对
    nbrs = NearestNeighbors(n_neighbors=2, algorithm='kd_tree').fit(features)
    distances, indices = nbrs.kneighbors(features)

    return distances, indices

# 测试数据
points = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
normals = np.array([[1, 0], [0, 1], [-1, 0], [0, -1]])

distances, indices = ppf_algorithm(points, normals)
print("距离：", distances)
print("索引：", indices)