python实现mean-shift聚类算法_muli

1. 新建MeanShift.py文件

import numpy as np

# 定义 预先设定 的阈值
STOP_THRESHOLD = 1e-4
CLUSTER_THRESHOLD = 1e-1


# 定义度量函数
def distance(a, b):
    return np.linalg.norm(np.array(a) - np.array(b))


# 定义高斯核函数
def gaussian_kernel(distance, bandwidth):
    return (1 / (bandwidth * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((distance / bandwidth)) ** 2)


# mean_shift类
class mean_shift(object):
    def __init__(self, kernel=gaussian_kernel):
        self.kernel = kernel

    def fit(self, points, kernel_bandwidth):

        shift_points = np.array(points)
        shifting = [True] * points.shape[0]

        while True:
            max_dist = 0
            for i in range(0, len(shift_points)):
                if not shifting[i]:
                    continue
                p_shift_init = shift_points[i].copy()
                shift_points[i] = self._shift_point(shift_points[i], points, kernel_bandwidth)
                dist = distance(shift_points[i], p_shift_init)
                max_dist = max(max_dist, dist)
                shifting[i] = dist > STOP_THRESHOLD

            if(max_dist < STOP_THRESHOLD):
                break
        cluster_ids = self._cluster_points(shift_points.tolist())
        return shift_points, cluster_ids

    def _shift_point(self, point, points, kernel_bandwidth):
        shift_x = 0.0
        shift_y = 0.0
        scale = 0.0
        for p in points:
            dist = distance(point, p)
            weight = self.kernel(dist, kernel_bandwidth)
            shift_x += p[0] * weight
            shift_y += p[1] * weight
            scale += weight
        shift_x = shift_x / scale
        shift_y = shift_y / scale
        return [shift_x, shift_y]

    def _cluster_points(self, points):
        cluster_ids = []
        cluster_idx = 0
        cluster_centers = []

        for i, point in enumerate(points):
            if(len(cluster_ids) == 0):
                cluster_ids.append(cluster_idx)
                cluster_centers.append(point)
                cluster_idx += 1
            else:
                for center in cluster_centers:
                    dist = distance(point, center)
                    if(dist < CLUSTER_THRESHOLD):
                        cluster_ids.append(cluster_centers.index(center))
                if(len(cluster_ids) < i + 1):
                    cluster_ids.append(cluster_idx)
                    cluster_centers.append(point)
                    cluster_idx += 1
        return cluster_ids
import numpy a