本文例項為大家分享了python實現mean-shift聚類演算法的具體程式碼，供大家參考，具體內容如下

1、新建MeanShift.py檔案

import numpy as np

# 定義 預先設定 的閾值
STOP_THRESHOLD = 1e-4
CLUSTER_THRESHOLD = 1e-1


# 定義度量函式
def distance(a,b):
 return np.linalg.norm(np.array(a) - np.array(b))


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


# mean_shift類
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],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,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

2、呼叫上述py檔案

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 09 11:02:08 2018

@author: muli
"""

from sklearn.datasets.samples_generator import make_blobs
import matplotlib.pyplot as plt 
import random
import numpy as np
import MeanShift


def colors(n):
 ret = []
 for i in range(n):
 ret.append((random.uniform(0,1),random.uniform(0,1)))
 return ret

def main():
 centers = [[-1,-1],[-1,1],[1,1]]
 X,_ = make_blobs(n_samples=300,centers=centers,cluster_std=0.4)

 mean_shifter = MeanShift.mean_shift()
 _,mean_shift_result = mean_shifter.fit(X,kernel_bandwidth=0.5)

 np.set_printoptions(precision=3)
 print('input: {}'.format(X))
 print('assined clusters: {}'.format(mean_shift_result))
 color = colors(np.unique(mean_shift_result).size)

 for i in range(len(mean_shift_result)):
  plt.scatter(X[i,0],X[i,color = color[mean_shift_result[i]])
 plt.show()


if __name__ == '__main__':
 main()

結果如圖所示：

參考連結

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支援我們。