Python implementation Canopy algorithm

Code

import math
import random
import numpy as np
from datetime import datetime
from pprint import pprint as p
import matplotlib.pyplot as plt

# 随机生成一个500个二维 [0,1)平面点
dataset = np.random.rand(500, 2)
# print(dataset)

# 设计一个Canopy类
class Canopy:
    def __init__(self, dataset):
        self.dataset = dataset
        self.t1 = 0
        self.t2 = 0
    
    # 设置初始阈值t1 和 t2
    def setThreshold(self, t1, t2):
        if t1 > t2:
            self.t1 = t1
            self.t2 = t2
        else:
            print("t1 needs to be larger than t2!")
    
    # 使用欧式距离进行距离计算
    def euclideanDistance(self, vec1, vec2):
        return math.sqrt(((vec1 - vec2) ** 2).sum())
    
    # 根据当前dataset的长度随机选择一个下标
    def getRandIndex(self):
        return np.random.randint(len(self.dataset))
        # return random.randint(0, len(self.dataset) - 1)
    
    # 核心算法
    def clustering(self):
        if self.t1 == 0:
            print('Please set the threshold t1 and t2!')
        else:
            canopies = [] # 用于存放最终归类的结果
            while len(self.dataset) != 0:
                # 获取一个随机下标
                rand_index = self.getRandIndex() 
                # 随机获取一个中心点，定为P点
                current_center = self.dataset[rand_index] 
                # 初始化P点的canopy类容器
                current_center_list = [] 
                # 初始化P点的删除容器
                delete_list = [] 
                # 删除随机选择的中心点P
                self.dataset = np.delete(self.dataset, rand_index, 0)
                for datum_j in range(len(self.dataset)):
                    datum = self.dataset[datum_j]
                    # 计算选取的中心点P到每个点之间的距离
                    distance = self.euclideanDistance(current_center, datum)
                    if distance < self.t1:
                        # 若距离小于t1，则将点归入P点的canopy类
                        current_center_list.append(datum)
                    if distance < self.t2:
                        # 若小于t2则归入删除容器
                        delete_list.append(datum_j)
                self.dataset = np.delete(self.dataset, delete_list, 0)
                canopies.append((current_center, current_center_list))
            return canopies
        
        
def showCanopy(canopies, dataset, t1, t2):
    fig = plt.figure(figsize=(10, 10))
    
    sc = fig.add_subplot(111)
    
    colors = ['brown', 'green', 'blue', 'y', 'r', 'tan', 'dodgerblue', 'deeppink', 'orangered', 'peru', 'blue', 'y', 'r',
              'gold', 'dimgray', 'darkorange', 'peru', 'blue', 'y', 'r', 'cyan', 'tan', 'orchid', 'peru', 'blue', 'y', 'r', 'sienna']
    markers = ['*', 'h', 'H', '+', 'o', '1', '2', '3', ',', 'v', 'H', '+', '1', '2', '^',
               '<', '>', '.', '4', 'H', '+', '1', '2', 's', 'p', 'x', 'D', 'd', '|', '_']
    for i in range(len(canopies)):
        canopy = canopies[i]
        center = canopy[0]
        components = canopy[1]
        sc.plot(center[0], center[1], marker=markers[i], color=colors[i], markersize=10)
        t1_circle = plt.Circle(
            xy=(center[0], center[1]), radius=t1, color='dodgerblue', fill=False)
        t2_circle = plt.Circle(
            xy=(center[0], center[1]), radius=t2, color='skyblue', alpha=0.2)
        sc.add_artist(t1_circle)
        sc.add_artist(t2_circle)
        for component in components:
            sc.plot(component[0], component[1], marker=markers[i], color=colors[i], markersize=1.5)
    
    maxvalue = np.amax(dataset)
    minvalue = np.amin(dataset)

    # print('maxvalue = ', maxvalue)
    # print('minvalue = ', minvalue)
    # print('t1 = ', t1)
    # print('t2 = ', t2)
    # print(minvalue - t1, maxvalue + t1)
    plt.axis('equal')
    plt.xlim((minvalue - t1, maxvalue + t1))
    plt.ylim((minvalue - t1, maxvalue + t1))
    # plt.axis('scaled')
    
    
    plt.savefig('Canopy.svg')
    plt.show()
    
def main():
    t1 = 0.6
    t2 = 0.4
    gc =Canopy(dataset)
    gc.setThreshold(t1, t2)
    canopies = gc.clustering()
    print('Get %s initial centers.' % len(canopies))
    showCanopy(canopies, dataset, t1, t2)
    
if __name__ == "__main__":
    main()

operation result