# !/usr/bin/env python3 import matplotlib.pyplot as plt import numpy as np from math import sqrt from collections import Counter #随机生成10个元素的数组 x = np.array([0.19233859 ,0.18626021 ,0.34556073 ,0.39676747 ,0.53881673 ,0.41919451, 0.6852195 ,0.20445225, 0.87811744, 0.32738759]) * 10 X = x.reshape(10,1) #随机生成10个不同元素的数组 y = np.array([0.62978754 ,0.93461034 ,0.56664329 ,0.99947942 ,0.59558408 ,0.84467021 ,0.95667363 ,0.41492526, 0.47581411 ,0.12934113]) * 10 Y = y.reshape(10,1) data = np.hstack((X,Y)) target = np.array([0 ,0 ,1 ,0 ,0 ,1 ,1, 0 ,1, 1]) result = np.array([4.823 ,7.09]) # plt.scatter(X,Y,c=target) # plt.scatter(result[0],result[1],c='r') # plt.show() # 写法一 # distances = [] # for i in data: # distance = sqrt(np.sum((i - result) ** 2)) # distances.append(distance) distances = [ sqrt(np.sum((i - result) ** 2)) for i in data] # 求出了距离 print(distances) index = np.argsort(distances) k = 3 print(index[:k]) top = [ target[i] for i in index[:k]] # 投票 votes = Counter(top) #根据第二个元素从大到小倒叙排序 print(votes.most_common()[0][0]) # y = np.random(0, 10, 100) # plt.scatter(x,y) # plt.show()
使用scikit-learn框架
from sklearn.neighbors import KNeighborsClassifier import numpy as np #随机生成10个元素的数组 x = np.array([0.19233859 ,0.18626021 ,0.34556073 ,0.39676747 ,0.53881673 ,0.41919451, 0.6852195 ,0.20445225, 0.87811744, 0.32738759]) * 10 X = x.reshape(10,1) #随机生成10个不同元素的数组 y = np.array([0.62978754 ,0.93461034 ,0.56664329 ,0.99947942 ,0.59558408 ,0.84467021 ,0.95667363 ,0.41492526, 0.47581411 ,0.12934113]) * 10 Y = y.reshape(10,1) data = np.hstack((X,Y)) target = np.array([0 ,0 ,1 ,0 ,0 ,1 ,1, 0 ,1, 1]) result = np.array([4.823 ,7.09]) kNN_classifier = KNeighborsClassifier(n_neighbors=3) # 训练 kNN_classifier.fit(data, target) '''预测 预存数据需要传入一个矩阵''' print(kNN_classifier.predict(result.reshape(1,2))[0])
