KNN算法练习

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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 10 11:02:33 2018

@author: Administrator
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(0)
x=np.sort( 5*np.random.rand(40,1),axis=0)
y=np.sin(x).ravel()
y[::5]=0.5-np.random.rand(8)
t=np.linspace(0,5,500)[:,np.newaxis]

from sklearn.neighbors import KNeighborsRegressor

n=5
for i,weights in enumerate( ['uniform','distance']):
    knn = KNeighborsRegressor(n_neighbors=n,weights=weights)
    pre=knn.fit(x,y).predict(t)
    
    plt.figure(figsize=(8,8))
    plt.subplot(2,1,i+1)
    plt.scatter(x,y,c='k',label='data')
    plt.plot(t,pre,c='g',label='predict')
    plt.title('KNeighborsRegressor(k=%d,weights=%s)'%(n,weights))
    plt.legend()
plt.show()