本文用python实现KNN算法
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 14 22:54:05 2019
"""
from sklearn import datasets
from collections import Counter # 为了做投票
from sklearn.model_selection import train_test_split
import numpy as np
# 导入iris数据
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2003)
def euc_dis(instance1, instance2):
"""
计算两个样本instance1和instance2之间的欧式距离
instance1: 第一个样本, array型
instance2: 第二个样本, array型
"""
# TODO
dist = np.sqrt(sum((instance1 - instance2)**2))
return dist
def knn_classify(X, y, testInstance, k):
"""
给定一个测试数据testInstance, 通过KNN算法来预测它的标签。
X: 训练数据的特征
y: 训练数据的标签
testInstance: 测试数据,这里假定一个测试数据 array型
k: 选择多少个neighbors?
"""
# TODO 返回testInstance的预测标签 = {0,1,2}
distances = [euc_dis(x, testInstance) for x in X]
kneighbors = np.argsort(distances)[:k]
count = Counter(y[kneighbors])
return count.most_common()[0][0]
# 预测结果。
predictions = [knn_classify(X_train, y_train, data, 3) for data in X_test]
correct = np.count_nonzero((predictions==y_test)==True)
print ("Accuracy is: %.3f" %(correct/len(X_test)))