手写代码是算法最好的实现机制：
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
"""
# 注意运算符的优先级
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}
y_distances=[euc_dis(x,testInstance) for x in X]
kn = np.argsort(y_distances)[:k]
count = Counter(y[kn])
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)))
不同K下的校验结果：
k = 3; Accuracy is:0.921
k = 5; Accuracy is:0.895
k = 7; Accuracy is:0.921
