16 knn.py

#import operator
#import numpy as np

## trainData - 训练集，testData - 测试集，labels - 分类
#def knn(trainData, testData, labels, k):
#    # 计算训练样本的行数
#    rowSize = trainData.shape[0]
#    # 计算训练样本和测试样本的差值
#    diff = np.tile(testData, (rowSize, 1)) - trainData
#    # 计算差值的平方和
#    sqrDiff = diff ** 2
#    sqrDiffSum = sqrDiff.sum(axis=1)
#    # 计算距离
#    distances = sqrDiffSum ** 0.5
#    # 对所得的距离从低到高进行排序
#    sortDistance = distances.argsort()
    
#    count = {}
    
#    for i in range(k):
#        vote = labels[sortDistance[i]]
#        count[vote] = count.get(vote, 0) + 1
#    # 对类别出现的频数从高到低进行排序
#    sortCount = sorted(count.items(), key=operator.itemgetter(1), reverse=True)
    
#    # 返回出现频数最高的类别
#    return sortCount[0][0]

#trainData = np.array([[5, 1], [4, 0], [1, 3], [0, 4]])
#labels = ['A', 'A', 'B', 'B']
#testData = [3, 2]
#X = knn(trainData, testData, labels, 3)
#print(X)

from sklearn.neighbors import KNeighborsClassifier

X = [[5, 1], [4, 0], [1, 3], [0, 4]]
y = ['A', 'A', 'B', 'B']

neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y) 
print(neigh.predict([[3, 2]]))