KNN学习笔记

下面，我们用Python实现一个简单的例子

import numpy as pd

import operator

# 创建数据集

def createDataSet():
    group = np.array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
    labels = [‘A‘, ‘A‘, ‘B‘, ‘B‘]
    return group, labels

# k-近邻算法
def classify0(inx, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    # np.tile(inx, (dataSetSize, 1)), 先沿着x轴的方向复制1， 再沿y轴复制dataSetSize
    diffMat = np.tile(inx, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances ** 0.5
    # 获取distances从小到大的索引
    sortedDistIndicies = distances.argsort()
    classCount = {}
    # 统计距离最小的k个标签出现的次数
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
    # key=operator.itemgetter(1): 按照值进行排序，降序
    sortedclassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedclassCount[0][0]

if ‘__name__‘ == ‘__main__‘:
　　group, labels = createDataSet()

　　print(classify0([0, 0], group, labels, 3)) # B

**刚刚开始学习，如有错误还请大神可以帮忙指正，更多例子可以参考《机器学习实战》这本书。