机器学习-KNN

先存个码
# -- coding: utf-8 --
import numpy as np
import operator


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


def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    # 根据欧式距离计算训练集中每个样本到测试点的距离
    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances ** 0.5
    # 计算完所有点的距离后，对数据按照从小到大的次序排序
    sortedDistIndicies = distances.argsort()
    # 确定前k个距离最小的元素所在的主要分类，最后返回发生频率最高的元素类别
    classCount = {}
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

if __name__ == ‘__main__‘:
    group ,labels = createDataSet()
    # 输入[0,0]点计算分类
    result = classify0([0,0], group, labels, 3)
    print(result)

机器学习-KNN

原文：https://www.cnblogs.com/haimingv/p/12634163.html