Knn算法(约会问题应用)

内容 = 机器学习书籍内容 + 个人实践过程

海伦约会问题
我们要输入一个人的特征，看看这个人是否适合做海伦的约会对象
(虽然这个看起来好像有点偏应用了，但是稍微懂点就知道，还有谁恋爱的时候统计这些数据？？？但是也有学习的价值不是？(坏笑))
这里相比于入门版的Knn用到了文件流
点此可以看之前那个入门版KNN的代码
样本特征取三个值
1.每周获得的飞行常客里程数
2.玩视频游戏所耗时间比例
3.每周消费的冰淇淋公升数
（要是有人真的在估计的话，也可以自己去找些因子来试一下emm，要是真靠这个算法找到真爱了，麻烦告知一声好吧！！）

代码比较长，我知道初学者肯定都不愿意看完(我也是)，但是推荐先把上面的入门版先看完再来看这个。(里面有配套讲解，可能会有较大帮助)
代码如下

from numpy import *import operatordef createDataSet():    group = array([[0.1, 0.1], [0, 0], [1, 0.9]])    lables = ['B', 'B', 'A']    return group, lablesdef classify0(inX, dataSet, labels, k):    # k means how many you need to get your P(x)    dataSetSize = dataSet.shape[0]    DiffMat = tile(inX, (dataSetSize, 1)) - dataSet    sqDiffMat = DiffMat**2    sqDistances = sqDiffMat.sum(axis=1)    distances = sqDistances**0.5  # 到这里求解了欧式距离(并构成了一个ndarray)    sortedDistances = distances.argsort()    classCount = {}    for i in range(k):        voteIlabel = labels[sortedDistances[i]]        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1    # 选出了距离最小的k个点，并且做了一个简单的统计    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) #按照第一个(从0开始数)进行排序    return sortedClassCount[0][0]   # 返回的出现次数最多的那个标签def file2Matrix(filename):    fr = open(filename)    arrayOLines = fr.readlines()    numberOfLines = len(arrayOLines)    returnMat = zeros((numberOfLines, 3))    classLabelVector = []    index = 0    for line in arrayOLines:        line = line.strip()  # 去除首末的空格        listFromLine = line.split('\t')  # 通过换行符做切片，主要是去掉那个换行符        returnMat[index, :] = listFromLine[0:3]  # 将整个行东西直接粘贴过来，注意，这里自动完成了类型转换的问题, 前三列        index += 1        classLabelVector.append(listFromLine[-1])  # 最后一个那个标签放到这列表中    return returnMat, classLabelVectorif __name__ == "__main__":    group, labels = createDataSet()    x = [1, 1]    print(classify0(x, group, labels, 2))    group, labels = file2Matrix("你自己把文件放到哪个地址了就自己输入")    x = [3000, 5, 0.9]    print(classify0(x, group, labels, 5))

注意到倒数第二行那个地址，本来是我的自己写的东西，然后，你要是想做的话，可以自己写一份数据或者是用我上传的一个数据集，自己去做做玩一玩
记得改写那个地址的时候，一定要写具体一点
如：
D:/DataSet.txt
上面，这样
有什么问题可以在评论区问。
//数据

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