机器学习1 KNN文本分类

思想
1.找到与数据最相近K个数据（根据余弦相似度）
2.分别找出K条数据的类别，同类别相加，得到最大值，则该类别为测试数据的所属类。

代码

# encoding=utf-8
from pylab import *
reload(sys)


def createDataSet():
    group = [[1.0, 1.1], [2.0, 2.1], [1.1, 1.2], [2.2, 2.2]]
    label = ['A', 'B', 'A', 'B']
    return group, label


def init(testdata, k):
    dataSet, labels = createDataSet()
    fg = plt.figure()
    ax = fg.add_subplot(111)
    index = 0
    for point in dataSet:
        if (labels[index] == 'A'):
            plt.scatter(point[0], point[1])
            plt.an
        elif (labels[index] == 'B'):
            plt.scatter(point[0], point[1])
        index += 1
    res = classify(testdata, dataSet, labels, k)
    plt.scatter(testdata[0][0],testdata[0][1])
    print res+"       pppppp"
    plt.show()


# 余弦距离
def cosdistance(vector1, vector2):
    return dot(vector1, vector2) / (linalg.norm(vector1) * linalg.norm(vector2))


def classify(testdata, trainSet, listClasses, k):
    res = array(zeros(k))
    reslabel = array(zeros(k))
    for label, data in enumerate(trainSet):
        for tdata in testdata:
            dis = cosdistance(tdata, data)
            for index, d in enumerate(res):
                if dis < d:
                    res[index] = dis
                    reslabel[index] = listClasses[label]
    freslabel = array(zeros(len(listClasses)))
    for index, cl in enumerate(listClasses):
        for fcl in reslabel:
            if cl == fcl:
                freslabel[index] += 1
    maxValue = freslabel[0]
    maxIndex = 0
    for index,val in enumerate(freslabel):
        if val > maxValue:
            maxIndex = index
    return listClasses[maxIndex]
if __name__ == '__main__':
    init([[1.5,1.3]],3)
`