KNN_predict计算方差的问题

[Edward-hb]

            这里的i在计算方差的时候是没有变化的计算的方差没有遍历所有的。

            i = 0
    for p in self.labeled_hudongList:  # 预先计算存储各分量相似度
        if p.title == item.title:   # 如果训练集已经有，直接返回label
            return p.label
        title_simi.append(self.get_title_simi(p, item))
        openTypeList_simi.append(self.get_openTypeList_simi(p, item))
        baseInfoKeyList_simi.append(self.get_baseInfoKeyList_simi(p, item))
        baseInfoValueList_simi.append(self.get_baseInfoValueList_simi(p, item))

        mean[0] += title_simi[i]
        mean[1] += openTypeList_simi[i]

        mean[2] += baseInfoKeyList_simi[i]
        maxx[2] = max(maxx[2],baseInfoKeyList_simi[i])
        minn[2] = min(minn[2],baseInfoKeyList_simi[i])

        mean[3] += baseInfoValueList_simi[i]
        maxx[3] = max(maxx[3],baseInfoValueList_simi[i])
        minn[3] = min(minn[3],baseInfoValueList_simi[i])

        i += 1

    for i in range(4):
        mean[i] /= len(self.labeled_hudongList)

    for p in self.labeled_hudongList: # 计算方差
        var[0] += (title_simi[i]-mean[0])*(title_simi[i]-mean[0])
        var[1] += (openTypeList_simi[i]-mean[1])*(openTypeList_simi[i]-mean[1])
        var[2] += (baseInfoKeyList_simi[i]-mean[2])*(baseInfoKeyList_simi[i]-mean[2])
        var[3] += (baseInfoValueList_simi[i]-mean[3])*(baseInfoValueList_simi[i]-mean[3])

修改为：

我这里修改了变量名所以和源码略有不同 for i in range(len(self.labeled_hudongList)): title_simi[i] = (title_simi[i] - mean[0]) / stand[0] openTypeList_simi[i] = (openTypeList_simi[i] - mean[1]) / stand[1] baseInfoKeyList_simi[i] = (baseInfoKeyList_simi[i] - mean[2]) / stand[2] baseInfoValueList_simi[i] = (baseInfoValueList_simi[i] - mean[3]) / stand[3]