<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[allen_work的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/allen_work</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; allen_work]]></copyright><item><title><![CDATA[神经网络的底层实现]]></title><link>https://blog.youkuaiyun.com/allen_work/article/details/132065105</link><guid>https://blog.youkuaiyun.com/allen_work/article/details/132065105</guid><author>allen_work</author><pubDate>Wed, 02 Aug 2023 16:14:32 +0800</pubDate><description><![CDATA[【代码】神经网络的底层实现。]]></description><category></category></item><item><title><![CDATA[聚类K_means的底层实现]]></title><link>https://blog.youkuaiyun.com/allen_work/article/details/132041572</link><guid>https://blog.youkuaiyun.com/allen_work/article/details/132041572</guid><author>allen_work</author><pubDate>Tue, 01 Aug 2023 14:42:59 +0800</pubDate><description><![CDATA[dist = np.sqrt(((x-c)**2).sum(axis=1)) # 欧式距离 计算公式。# 如果计算的质心与新的中心点位置一样，质心不需要在移动位置了。# 求归属 判断每个样本点距离那个质心更近。# 展示处理之前的样本点和质心的距离。# 展示处理之后的样本点和质心的距离。#计算每个样本到质心的距离。# 生成样本点==>特征。]]></description><category></category></item></channel></rss>