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AirPods开箱

　　懒癌发作，好久没更博客了，发一篇开箱（伪）凑数吧……

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类似“超强智商”之类的答题游戏作弊思路
不务正业
5 分钟 读完 (大约 726 个字)

类似“超强智商”之类的答题游戏作弊思路

　　今年年初时答题游戏比较火，对于一些比较热门的游戏，已经有不少成熟的辅助程序了。然而这个实验室同学转给我的这个小程序，似乎热度还达不到做辅助的标准，而题目又比较恶心，自己答起来比较费劲，所以在这里探讨一下作弊的思路。

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将SMILES转成InChiKey
心得体会
4 分钟 读完 (大约 654 个字)

将SMILES转成InChiKey

　　最近在项目中需要将SMILES转成InChiKey，通常可以在OpenBabelGUI中操作完成，但是对于大量数据在OpenBabelGUI中操作就有些不方便且容易失误。这里记录一下我解决这个问题的过程。

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Aero15x开箱
昔情难追
4 分钟 读完 (大约 565 个字)

Aero15x开箱

　　之前一直在gs65和aero15x中摇摆不定，在已经付了gs65的定金后还是选择了aero15x。主要原因就是aero15x比较好拆，实在不想装个内存、固态或者清灰都跑一趟售后，于是就损失了100定金~ \ (╯-╰) /

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FP-growth算法
学习笔记
34 分钟 读完 (大约 5160 个字)

FP-growth算法

　　你用过搜索引擎吗？输入一个单词或者一个单词的一部分，搜索引擎就会自动补全查询词项。用户甚至事先都不知道搜索引擎推荐的东西是否存在，反而会去查找推荐词项。那么这些推荐词项是如何被搜索引擎找到的？那是因为研究人员使用了FP-growth算法，它是基于Apriori构建的，但完成相同任务时将数据集存储在一个特定的称作FP树的结构之后发现频繁项集或频繁项对，即常在一起出现的元素项的集合FP树。这是一种比Apriori执行速度更快的算法，能高效地发现频繁项集，但不能用于发现关联规则。

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记录实用的python库
心得体会
8 分钟 读完 (大约 1183 个字)

记录实用的python库

　　本文专门用来记录一下python中一些好用方法/库，可以在日常使用中提高效率。

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金缕曲
词海拾珍
12 分钟 读完 (大约 1798 个字)

金缕曲

　　搬运的旧文之二：

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临终歌
词海拾珍
4 分钟 读完 (大约 599 个字)

临终歌

　　庆祝博客开通，搬篇旧文凑个数：

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Apriori算法
学习笔记
38 分钟 读完 (大约 5689 个字)

Apriori算法

　　很多人都听说过“尿布和啤酒”的故事：据说，美国中西部的一家连锁店发现，男人们去超市买尿布的同时，往往会顺便给自己购买啤酒。由此，卖场开始把啤酒和尿布摆放在相同区域，让男人可以同时找到这两件商品，从而获得了很好的销售收入。虽然并没有商店真的把这两样东西放在一起，但是很多商家确实将大家经常购买的物品放在一起捆绑销售以鼓励大家购买。那么我们如何在繁杂的数据发现这些隐含关系呢？这就需要关联分析（association analysis），本文所讨论的Apriori便是其中一种关联分析算法。

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