todwang.blogspot.com

王元涛的Blog

Fwd: An interesting property of AUC. Get definition of AUC here. Http:/ en.wikipedia.org/wiki/Receiver operating characteristic. Property: if you flip ground truth only, or flip prediction only, you will get 1-original AUC as new AUC. Take original ROC curve as function of moving score threshold (x(s),y(s) =(fp(s)/N, tp(s)/P). Where fp(s) is false positive=number of cases predicted as positive under score threshold s but actually negative. N is number of negative cases and P is number of positive cases.

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Fwd: An interesting property of AUC. Get definition of AUC here. Http:/ en.wikipedia.org/wiki/Receiver operating characteristic. Property: if you flip ground truth only, or flip prediction only, you will get 1-original AUC as new AUC. Take original ROC curve as function of moving score threshold (x(s),y(s) =(fp(s)/N, tp(s)/P). Where fp(s) is false positive=number of cases predicted as positive under score threshold s but actually negative. N is number of negative cases and P is number of positive cases.

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王元涛的blog,wangyuantao@msn com,proof,so auc auc=1,enjoy,如果需要频繁检查某个数是否是素数，可以把查过的素数缓存下来,class primecheckerwithcache,def init self,selfprimeset=set 2 3,selfmaxtested=4,cache hit,selfprimeset add i,selfmaxtested=test,正确性证明：,不变量：primeset中是不超过maxtested的全部素数,body

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王元涛的Blog | todwang.blogspot.com Reviews

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Fwd: An interesting property of AUC. Get definition of AUC here. Http:/ en.wikipedia.org/wiki/Receiver operating characteristic. Property: if you flip ground truth only, or flip prediction only, you will get 1-original AUC as new AUC. Take original ROC curve as function of moving score threshold (x(s),y(s) =(fp(s)/N, tp(s)/P). Where fp(s) is false positive=number of cases predicted as positive under score threshold s but actually negative. N is number of negative cases and P is number of positive cases.

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王元涛的Blog: Fwd: Copy path in context menu

http://todwang.blogspot.com/2013/12/fwd-copy-path-in-context-menu.html

Fwd: Copy path in context menu. Copy, save and click to add into regedit. Add Copy as Path Option.reg. Windows Registry Editor Version 5.00. Created by Vishal Gupta for AskVG.com. HKEY CLASSES ROOT * shell Copy Full Path]. HKEY CLASSES ROOT * shell Copy Full Path command]. Cmdexe /c echo %1 clip. HKEY CLASSES ROOT * shell Copy Filename]. HKEY CLASSES ROOT * shell Copy Filename command]. Cmd /c for % i in ( %1 ) do @echo % nxi clip. HKEY CLASSES ROOT * shell Copy Dir Path]. Cmdexe /c echo %1 clip.

2

王元涛的Blog: 五月 2012

http://todwang.blogspot.com/2012_05_01_archive.html

遍历长度为n的所有可能的0-1向量（N=2 n个），使得相邻输出的两个向量可以通过某一个下标取反得到。 For step in xrange(1,N):. For p in xrange(1,n 1):. If step%pow(2,p)= pow(2,p-1):. 虽然两重循环，看似时间复杂度为O(nN)，其实有内层循环有1/2的概率第一次if就break了，有1/4的概率第2次就break了，。。。有1/2 k的概率第k次就break了。而级数sum{k/2 k}收敛，有上界，上界与n无关。故均摊内层循环时间为O(1)，总时间就是O(N)了。 Google App Engine需要装SDK配yaml，AWS需要有git插件和信用卡绑定。有一天晚上，老婆说想弄个网站，搞基于数据分析的新闻调查。我觉得这个交叉学科的小项目很有意思，就开始忙活起来。俗话说，名不正，言不顺。从国内某个从来没听说过的小破网站买了一个看起来很权威的域名 www.data-journalist.com. 订阅：帖子 (Atom). Simple模板. 由 Blogger.

3

王元涛的Blog: 八月 2012

http://todwang.blogspot.com/2012_08_01_archive.html

大概要从trillion量级的时间序列中找到query的时间序列子列，当然定义了两个时间序列子列之间的距离，是最近邻搜素。实验数据大多来自生物医学，比如DNA序列，脑电波等。用到了一些技术：z-normalization，每个点减均值除方差。还有是用两条平行的时间序列将目标时间序列夹住，夹得越紧越好，当然被夹的序列抖动比较厉害，夹人的比较平滑。然后用夹人的序列的特征刻画被夹的序列的特征。还有各种剪枝，由于距离定义的可加性，如果当前子列的距离已经大于已知的最短距离，就可以放弃当前位置，换个地方重新计算。采样技术，将时间序列连续地采一部分出来，得到了一个较短的子列，如果子列距离就已经超了，这个样本所在的总体就不用考虑了。还有各种并行化技术，比如有的进程从左往右找，有的进程从右往左找。不明白索引结构是怎么样的，难道都是这种很苦逼的遍历么，这叫哪门子搜索啊。 Display ads做的就是browsing pattern。个人感觉他们Engineering做得很好，有系统有效的Monitoring，运维的人的成本很低。以前人们建模通常把感兴趣的概率作为model score，作者认...

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王元涛的Blog: 七月 2012

http://todwang.blogspot.com/2012_07_01_archive.html

几天不写js就难受，正好论坛页面改版，原来的脚本不能用了。改了一下，还是通过自动访问其他用户的主页来赚K币。有两个很好用的技巧：异步加载外部脚本；用jQuery Attribute Selector获得所需页面元素。 Name KLD K coins. Include http:/ www.kldjy.com/home.php#3. Description open 10 space. Function ingest(precondition, url, dst){. Precondition() {setTimeout(function(){ingest(precondition, url, dst)},100);return}. Script node.setAttribute(type, text/javascript);. Script node.setAttribute(src, url). Ingest(function(){return true}, http:/ code.jquery.com/jquery-git.js. For(i=0;i ls.length; i).

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王元涛的Blog: 三月 2012

http://todwang.blogspot.com/2012_03_01_archive.html

昨天午饭后又是没人愿意去刷碗。于是老婆说："这次换个方法掷骰子吧。如果我连掷三次都没掷到刷碗，你就去刷，否则我去"。我说好。我算了一下，说，不行，要4次。然后。。。我就去刷碗了。一天同事考我一个C 的问题，关于STL容器的拷贝构造。 Std: vector int v ;. Av push back(9);. Printf(%d %d %d %d n, a.v .size(), b.v .size(), a.v [0], b.v [0]);. Av [0] = 8;. Printf(%d %d %d %d n, a.v .size(), b.v .size(), a.v [0], b.v [0]);. Https:/ www.kddcup2012.org/c/kddcup2012-track2/. 广告的静态属性，引用 purchasedkeyword tokensid.txt. 8203;花了一晚上下载了2G的数据文件，解压缩后 11G. 0 1 9279176529507935999 10810874 23649 1 1 1647439 373861 816959 727055 766180.

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xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 五月 2009

http://xlnetflixprize.blogspot.com/2009_05_01_archive.html

Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。订阅：帖子 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Netflix Grand Prize technical presentation. Follow me on Twitter. 中关村, 北京, China.

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 关于评分分布的思考

http://xlnetflixprize.blogspot.com/2009/04/blog-post.html

Xlvector's solution of NetflixPrize. 对于一部电影，它被一堆人评分了，这些评分具有一些属性，均值，方差，偏差，等等。目前我在设计一个新的模型，对于每一部电影，我们计算他的平均得分，记为m(i)，那么对于一个用户-电影(u,i)，. R(u,i) = mu bu bi dot(p[u], q[i]) * h[k]. 其中，k = m(i). 这个模型可以不断的变换，比如我们也可以令k = var(i)，也就是说. Dot(p[u], q[i]) * h[k]. 表示了用户u对具有k属性的电影i的看法。我仍然用梯度法训练这个模型。结果稍后公布。 Other than SVD, what other algorithms are you currently using? KNN, clustering, global effects. What does FLC stand for? FLC融合 you mentioned in March) Is this a special Chinese terminology? 订阅：帖子评论 (Atom).

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 四月 2009

http://xlnetflixprize.blogspot.com/2009_04_01_archive.html

Xlvector's solution of NetflixPrize. Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors? By SVD model, we can calculate latent factors for users and items. p(u) is latent factor for u, while q(i) is latent factor for i. S(u,v) = f(p(u), p(v)? 其中，k = m(i). 在预测某个电影的评分时，...

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 推荐两篇关于RBM的中文介绍

http://xlnetflixprize.blogspot.com/2009/04/rbm.html

Xlvector's solution of NetflixPrize. Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Netflix Grand Prize technical presentation. Follow me on Twitter. 中关村, 北京, China. Clustering items and users by latent factors? An improved item-based KNN predictor.

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: clustering items and users by latent factors?

http://xlnetflixprize.blogspot.com/2009/04/clustering-items-and-users-by-latent.html

Xlvector's solution of NetflixPrize. Clustering items and users by latent factors? By SVD model, we can calculate latent factors for users and items. p(u) is latent factor for u, while q(i) is latent factor for i. Recently, I thought about calculate user similarity by latent factor,. S(u,v) = f(p(u), p(v)? I am testing this idea now, and I hope this idea can improve prediction accuracy. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Follow me on Twitter.

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: An improved item-based KNN predictor

http://xlnetflixprize.blogspot.com/2009/04/improved-item-based-knn-predictor.html

Xlvector's solution of NetflixPrize. An improved item-based KNN predictor. Today, I revise the item-based KNN predictor and get RMSE = 0.8730 in quiz with other 39 predictors. The classical item-based kNN will firstly calculate similarity between item i and j by：. The, the rating r(u,i) will be predicted by:. However, I revise this predictor by:. This predictor can produce more accurate prediction by choosing adequate alpha. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Follow me on Twitter.

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 程序并行化 multi-thread

http://xlnetflixprize.blogspot.com/2009/04/multi-thread.html

Xlvector's solution of NetflixPrize. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Netflix Grand Prize technical presentation. Follow me on Twitter. 中关村, 北京, China. Clustering items and users by latent factors? An improved item-based KNN predictor.

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: 三月 2009

http://xlnetflixprize.blogspot.com/2009_03_01_archive.html

Xlvector's solution of NetflixPrize. 最近一直在写一篇关于如何使用时间信息的论文，准备投4月份的WI，今天将论文中的一些结果加入到以前的预测器集合中，RMSE从 0.8770. 12290;以前的预测器集合中已经有了很多考虑时间信息的预测器，不过这个这个预测器效果是最好的。根据我的估计，时间信息对最终结果的影响大概在0.007 - 0.01之间，在我的论文中将会阐述这个结果的由来。 Global Effects in Netflix Prize. 忙活了几天，终于把那个global effects给搞定了，不容易啊，嘿嘿. The BigChaos Solution to the Netflix Prize 2008. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. B0(u,i) = global mean. X(u,i) = average(i) = sum u {r(u,i) - b0(u,i)} / n(i). X(u,i) =...

xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize: Top40

http://xlnetflixprize.blogspot.com/2009/04/top40.html

Xlvector's solution of NetflixPrize. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Netflix Grand Prize technical presentation. Follow me on Twitter. 中关村, 北京, China. Clustering items and users by latent factors? An improved item-based KNN predictor.

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Mot de passe :. J'ai oublié mon mot de passe. Tod and son blog. Basket, sauce, famille, son, notre vie koi.skyblog cool fait par un couple cool pour les gens cool. Mise à jour :. Abonne-toi à mon blog! Voila comme sa jsui sur kiora tt lmonde parceke ien a il ose pa metr papa maman. Ou poster avec :. Retape dans le champ ci-dessous la suite de chiffres et de lettres qui apparaissent dans le cadre ci-contre. Posté le dimanche 16 avril 2006 16:22. Modifié le mardi 24 juillet 2007 04:20. Ou poster avec :.

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王元涛的Blog

Fwd: An interesting property of AUC. Get definition of AUC here. Http:/ en.wikipedia.org/wiki/Receiver operating characteristic. Property: if you flip ground truth only, or flip prediction only, you will get 1-original AUC as new AUC. Take original ROC curve as function of moving score threshold (x(s),y(s) =(fp(s)/N, tp(s)/P). Where fp(s) is false positive=number of cases predicted as positive under score threshold s but actually negative. N is number of negative cases and P is number of positive cases.

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