Anant, I got rid of those increment/ decrements functions and now code is much cleaner. Please check. All your comments have been looked after.
https://github.com/codeAshu/Outlier-Detection-with-AVF-Spark/blob/master/OutlierWithAVFModel.scala _Ashu [https://avatars3.githubusercontent.com/u/5406975?v=2&s=400]<https://github.com/codeAshu/Outlier-Detection-with-AVF-Spark/blob/master/OutlierWithAVFModel.scala> Outlier-Detection-with-AVF-Spark/OutlierWithAVFModel.scala at master · codeAshu/Outlier-Detection-with-AVF-Spark · GitHub Contribute to Outlier-Detection-with-AVF-Spark development by creating an account on GitHub. Read more...<https://github.com/codeAshu/Outlier-Detection-with-AVF-Spark/blob/master/OutlierWithAVFModel.scala> ________________________________ From: slcclimber [via Apache Spark Developers List] <ml-node+s1001551n9037...@n3.nabble.com> Sent: Friday, October 31, 2014 10:09 AM To: Ashutosh Trivedi (MT2013030) Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection You should create a jira ticket to go with it as well. Thanks On Oct 30, 2014 10:38 PM, "Ashutosh [via Apache Spark Developers List]" <[hidden email]</user/SendEmail.jtp?type=node&node=9037&i=0>> wrote: ?Okay. I'll try it and post it soon with test case. After that I think we can go ahead with the PR. ________________________________ From: slcclimber [via Apache Spark Developers List] <ml-node+[hidden email]<http://user/SendEmail.jtp?type=node&node=9036&i=0>> Sent: Friday, October 31, 2014 10:03 AM To: Ashutosh Trivedi (MT2013030) Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection Ashutosh, A vector would be a good idea vectors are used very frequently. Test data is usually stored in the spark/data/mllib folder On Oct 30, 2014 10:31 PM, "Ashutosh [via Apache Spark Developers List]" <[hidden email]<http://user/SendEmail.jtp?type=node&node=9035&i=0>> wrote: Hi Anant, sorry for my late reply. Thank you for taking time and reviewing it. I have few comments on first issue. You are correct on the string (csv) part. But we can not take input of type you mentioned. We calculate frequency in our function. Otherwise user has to do all this computation. I realize that taking a RDD[Vector] would be general enough for all. What do you say? I agree on rest all the issues. I will correct them soon and post it. I have a doubt on test cases. Where should I put data while giving test scripts? or should i generate synthetic data for testing with in the scripts, how does this work? Regards, Ashutosh ________________________________ If you reply to this email, your message will be added to the discussion below: http://apache-spark-developers-list.1001551.n3.nabble.com/MLlib-Contributing-Algorithm-for-Outlier-Detection-tp8880p9034.html To unsubscribe from [MLlib] Contributing Algorithm for Outlier Detection, click here. 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