[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=410121&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-410121 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 26/Mar/20 07:34 Start Date: 26/Mar/20 07:34 Worklog Time Spent: 10m Work Description: chentao106 commented on pull request #129: #MATH-1509: Add missing documentation to class MiniBatchKMeansCluster… URL: https://github.com/apache/commons-math/pull/129 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 410121) Time Spent: 1h 40m (was: 1.5h) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 1h 40m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=410120&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-410120 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 26/Mar/20 07:34 Start Date: 26/Mar/20 07:34 Worklog Time Spent: 10m Work Description: chentao106 commented on issue #129: #MATH-1509: Add missing documentation to class MiniBatchKMeansCluster… URL: https://github.com/apache/commons-math/pull/129#issuecomment-604275377 Replace by another PR This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 410120) Time Spent: 1.5h (was: 1h 20m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 1.5h > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=409613&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-409613 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 25/Mar/20 16:11 Start Date: 25/Mar/20 16:11 Worklog Time Spent: 10m Work Description: asfgit commented on pull request #132: MATH-1509: Add missing documentation to class ImprovementEvaluator URL: https://github.com/apache/commons-math/pull/132 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 409613) Time Spent: 1h 20m (was: 1h 10m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 1h 20m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=409577&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-409577 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 25/Mar/20 15:24 Start Date: 25/Mar/20 15:24 Worklog Time Spent: 10m Work Description: coveralls commented on issue #132: MATH-1509: Add missing documentation to class ImprovementEvaluator URL: https://github.com/apache/commons-math/pull/132#issuecomment-603903887 [](https://coveralls.io/builds/29609723) Coverage increased (+0.005%) to 90.553% when pulling **01227337f8d6645550a9559bef1a57297feab7b6 on chentao106:ImprovementEvaluatorDoc** into **6b0395898e9469fda20f011ded8dce3f9d0df907 on apache:master**. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 409577) Time Spent: 1h 10m (was: 1h) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 1h 10m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=409548&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-409548 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 25/Mar/20 14:47 Start Date: 25/Mar/20 14:47 Worklog Time Spent: 10m Work Description: chentao106 commented on pull request #132: MATH-1509: Add missing documentation to class ImprovementEvaluator URL: https://github.com/apache/commons-math/pull/132 Add missing documentation to class ImprovementEvaluator This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 409548) Time Spent: 1h (was: 50m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 1h > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=408538&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-408538 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 24/Mar/20 04:28 Start Date: 24/Mar/20 04:28 Worklog Time Spent: 10m Work Description: coveralls commented on issue #129: #MATH-1509: Add missing documentation to class MiniBatchKMeansCluster… URL: https://github.com/apache/commons-math/pull/129#issuecomment-603007764 [](https://coveralls.io/builds/29569884) Coverage increased (+0.008%) to 90.556% when pulling **e5fb5e16a25fd408f673eeb5c257c8bdce715f84 on chentao106:MiniBatchImprovementEvaluator** into **6b0395898e9469fda20f011ded8dce3f9d0df907 on apache:master**. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 408538) Time Spent: 50m (was: 40m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 50m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=408529&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-408529 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 24/Mar/20 04:18 Start Date: 24/Mar/20 04:18 Worklog Time Spent: 10m Work Description: chentao106 commented on pull request #129: #MATH-1509: Add missing documentation to class MiniBatchKMeansCluster… URL: https://github.com/apache/commons-math/pull/129 Add missing documentation to class MiniBatchKMeansCluster.ImprovementEvaluator. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 408529) Time Spent: 40m (was: 0.5h) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 40m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=407613&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-407613 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 22/Mar/20 15:11 Start Date: 22/Mar/20 15:11 Worklog Time Spent: 10m Work Description: asfgit commented on pull request #128: #MATH-1509: Implement the MiniBatchKMeansClusterer. URL: https://github.com/apache/commons-math/pull/128 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 407613) Time Spent: 0.5h (was: 20m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 0.5h > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=407536&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-407536 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 22/Mar/20 04:19 Start Date: 22/Mar/20 04:19 Worklog Time Spent: 10m Work Description: coveralls commented on issue #128: #MATH-1509: Implement the MiniBatchKMeansClusterer. URL: https://github.com/apache/commons-math/pull/128#issuecomment-602145882 [](https://coveralls.io/builds/29527552) Coverage increased (+0.04%) to 90.559% when pulling **cd7df89611d0d6e60f0133bb155894e391c8b3f8 on chentao106:feature-minibatchkmeans++** into **22373aeb76811aae77f581143e9fed34580316eb on apache:master**. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 407536) Time Spent: 20m (was: 10m) > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 20m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Work logged] (MATH-1509) Implement the MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=407535&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-407535 ] ASF GitHub Bot logged work on MATH-1509: Author: ASF GitHub Bot Created on: 22/Mar/20 04:09 Start Date: 22/Mar/20 04:09 Worklog Time Spent: 10m Work Description: chentao106 commented on pull request #128: #MATH-1509: Implement the MiniBatchKMeansClusterer. URL: https://github.com/apache/commons-math/pull/128 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking --- Worklog Id: (was: 407535) Remaining Estimate: 0h Time Spent: 10m > Implement the MiniBatchKMeansClusterer > -- > > Key: MATH-1509 > URL: https://issues.apache.org/jira/browse/MATH-1509 > Project: Commons Math > Issue Type: New Feature >Reporter: Chen Tao >Priority: Major > Attachments: compare.png, intensive-data-comparsion-badcase.png, > intensive-data-comparsion.png, random-data-comparison.png > > Time Spent: 10m > Remaining Estimate: 0h > > MiniBatchKMeans is a fast clustering algorithm, > which use partial points in initialize cluster centers, and mini batch in > training iterations. > It can finish in few seconds on clustering millions of data, and has few > differences between KMeans. > I have implemented it by Kotlin in my own project, and I'd like to contribute > the code to Apache Commons Math, of course in java. > My implemention is base on Apache Commons Math3, refer to Python > sklearn.cluster.MiniBatchKMeans > Thought test I found it works well on intensive data, significant performance > improvement and return value has few difference to KMeans++, but has many > difference on sparse data. > > Below is the comparation of my implemention and KMeansPlusPlusClusterer > !compare.png! > > I have created a pull request on > [https://github.com/apache/commons-math/pull/117], for reference only. -- This message was sent by Atlassian Jira (v8.3.4#803005)
