[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: MAHOUT-1214.patch removed the string input format, let's not include in this fix then. > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: (was: MAHOUT-1214.patch) > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: MAHOUT-1214.patch updated version, same uploaded to reviewboard > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: (was: MAHOUT-1214.patch) > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Commented] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13686529#comment-13686529 ] zhang da commented on MAHOUT-1214: -- i believe the dot product is a false alarm and the problem is in our patch. let me fix it and update the patch tonight. > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Commented] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13686355#comment-13686355 ] zhang da commented on MAHOUT-1214: -- thanks. Review Request #11931 > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Commented] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13686341#comment-13686341 ] zhang da commented on MAHOUT-1214: -- i uploaded the patch to the reviewboard, but it has to be assigned to someone or some group as reviewer before i can publish, who am i supposed to assign to? > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: MAHOUT-1214.patch patch uploaded > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Commented] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13682240#comment-13682240 ] zhang da commented on MAHOUT-1214: -- hi, I finish the patch against v0.8 head today, but somehow it breaks one of the DistributedLanczosSolver unit test case. Think need a day or two to fix that. i'll keep you posted. > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: (was: MAHOUT-1214.patch) > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: matrix_1, matrix_2 > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Updated] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] zhang da updated MAHOUT-1214: - Attachment: MAHOUT-1214.patch 1. removed the commented out codes 2. used the latest formatter 3. add in unit tests, TestAffinityMatrixGenericInputJob.java TestEigenSeedGenerator.java it's based on v0.7 branch > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: MAHOUT-1214.patch, matrix_1, matrix_2, > SpectralKMeans.patch > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
[jira] [Commented] (MAHOUT-1214) Improve the accuracy of the Spectral KMeans Method
[ https://issues.apache.org/jira/browse/MAHOUT-1214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13679340#comment-13679340 ] zhang da commented on MAHOUT-1214: -- regarding to point 2, I notice that the project(version 0.7 branch) is not 100% following the lucene coding style. I could format the files I have modified in eclipse, but that would introduce quite a number of unnecessary discrepancies. Is that ok? > Improve the accuracy of the Spectral KMeans Method > -- > > Key: MAHOUT-1214 > URL: https://issues.apache.org/jira/browse/MAHOUT-1214 > Project: Mahout > Issue Type: Improvement > Components: Clustering >Affects Versions: 0.7 > Environment: Mahout 0.7 >Reporter: Yiqun Hu >Assignee: Robin Anil > Labels: clustering, improvement > Fix For: 0.8 > > Attachments: matrix_1, matrix_2, SpectralKMeans.patch > > > The current implementation of the spectral KMeans algorithm (Andrew Ng. etc. > NIPS 2002) in version 0.7 has two serious issues. These two incorrect > implementations make it fail even for a very obvious trivial dataset. We have > implemented a solution to resolve these two issues and hope to contribute > back to the community. > # Issue 1: > The EigenVerificationJob in version 0.7 does not check the orthogonality of > eigenvectors, which is necessary to obtain the correct clustering results for > the case of K>1; We have an idea and implementation to select based on > cosAngle/orthogonality; > # Issue 2: > The random seed initialization of KMeans algorithm is not optimal and > sometimes a bad initialization will generate wrong clustering result. In this > case, the selected K eigenvector actually provides a better way to initalize > cluster centroids because each selected eigenvector is a relaxed indicator of > the memberships of one cluster. For every selected eigenvector, we use the > data point whose eigen component achieves the maximum absolute value. > We have already verified our improvement on synthetic dataset and it shows > that the improved version get the optimal clustering result while the current > 0.7 version obtains the wrong result. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators For more information on JIRA, see: http://www.atlassian.com/software/jira
