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• python-cluster 1.1.1b3 file released: cluster-1.1.1b3.win32.exe

  1.1.1b3 - Fixed bug #1727558 - Some more unit-tests - ValueError changed to ClusteringError where appropriate 1.1.1b2 - Fixed bug #1604859 (thanks to Willi Richert for reporting it) 1.1.1b1 - Applied patch [1535137] (thanks ajaksu) --> Topology output supported --> data and raw_data are now properties. 1.1.0b1 - KMeans Clustering implemented for simple numeric tuples. Data in the form [(1,1), (2,1), (5,3), ...] can be clustered. Usage: >>> from cluster import KMeansClustering >>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...]) >>> clusters = cl.getclusters(2) the method "getclusters" takes the amount of clusters you would like to have as parameter. Only numeric values are supported in the tuples. The reason for this is that the "centroid" method which I use, essentially returns a tuple of floats. So you will lose any other kind of metadata. Once I figure out a way how to recode that method, other types should be possible. 1.0.1b2 - Optimized calculation of the hierarchical clustering by using the fact, that the generated matrix is symmetrical. 1.0.1b1 - Implemented complete-, average-, and uclus-linkage methods. You can select one by specifying it in the constructor, for example: cl = HierarchicalClustering(data, distfunc, linkage='uclus') or by setting it before starting the clustering process: cl = HierarchicalClustering(data, distfunc) cl.setLinkageMethod('uclus') cl.cluster() - Clustering is not executed on object creation, but on the first call of "getlevel". You can force the creation of the clusters by calling the "cluster" method as shown above.

  posted 763 days ago

• python-cluster 1.1.1b3 file released: cluster-1.1.1b3.tar.gz

  1.1.1b3 - Fixed bug #1727558 - Some more unit-tests - ValueError changed to ClusteringError where appropriate 1.1.1b2 - Fixed bug #1604859 (thanks to Willi Richert for reporting it) 1.1.1b1 - Applied patch [1535137] (thanks ajaksu) --> Topology output supported --> data and raw_data are now properties. 1.1.0b1 - KMeans Clustering implemented for simple numeric tuples. Data in the form [(1,1), (2,1), (5,3), ...] can be clustered. Usage: >>> from cluster import KMeansClustering >>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...]) >>> clusters = cl.getclusters(2) the method "getclusters" takes the amount of clusters you would like to have as parameter. Only numeric values are supported in the tuples. The reason for this is that the "centroid" method which I use, essentially returns a tuple of floats. So you will lose any other kind of metadata. Once I figure out a way how to recode that method, other types should be possible. 1.0.1b2 - Optimized calculation of the hierarchical clustering by using the fact, that the generated matrix is symmetrical. 1.0.1b1 - Implemented complete-, average-, and uclus-linkage methods. You can select one by specifying it in the constructor, for example: cl = HierarchicalClustering(data, distfunc, linkage='uclus') or by setting it before starting the clustering process: cl = HierarchicalClustering(data, distfunc) cl.setLinkageMethod('uclus') cl.cluster() - Clustering is not executed on object creation, but on the first call of "getlevel". You can force the creation of the clusters by calling the "cluster" method as shown above.

  posted 763 days ago

• python-cluster 1.1.1b3 file released: cluster-1.1.1b3.linux-i686.tar.gz

  1.1.1b3 - Fixed bug #1727558 - Some more unit-tests - ValueError changed to ClusteringError where appropriate 1.1.1b2 - Fixed bug #1604859 (thanks to Willi Richert for reporting it) 1.1.1b1 - Applied patch [1535137] (thanks ajaksu) --> Topology output supported --> data and raw_data are now properties. 1.1.0b1 - KMeans Clustering implemented for simple numeric tuples. Data in the form [(1,1), (2,1), (5,3), ...] can be clustered. Usage: >>> from cluster import KMeansClustering >>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...]) >>> clusters = cl.getclusters(2) the method "getclusters" takes the amount of clusters you would like to have as parameter. Only numeric values are supported in the tuples. The reason for this is that the "centroid" method which I use, essentially returns a tuple of floats. So you will lose any other kind of metadata. Once I figure out a way how to recode that method, other types should be possible. 1.0.1b2 - Optimized calculation of the hierarchical clustering by using the fact, that the generated matrix is symmetrical. 1.0.1b1 - Implemented complete-, average-, and uclus-linkage methods. You can select one by specifying it in the constructor, for example: cl = HierarchicalClustering(data, distfunc, linkage='uclus') or by setting it before starting the clustering process: cl = HierarchicalClustering(data, distfunc) cl.setLinkageMethod('uclus') cl.cluster() - Clustering is not executed on object creation, but on the first call of "getlevel". You can force the creation of the clusters by calling the "cluster" method as shown above.

  posted 763 days ago

• Code committed

  exhuma committed revision 5 to the python-cluster SVN repository, changing 1 files

  posted by exhuma 763 days ago

• File released: /python-cluster/1.1.1b3/cluster-1.1.1b3.win32.exe

  posted 763 days ago

• File released: /python-cluster/1.1.1b3/cluster-1.1.1b3.tar.gz

  posted 763 days ago

• File released: /python-cluster/1.1.1b3/cluster-1.1.1b3.linux-i686.tar.gz

  posted 763 days ago

• Code committed

  exhuma committed revision 4 to the python-cluster SVN repository, changing 2 files

  posted by exhuma 763 days ago

• Code committed

  exhuma committed revision 3 to the python-cluster SVN repository, changing 2 files

  posted by exhuma 763 days ago

• Tracker comment added

  exhuma commented on the Duplicate items returned when using getBiggestCluster artifact

  posted by exhuma 763 days ago