On Tue, Aug 17, 2010 at 3:35 PM, Jeff Eastman <j...@windwardsolutions.com>wrote:
> Ok, so if a new ClusterClassifier could read a set of Clusters from > clusters-i storage into memory and do what is now being done in each > driver/clusterer then this would facilitate the integration? Absolutely. > It's just a different slice of the current clusteredPoints output; a vector > of probabilities for the given input vector and Clusters. Sounds like this > would not need a command line interface at all, just a Path reference to the > clusters to be read-in at initialization time. > Yes. > Dirichlet models already implement pdf(Vector) methods which support > classification from their persistent state. The other types of Cluster > cannot since their persistent state does not include the DistanceMeasure > they used. Some further refactoring of Cluster, AbstractCluster and Model > along the lines I discussed earlier might make all this come together. I > think the current set of Dirichlet models needs to be cleaned up anyway; > AbstractClusters look way too much like AsymmetricSampledNormalDistributions > to ignore the redundancy. I will noodle on this some more and see where it > takes me. > Sounds great.
