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Packages that use Model | |
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org.apache.mahout.clustering | |
org.apache.mahout.clustering.canopy | |
org.apache.mahout.clustering.dirichlet | |
org.apache.mahout.clustering.dirichlet.models | |
org.apache.mahout.clustering.fuzzykmeans | |
org.apache.mahout.clustering.kmeans | This package provides an implementation of the k-means clustering algorithm. |
org.apache.mahout.clustering.meanshift |
Uses of Model in org.apache.mahout.clustering |
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Subinterfaces of Model in org.apache.mahout.clustering | |
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interface |
Cluster
Implementations of this interface have a printable representation and certain attributes that are common across all clustering implementations |
Classes in org.apache.mahout.clustering that implement Model | |
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class |
AbstractCluster
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class |
DistanceMeasureCluster
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Methods in org.apache.mahout.clustering that return Model | |
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Model<VectorWritable> |
Model.sampleFromPosterior()
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Model<VectorWritable> |
DistanceMeasureCluster.sampleFromPosterior()
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Model<O>[] |
ModelDistribution.sampleFromPosterior(Model<O>[] posterior)
Return a list of models sampled from the posterior |
Model<O>[] |
ModelDistribution.sampleFromPrior(int howMany)
Return a list of models sampled from the prior |
Methods in org.apache.mahout.clustering with parameters of type Model | |
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void |
Model.observe(Model<O> x)
Observe the given model, retaining information about its observations |
void |
AbstractCluster.observe(Model<VectorWritable> x)
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Model<O>[] |
ModelDistribution.sampleFromPosterior(Model<O>[] posterior)
Return a list of models sampled from the posterior |
Uses of Model in org.apache.mahout.clustering.canopy |
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Classes in org.apache.mahout.clustering.canopy that implement Model | |
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class |
Canopy
This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed. |
Uses of Model in org.apache.mahout.clustering.dirichlet |
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Classes in org.apache.mahout.clustering.dirichlet that implement Model | |
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class |
DirichletCluster
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Methods in org.apache.mahout.clustering.dirichlet that return Model | |
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Model<VectorWritable>[] |
DirichletState.getModels()
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Model<VectorWritable> |
DirichletCluster.sampleFromPosterior()
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protected Model<VectorWritable>[] |
DirichletClusterer.samplePosteriorModels()
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Methods in org.apache.mahout.clustering.dirichlet with parameters of type Model | |
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void |
DirichletCluster.observe(Model<VectorWritable> x)
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protected void |
DirichletClusterer.observe(Model<VectorWritable>[] newModels,
VectorWritable observation)
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static void |
DirichletCluster.writeModel(DataOutput out,
Model<?> model)
Writes a typed Model instance to the output stream |
Uses of Model in org.apache.mahout.clustering.dirichlet.models |
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Classes in org.apache.mahout.clustering.dirichlet.models that implement Model | |
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class |
GaussianCluster
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Methods in org.apache.mahout.clustering.dirichlet.models that return Model | |
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Model<VectorWritable> |
GaussianCluster.sampleFromPosterior()
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Model<VectorWritable>[] |
GaussianClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)
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Model<VectorWritable>[] |
DistanceMeasureClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)
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Model<VectorWritable>[] |
GaussianClusterDistribution.sampleFromPrior(int howMany)
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Model<VectorWritable>[] |
DistanceMeasureClusterDistribution.sampleFromPrior(int howMany)
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Methods in org.apache.mahout.clustering.dirichlet.models with parameters of type Model | |
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Model<VectorWritable>[] |
GaussianClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)
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Model<VectorWritable>[] |
DistanceMeasureClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)
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Uses of Model in org.apache.mahout.clustering.fuzzykmeans |
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Classes in org.apache.mahout.clustering.fuzzykmeans that implement Model | |
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class |
SoftCluster
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Uses of Model in org.apache.mahout.clustering.kmeans |
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Classes in org.apache.mahout.clustering.kmeans that implement Model | |
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class |
Cluster
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Uses of Model in org.apache.mahout.clustering.meanshift |
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Classes in org.apache.mahout.clustering.meanshift that implement Model | |
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class |
MeanShiftCanopy
This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed. |
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