public class GmmTrainer extends SingleLabelDatasetTrainer<GmmModel>
DatasetTrainer.EmptyDatasetException
envBuilder, environment
Constructor and Description |
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GmmTrainer()
Creates an instance of GmmTrainer.
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GmmTrainer(int countOfComponents)
Creates an instance of GmmTrainer.
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GmmTrainer(int countOfComponents,
int maxCountOfIterations)
Creates an instance of GmmTrainer.
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Modifier and Type | Method and Description |
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<K,V> GmmModel |
fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
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boolean |
isUpdateable(GmmModel mdl) |
protected <K,V> GmmModel |
updateModel(GmmModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains new model taken previous one as a first approximation.
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GmmTrainer |
withEps(double eps)
Sets min divergence beween iterations.
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GmmTrainer |
withInitialCountOfComponents(int numberOfComponents)
Sets numberOfComponents.
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GmmTrainer |
withInitialMeans(List<Vector> means)
Sets initial means.
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GmmTrainer |
withMaxCountIterations(int maxCountOfIterations)
Sets max count of iterations
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GmmTrainer |
withMaxCountOfClusters(int maxCountOfClusters)
Sets maximum number of clusters in GMM.
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GmmTrainer |
withMaxCountOfInitTries(int maxCountOfInitTries)
Sets MaxCountOfInitTries parameter.
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GmmTrainer |
withMaxLikelihoodDivergence(double maxLikelihoodDivergence)
Sets maximum divergence between maximum of likelihood of vector in dataset and other for anomalies
identification.
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GmmTrainer |
withMinClusterProbability(double minClusterProbability)
Sets minimum requred probability for cluster.
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GmmTrainer |
withMinElementsForNewCluster(int minElementsForNewCluster)
Sets minimum required anomalies in terms of maxLikelihoodDivergence for creating new cluster.
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fit, fit, fit, fit, fit, fit, getLastTrainedModelOrThrowEmptyDatasetException, identityTrainer, learningEnvironment, update, update, update, update, update, withConvertedLabels, withEnvironmentBuilder
public GmmTrainer()
public GmmTrainer(int countOfComponents, int maxCountOfIterations)
countOfComponents
- Count of components.maxCountOfIterations
- Max count of iterations.public GmmTrainer(int countOfComponents)
countOfComponents
- Count of components.public <K,V> GmmModel fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)
fitWithInitializedDeployingContext
in class DatasetTrainer<GmmModel,Double>
K
- Type of a key in upstream
data.V
- Type of a value in upstream
data.datasetBuilder
- Dataset builder.extractor
- Extractor of UpstreamEntry
into LabeledVector
.public GmmTrainer withInitialCountOfComponents(int numberOfComponents)
numberOfComponents
- Number of components.public GmmTrainer withInitialMeans(List<Vector> means)
means
- Initial means for clusters.public GmmTrainer withMaxCountIterations(int maxCountOfIterations)
maxCountOfIterations
- Max count of iterations.public GmmTrainer withEps(double eps)
eps
- Eps.public GmmTrainer withMaxCountOfInitTries(int maxCountOfInitTries)
maxCountOfInitTries
- Max count of init tries.public GmmTrainer withMaxCountOfClusters(int maxCountOfClusters)
maxCountOfClusters
- Max count of clusters.public GmmTrainer withMaxLikelihoodDivergence(double maxLikelihoodDivergence)
maxLikelihoodDivergence
- Max likelihood divergence.public GmmTrainer withMinElementsForNewCluster(int minElementsForNewCluster)
minElementsForNewCluster
- Min elements for new cluster.public GmmTrainer withMinClusterProbability(double minClusterProbability)
minClusterProbability
- Min cluster probability.public boolean isUpdateable(GmmModel mdl)
isUpdateable
in class DatasetTrainer<GmmModel,Double>
mdl
- Model.protected <K,V> GmmModel updateModel(GmmModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)
updateModel
in class DatasetTrainer<GmmModel,Double>
K
- Type of a key in upstream
data.V
- Type of a value in upstream
data.mdl
- Learned model.datasetBuilder
- Dataset builder.extractor
- Extractor of UpstreamEntry
into LabeledVector
.
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024