Class AbstractSGDModel<T extends Output<T>>
java.lang.Object
org.tribuo.Model<T>
org.tribuo.common.sgd.AbstractSGDModel<T>
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>
,Serializable
- Direct Known Subclasses:
AbstractLinearSGDModel
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Nested Class Summary
Nested ClassesModifier and TypeClassDescriptionprotected static final class
A nominal tuple used to capture the prediction and the number of active features used by the model. -
Field Summary
FieldsModifier and TypeFieldDescriptionprotected boolean
protected FeedForwardParameters
The weights for this model.Fields inherited from class org.tribuo.Model
ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput
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Constructor Summary
ConstructorsModifierConstructorDescriptionprotected
AbstractSGDModel
(String name, ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, FeedForwardParameters weights, boolean generatesProbabilities, boolean addBias) Constructs a linear model trained via SGD. -
Method Summary
Modifier and TypeMethodDescriptionReturns a copy of the model parameters.protected AbstractSGDModel.PredAndActive
predictSingle
(Example<T> example) Generates the dense vector prediction from the supplied example.Methods inherited from class org.tribuo.Model
copy, copy, generatesProbabilities, getExcuse, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, getTopFeatures, innerPredict, predict, predict, predict, setName, toString, validate
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Field Details
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modelParameters
The weights for this model. -
addBias
protected boolean addBias
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Constructor Details
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AbstractSGDModel
protected AbstractSGDModel(String name, ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, FeedForwardParameters weights, boolean generatesProbabilities, boolean addBias) Constructs a linear model trained via SGD.- Parameters:
name
- The model name.provenance
- The model provenance.featureIDMap
- The feature domain.outputIDInfo
- The output domain.weights
- The model weights.generatesProbabilities
- Does this model generate probabilities?addBias
- Should the model add a bias feature to the feature vector?
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Method Details
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predictSingle
Generates the dense vector prediction from the supplied example.- Parameters:
example
- The example to use for prediction.- Returns:
- The prediction and the number of features involved.
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getModelParameters
Returns a copy of the model parameters.- Returns:
- A copy of the model parameters.
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