public static class BayesianRobustLinearRegression.IncrementalEstimator extends BayesianRobustLinearRegression implements IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic>
Modifier and Type | Class and Description |
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class |
BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic
SufficientStatistic for incremental Bayesian linear regression
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BayesianRobustLinearRegression.IncrementalEstimator, BayesianRobustLinearRegression.PredictiveDistribution
DEFAULT_WEIGHT_VARIANCE
Constructor and Description |
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IncrementalEstimator(int dimensionality)
Creates a new instance of IncrementalEstimator
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IncrementalEstimator(InverseGammaDistribution outputVariance,
MultivariateGaussian weightPrior)
Creates a new instance of IncrementalEstimator
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Modifier and Type | Method and Description |
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BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic |
createInitialLearnedObject()
Creates a new initial learned object, before any data is given.
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MultivariateGaussianInverseGammaDistribution |
learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
void |
update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target,
InputOutputPair<? extends Vectorizable,java.lang.Double> data)
The
update method updates an object of ResultType using
the given new data of type DataType , using some form of
"learning" algorithm. |
void |
update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target,
java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
The
update method updates an object of ResultType using
the given new Iterable containing some number of type DataType ,
using some form of "learning" algorithm. |
clone, createConditionalDistribution, createPredictiveDistribution, getOutputVariance, getWeightPrior, setOutputVariance, setWeightPrior
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public IncrementalEstimator(int dimensionality)
dimensionality
- Sets up the parameters (except featureMap) for the given dimensionality
of objects in feature space.public IncrementalEstimator(InverseGammaDistribution outputVariance, MultivariateGaussian weightPrior)
outputVariance
- Distribution of the output (measurement) varianceweightPrior
- Prior distribution of the weights, typically a zero-mean,
diagonal-variance distribution.public BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic createInitialLearnedObject()
IncrementalLearner
createInitialLearnedObject
in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic>
public MultivariateGaussianInverseGammaDistribution learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
BatchLearner
learn
method creates an object of ResultType
using
data of type DataType
, using some form of "learning" algorithm.learn
in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>>,MultivariateGaussianInverseGammaDistribution>
learn
in class BayesianRobustLinearRegression
data
- The data that the learning algorithm will use to create an
object of ResultType
.public void update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target, InputOutputPair<? extends Vectorizable,java.lang.Double> data)
IncrementalLearner
update
method updates an object of ResultType
using
the given new data of type DataType
, using some form of
"learning" algorithm.update
in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic>
target
- The object to update.data
- The new data for the learning algorithm to use to update
the object.public void update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
IncrementalLearner
update
method updates an object of ResultType
using
the given new Iterable containing some number of type DataType
,
using some form of "learning" algorithm.update
in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic>
target
- The object to update.data
- The Iterable containing data for the learning algorithm to use to
update the object.