public class RegressionLearner extends AbstractMinimizingOnlineLearner<RegressionModel>
minimizer, numPasses, random, sparseWeightsfeatureDimension, numOutcomeClasses, outcomeDimension, streamSupplier, verbose| Constructor and Description |
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RegressionLearner(StochasticMinimizer minimizer,
de.jungblut.math.activation.ActivationFunction activationFunction,
de.jungblut.math.loss.LossFunction lossFunction) |
| Modifier and Type | Method and Description |
|---|---|
RegressionModel |
createModel(de.jungblut.math.DoubleVector weights)
Creates a model with the given minimized weights.
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protected de.jungblut.math.minimize.CostGradientTuple |
observeExample(de.jungblut.online.ml.FeatureOutcomePair next,
de.jungblut.math.DoubleVector weights)
Observes the next example.
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observeExampleSafe, randomInitialize, setNumPasses, setRandom, train, useSparseWeightsinit, peekDimensions, setVerbose, verbosepublic RegressionLearner(StochasticMinimizer minimizer, de.jungblut.math.activation.ActivationFunction activationFunction, de.jungblut.math.loss.LossFunction lossFunction)
protected de.jungblut.math.minimize.CostGradientTuple observeExample(de.jungblut.online.ml.FeatureOutcomePair next,
de.jungblut.math.DoubleVector weights)
AbstractMinimizingOnlineLearnerobserveExample in class AbstractMinimizingOnlineLearner<RegressionModel>next - the next feature/outcome pair.weights - the current weights.public RegressionModel createModel(de.jungblut.math.DoubleVector weights)
AbstractMinimizingOnlineLearnercreateModel in class AbstractMinimizingOnlineLearner<RegressionModel>weights - the learned weights.Copyright © 2015. All rights reserved.