|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Objectedu.stanford.nlp.optimization.AbstractCachingDiffFunction
edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
edu.stanford.nlp.optimization.AbstractStochasticCachingDiffUpdateFunction
edu.stanford.nlp.classify.LogConditionalObjectiveFunction<L,F>
edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction<L,F>
L
- The type of the labels in the Dataset (one can be passed in to the constructor)F
- The type of the features in the Datasetpublic class AdaptedGaussianPriorObjectiveFunction<L,F>
Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights
Nested Class Summary |
---|
Nested classes/interfaces inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction |
---|
AbstractStochasticCachingDiffFunction.SamplingMethod |
Field Summary |
---|
Fields inherited from class edu.stanford.nlp.classify.LogConditionalObjectiveFunction |
---|
data, dataIterable, dataweights, derivativeAD, derivativeNumerator, featureIndex, labelIndex, labels, numClasses, numFeatures, prior, priorDerivative, probs, sums, useIterable, useSummedConditionalLikelihood, values, xAD |
Fields inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction |
---|
allIndices, curElement, finiteDifferenceStepSize, gradPerturbed, hasNewVals, HdotV, lastBatch, lastBatchSize, lastElement, lastVBatch, lastXBatch, method, randGenerator, recalculatePrevBatch, returnPreviousValues, sampleMethod, scaleUp, thisBatch, xPerturbed |
Fields inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction |
---|
derivative, value |
Constructor Summary | |
---|---|
AdaptedGaussianPriorObjectiveFunction(GeneralDataset<L,F> dataset,
LogPrior prior,
double[][] weights)
|
Method Summary | |
---|---|
protected void |
calculate(double[] x)
Calculate the conditional likelihood. |
protected void |
rvfcalculate(double[] x)
Calculate conditional likelihood for datasets with real-valued features. |
double[] |
to1D(double[][] x2)
|
Methods inherited from class edu.stanford.nlp.classify.LogConditionalObjectiveFunction |
---|
calculateStochastic, calculateStochasticAlgorithmicDifferentiation, calculateStochasticFiniteDifference, calculateStochasticGradientOnly, calculateStochasticUpdate, dataDimension, domainDimension, indexOf, setPrior, setUseSumCondObjFun, to2D, valueAt |
Methods inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffUpdateFunction |
---|
calculateStochasticUpdate, getSample |
Methods inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction |
---|
clearCache, decrementBatch, derivativeAt, derivativeAt, getBatch, HdotVAt, HdotVAt, HdotVAt, incrementBatch, incrementRandom, initial, lastDerivative, lastValue, scaleUp, setValue, valueAt, valueAt |
Methods inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction |
---|
copy, derivativeAt, valueAt |
Methods inherited from class java.lang.Object |
---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
---|
public AdaptedGaussianPriorObjectiveFunction(GeneralDataset<L,F> dataset, LogPrior prior, double[][] weights)
Method Detail |
---|
protected void calculate(double[] x)
calculate
in class LogConditionalObjectiveFunction<L,F>
protected void rvfcalculate(double[] x)
LogConditionalObjectiveFunction
rvfcalculate
in class LogConditionalObjectiveFunction<L,F>
public double[] to1D(double[][] x2)
|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |