Package samer.models
Interface Model
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- All Known Implementing Classes:
- AlignedGaussian, DiffScaler, GeneralisedExponential, ICA, ICAWithScaler, IIDPrior, Mixture, NoisyICA, Scaler, SmoothGeneralisedExponential, SparseICA, VarianceICA
public interface Model
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Nested Class Summary
Nested Classes Modifier and Type Interface and Description static interfaceModel.TrainerThis represents a training algorithm for a Model Trainer is responsible for counting calls to accumulate() between flushes
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method and Description voidcompute()contract is that getEnergy and getGradient must return correct values for current x after infer and compute has been called, but not necessarily before.voiddispose()Functionxfunctionx()This presents a more functional interface to the model so that it can be driven by an optimiser.doublegetEnergy()return E = -log p(x)double[]getGradient()return dE/dxintgetSize()return size of vector this model expectsvoidinfer()should infer values latent variablesvoidsetInput(Vec x)model should begin observing this vector
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Method Detail
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getSize
int getSize()
return size of vector this model expects
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setInput
void setInput(Vec x)
model should begin observing this vector
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infer
void infer()
should infer values latent variables
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compute
void compute()
contract is that getEnergy and getGradient must return correct values for current x after infer and compute has been called, but not necessarily before. This is to give model an opportunity to cache values of energy and gradient to avoid repeated computations.
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getEnergy
double getEnergy()
return E = -log p(x)
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getGradient
double[] getGradient()
return dE/dx
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dispose
void dispose()
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functionx
Functionx functionx()
This presents a more functional interface to the model so that it can be driven by an optimiser. See classes Functionx and MinimiserBase in package samer.maths.opt.
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