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Namespaces | |
| shark | |
| AbstractMultiObjectiveOptimizer. | |
Enumerations | |
| enum | shark::PartitionEstimationAlgorithm { shark::AIS, shark::AISMean, shark::TwoSidedAISMean, shark::AcceptanceRatio, shark::AcceptanceRatioMean } |
Functions | |
| template<class RBMType > | |
| double | shark::logPartitionFunction (RBMType const &rbm, double beta=1.0) |
| Calculates the value of the partition function $Z$. More... | |
| template<class RBMType > | |
| double | shark::negativeLogLikelihoodFromLogPartition (RBMType const &rbm, UnlabeledData< RealVector > const &inputs, double logPartition, double beta=1.0) |
| Estimates the negative log-likelihood of a set of input vectors under the models distribution using the partition function. More... | |
| template<class RBMType > | |
| double | shark::negativeLogLikelihood (RBMType const &rbm, UnlabeledData< RealVector > const &inputs, double beta=1.0) |
| Estimates the negative log-likelihood of a set of input vectors under the models distribution. More... | |
| double | shark::estimateLogFreeEnergyFromEnergySamples (RealMatrix const &energyDiffUp, RealMatrix const &energyDiffDown, PartitionEstimationAlgorithm algorithm=AIS) |
| template<class RBMType > | |
| double | shark::estimateLogFreeEnergy (RBMType &rbm, Data< RealVector > const &initDataset, RealVector const &beta, std::size_t samples, PartitionEstimationAlgorithm algorithm=AcceptanceRatioMean, float burnInPercentage=0.1) |
| template<class RBMType > | |
| double | shark::annealedImportanceSampling (RBMType &rbm, RealVector const &beta, std::size_t samples) |
| template<class RBMType > | |
| double | shark::estimateLogFreeEnergy (RBMType &rbm, Data< RealVector > const &initDataset, std::size_t chains, std::size_t samples, PartitionEstimationAlgorithm algorithm=AIS, float burnInPercentage=0.1) |
| template<class RBMType > | |
| double | shark::annealedImportanceSampling (RBMType &rbm, std::size_t chains, std::size_t samples) |