Details and Options
- The following functions can be used on a LearnedDistribution[…]:
PDF[dist,…] probability or probability density for data RandomVariate[dist] random samples generated from the distribution SynthesizeMissingValues[dist,…] fill in missing values according to the distribution RarerProbability[dist,…] compute the probability to generate a sample with lower PDF than a given example
- When acting on a LearnedDistribution[…], the functions PDF and RarerProbability can be used with the following options:
PerformanceGoal Automatic aspect of performance to optimize MaxIterations Automatic number of iterations to use when a Monte Carlo integration is performed ComputeUncertainty False whether to return probabilities with their uncertainty
- Possible settings for PerformanceGoal include:
"Quality" maximize the quality of the result "Speed" maximize the speed of the result Automatic automatic tradeoff between speed and quality
- Information[LearnedDistribution[…]] generates an information panel about the distribution and its estimated performances.
- Information[LearnedDistribution[…],prop] can be used to obtain specific properties.
- Information of a LearnedDistribution may include the following properties:
"BatchPDFTime" marginal time to appy PDF to one example when a batch is given "BatchSamplingTime" marginal time to generate one example in a batch "Entropy" estimated entropy of the distribution "ExampleNumber" number of training examples "FeatureTypes" types of the distribution variables "FunctionMemory" memory needed to store the distribution "LearningCurve" performance as a function of the training set size "MaxTrainingMemory" maximum memory used during training "Method" value of Method used by LearnDistribution "MethodDescription" summary of the method "MethodOption" full method option to be reused in a new training "PDFTime" time to apply PDF to a unique example "Properties" all information properties available for this distribution "SamplingTime" time to sample one example "TrainingTime" time used by LearnDistribution to generate the distribution
- Information properties also include all method suboptions.
Examplesopen all close all
Basic Examples (3)
Train a LearnedDistribution[…] on a numeric dataset:
Look at the distribution Information:
Train a LearnedDistribution[…] on a nominal dataset:
Train a LearnedDistribution[…] on a two-dimensional dataset:
Introduced in 2019