ActivePrediction

ActivePrediction[f,{conf1,conf2, }]

gives an object representing the result of active prediction obtained by using the function f to determine values for the example configurations confi.

ActivePrediction[f,reg]

generates configurations within the region specified by reg.

ActivePrediction[f,sampler]

generates configurations by applying the function sampler.

ActivePrediction[f,{conf1,conf2,}nsampler]

applies the function nsampler to successively generate configurations starting from one of the confi.

Details and Options

  • ActivePrediction[] returns an ActivePredictionObject[] whose properties can be obtained using ActivePredictionObject[]["prop"].
  • Possible properties include:
  • "EvaluationHistory"configurations explored and values corresponding to them
    "Method"method used for active prediction
    "PredictorFunction"best PredictorFunction[] obtained
    "PredictorMeasurementsObject"latest PredictorMeasurementsObject[] obtained
    "OracleFunction"original function f used to determine values
    "LearningCurve"plot of mean cross-entropy evolution
    "Properties"list of all available properties
  • Configurations can be of any form accepted by Predict (single data element, list of data elements, association of data elements, etc.) and of any type accepted by Predict (numerical, textual, sounds, images, etc.).
  • When applied to a configuration conf, the output of the function f must be a real-number value.
  • In ActivePrediction[f,spec], spec defines the domain of the function f. A domain can be defined by a list of configurations, a geometric region or a configuration generator function.
  • In ActivePrediction[f,sampler], sampler[] must output a configuration suitable for f to be applied to it.
  • In ActivePrediction[f,{conf1,conf2,}nsampler], nsampler[conf] must output a configuration.
  • ActivePrediction has the same options as Predict, with the following additions and changes:
  • InitialEvaluationHistoryNoneinitial set of configurations and values
    MaxIterations2000maximum number of iterations
    MethodAutomaticmethod used to determine configurations to query and the prediction algorithm to use
    RandomSeeding1234what seeding of pseudorandom generators should be done internally
  • Possible settings for Method include:
  • Automaticautomatically choose method
    "Randomized"choose random configurations from the domain
    "MaxEntropy"choose configurations for which the predictor has maximum uncertainty
    assocassociation specifying the evaluation strategy and prediction method
  • In the form Methodassoc, the association can have elements:
  • "EvaluationStrategy"method for determining which configurations to query
    "PredictionMethod"method to use for prediction
  • Possible settings for RandomSeeding include:
  • Automaticautomatically reseed every time the function is called
    Inheriteduse externally seeded random numbers
    seeduse an explicit integer or strings as a seed

Examples

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Basic Examples  (3)

Train an ActivePredictionObject[] to find the predictor for a function, given a set of configurations:

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Extract the resulting predictor:

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Predict new examples:

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Train a prediction object to find the predictor for a function whose domain is defined by an interval:

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Extract the predictor:

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Predict new examples:

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Train a prediction object to find the predictor for the Det function, with the domain defined by a configuration generator:

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Extract the predictor:

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Predict new examples:

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Scope  (3)

Options  (3)

Applications  (1)

See Also

Predict  ActivePredictionObject  BayesianMinimization  BayesianMaximization

Introduced in 2017
(11.1)
| Updated in 2017
(11.2)