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CompositionFunctionApproximator¶

Subclasses

  • RegressionCFA

Related

  • OptimizationControlMechanism

Contents¶

  • Overview

  • Class Reference

Overview¶

A CompositionFunctionApproximator is an abstract subclass of Composition that, over calls to its adapt method, parameterizes its function to predict the net_outcome of the Composition (or part of one) controlled by an OptimizationControlMechanism, for a given set of state_feature_values and a control_allocation provided by the OptimizationControlMechanism. Its evaluate method calls its function to generate and return the predicted net_outcome for a given set of state_feature_values, control_allocation, num_estimates, and num_trials_per_estimate.

Class Reference¶

class psyneulink.core.compositions.compositionfunctionapproximator.CompositionFunctionApproximator(name=None, **param_defaults)¶

Subclass of Composition that implements a FunctionApproximator as the agent_rep of an OptimizationControlMechanism.

Parameterizes its function to predict a net_outcome for a set of state_feature_values and a control_allocation provided by an OptimizationControlMechanism.

See Composition for additional arguments and attributes.

Parameters

param_defaults (LearningFunction, function or method) – specifies the function parameterized by the CompositionFunctionApproximator’s adapt method, and used by its evaluate method to generate and return a predicted net_outcome for a set of state_feature_values and a control_allocation provided by an OptimizationControlMechanism.

function¶

parameterized by the CompositionFunctionApproximator’s <adapt <CompositionFunctionApproximator.adapt>` method, and used by its evaluate method to generate and return a predicted net_outcome for a set of state_feature_values and a control_allocation provided by an OptimizationControlMechanism.

Type

LearningFunction, function or method

prediction_parameters¶

parameters adjusted by adapt method, and used by function to predict the net_outcome for a given set of state_feature_values and control_allocation.

Type

1d array

adapt(feature_values, control_allocation, net_outcome, context=None)¶

Adjust parameters of function to improve prediction of target from input.

evaluate(feature_values, control_allocation, num_estimates, num_trials_per_estimate, base_context=<psyneulink.core.globals.context.Context object>, context=None)¶

Return target predicted by function.

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© Copyright 2016, Jonathan D. Cohen.

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  • CompositionFunctionApproximator
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    • Class Reference
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