NelderMead Class Reference

Nealder-Mead algorithm for finding suitable hyperparameters. More...

List of all members.

Static Public Member Functions
static double	FindMinimum (string error_measure, RatingPredictor recommender)
	Find best hyperparameter (according to an error measure) using Nelder-Mead search.
static double	FindMinimum (string evaluation_measure, IList< string > hp_names, IList< DenseVector > initial_hp_values, RatingPredictor recommender, ISplit< IRatings > split)
	Find the the parameters resulting in the minimal results for a given evaluation measure.

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Detailed Description

Nealder-Mead algorithm for finding suitable hyperparameters.

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Member Function Documentation

static double FindMinimum	(	string	error_measure,
		RatingPredictor	recommender
	)		[inline, static]

Find best hyperparameter (according to an error measure) using Nelder-Mead search.

Parameters:

error_measure	an error measure (lower is better)
recommender	a rating predictor (will be set to best hyperparameter combination)

Returns:

the estimated error of the best hyperparameter combination

static double FindMinimum	(	string	evaluation_measure,
		IList< string >	hp_names,
		IList< DenseVector >	initial_hp_values,
		RatingPredictor	recommender,
		ISplit< IRatings >	split
	)		[inline, static]

Find the the parameters resulting in the minimal results for a given evaluation measure.

The recommender will be set to the best parameter value after calling this method.

Parameters:

evaluation_measure	the name of the evaluation measure
hp_names	the names of the hyperparameters to optimize
initial_hp_values	the values of the hyperparameters to try out first
recommender	the recommender
split	the dataset split to use

Returns:

the best (lowest) average value for the hyperparameter

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The documentation for this class was generated from the following file:

• NelderMead.cs