Class: MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::SNES
- Inherits:
-
Base
- Object
- Base
- MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::SNES
- Defined in:
- lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb
Overview
Separable Natural Evolution Strategies
Instance Attribute Summary collapse
-
#variances ⇒ Object
readonly
Returns the value of attribute variances.
Attributes inherited from Base
#best, #eye, #last_fits, #mu, #ndims, #obj_fn, #opt_type, #parallel_fit, #rescale_lrate, #rescale_popsize, #rng, #sigma
Instance Method Summary collapse
-
#convergence ⇒ Object
Estimate algorithm convergence as total variance.
- #initialize_distribution(mu_init: 0, sigma_init: 1) ⇒ Object
- #load(data) ⇒ Object
- #save ⇒ Object
- #train(picks: sorted_inds) ⇒ Object
Methods inherited from Base
#cmaes_lrate, #cmaes_popsize, #cmaes_utilities, #initialize, #interface_methods, #lrate, #move_inds, #popsize, #sorted_inds, #standard_normal_sample, #standard_normal_samples, #utils
Constructor Details
This class inherits a constructor from MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::Base
Instance Attribute Details
#variances ⇒ Object (readonly)
Returns the value of attribute variances.
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 7 def variances @variances end |
Instance Method Details
#convergence ⇒ Object
Estimate algorithm convergence as total variance
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 41 def convergence variances.sum end |
#initialize_distribution(mu_init: 0, sigma_init: 1) ⇒ Object
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 9 def initialize_distribution mu_init: 0, sigma_init: 1 @mu = case mu_init when Array raise ArgumentError unless mu_init.size == ndims NArray[mu_init] when Numeric NArray.new([1,ndims]).fill mu_init else raise ArgumentError, "Something is wrong with mu_init: #{mu_init}" end @variances = case sigma_init when Array raise ArgumentError unless sigma_init.size == ndims NArray[*sigma_init] when Numeric NArray.new([ndims]).fill(sigma_init) else raise ArgumentError, "Something is wrong with sigma_init: #{sigma_init}" \ "(did you remember to copy the other cases from XNES?)" end @sigma = @variances.diag end |
#load(data) ⇒ Object
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 49 def load data raise ArgumentError unless data.size == 2 @mu, @variances = data.map &:to_na @sigma = variances.diag end |
#save ⇒ Object
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 45 def save [mu.to_a, variances.to_a] end |
#train(picks: sorted_inds) ⇒ Object
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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/snes.rb', line 32 def train picks: sorted_inds g_mu = utils.dot(picks) g_sigma = utils.dot(picks**2 - 1) @mu += sigma.dot(g_mu.transpose).transpose * lrate @variances *= (g_sigma * lrate / 2).exponential.flatten @sigma = @variances.diag end |