class MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::RNES
Radial Natural Evolution Strategies
Attributes
variance[R]
Public Instance Methods
convergence()
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Estimate algorithm convergence based on variance
# File lib/machine_learning_workbench/optimizer/natural_evolution_strategies/rnes.rb, line 40 def convergence variance end
initialize_distribution(mu_init: 0, sigma_init: 1)
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# File lib/machine_learning_workbench/optimizer/natural_evolution_strategies/rnes.rb, line 8 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 @variance = sigma_init @sigma = case sigma_init when Array raise ArgumentError "RNES uses single global variance" when Numeric NArray.new([ndims]).fill(variance).diag else raise ArgumentError, "Something is wrong with sigma_init: #{sigma_init}" end end
load(data)
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# File lib/machine_learning_workbench/optimizer/natural_evolution_strategies/rnes.rb, line 48 def load data raise ArgumentError unless data.size == 2 mu_ary, @variance = data @mu = mu_ary.to_na @sigma = eye * variance end
save()
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# File lib/machine_learning_workbench/optimizer/natural_evolution_strategies/rnes.rb, line 44 def save [mu.to_a, variance] end
train(picks: sorted_inds)
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# File lib/machine_learning_workbench/optimizer/natural_evolution_strategies/rnes.rb, line 29 def train picks: sorted_inds g_mu = utils.dot(picks) # g_sigma = utils.dot(picks.row_norms**2 - ndims).first # back to scalar row_norms = NLinalg.norm picks, 2, axis:1 g_sigma = utils.dot(row_norms**2 - ndims)[0] # back to scalar @mu += sigma.dot(g_mu.transpose).transpose * lrate @variance *= Math.exp(g_sigma * lrate / 2) @sigma = NArray.new([ndims]).fill(variance).diag end