class MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::SNES

Separable Natural Evolution Strategies

Attributes

variances[R]

Public Instance Methods

convergence() click to toggle source

Estimate algorithm convergence as total variance

# 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) click to toggle source
# 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) click to toggle source
# 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() click to toggle source
# 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) click to toggle source
# 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