Class: MachineLearningWorkbench::Optimizer::NaturalEvolutionStrategies::XNES

Inherits:
Base
  • Object
show all
Defined in:
lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb

Overview

Exponential Natural Evolution Strategies

Instance Attribute Summary collapse

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

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

#log_sigma ⇒ Object (readonly)

Returns the value of attribute log_sigma.



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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb', line 6

def log_sigma
  @log_sigma
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/xnes.rb', line 56

def convergence
  sigma.trace
end

#initialize_distribution(mu_init: 0, sigma_init: 1) ⇒ Object 



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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb', line 8

def initialize_distribution mu_init: 0, sigma_init: 1
  @mu = case mu_init
    when Range # initialize with random in range
      raise ArgumentError, "mu_init: `Range` start/end in `Float`s" \
        unless mu_init.first.kind_of?(Float) && mu_init.last.kind_of?(Float)
      mu_rng = Random.new rng.rand 10**Random.new_seed.size
      NArray[*ndims.times.map { mu_rng.rand mu_init }]
    when Array
      raise ArgumentError unless mu_init.size == ndims
      NArray[mu_init]
    when Numeric
      NArray.new([1,ndims]).fill mu_init
    when NArray
      raise ArgumentError unless mu_init.size == ndims
      mu_init.ndim < 2 ? mu_init.reshape(1, ndims) : mu_init
    else
      raise ArgumentError, "Something is wrong with mu_init: #{mu_init}"
  end
  @sigma = case sigma_init
    when Array
      raise ArgumentError unless sigma_init.size == ndims
      NArray[*sigma_init].diag
    when Numeric
      NArray.new([ndims]).fill(sigma_init).diag
    when NArray
      raise ArgumentError unless sigma_init.size == ndims**2
      sigma_init.ndim < 2 ? sigma_init.reshape(ndims, ndims) : sigma_init
    else
      raise ArgumentError, "Something is wrong with sigma_init: #{sigma_init}"
  end
  # Works with the log of sigma to avoid continuous decompositions (thanks Sun Yi)
  @log_sigma = NMath.log(sigma.diagonal).diag
end

#load(data) ⇒ Object

Raises:

  • (ArgumentError) 


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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb', line 64

def load data
  raise ArgumentError unless data.size == 2
  @mu, @log_sigma = data.map &:to_na
  @sigma = log_sigma.exponential
end

#save ⇒ Object 



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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb', line 60

def save
  [mu.to_a, log_sigma.to_a]
end

#train(picks: sorted_inds) ⇒ Object 



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# File 'lib/machine_learning_workbench/optimizer/natural_evolution_strategies/xnes.rb', line 42

def train picks: sorted_inds
  g_mu = utils.dot(picks)
  g_log_sigma = popsize.times.inject(NArray.zeros sigma.shape) do |sum, i|
    u = utils[i]
    ind = picks[i, true]
    ind_sq = ind.outer_flat(ind, &:*)
    sum + (ind_sq - eye) * u
  end
  @mu += sigma.dot(g_mu.transpose).transpose * lrate
  @log_sigma += g_log_sigma * (lrate/2)
  @sigma = log_sigma.exponential
end