Class: Desiru::Optimizers::MIPROv2::GaussianProcess

Inherits:
Object
  • Object
show all
Defined in:
lib/desiru/optimizers/mipro_v2.rb

Overview

Simplified Gaussian Process implementation without matrix library

Instance Method Summary collapse

Constructor Details

#initialize(kernel = :rbf, length_scale = 1.0, noise = 0.1) ⇒ GaussianProcess 



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# File 'lib/desiru/optimizers/mipro_v2.rb', line 814

def initialize(kernel = :rbf, length_scale = 1.0, noise = 0.1)
  @kernel = kernel
  @length_scale = length_scale
  @noise = noise
  @observations = []
  @trained = false
end

Instance Method Details

#add_observation(features, value) ⇒ Object 



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# File 'lib/desiru/optimizers/mipro_v2.rb', line 822

def add_observation(features, value)
  @observations << { features: features, value: value }
  @trained = false
end

#predict(features) ⇒ Object 



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# File 'lib/desiru/optimizers/mipro_v2.rb', line 835

def predict(features)
  return { mean: 0.0, std: 1.0 } unless @trained && !@observations.empty?

  # Simplified prediction using weighted average based on kernel similarity
  weights = @observations.map do |obs|
    kernel_function(features, obs[:features])
  end

  total_weight = weights.sum
  return { mean: 0.0, std: 1.0 } if total_weight.zero?

  # Normalize weights
  weights = weights.map { |w| w / total_weight }

  # Compute weighted mean
  mean = @observations.zip(weights).map { |obs, w| obs[:value] * w }.sum

  # Compute weighted variance for uncertainty
  variance = @observations.zip(weights).map do |obs, w|
    w * ((obs[:value] - mean)**2)
  end.sum

  std = Math.sqrt([variance + @noise, 0].max)

  { mean: mean, std: std }
rescue StandardError => e
  Desiru.logger&.warn("Gaussian Process prediction failed: #{e.message}")
  { mean: 0.0, std: 1.0 }
end

#update ⇒ Object 



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# File 'lib/desiru/optimizers/mipro_v2.rb', line 827

def update
  # Simplified update - just mark as trained
  @trained = !@observations.empty?
rescue StandardError => e
  Desiru.logger&.warn("Gaussian Process update failed: #{e.message}")
  @trained = false
end