Class: DataModeler::Model::FANN

Inherits:
Object
  • Object
show all
Defined in:
lib/data_modeler/model/fann.rb

Overview

Model the data using an artificial neural network, based on the Fast Artificial Neural Networks (FANN) implementation

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(ngens:, hidden_layers:, ninputs:, noutputs:, algo: nil, actfn: nil, init_weights_range: nil) ⇒ FANN

Returns a new instance of FANN.

Parameters:

  • ngens (Integer) —

    number of generations (repetitions) alloted for training

  • hidden_layers (Array<Integer>) —

    list of number of hidden neurons per each hidden layer in the network

  • ninputs (Integer) —

    number of inputs in the network

  • noutputs (Integer) —

    number of outputs in the network

  • algo (:rprop, :rwg, ...) (defaults to: nil) —

    training algorithm

  • actfn (:sigmoid, ...) (defaults to: nil) —

    activation function

  • init_weights_range (Array<min_w, max_w>) (defaults to: nil) —

    minimum and maximum value for weight initialization range



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# File 'lib/data_modeler/model/fann.rb', line 17

def initialize ngens:, hidden_layers:, ninputs:, noutputs:, algo: nil, actfn: nil, init_weights_range: nil
  @fann_opts = {
    num_inputs: ninputs,
    hidden_neurons: hidden_layers,
    num_outputs: noutputs
  }
  @ngens = ngens
  @algo = algo
  @actfn = actfn
  @init_weights_range = init_weights_range
  reset
end

Instance Attribute Details

#actfn ⇒ Object (readonly)

Returns the value of attribute actfn.



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# File 'lib/data_modeler/model/fann.rb', line 7

def actfn
  @actfn
end

#algo ⇒ Object (readonly)

Returns the value of attribute algo.



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# File 'lib/data_modeler/model/fann.rb', line 7

def algo
  @algo
end

#fann ⇒ Object (readonly)

Returns the value of attribute fann.



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# File 'lib/data_modeler/model/fann.rb', line 7

def fann
  @fann
end

#fann_opts ⇒ Object (readonly)

Returns the value of attribute fann_opts.



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# File 'lib/data_modeler/model/fann.rb', line 7

def fann_opts
  @fann_opts
end

#init_weights_range ⇒ Object (readonly)

Returns the value of attribute init_weights_range.



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# File 'lib/data_modeler/model/fann.rb', line 7

def init_weights_range
  @init_weights_range
end

#ngens ⇒ Object (readonly)

Returns the value of attribute ngens.



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# File 'lib/data_modeler/model/fann.rb', line 7

def ngens
  @ngens
end

Instance Method Details

#reset ⇒ void

This method returns an undefined value.

Resets / initializes the model



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# File 'lib/data_modeler/model/fann.rb', line 32

def reset
  @fann = RubyFann::Standard.new fann_opts
  if algo && algo != :rwg
    fann.set_training_algorithm(algo)
  end
  if actfn
    fann.set_activation_function_hidden(actfn)
    fann.set_activation_function_output(actfn)
  end
  if init_weights_range
    fann.randomize_weights(*init_weights_range.map(&method(:Float)))
  end
end

#save(filename) ⇒ void

This method returns an undefined value.

Saves the model

Parameters:

  • filename (String/path) —

    where to save the model



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# File 'lib/data_modeler/model/fann.rb', line 110

def save filename
  # can do filename check here...?
  # TODO: I'd like to have a kind of `to_s`, and do all the saving in the modeler...
  fann.save filename.to_s
end

#test(inputs) ⇒ Array<Array<outputs>>

Tests the model on inputs.

Parameters:

  • inputs (Array<Array<inputs>>) —

    sequence of inputs for the model

Returns:

  • (Array<Array<outputs>>) —

    outputs corresponding to each input



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# File 'lib/data_modeler/model/fann.rb', line 103

def test inputs
  inputs.collect &fann.method(:run)
end

#train(trainset, ngens = @ngens, report_interval: 1000, desired_error: 1e-10) ⇒ void

This method returns an undefined value.

Trains the model for ngens on the trainset

Parameters:

  • trainset (Hash<input: Array, target: Array>) —

    training set

  • ngens (Integer) (defaults to: @ngens) —

    number of training generations



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# File 'lib/data_modeler/model/fann.rb', line 50

def train trainset, ngens=@ngens, report_interval: 1000, desired_error: 1e-10
  # it makes sense to temporarily disable the `report_interval` with `false` or `nil`
  report_interval ||= 0
  # special case: not implemented in FANN
  if algo == :rwg
    return train_rwg(trainset, ngens,
      report_interval: report_interval, desired_error: desired_error)
  end
  # TODO: optimize maybe?
  times, inputs, targets = trainset.values
  tset = RubyFann::TrainData.new inputs: inputs, desired_outputs: targets
  # fann.init_weights tset # test this weights initialization

  # params: train_data, max_epochs, report_interval, desired_error
  fann.train_on_data(tset, ngens, report_interval, desired_error)
end

#train_rwg(trainset, ngens = @ngens, report_interval: 1000, desired_error: 1e-10) ⇒ void

This method returns an undefined value.

Trains the model for ngens on the trainset using Random Weight Guessing

Parameters:

  • trainset (Hash-like<input: Array, target: Array>) —

    training set

  • ngens (Integer) (defaults to: @ngens) —

    number of training generations



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# File 'lib/data_modeler/model/fann.rb', line 71

def train_rwg trainset, ngens=@ngens, report_interval: 1000, desired_error: 1e-10
  # TODO: use report_interval and desired_error
  # initialize weight with random values in an interval [min_weight, max_weight]
  # NOTE: if the RWG training is unsuccessful, this range is the first place to
  # check to improve performance
  fann.randomize_weights(*init_weights_range.map(&method(:Float)))
  # test it on inputs
  times, inputs, targets = trainset.values
  outputs = test(inputs)
  # calculate RMSE
  rmse_fn = -> (outs) do
    sq_err = outs.zip(targets).flat_map do |os,ts|
      os.zip(ts).collect { |o,t| (t-o)**2 }
    end
    Math.sqrt(sq_err.reduce(:+) / sq_err.size)
  end
  rmse = rmse_fn.call(outputs)
  # initialize best
  best = [fann,rmse]
  # rinse and repeat
  ngens.times do
    outputs = test(inputs)
    rmse = rmse_fn.call(outputs)
    (best = [fann,rmse]; puts rmse) if rmse < best.last
  end
  # expose the best to the interface
  fann = best.first
end