Class: DeepMiner::Perceptron

Inherits:
Object
  • Object
show all
Defined in:
lib/deep_miner/perceptron.rb

Overview

ajcost

Perceptron is class that implements a single layer neural net Implemenation is made with vertex_matrix class

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(input, hidden, output) ⇒ Perceptron

Returns a new instance of Perceptron.



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# File 'lib/deep_miner/perceptron.rb', line 13

def initialize(input, hidden, output)
  fail ArgumentError, 'Input must be Array' unless input.is_a? Array
  fail ArgumentError, 'Output must be Array' unless output.is_a? Array
  @input = input
  @hidden = hidden
  @output = output
  # Create activation vectors and weight matrices and change matrices
  init_activations
  init_weight_matrices
  init_change_matrices
end

Instance Attribute Details

#change_matrix_i ⇒ Object (readonly)

Returns the value of attribute change_matrix_i.



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# File 'lib/deep_miner/perceptron.rb', line 10

def change_matrix_i
  @change_matrix_i
end

#change_matrix_o ⇒ Object (readonly)

Returns the value of attribute change_matrix_o.



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# File 'lib/deep_miner/perceptron.rb', line 10

def change_matrix_o
  @change_matrix_o
end

#hidden ⇒ Object (readonly)

Returns the value of attribute hidden.



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# File 'lib/deep_miner/perceptron.rb', line 7

def hidden
  @hidden
end

#hidden_delta ⇒ Object (readonly)

Returns the value of attribute hidden_delta.



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# File 'lib/deep_miner/perceptron.rb', line 11

def hidden_delta
  @hidden_delta
end

#hidden_delta_v ⇒ Object (readonly)

Returns the value of attribute hidden_delta_v.



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# File 'lib/deep_miner/perceptron.rb', line 9

def hidden_delta_v
  @hidden_delta_v
end

#input ⇒ Object (readonly)

Returns the value of attribute input.



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# File 'lib/deep_miner/perceptron.rb', line 7

def input
  @input
end

#out_delta ⇒ Object (readonly)

Returns the value of attribute out_delta.



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# File 'lib/deep_miner/perceptron.rb', line 11

def out_delta
  @out_delta
end

#output ⇒ Object (readonly)

Returns the value of attribute output.



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# File 'lib/deep_miner/perceptron.rb', line 7

def output
  @output
end

#output_delta_v ⇒ Object (readonly)

Returns the value of attribute output_delta_v.



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# File 'lib/deep_miner/perceptron.rb', line 9

def output_delta_v
  @output_delta_v
end

#weight_matrix_ho ⇒ Object (readonly)

Returns the value of attribute weight_matrix_ho.



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# File 'lib/deep_miner/perceptron.rb', line 8

def weight_matrix_ho
  @weight_matrix_ho
end

#weight_matrix_ih ⇒ Object (readonly)

Returns the value of attribute weight_matrix_ih.



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# File 'lib/deep_miner/perceptron.rb', line 8

def weight_matrix_ih
  @weight_matrix_ih
end

Instance Method Details

#back_propogate(target, eta, momentum) ⇒ Object 



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# File 'lib/deep_miner/perceptron.rb', line 38

def back_propogate(target, eta, momentum)
  fail ArgumentError, 'Target must be Array' unless target.is_a? Array
  fail ArgumentError, 'Target and Output must have same size' unless target.size == @output.size
  # Calculate errors for output layer
  errors_output(target)

  # Calculate errors for hidden layer
  errors_hidden

  # Update weight vector from hidden to outputs
  update_output_weight_vector(eta, momentum)

  # Update weight vector from inputs to hidden
  update_hidden_weight_vector(eta, momentum)

  calculate_error(target)
end

#predict(input_vector) ⇒ Object 



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# File 'lib/deep_miner/perceptron.rb', line 25

def predict(input_vector)
  fail ArgumentError, 'Input vector must be Array' unless input_vector.is_a? Array
  fail ArgumentError, 'Predict input must be same size as input' unless input_vector.size == @input.size
  # Set activation vector for the input layer
  @act_input.apply_new_matrix([] << input_vector)

  # Calculate activation vector for the hidden layer
  calculate_activation_hidden

  # Calculate activation vector for the output layer
  calculate_activation_output
end

#train(train_data, expect, epoch_iter = 50, eta, m) ⇒ Object

Train the network with training input, expectations, input, and epoch num



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# File 'lib/deep_miner/perceptron.rb', line 57

def train(train_data, expect, epoch_iter = 50, eta, m)
  fail ArgumentError, 'Training must be Array' unless train_data.is_a? Array
  fail ArgumentError, 'Expectation must be Array' unless expect.is_a? Array
  fail ArgumentError, 'Data and Expectation must be same size' unless train_data.size == expect.size

  1.upto(epoch_iter) do
    error = 0.0
    train_data.zip(expect) do |sample, target|
      predict(sample)
      error += back_propogate(target, eta, m)
    end
  end
end