Class: Newral::Data::Base

Inherits:
Object
  • Object
show all
Defined in:
lib/newral/data/base.rb

Direct Known Subclasses

Csv, Idx

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(inputs: [], outputs: [], labels: []) ⇒ Base

Returns a new instance of Base.

Raises:



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# File 'lib/newral/data/base.rb', line 14

def initialize( inputs: [], outputs: [], labels:[] )
  @inputs = inputs
  @outputs = outputs
  @labels = labels
  raise Errors::InputOutputSizeMismatch unless @inputs.size == @outputs.size
end

Instance Attribute Details

#inputs ⇒ Object

Returns the value of attribute inputs.



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# File 'lib/newral/data/base.rb', line 13

def inputs
  @inputs
end

#labels ⇒ Object

Returns the value of attribute labels.



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# File 'lib/newral/data/base.rb', line 13

def labels
  @labels
end

#outputs ⇒ Object

Returns the value of attribute outputs.



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# File 'lib/newral/data/base.rb', line 13

def outputs
  @outputs
end

Instance Method Details

#add_input(input, output: nil, label: nil) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 21

def add_input( input, output: nil, label:nil )
  @labels << label
  @outputs << output
  @inputs << input
end

#count_outputs ⇒ Object 



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# File 'lib/newral/data/base.rb', line 117

def count_outputs
  output_hash = {}
  @outputs.each do |output| 
    output_key = output.size == 1 ? output.first.to_s.to_sym : output.join('-')
    output_hash[output_key] = (output_hash[output_key] || 0) + 1
  end
  output_hash 
end

#downsample_input!(height: 1, width: 1, width_of_line: nil) ⇒ Object

Raises:

  • (DownSamplingImpossible) 


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# File 'lib/newral/data/base.rb', line 130

def downsample_input!( height:1, width: 1, width_of_line: nil )
  raise DownSamplingImpossible unless @inputs.first.size % ( width*height ) == 0
  total_height = @inputs.first.size/width_of_line
  
  @inputs.collect! do |input| 
    downsampled = []
    pos = 0
    while pos < input.size do 
      matrix = []
      height.times do |h|
        start_pos = pos+(width_of_line*h)
        end_pos = pos+width+(width_of_line)*h-1
        matrix = matrix+input[start_pos..end_pos]
      end 
      downsampled << (  matrix.inject(0){|sum,e| sum+e }/matrix.length.to_f > 0.5 ? 1 : 0 )
      pos = pos+width
      pos=pos+width_of_line*(height-1) if (pos%width_of_line) == 0
    end
    downsampled 
  end 
end

#inputs_for_output(output) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 65

def inputs_for_output( output )
  values_for output, search_objects: @outputs, return_objects: @inputs
end

#label_for_input(input) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 61

def label_for_input( input )
  values_for input, search_objects: @inputs, return_objects: @labels, only_first:  true
end

#normalized_inputs(normalized_high: 1, normalized_low: -1 )) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 69

def normalized_inputs(normalized_high: 1, normalized_low:-1 )
  return [] if @inputs.size == 0 || !@inputs.first.kind_of?( Array )
  max_values = [Float::MIN]*@inputs.first.size
  min_values = [Float::MAX]*@inputs.first.size
  @inputs.each do |input|
    input.each_with_index do |value,idx|
      max_values[idx] = value.to_f if value > max_values[idx]
      min_values[idx] = value.to_f if value < min_values[idx]
    end
  end
  @inputs.collect do |input|
    row = [0]*input.size
    input.each_with_index do |value,idx|
      row[idx] = (value-min_values[idx])/(max_values[idx]-min_values[idx]).to_f*(normalized_high-normalized_low)+normalized_low
    end
    row 
  end
end

#output_as_vector(category: :all) ⇒ Object

this will make it easier to use outputs for neura networks as it translates them to vectors like [1,0,0] if you have 3 possible outputs this will return [1,0,0],,[0,0,1]



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# File 'lib/newral/data/base.rb', line 108

def output_as_vector( category: :all )
  hash = output_hash
  sub_set( set: :outputs, category: category ).collect do  |k| 
    vector = [0]*output_hash.keys.size
    vector[ output_hash.keys.index( k ) ] = 1 # output_hash.keys 
    vector
  end 
end

#output_for_input(input) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 57

def output_for_input( input )
  values_for input, search_objects: @inputs, return_objects: @outputs, only_first:  true 
end

#output_hash(normalized_high: 1, normalized_low: -1 )) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 88

def output_hash( normalized_high: 1, normalized_low:-1 )
  @output_hash = @outputs.inject({}) do |hash,output|
    hash[output] = ( hash[output] || 0 )+1
    hash
  end 
  new_hash = {}
  @output_hash.keys.sort.each_with_index do |key,idx|
    new_hash[ key ] = normalized_low+((normalized_high.to_f-normalized_low)*idx)/( [@output_hash.keys.length-1,1].max )
  end 
  @output_hash = new_hash
end

#output_normalized ⇒ Object 



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# File 'lib/newral/data/base.rb', line 100

def output_normalized
   hash = output_hash
   @outputs.collect{ |k| hash[k]}
end

#sample(offset: 0, limit: 100) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 126

def sample( offset:0,limit:100 )
  Base.new( inputs:  @inputs[offset..limit+offset] , outputs: @outputs[offset..limit+offset] )
end

#sub_set(set: :inputs, category: :all) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 27

def sub_set( set: :inputs, category: :all )
  data = case set
    when :inputs then @inputs
    when :outputs then @outputs 
    else
      raise Errors::UnknownSet
    end 
  
  case category
    when :all then data 
    when :training then data[0..(data.size.to_f*0.7).to_i]
    when :validation then  data[(data.size.to_f*0.7).to_i+1..(data.size.to_f*0.8).to_i ]
    when :testing then  data[(data.size.to_f*0.8).to_i+1,data.size ]
  else
    raise Errors::UnknownCategory, category.to_s
  end
end

#values_for(searched_value, only_first: false, return_objects: [], search_objects: []) ⇒ Object 



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# File 'lib/newral/data/base.rb', line 45

def values_for( searched_value, only_first: false, return_objects: [], search_objects: [] )
  results = []
  search_objects.each_with_index do |each_value,idx|
    if only_first 
      return return_objects[idx] if searched_value == each_value || [searched_value] == each_value
    else 
      results << return_objects[idx] if searched_value == each_value || [searched_value] == each_value
    end
  end
  results unless only_first
end