Class: Newral::Networks::Perceptron

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

Direct Known Subclasses

Sigmoid

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(weights: [], bias: 0, weight_length: nil, max_random_weight: 1, min_random_weight: -1 )) ⇒ Perceptron

to create random weights just specify weight_length

Raises:



12
13
14
15
16
17
18
19
20
21
 
# File 'lib/newral/networks/perceptron.rb', line 12

def initialize(weights:[],bias:0, weight_length: nil, max_random_weight:1, min_random_weight:-1 )
  raise Errors::WeightsAndWeightLengthGiven if weight_length && ( weights || ( weights && weights.size > 0 ))
  @max_random_weight = max_random_weight
  @min_random_weight=  min_random_weight
  @weights =weights || []
  @weights = weight_length.times.collect{ |i| (max_random_weight-min_random_weight)*rand()+min_random_weight } if weight_length
  @bias = bias || (weight_length && (max_random_weight-min_random_weight)*rand()+min_random_weight ) || 0
  @inputs = []
  @last_output = nil
end

Instance Attribute Details

#bias ⇒ Object (readonly)

Returns the value of attribute bias.



10
11
12
 
# File 'lib/newral/networks/perceptron.rb', line 10

def bias
  @bias
end

#inputs ⇒ Object (readonly)

Returns the value of attribute inputs.



10
11
12
 
# File 'lib/newral/networks/perceptron.rb', line 10

def inputs
  @inputs
end

#last_output ⇒ Object (readonly)

Returns the value of attribute last_output.



10
11
12
 
# File 'lib/newral/networks/perceptron.rb', line 10

def last_output
  @last_output
end

#weights ⇒ Object (readonly)

Returns the value of attribute weights.



10
11
12
 
# File 'lib/newral/networks/perceptron.rb', line 10

def weights
  @weights
end

Instance Method Details

#add_input(object) ⇒ Object 



51
52
53
54
55
56
57
58
59
 
# File 'lib/newral/networks/perceptron.rb', line 51

def add_input( object )
  if object.kind_of?( Array ) 
     @inputs=object
  else 
    @inputs << object
  end
  # automatically add a weight if number of inputs exceeds weight size 
  weights << (@max_random_weight-@min_random_weight)*rand()+@min_random_weight if weights.length < @inputs.length
end

#calculate_input(input) ⇒ Object 



38
39
40
 
# File 'lib/newral/networks/perceptron.rb', line 38

def calculate_input( input )
  input.kind_of?( Perceptron ) ? input.last_output : input
end

#calculate_value ⇒ Object

Raises:



28
29
30
31
32
33
34
35
36
 
# File 'lib/newral/networks/perceptron.rb', line 28

def calculate_value 
  raise Errors::InputAndWeightSizeDiffer, "weights: #{@weights.size  }, #{ @inputs.size }" unless @weights.size == @inputs.size
  value = 0
  @inputs.each_with_index do |input,idx|
    val = calculate_input( input )
    value = value+val*@weights[idx]
  end
  value = value+@bias 
end

#move(direction: 0, step: 0.01, step_percentage: nil) ⇒ Object

Raises:

  • (Errors::InvalidDirection) 


69
70
71
72
73
74
75
76
77
 
# File 'lib/newral/networks/perceptron.rb', line 69

def move( direction: 0, step:0.01, step_percentage: nil )
  raise Errors::InvalidDirection if direction >= number_of_directions
  if direction < @weights.size 
    @weights[direction] = step_percentage ?  @weights[direction]*(1+step_percentage.to_f/100) : @weights[direction]+step
  else
    @bias = step_percentage ?  @bias*(1+step_percentage.to_f/100) : @bias+step
  end
  self
end

#number_of_directions ⇒ Object

move + number of directions are just needed for some training algorithms not typically used for neural networks (like greedy) mainly implemented here for a proove of concept



64
65
66
 
# File 'lib/newral/networks/perceptron.rb', line 64

def number_of_directions
  @weights.size+1
end

#output ⇒ Object 



42
43
44
 
# File 'lib/newral/networks/perceptron.rb', line 42

def output
  @last_output = calculate_value <= 0 ? 0 : 1
end

#set_weights_and_bias(weights: [], bias: nil) ⇒ Object 



23
24
25
26
 
# File 'lib/newral/networks/perceptron.rb', line 23

def set_weights_and_bias( weights:[], bias: nil )
  @weights = weights
  @bias = bias || 0 # if none specified
end

#update_with_vector(inputs) ⇒ Object 



46
47
48
49
 
# File 'lib/newral/networks/perceptron.rb', line 46

def update_with_vector( inputs )
  @inputs = inputs
  output
end