Module: Newral::Tools

Defined in:
lib/newral/tools.rb

Defined Under Namespace

Modules: Errors

Class Method Summary collapse

Class Method Details

.denormalize(data, high: nil, low: nil, normalized_high: 1, normalized_low: -1)) ⇒ Object 



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# File 'lib/newral/tools.rb', line 30

def self.denormalize( data, high: nil, low:nil,normalized_high:1,normalized_low:-1)
  normalize( data, normalized_high: high, normalized_low: low, high: normalized_high, low: normalized_low)
end

.euclidian_distance(v1, v2) ⇒ Object 



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# File 'lib/newral/tools.rb', line 34

def self.euclidian_distance( v1, v2 )
  total = 0.0
  v1.each_with_index do   |value,idx|
    total=total+(value-v2[idx])**2
  end
  total**0.5
end

.gaussian_density(x, mu: nil, sigma: nil, elements: nil) ⇒ Object

youtu.be/_DhelJs0BFc?t=4m10s keisan.casio.com/exec/system/1180573188

Raises:



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# File 'lib/newral/tools.rb', line 124

def self.gaussian_density( x, mu: nil , sigma: nil, elements:nil )
  raise Errors::NoElements if ( mu.nil?  || sigma.nil? ) && elements.nil?
  mu = mean(  elements ) unless mu 
  sigma = sigma(  elements ) unless sigma 
  (1.0/((2*Math::PI)**0.5*sigma.to_f))*Math.exp((-1.0/2)*((x-mu)**2/sigma.to_f**2 ))
end

.general_to_specific(samples) ⇒ Object

example movies user does not likes with 3 properties action, comedy, romance

[[1,0,1],[0,0,1]] => user does not like action+romance, romance

output be [-1,1,-1] => action no, romance no



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# File 'lib/newral/tools.rb', line 106

def self.general_to_specific( samples )
  hypotheses=[0]*samples.first.length
  samples.each do |sample|
    sample.each_with_index do |v,idx|
      if v == 1
        hypotheses[idx] = -1 unless hypotheses[idx] == 1
      else 
        hypotheses[idx] = 1 if hypotheses[idx] == 0
      end
    end
  end
  hypotheses
end

.k_nearest_neighbour(point, clusters, number_of_neighbours: 5) ⇒ Object

point is a vector clusters have a key , values are an array of point we iterate over all points and return the closest ones as an Array of [key,distance,point] to classify you can then count the nearest neighbours by key or use the weighted k_nearest_neighbour approach



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# File 'lib/newral/tools.rb', line 70

def self.k_nearest_neighbour( point, clusters, number_of_neighbours: 5 )
  distances = []
  
  clusters.each do |key,values|
    values.each do |value|
      # we optimize here by sorting on insert, or sort and slice after distances
      # array exceeds number_of_neighbours*5
      distances = sort_and_slice( distances, number_of_entries: number_of_neighbours ) if distances.length > number_of_neighbours*5
      distances << [key,euclidian_distance( point, value ),value]
    end
  end
  sort_and_slice( distances, number_of_entries: number_of_neighbours )
end

.max_distance(v1, v2) ⇒ Object 



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# File 'lib/newral/tools.rb', line 42

def self.max_distance( v1, v2 )
  max = 0.0
  v1.each_with_index do   |value,idx|
    dist=(value-v2[idx]).abs
    max = dist unless max > dist 
  end
  max
end

.mean(array) ⇒ Object 



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# File 'lib/newral/tools.rb', line 9

def self.mean( array )
  array.sum.to_f/array.length
end

.more_general_than_or_equal(samples) ⇒ Object

input: array of samples which lead to positive result example movies user likes with 3 properties action, comedy, romance

[[1,0,1],[0,0,1]] => user likes action+romance, romance

output be [0,-1,1] => action y/n does not matter, comedy no, romance yes



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# File 'lib/newral/tools.rb', line 88

def self.more_general_than_or_equal( samples )
  hypotheses=[-1]*samples.first.length
  samples.each do |sample|
    sample.each_with_index do |v,idx|
      if v == 1
        hypotheses[idx] = 1 unless hypotheses[idx] == 0
      else 
        hypotheses[idx] = 0 if hypotheses[idx] == 1
      end
    end
  end
  hypotheses
end

.normalize(data, high: nil, low: nil, normalized_high: 1, normalized_low: -1)) ⇒ Object 



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# File 'lib/newral/tools.rb', line 19

def self.normalize( data, high: nil, low:nil,normalized_high:1,normalized_low:-1)
  unless high && low 
    data.sort!
    high ||= [data[data.length-1],data[0].abs].max
    low  ||= data[0] >= 0 ? data[0] : [high*-1,data[0]].min
  end 
  data.collect do |data_point|
    (data_point-low)/(high-low).to_f*(normalized_high-normalized_low)+normalized_low
  end
end

.sigma(array) ⇒ Object 



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# File 'lib/newral/tools.rb', line 13

def self.sigma( array )
  mean = self.mean( array )
  sigma_square = array.inject(0){ |value,el| value+(el-mean)**2 }
  (sigma_square/array.length)**0.5
end

.sort_and_slice(distances, number_of_entries: 5, element_at_index: 1) ⇒ Object 



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# File 'lib/newral/tools.rb', line 59

def self.sort_and_slice( distances, number_of_entries:5,element_at_index:1 )
    distances.sort do |v1,v2|
      v1[ element_at_index ]<=>v2[ element_at_index ]
    end[0..number_of_entries-1]
end

.taxi_cab_distance(v1, v2) ⇒ Object 



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# File 'lib/newral/tools.rb', line 51

def self.taxi_cab_distance( v1, v2 )
  total = 0.0
  v1.each_with_index do   |value,idx|
    total=total+(value-v2[idx]).abs
  end
  total
end