class Similarity

  # Get Tanimoto similarity
  # @param [Array<Array<String>>]
  # @return [Float]
  def self.tanimoto fingerprints
    ( fingerprints[0] & fingerprints[1] ).size/( fingerprints[0] | fingerprints[1] ).size.to_f
  end

  # Get Euclidean distance 
  # @param [Array<Array<Float>>]
  # @return [Float]
  def self.euclid variables
    sq = variables[0].zip(variables[1]).map{|a,b| (a - b) ** 2}
    Math.sqrt(sq.inject(0) {|s,c| s + c})
  end

  # Get cosine similarity
  #   http://stackoverflow.com/questions/1838806/euclidean-distance-vs-pearson-correlation-vs-cosine-similarity
  # @param [Array<Array<Float>>]
  # @return [Float]
  def self.cosine variables
    variables[0].dot_product(variables[1]) / (variables[0].magnitude * variables[1].magnitude)
  end

=begin
  # Get weighted cosine similarity
  #   http://stackoverflow.com/questions/1838806/euclidean-distance-vs-pearson-correlation-vs-cosine-similarity
  # @param [Array<Array<Float>>] [a,b,weights]
  # @return [Float]
  def self.weighted_cosine scaled_properties 
    a,b,w = remove_nils scaled_properties
    return cosine(scaled_properties) if w.uniq.size == 1
    dot_product = 0
    magnitude_a = 0
    magnitude_b = 0
    (0..a.size-1).each do |i|
      dot_product += w[i].abs*a[i]*b[i]
      magnitude_a += w[i].abs*a[i]**2
      magnitude_b += w[i].abs*b[i]**2
    end
    dot_product/(Math.sqrt(magnitude_a)*Math.sqrt(magnitude_b))
  end

  # Remove nil values
  # @param [Array<Array<Float>>] [a,b,weights]
  # @return [Array<Array<Float>>] [a,b,weights]
  def self.remove_nils scaled_properties
    a =[]; b = []; w = []
    (0..scaled_properties.first.size-1).each do |i|
      if scaled_properties[0][i] and scaled_properties[1][i] and !scaled_properties[0][i].nan? and !scaled_properties[1][i].nan?
        a << scaled_properties[0][i]
        b << scaled_properties[1][i]
        w << scaled_properties[2][i]
      end
    end
    [a,b,w]
  end
=end

end