Module: Similar

Defined in:
lib/similar.rb,
lib/similar/version.rb

Constant Summary collapse

VERSION = 
"0.0.5"

Class Method Summary collapse

Class Method Details

.euclidean_distance(a, b) ⇒ Object

Calculate the distance between two Arrays of values.

Distance is inverted, so higher values are better.



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# File 'lib/similar.rb', line 49

def self.euclidean_distance(a, b)
  # sum, of the squares of the differences...
  sum = a.zip(b).map { |x, y| ( (x - y) ** 2) }.inject(:+)
  1 / (1+ sum)
end

.pearson_score(a, b) ⇒ Object

Calculate the pearson score for the values in two Arrays.

Each Array must contain the same number of elements.

Raises:

  • (ArgumentError) 


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# File 'lib/similar.rb', line 8

def self.pearson_score(a, b)
  n = a.length

  # There is nothing to compare.
  return 0 unless n > 0

  raise ArgumentError.new("Arrays not of equal length") if n != b.length

  # There is a case with pearson score, where if the two arrays
  # are exactly the same it returns 0, when really the score should be 1.0
  # as there is an exact correlation between the values.
  #
  # Zero is returned in this case because determining the difference between
  # points shows that there is no difference at all... zero.
  #
  # I am returning 1.0 to show extremely high similarity.
  return 1.0 if a == b

  # sum of the values
  sum_1 = a.inject(0) { |sum, c| sum + c }
  sum_2 = b.inject(0) { |sum, c| sum + c }

  # sum of the squares
  sum_1_sq = a.inject(0) { |sum, c| sum + c ** 2 }
  sum_2_sq = b.inject(0) { |sum, c| sum + c ** 2 }

  # sum of the product
  prod_sum = a.zip(b).inject(0) { |sum, ab| sum + ab[0] * ab[1] }

  # calculate the Pearson score
  num = prod_sum - (sum_1 * sum_2 / n)
  den = Math.sqrt((sum_1_sq - (sum_1 ** 2) / n) * (sum_2_sq - (sum_2 ** 2) / n))

  return 0 if den == 0

  num / den.to_f
end