From 1f789133d961c29d3babfaf69cdde3d675288537 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Sat, 24 Aug 2019 14:44:52 +0200
Subject: initial refactored version for mutagenicity paper

---
 lib/similarity.rb | 128 ++++++++++++++++++++++--------------------------------
 1 file changed, 52 insertions(+), 76 deletions(-)

(limited to 'lib/similarity.rb')

diff --git a/lib/similarity.rb b/lib/similarity.rb
index ccbc9d6..deeb81f 100644
--- a/lib/similarity.rb
+++ b/lib/similarity.rb
@@ -1,85 +1,61 @@
-module OpenTox
-  module Algorithm
+class Similarity
 
-    class Vector
-      # Get dot product 
-      # @param [Vector]
-      # @param [Vector]
-      # @return [Numeric]
-      def self.dot_product(a, b)
-        products = a.zip(b).map{|a, b| a * b}
-        products.inject(0) {|s,p| s + p}
-      end
-
-      def self.magnitude(point)
-        squares = point.map{|x| x ** 2}
-        Math.sqrt(squares.inject(0) {|s, c| s + c})
-      end
-    end
-
-    class Similarity
-
-      # Get Tanimoto similarity
-      # @param [Array<Array<Float>>]
-      # @return [Float]
-      def self.tanimoto fingerprints
-        ( fingerprints[0] & fingerprints[1]).size/(fingerprints[0]|fingerprints[1]).size.to_f
-      end
-
-      #def self.weighted_tanimoto fingerprints
-        #( fingerprints[0] & fingerprints[1]).size/(fingerprints[0]|fingerprints[1]).size.to_f
-      #end
+  # 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 scaled_properties
-        sq = scaled_properties[0].zip(scaled_properties[1]).map{|a,b| (a - b) ** 2}
-        Math.sqrt(sq.inject(0) {|s,c| s + c})
-      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 scaled_properties
-        scaled_properties = remove_nils scaled_properties
-        Algorithm::Vector.dot_product(scaled_properties[0], scaled_properties[1]) / (Algorithm::Vector.magnitude(scaled_properties[0]) * Algorithm::Vector.magnitude(scaled_properties[1]))
-      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
 
-      # 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
+=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]
+  # 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
-- 
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