From b614689b3c293be1cbd566f28996e6c3a41f70a3 Mon Sep 17 00:00:00 2001
From: gebele <gebele@in-silico.ch>
Date: Wed, 9 Aug 2017 12:31:27 +0000
Subject: deleted obsolete files

---
 nanoparticles.rb | 182 -------------------------------------------------------
 1 file changed, 182 deletions(-)
 delete mode 100644 nanoparticles.rb

(limited to 'nanoparticles.rb')

diff --git a/nanoparticles.rb b/nanoparticles.rb
deleted file mode 100644
index d3399e9..0000000
--- a/nanoparticles.rb
+++ /dev/null
@@ -1,182 +0,0 @@
-require 'rserve'
-require 'json'
-require 'yaml'
-require 'csv'
-
-ENDPOINT = "Cell.association (Net cell association [mL/ug(Mg)])"
-
-def query_features
-  relevant_features = JSON.parse(File.read("./relevant-features.json"))
-end
-
-def predict params
-  # calculate difference parameters
-  diff_features = JSON.parse(File.read("./diff-features.json"))
-  diff_features.each do |feature,originals|
-    params[feature] = params[originals[1]]-params[originals[0]] # causes rounding errors!
-  end
-  neighbors = []
-  sim_sum = 0
-  weighted_sum = 0
-  match = nil
-  relevant_features = JSON.parse(File.read("./relevant-features.json"))
-  weights = relevant_features.values.collect{|v| v["r"]}
-  JSON.parse(File.read("./data.json")).each do |id,categories|
-    query_values = []
-    neighbor_values = []
-    relevant_features.keys.each do |f|
-      query_values << params[f]
-      neighbor_values << categories["physchem"][f]
-    end
-    sim = weighted_cosine_similarity(query_values,neighbor_values,weights)
-    if sim > 0.9999 # no exact match because of rounding errors
-      match = {id => categories}
-    elsif sim > 0.95
-      neighbor = categories
-      neighbor["similarity"] = sim
-      neighbor["sim"] = cosine_similarity(query_values,neighbor_values)
-      neighbor["id"] = id
-      sim_sum += sim
-      weighted_sum += sim*Math.log10(categories["tox"][ENDPOINT])
-      #weighted_sum += sim*categories["tox"][ENDPOINT]
-      neighbors << neighbor
-    end
-  end
-  neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
-  sim_sum == 0 ? prediction = nil : prediction =  10**(weighted_sum/sim_sum)
-  {
-    :query => params,
-    :match => match,
-    :prediction => {ENDPOINT => prediction},
-    :neighbors => neighbors
-  }
-end
-
-class Object
-  def numeric?
-    true if Float(self) rescue false
-  end
-end
-
-def euclidean_distance(a, b)
-  sq = a.zip(b).map{|a,b| (a - b) ** 2}
-  Math.sqrt(sq.inject(0) {|s,c| s + c})
-end
-
-def dot_product(a, b)
-  products = a.zip(b).map{|a, b| a * b}
-  products.inject(0) {|s,p| s + p}
-end
-
-def magnitude(point)
-  squares = point.map{|x| x ** 2}
-  Math.sqrt(squares.inject(0) {|s, c| s + c})
-end
-
-# http://stackoverflow.com/questions/1838806/euclidean-distance-vs-pearson-correlation-vs-cosine-similarity
-def cosine_similarity(a, b)
-  dot_product(a, b) / (magnitude(a) * magnitude(b))
-end
-
-def weighted_cosine_similarity(a, b, w)
-  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*magnitude_b)
-
-end
-
-#@endpoint = @data.collect{|r| r[5]}
-
-def neighbors query
-end
-
-def csv2json
-  csv = CSV.read("data/MergedSheets_edit.csv")
-  csv.collect!{|row| row[0..36].collect{|c| c.numeric? ? c.to_f : c } }.compact
-  feature_names = [
-    "ID",
-     csv[0][1],
-     csv[0][2],
-     csv[0][3],
-     csv[6][4],
-     "#{csv[0][5]} (#{csv[6][5]} [#{csv[11][5]}])", # endpoint
-     "#{csv[0][6]} (#{csv[6][6]})", # endpoint
-     "#{csv[6][7]} [#{csv[11][7]}]",
-     "#{csv[6][8]} [#{csv[11][8]}]",
-     "#{csv[6][9]} [#{csv[11][9]}]",
-  ]
-  (10..10+5*3).step(3) do |i|
-    feature_names += [
-     "#{csv[6][i]} [#{csv[11][i]}]",
-     "#{csv[6][i+1]} #{csv[8][i+1]} [#{csv[11][i+1]}]",
-     "#{csv[6][i+2]} #{csv[8][i+2]}",
-    ]
-  end
-  feature_names += [
-   "#{csv[6][28]}",
-   "#{csv[6][29]} #{csv[8][29]}",
-   "#{csv[6][30]} #{csv[8][30]}",
-  ]
-  (31..34).each do |i|
-    feature_names << "#{csv[6][i]} #{csv[8][i]} [#{csv[11][i]}]"
-  end
-  (35..36).each do |i|
-    feature_names << "#{csv[6][i]} #{csv[8][i]} #{csv[10][i]} [#{csv[11][i]}]"
-  end
-  data = {}
-  csv.drop(12).each do |row|
-    id = row.first
-    if id.match /^G/ # skip Ag, too many missing values
-      data[id] = {}
-      row.each_with_index do |col,i|
-        if i == 0
-          data[id][:composition] = {}
-        elsif i < 5
-          data[id][:composition][feature_names[i]] = col
-        elsif i == 5
-          data[id][:tox] ||= {}
-          data[id][:tox][feature_names[i]] = col
-        elsif i > 6
-          data[id][:physchem] ||= {}
-          data[id][:physchem][feature_names[i]] = col
-        end
-      end
-    end
-  end
-  File.open("data.json","w+"){|f| f.puts data.to_json}
-  data
-end
-
-#puts data.to_yaml
-=begin
-R.assign "endpoint", endpoint
-(0..data[0].size).each do |c|
-  if data.collect{|r| r[c]}.uniq.size > 1
-    begin
-    R.assign "feature", data.collect{|r| r[c]}
-    R.eval "r <- cor(-log(endpoint),-log(feature),use='complete')"
-    r = R.eval("r").to_ruby
-    p "#{c}: #{r}" if r > 0.3 or r < -0.3
-    rescue
-    end
-  end
-end
-
-
-csv[0..13].each do |row|
-  row.each_with_index do |col,i|
-    features[i] = features[i].to_s+", "+col.to_s
-  end
-end
-
-puts features.select{|f| f.match(/Mean/)}.to_yaml
-
-  #n+=1
-  #p n,row.first unless row.first.match /^[G|S]/
-=end
-- 
cgit v1.2.3