1 files changed, 147 insertions, 0 deletions
diff --git a/nanoparticles.rb b/nanoparticles.rb
new file mode 100644
index 0000000..890b3ca
--- /dev/null
+++ b/nanoparticles.rb
@@ -0,0 +1,147 @@
+require 'json'
+require 'yaml'
+require 'csv'
+
+ENDPOINT = "Cell.association (Net cell association [mL/ug(Mg)])"
+
+def predict params
+  neighbors = []
+  sim_sum = 0
+  weighted_sum = 0
+  match = nil
+  JSON.parse(File.read("./data.json")).each do |id,categories|
+    if params.values == categories["physchem"].values
+      match = {:id => categories}
+    else
+      sim = cosine_similarity(params.values,categories["physchem"].values)
+      neighbor = categories
+      neighbor["similarity"] = sim
+      neighbor["id"] = id
+      sim_sum += sim
+      weighted_sum += sim*Math.log(categories["tox"][ENDPOINT])
+      neighbors << neighbor
+    end
+  end
+  neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
+  {
+    :query => params,
+    :match => match,
+    :prediction => {ENDPOINT => 10**(weighted_sum/sim_sum)},
+    :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
+
+def cosine_similarity(a, b)
+  dot_product(a, b) / (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