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| author | Andreas Maunz <andreas@maunz.de> | 2011-07-21 11:13:44 +0200 |
|---|---|---|
| committer | Andreas Maunz <andreas@maunz.de> | 2011-07-21 11:13:44 +0200 |
| commit | ce30df7393b3a8d57faa21b441ac0c57dd766691 (patch) | |
| tree | f061f276a1038c02cc37677c4c520ae95b306b76 | |
| parent | b1e6b8aaeaed95797cd67e13567ac72344e89707 (diff) | |
Minor improvements
| -rw-r--r-- | lib/algorithm.rb | 19 |
1 files changed, 16 insertions, 3 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index df010e1..73f99ae 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -180,7 +180,7 @@ module OpenTox common_p_sum/all_p_sum else #LOGGER.debug "common_features : #{common_features}, all_features: #{all_features}, c/a: #{(common_features.size/all_features.size).to_f}" - (common_features.size/all_features.size).to_f + common_features.size.to_f/all_features.size.to_f end else 0.0 @@ -237,6 +237,10 @@ module OpenTox end + # Multi-linear regression weighted by similarity. + # Objective Feature Selection, Principal Components Analysis, Scaling of Axes. + # @param [Hash] params Keys `:n_prop, :q_prop, :sims, :acts` are required + # @return [Numeric] A prediction value. def self.mlr(params) # GSL matrix operations: @@ -290,8 +294,6 @@ module OpenTox confidence_sum = 0.0 confidence = 0.0 prediction = nil - positive_map_value= nil - negative_map_value= nil params[:neighbors].each do |neighbor| neighbor_weight = Algorithm.gauss(neighbor[:similarity]).to_f @@ -387,9 +389,20 @@ module OpenTox else # gram matrix (0..(neighbor_matches.length-1)).each do |i| + #neighbor_i_hits = params[:fingerprints][params[:neighbors][i]] + puts + puts params[:fingerprints][params[:neighbors][i]] + puts gram_matrix[i] = [] unless gram_matrix[i] # upper triangle ((i+1)..(neighbor_matches.length-1)).each do |j| + #neighbor_j_hits= params[:fingerprints][params[:neighbors][j]] + puts + puts params[:fingerprints][params[:neighbors][j]] + sim_params = {} + sim_params[:compound_features_hits] = neighbor_i_hits + sim_params[:training_compound_features_hits] = neighbor_j_hits + #sim = eval("#{params[:similarity_algorithm]}(neighbor_matches[i], neighbor_matches[j], params[:p_values], sim_params)") sim = eval("#{params[:similarity_algorithm]}(neighbor_matches[i], neighbor_matches[j], params[:p_values])") gram_matrix[i][j] = Algorithm.gauss(sim) gram_matrix[j] = [] unless gram_matrix[j] |