diff options
Diffstat (limited to 'lib/algorithm.rb')
| -rw-r--r-- | lib/algorithm.rb | 62 |
1 files changed, 52 insertions, 10 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index 3170efb..528c426 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -230,21 +230,17 @@ module OpenTox raise "No neighbors found." unless params[:neighbors].size>0 begin - props = params[:prop_kernel] ? get_props(params) : nil acts = params[:neighbors].collect { |n| act = n[:activity].to_f } sims = params[:neighbors].collect { |n| Algorithm.gauss(n[:similarity]) } - LOGGER.debug "Local MLR (Propositionalization / GSL)." prediction = mlr( {:n_prop => props[0], :q_prop => props[1], :sims => sims, :acts => acts} ) transformer = eval "OpenTox::Algorithm::Transform::#{params[:transform]["class"]}.new ([#{prediction}], #{params[:transform]["offset"]})" prediction = transformer.values[0] LOGGER.debug "Prediction is: '" + prediction.to_s + "'." - sims = params[:neighbors].collect{ |n| Algorithm.gauss(n[:similarity]) } # similarity values btwn q and nbors - conf = sims.inject{|sum,x| sum + x } - confidence = conf/params[:neighbors].size if params[:neighbors].size > 0 + params[:conf_stdev] = "false" if params[:conf_stdev].nil? + confidence = get_confidence({:sims => sims, :acts => acts, :neighbors => params[:neighbors], :conf_stdev => params[:conf_stdev]}) {:prediction => prediction, :confidence => confidence} - rescue Exception => e LOGGER.debug "#{e.class}: #{e.message}" end @@ -351,8 +347,8 @@ module OpenTox transformer = eval "OpenTox::Algorithm::Transform::#{params[:transform]["class"]}.new ([#{prediction}], #{params[:transform]["offset"]})" prediction = transformer.values[0] LOGGER.debug "Prediction is: '" + prediction.to_s + "'." - conf = sims.inject{|sum,x| sum + x } - confidence = conf/params[:neighbors].size + params[:conf_stdev] = "false" if params[:conf_stdev].nil? + confidence = get_confidence({:sims => sims, :acts => acts, :neighbors => params[:neighbors], :conf_stdev => params[:conf_stdev]}) {:prediction => prediction, :confidence => confidence} rescue Exception => e LOGGER.debug "#{e.class}: #{e.message}" @@ -517,6 +513,29 @@ module OpenTox prediction end + # Get confidence for regression, with standard deviation of neighbor activity if conf_stdev is set. + # @param[Hash] Required keys: :sims, :acts, :neighbors, :conf_stdev + # @return[Float] Confidence + def self.get_confidence(params) + if params[:conf_stdev] == "true" + sim_median = Algorithm.median(params[:sims]) + if sim_median.nil? + confidence = nil + else + standard_deviation = params[:acts].std_dev + confidence = (sim_median*Math.exp(-1*standard_deviation)).abs + if confidence.nan? + confidence = nil + end + end + else + conf = params[:sims].inject{|sum,x| sum + x } + confidence = conf/params[:neighbors].size + end + LOGGER.debug "Confidence is: '" + confidence.to_s + "'." + return confidence + end + # Get X and Y size of a nested Array (Matrix) def self.get_sizes(matrix) begin @@ -545,7 +564,7 @@ module OpenTox row = [] params[:features].each do |f| if ! params[:fingerprints][n].nil? - row << (params[:fingerprints][n].include?(f) ? 0.0 : params[:p_values][f]) + row << (params[:fingerprints][n].include?(f) ? params[:p_values][f] : 0.0) else row << 0.0 end @@ -778,9 +797,14 @@ module OpenTox Math.exp(-(d*d)/(2*sigma*sigma)) end + # For symbolic features + # @param [Array] Array to test, must indicate non-occurrence with 0. + # @return [Boolean] Whether the feature is singular or non-occurring or present everywhere. def self.isnull_or_singular?(array) nr_zeroes = array.count(0) - return ((nr_zeroes == array.size) || (nr_zeroes == 0) || (nr_zeroes == 1) || (nr_zeroes == array.size-1) ) + return (nr_zeroes == array.size) || # remove non-occurring feature + (nr_zeroes == array.size-1) || # remove singular feature + (nr_zeroes == 0) # also remove feature present everywhere end # Median of an array @@ -865,6 +889,24 @@ module OpenTox p_sum end + # Adds variance, mean and standard deviation calculation to Array class + module Variance + def sum(&blk) + map(&blk).inject { |sum, element| sum + element } + end + def mean + (sum.to_f / size.to_f) + end + def variance + m = mean + sum { |i| ( i - m )**2 } / (size-1).to_f + end + def std_dev + Math.sqrt(variance) + end + end + Array.send :include, Variance + end end |