diff options
Diffstat (limited to 'lib/regression.rb')
| -rw-r--r-- | lib/regression.rb | 33 |
1 files changed, 29 insertions, 4 deletions
diff --git a/lib/regression.rb b/lib/regression.rb index 4bade40..891d7f9 100644 --- a/lib/regression.rb +++ b/lib/regression.rb @@ -1,13 +1,38 @@ -#require "rinruby" - # TODO install R packages kernlab, caret, doMC, class, e1071 -# TODO use Rserve + + # log transform activities (create new dataset) + # scale, normalize features, might not be necessary + # http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised-before-making-a-model-when-is + # http://stats.stackexchange.com/questions/7112/when-and-how-to-use-standardized-explanatory-variables-in-linear-regression + # zero-order correlation and the semi-partial correlation + # seems to be necessary for svm + # http://stats.stackexchange.com/questions/77876/why-would-scaling-features-decrease-svm-performance?lq=1 + # http://stackoverflow.com/questions/15436367/svm-scaling-input-values + # use lasso or elastic net?? + # select relevant features + # remove features with a single value + # remove correlated features + # remove features not correlated with endpoint module OpenTox module Algorithm class Regression -require "rserve" + + def self.weighted_average neighbors + weighted_sum = 0.0 + sim_sum = 0.0 + neighbors.each do |row| + n,sim,acts = row + acts.each do |act| + weighted_sum += sim*Math.log10(act) + sim_sum += sim + end + end + confidence = sim_sum/neighbors.size.to_f + sim_sum == 0 ? prediction = nil : prediction = 10**(weighted_sum/sim_sum) + [prediction,confidence] + end # Local support vector regression from neighbors # @param [Hash] params Keys `:props, :activities, :sims, :min_train_performance` are required |