# R integration
# workaround to initialize R non-interactively (former rinruby versions did this by default)
# avoids compiling R with X
R = nil
require "rinruby" 

module OpenTox

  # Wrapper for OpenTox Algorithms
  module Algorithm 

    include OpenTox

    # Execute algorithm with parameters, please consult the OpenTox API and the webservice documentation for acceptable parameters
    # @param [optional,Hash] params Algorithm parameters
    # @param [optional,OpenTox::Task] waiting_task (can be a OpenTox::Subtask as well), progress is updated accordingly
    # @return [String] URI of new resource (dataset, model, ...)
    def run(params=nil, waiting_task=nil)
      RestClientWrapper.post(@uri, params, {:accept => 'text/uri-list'}, waiting_task).to_s
    end
    
    # Get OWL-DL representation in RDF/XML format
    # @return [application/rdf+xml] RDF/XML representation
    def to_rdfxml
      s = Serializer::Owl.new
      s.add_algorithm(@uri,@metadata)
      s.to_rdfxml
    end

    # Generic Algorithm class, should work with all OpenTox webservices
    class Generic 
      include Algorithm
      
      # Find Generic Opentox Algorithm via URI, and loads metadata
      # @param [String] uri Algorithm URI
      # @return [OpenTox::Algorithm::Generic] Algorithm instance, nil if alogrithm was not found
      def self.find(uri, subjectid=nil)
        return nil unless uri
        alg = Generic.new(uri)
        alg.load_metadata( subjectid )
        if alg.metadata==nil or alg.metadata.size==0
          nil
        else
          alg
        end
      end
      
    end

    # Fminer algorithms (https://github.com/amaunz/fminer2)
    module Fminer
      include Algorithm

      # Backbone Refinement Class mining (http://bbrc.maunz.de/)
      class BBRC
        include Fminer
        # Initialize bbrc algorithm
        def initialize
          super File.join(CONFIG[:services]["opentox-algorithm"], "fminer/bbrc")
          load_metadata
        end
      end

      # LAtent STructure Pattern Mining (http://last-pm.maunz.de)
      class LAST
        include Fminer
        # Initialize last algorithm
        def initialize
          super File.join(CONFIG[:services]["opentox-algorithm"], "fminer/last")
          load_metadata
        end
      end

    end

    # Create lazar prediction model
    class Lazar
      include Algorithm
      # Initialize lazar algorithm
      def initialize
        super File.join(CONFIG[:services]["opentox-algorithm"], "lazar")
        load_metadata
      end
    end

    # Utility methods without dedicated webservices

    # Similarity calculations
    module Similarity
      include Algorithm

      # Tanimoto similarity
      # @param [Array] features_a Features of first compound
      # @param [Array] features_b Features of second compound
      # @param [optional, Hash] weights Weights for all features
      # @return [Float] (Weighted) tanimoto similarity
      def self.tanimoto(features_a,features_b,weights=nil)
        common_features = features_a & features_b
        all_features = (features_a + features_b).uniq
        common_p_sum = 0.0
        if common_features.size > 0
          if weights
            common_features.each{|f| common_p_sum += Algorithm.gauss(weights[f])}
            all_p_sum = 0.0
            all_features.each{|f| all_p_sum += Algorithm.gauss(weights[f])}
            common_p_sum/all_p_sum
          else
            common_features.to_f/all_features
          end
        else
          0.0
        end
      end

      # Euclidean similarity
      # @param [Hash] properties_a Properties of first compound
      # @param [Hash] properties_b Properties of second compound
      # @param [optional, Hash] weights Weights for all properties
      # @return [Float] (Weighted) euclidean similarity
      def self.euclidean(properties_a,properties_b,weights=nil)
        common_properties = properties_a.keys & properties_b.keys
        if common_properties.size > 1
          dist_sum = 0
          common_properties.each do |p|
            if weights
              dist_sum += ( (properties_a[p] - properties_b[p]) * Algorithm.gauss(weights[p]) )**2
            else
              dist_sum += (properties_a[p] - properties_b[p])**2
            end
          end
          1/(1+Math.sqrt(dist_sum))
        else
          0.0
        end
      end
    end

    module Neighbors

      # Classification with majority vote from neighbors weighted by similarity
      # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity`
      # @param [optional] params Ignored (only for compatibility with local_svm_regression)
      # @return [Hash] Hash with keys `:prediction, :confidence`
      def self.weighted_majority_vote(neighbors,params={})
        conf = 0.0
        confidence = 0.0
        neighbors.each do |neighbor|
          case neighbor[:activity].to_s
          when 'true'
            conf += Algorithm.gauss(neighbor[:similarity])
          when 'false'
            conf -= Algorithm.gauss(neighbor[:similarity])
          end
        end
        if conf > 0.0
          prediction = true
        elsif conf < 0.0
          prediction = false
        else
          prediction = nil
        end
        confidence = conf/neighbors.size if neighbors.size > 0
        {:prediction => prediction, :confidence => confidence.abs}
      end

      # Local support vector regression from neighbors 
      # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity, :features`
      # @param [Hash] params Keys `:similarity_algorithm,:p_values` are required
      # @return [Hash] Hash with keys `:prediction, :confidence`
      def self.local_svm_regression(neighbors,params )
        sims = neighbors.collect{ |n| n[:similarity] } # similarity values between query and neighbors
        conf = sims.inject{|sum,x| sum + x }
        acts = neighbors.collect do |n|
          act = n[:activity] 
          Math.log10(act.to_f)
        end # activities of neighbors for supervised learning

        neighbor_matches = neighbors.collect{ |n| n[:features] } # as in classification: URIs of matches
        gram_matrix = [] # square matrix of similarities between neighbors; implements weighted tanimoto kernel
        if neighbor_matches.size == 0
          raise "No neighbors found"
        else
          # gram matrix
          (0..(neighbor_matches.length-1)).each do |i|
            gram_matrix[i] = [] unless gram_matrix[i]
            # upper triangle
            ((i+1)..(neighbor_matches.length-1)).each do |j|
              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] 
              gram_matrix[j][i] = gram_matrix[i][j] # lower triangle
            end
            gram_matrix[i][i] = 1.0
          end

          LOGGER.debug gram_matrix.to_yaml

          @r = RinRuby.new(false,false) # global R instance leads to Socket errors after a large number of requests
          @r.eval "library('kernlab')" # this requires R package "kernlab" to be installed
          LOGGER.debug "Setting R data ..."
          # set data
          @r.gram_matrix = gram_matrix.flatten
          @r.n = neighbor_matches.size
          @r.y = acts
          @r.sims = sims

          LOGGER.debug "Preparing R data ..."
          # prepare data
          @r.eval "y<-as.vector(y)"
          @r.eval "gram_matrix<-as.kernelMatrix(matrix(gram_matrix,n,n))"
          @r.eval "sims<-as.vector(sims)"
          
          # model + support vectors
          LOGGER.debug "Creating SVM model ..."
          @r.eval "model<-ksvm(gram_matrix, y, kernel=matrix, type=\"nu-svr\", nu=0.8)"
          @r.eval "sv<-as.vector(SVindex(model))"
          @r.eval "sims<-sims[sv]"
          @r.eval "sims<-as.kernelMatrix(matrix(sims,1))"
          LOGGER.debug "Predicting ..."
          @r.eval "p<-predict(model,sims)[1,1]"
          prediction = 10**(@r.p.to_f)
          LOGGER.debug "Prediction is: '" + @prediction.to_s + "'."
          @r.quit # free R
        end
        confidence = conf/neighbors.size if neighbors.size > 0
        {:prediction => prediction, :confidence => confidence}
        
      end

    end

    module Substructure
      include Algorithm
      # Substructure matching
      # @param [OpenTox::Compound] compound Compound
      # @param [Array] features Array with Smarts strings
      # @return [Array] Array with matching Smarts
      def self.match(compound,features)
        compound.match(features)
      end
    end

    module Dataset
      include Algorithm
      # API should match Substructure.match
      def features(dataset_uri,compound_uri)
      end
    end
    
    # Gauss kernel
    # @return [Float] 
    def self.gauss(x, sigma = 0.3) 
      d = 1.0 - x
      Math.exp(-(d*d)/(2*sigma*sigma))
    end
    
    # Median of an array
    # @param [Array] Array with values
    # @return [Float] Median
    def self.median(array)
      return nil if array.empty?
      array.sort!
      m_pos = array.size / 2
      return array.size % 2 == 1 ? array[m_pos] : (array[m_pos-1] + array[m_pos])/2
    end

  end
end