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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
def run(params=nil)
RestClientWrapper.post(@uri, params)
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
end
module Fminer
include Algorithm
class BBRC
include Fminer
# Initialize bbrc algorithm
def initialize
super File.join(CONFIG[:services]["opentox-algorithm"], "fminer/bbrc")
load_metadata
end
end
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
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] (Wighted) 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
def self.euclidean(prop_a,prop_b,weights=nil)
common_properties = prop_a.keys & prop_b.keys
if common_properties.size > 1
dist_sum = 0
common_properties.each do |p|
if weights
dist_sum += ( (prop_a[p] - prop_b[p]) * Algorithm.gauss(weights[p]) )**2
else
dist_sum += (prop_a[p] - prop_b[p])**2
end
end
1/(1+Math.sqrt(dist_sum))
else
0.0
end
end
end
# Gauss kernel
def self.gauss(sim, sigma = 0.3)
x = 1.0 - sim
Math.exp(-(x*x)/(2*sigma*sigma))
end
# Median of an array
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
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