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#!/usr/bin/env ruby
require 'nlp_ruby'
require 'json'
module HG
class HG::Node
attr_accessor :label, :cat, :outgoing, :incoming, :score
def initialize label=nil, cat=nil, outgoing=[], incoming=[], score=nil
@label = label
@cat = cat
@outgoing = outgoing
@incoming = incoming
@score = nil
end
def to_s
"Node<label:\"#{@label}\", cat:\"#{@cat}\", outgoing:#{@outgoing.size}, incoming:#{@incoming.size}>"
end
end
class HG::Hypergraph
attr_accessor :nodes, :edges
def initialize nodes=[], edges=[]
@nodes = nodes
@edges = edges
end
def arity
@edges.map { |e| e.arity }.max
end
def to_s
"Hypergraph<nodes:[#{@nodes.to_s}], edges:[#{@edges.to_s}], arity:#{arity}>"
end
end
class HG::Hyperedge
attr_accessor :head, :tails, :weight, :f, :mark
def initialize head=nil, tails=[], weight=0.0, f={}
@head = head
@tails = tails
@weight = weight
@f = f
@mark = 0
end
def arity
return @tails.size
end
def marked?
arity == @mark
end
def to_s
"Hyperedge<head:\"#{@head.label}\", tails:#{@tails.map{|n|n.label}}, arity:#{arity}, weight:#{@weight}, f:#{f.to_s}, mark:#{@mark}>"
end
end
def HG::topological_sort nodes
sorted = []
s = nodes.reject { |n| !n.incoming.empty? }
while !s.empty?
sorted << s.shift
sorted.last.outgoing.each { |e|
next if e.marked?
e.mark += 1
s << e.head if e.head.incoming.reject{ |f| f.mark==f.arity }.empty?
}
end
return sorted
end
def HG::init nodes, semiring, root
nodes.each { |n| n.score=semiring.null }
root.score = semiring.one
end
def HG::viterbi hypergraph, root, semiring=ViterbiSemiring.new
toposorted = topological_sort hypergraph.nodes
init toposorted, semiring, root
toposorted.each { |n|
n.incoming.each { |e|
s = semiring.one
e.tails.each { |m|
s = semiring.multiply.call(s, m.score)
}
n.score = semiring.add.call(n.score, semiring.multiply.call(s, e.weight))
}
}
end
def HG::read_hypergraph_from_json fn, semiring=RealSemiring.new, log_weights=false
nodes = []
edges = []
nodes_by_label = {}
nodes_by_index = []
h = JSON.parse File.new(fn).read
w = SparseVector.from_h h['weights']
h['nodes'].each { |i|
n = Node.new i['label'], i['cat']
nodes << n
nodes_by_label[n.label] = n
nodes_by_index << n
}
h['edges'].each { |i|
e = Hyperedge.new nodes_by_label[i['head']], i['tails'].map{|j| nodes_by_label[j]}.to_a, semiring.convert.call(i['weight'].to_f)
e.f = SparseVector.from_h i['f']
if log_weights
e.weight = Math.exp(w.dot(e.f))
else
e.weight = w.dot(e.f)
end
e.tails.each { |m|
m.outgoing << e
}
e.head.incoming << e
edges << e
}
return Hypergraph.new(nodes, edges), nodes_by_label, nodes_by_index
end
end #module
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