=> 0.3; License and README updated; some from_* methods for SparseVector; ttable.rb => Translation.rb; moved some misc. stuff to misc.rb; monkey patched String

1 files changed, 205 insertions, 0 deletions

diff --git a/lib/nlp_ruby/dag.rb b/lib/nlp_ruby/dag.rb
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+++ b/lib/nlp_ruby/dag.rb
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+module DAG
+
+require 'json'
+
+
+class DAG::Node
+  attr_accessor :label, :edges, :incoming, :score, :mark
+
+  def initialize label=nil, edges=[], incoming=[], score=nil
+    @label    = label
+    @edges    = edges # outgoing
+    @incoming = incoming
+    @score    = nil
+  end
+
+  def add_edge head, weight=0
+    exit if self==head # no self-cycles!
+    @edges << DAG::Edge.new(self, head, weight)
+    return @edges.last
+  end
+
+  def to_s
+    "DAG::Node<label:#{label}, edges:#{edges.size}, incoming:#{incoming.size}>"
+  end
+
+  def repr
+    "#{to_s} #{@score} out:#{@edges} in:[#{@incoming.map{|e| e.to_s}.join ', '}]"
+  end
+end
+
+class DAG::Edge
+  attr_accessor :tail, :head, :weight, :mark
+
+  def initialize tail=nil, head=nil, weight=0
+    @tail   = tail
+    @head   = head
+    @weight = weight
+    @mark   = false # did we already follow this edge? -- for topological sorting
+  end
+
+  def to_s
+    s = "DAG::Edge<#{@tail} ->[#{weight}] #{@head}"
+    s += " x" if @mark
+    s += ">"
+    s
+  end
+end
+
+# depth-first search
+#  w/o markings as we do not have cycles
+def DAG::dfs n, target_label
+  return n if n.label==target_label # assumes uniq labels!
+  stack = n.edges.map { |i| i.head }
+  while !stack.empty?
+    m = stack.pop
+    return DAG::dfs m, target_label
+  end
+  return nil
+end
+
+# breadth-first search
+#  w/o markings as we do not have cycles
+def DAG::bfs n, target_label
+  queue = [n]
+  while !queue.empty?
+    m = queue.shift
+    return m if m.label==target_label
+    m.edges.each { |e| queue << e.head }
+  end
+  return nil
+end
+
+# topological sort
+def DAG::topological_sort graph
+  sorted = []
+  s = graph.reject { |n| !n.incoming.empty? }
+  while !s.empty?
+    sorted << s.shift
+    sorted.last.edges.each { |e|
+      e.mark = true
+      s << e.head if e.head.incoming.reject{|f| f.mark}.empty?
+    }
+  end
+  return sorted
+end
+
+# initialize graph scores with semiring One
+def DAG::init graph, semiring, source_node
+  graph.each {|n| n.score=semiring.null}
+  source_node.score = semiring.one
+end
+
+# viterbi
+def DAG::viterbi graph, semiring=ViterbiSemiring, source_node
+  toposorted = DAG::topological_sort(graph)
+  DAG::init(graph, semiring, source_node)
+  toposorted.each { |n|
+    n.incoming.each { |e|
+      # update
+      n.score = \
+        semiring.add.call(n.score, \
+                          semiring.multiply.call(e.tail.score, e.weight)
+        )
+    }
+  }
+end
+
+# forward viterbi
+def DAG::viterbi_forward graph, semiring=ViterbiSemiring, source_node
+  toposorted = DAG::topological_sort(graph)
+  DAG::init(graph, semiring, source_node)
+  toposorted.each { |n|
+    n.edges.each { |e|
+      e.head.score = \
+        semiring.add.call(e.head.score, \
+                          semiring.multiply.call(n.score, e.weight)
+        )
+    }
+  }
+end
+
+# Dijkstra algorithm
+#  for A*-search we would need an optimistic estimate of
+#  future cost at each node
+def DAG::dijkstra graph, semiring=RealSemiring.new, source_node
+  DAG::init(graph, semiring, source_node)
+  q = PriorityQueue.new graph
+  while !q.empty?
+    n = q.pop
+    n.edges.each { |e|
+      e.head.score = \
+        semiring.add.call(e.head.score, \
+                          semiring.multiply.call(n.score, e.weight))
+      q.sort!
+    }
+  end
+end
+
+# Bellman-Ford algorithm
+def DAG::bellman_ford(graph, semiring=RealSemiring.new, source_node)
+  DAG::init(graph, semiring, source_node)
+  edges = []
+  graph.each { |n| edges |= n.edges }
+  # relax edges
+  (graph.size-1).times{ |i|
+    edges.each { |e|
+      e.head.score = \
+        semiring.add.call(e.head.score, \
+                          semiring.multiply.call(e.tail.score, e.weight))
+    }
+  }
+  # we do not allow cycles (negative or positive)
+end
+
+# Floyd algorithm
+def DAG::floyd(graph, semiring=nil)
+  dist_matrix = []
+  graph.each_index { |i|
+    dist_matrix << []
+    graph.each_index { |j|
+      val = 1.0/0.0
+      val = 0.0 if i==j
+      dist_matrix.last << val
+    }
+  }
+  edges = []
+  graph.each { |n| edges |= n.edges }
+  edges.each { |e|
+    dist_matrix[graph.index(e.tail)][graph.index(e.head)] = e.weight
+  }
+  0.upto(graph.size-1) { |k|
+    0.upto(graph.size-1) { |i|
+      0.upto(graph.size-1) { |j|
+        if dist_matrix[i][k] + dist_matrix[k][j] < dist_matrix[i][j]
+          dist_matrix  [i][j] = dist_matrix[i][k] + dist_matrix[k][j]
+        end
+      }
+    }
+  }
+  return dist_matrix
+end
+
+
+# returns a list of nodes (graph) and a hash for finding
+# nodes by their label (these need to be unique!)
+def DAG::read_graph_from_json fn, semiring=RealSemiring.new
+  graph = []
+  nodes_by_label = {}
+  h = JSON.parse File.new(fn).read
+  h['nodes'].each { |i|
+    n = DAG::Node.new i['label']
+    graph << n
+    nodes_by_label[n.label] = n
+  }
+  h['edges'].each { |i|
+    n = nodes_by_label[i['tail']]
+    a = n.add_edge(nodes_by_label[i['head']], semiring.convert.call(i['weight'].to_f))
+    nodes_by_label[i['head']].incoming << a
+  }
+  return graph, nodes_by_label
+end
+
+
+end # module
+