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cimport hypergraph
cimport kbest
cdef class Hypergraph:
cdef hypergraph.Hypergraph* hg
cdef MT19937* rng
def __dealloc__(self):
del self.hg
if self.rng != NULL:
del self.rng
cdef MT19937* _rng(self):
if self.rng == NULL:
self.rng = new MT19937()
return self.rng
def viterbi(self):
cdef vector[WordID] trans
hypergraph.ViterbiESentence(self.hg[0], &trans)
return unicode(GetString(trans).c_str(), 'utf8')
def viterbi_trees(self):
f_tree = unicode(hypergraph.ViterbiFTree(self.hg[0]).c_str(), 'utf8')
e_tree = unicode(hypergraph.ViterbiETree(self.hg[0]).c_str(), 'utf8')
return (f_tree, e_tree)
def viterbi_features(self):
cdef SparseVector fmap = SparseVector.__new__(SparseVector)
fmap.vector = new FastSparseVector[weight_t](hypergraph.ViterbiFeatures(self.hg[0]))
return fmap
def viterbi_joshua(self):
return unicode(hypergraph.JoshuaVisualizationString(self.hg[0]).c_str(), 'utf8')
def kbest(self, size):
cdef kbest.KBestDerivations[vector[WordID], kbest.ESentenceTraversal]* derivations = new kbest.KBestDerivations[vector[WordID], kbest.ESentenceTraversal](self.hg[0], size)
cdef kbest.KBestDerivations[vector[WordID], kbest.ESentenceTraversal].Derivation* derivation
cdef unsigned k
try:
for k in range(size):
derivation = derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
if not derivation: break
yield unicode(GetString(derivation._yield).c_str(), 'utf8')
finally:
del derivations
def kbest_trees(self, size):
cdef kbest.KBestDerivations[vector[WordID], kbest.FTreeTraversal]* f_derivations = new kbest.KBestDerivations[vector[WordID], kbest.FTreeTraversal](self.hg[0], size)
cdef kbest.KBestDerivations[vector[WordID], kbest.FTreeTraversal].Derivation* f_derivation
cdef kbest.KBestDerivations[vector[WordID], kbest.ETreeTraversal]* e_derivations = new kbest.KBestDerivations[vector[WordID], kbest.ETreeTraversal](self.hg[0], size)
cdef kbest.KBestDerivations[vector[WordID], kbest.ETreeTraversal].Derivation* e_derivation
cdef unsigned k
try:
for k in range(size):
f_derivation = f_derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
e_derivation = e_derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
if not f_derivation or not e_derivation: break
f_tree = unicode(GetString(f_derivation._yield).c_str(), 'utf8')
e_tree = unicode(GetString(e_derivation._yield).c_str(), 'utf8')
yield (f_tree, e_tree)
finally:
del f_derivations
del e_derivations
def kbest_features(self, size):
cdef kbest.KBestDerivations[FastSparseVector[weight_t], kbest.FeatureVectorTraversal]* derivations = new kbest.KBestDerivations[FastSparseVector[weight_t], kbest.FeatureVectorTraversal](self.hg[0], size)
cdef kbest.KBestDerivations[FastSparseVector[weight_t], kbest.FeatureVectorTraversal].Derivation* derivation
cdef SparseVector fmap
cdef unsigned k
try:
for k in range(size):
derivation = derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
if not derivation: break
fmap = SparseVector.__new__(SparseVector)
fmap.vector = new FastSparseVector[weight_t](derivation._yield)
yield fmap
finally:
del derivations
def sample(self, unsigned n):
cdef vector[hypergraph.Hypothesis]* hypos = new vector[hypergraph.Hypothesis]()
hypergraph.sample_hypotheses(self.hg[0], n, self._rng(), hypos)
cdef unsigned k
try:
for k in range(hypos.size()):
yield unicode(GetString(hypos[0][k].words).c_str(), 'utf8')
finally:
del hypos
def sample_trees(self, unsigned n):
cdef vector[string]* trees = new vector[string]()
hypergraph.sample_trees(self.hg[0], n, self._rng(), trees)
cdef unsigned k
try:
for k in range(trees.size()):
yield unicode(trees[0][k].c_str(), 'utf8')
finally:
del trees
def intersect(self, Lattice lat):
return hypergraph.Intersect(lat.lattice[0], self.hg)
def prune(self, beam_alpha=0, density=0, **kwargs):
cdef hypergraph.EdgeMask* preserve_mask = NULL
if 'csplit_preserve_full_word' in kwargs:
preserve_mask = new hypergraph.EdgeMask(self.hg.edges_.size())
preserve_mask[0][hypergraph.GetFullWordEdgeIndex(self.hg[0])] = True
self.hg.PruneInsideOutside(beam_alpha, density, preserve_mask, False, 1, False)
if preserve_mask:
del preserve_mask
def lattice(self): # TODO direct hg -> lattice conversion in cdec
cdef bytes plf = hypergraph.AsPLF(self.hg[0], True).c_str()
return Lattice(eval(plf))
def reweight(self, weights):
if isinstance(weights, SparseVector):
self.hg.Reweight((<SparseVector> weights).vector[0])
elif isinstance(weights, DenseVector):
self.hg.Reweight((<DenseVector> weights).vector[0])
else:
raise TypeError('cannot reweight hypergraph with %s' % type(weights))
property edges:
def __get__(self):
cdef unsigned i
for i in range(self.hg.edges_.size()):
yield HypergraphEdge().init(self.hg, i)
property nodes:
def __get__(self):
cdef unsigned i
for i in range(self.hg.nodes_.size()):
yield HypergraphNode().init(self.hg, i)
property goal:
def __get__(self):
return HypergraphNode().init(self.hg, self.hg.GoalNode())
property npaths:
def __get__(self):
return self.hg.NumberOfPaths()
def inside_outside(self):
cdef FastSparseVector[prob_t]* result = new FastSparseVector[prob_t]()
cdef prob_t z = hypergraph.InsideOutside(self.hg[0], result)
result[0] /= z
cdef SparseVector vector = SparseVector.__new__(SparseVector)
vector.vector = new FastSparseVector[double]()
cdef FastSparseVector[prob_t].const_iterator* it = new FastSparseVector[prob_t].const_iterator(result[0], False)
cdef unsigned i
for i in range(result.size()):
vector.vector.set_value(it[0].ptr().first, log(it[0].ptr().second))
pinc(it[0]) # ++it
del it
del result
return vector
cdef class HypergraphEdge:
cdef hypergraph.Hypergraph* hg
cdef hypergraph.HypergraphEdge* edge
cdef public TRule trule
cdef init(self, hypergraph.Hypergraph* hg, unsigned i):
self.hg = hg
self.edge = &hg.edges_[i]
self.trule = TRule.__new__(TRule)
self.trule.rule = new shared_ptr[grammar.TRule](self.edge.rule_)
return self
def __len__(self):
return self.edge.tail_nodes_.size()
property head_node:
def __get__(self):
return HypergraphNode().init(self.hg, self.edge.head_node_)
property tail_nodes:
def __get__(self):
cdef unsigned i
for i in range(self.edge.tail_nodes_.size()):
yield HypergraphNode().init(self.hg, self.edge.tail_nodes_[i])
property span:
def __get__(self):
return (self.edge.i_, self.edge.j_)
property feature_values:
def __get__(self):
cdef SparseVector vector = SparseVector.__new__(SparseVector)
vector.vector = new FastSparseVector[double](self.edge.feature_values_)
return vector
property prob:
def __get__(self):
return self.edge.edge_prob_.as_float()
def __richcmp__(HypergraphEdge x, HypergraphEdge y, int op):
if op == 2: # ==
return x.edge == y.edge
elif op == 3: # !=
return not (x == y)
raise NotImplemented('comparison not implemented for HypergraphEdge')
cdef class HypergraphNode:
cdef hypergraph.Hypergraph* hg
cdef hypergraph.HypergraphNode* node
cdef init(self, hypergraph.Hypergraph* hg, unsigned i):
self.hg = hg
self.node = &hg.nodes_[i]
return self
property in_edges:
def __get__(self):
cdef unsigned i
for i in range(self.node.in_edges_.size()):
yield HypergraphEdge().init(self.hg, self.node.in_edges_[i])
property out_edges:
def __get__(self):
cdef unsigned i
for i in range(self.node.out_edges_.size()):
yield HypergraphEdge().init(self.hg, self.node.out_edges_[i])
property span:
def __get__(self):
return next(self.in_edges).span
property cat:
def __get__(self):
if self.node.cat_:
return str(TDConvert(-self.node.cat_).c_str())
def __richcmp__(HypergraphNode x, HypergraphNode y, int op):
if op == 2: # ==
return x.node == y.node
elif op == 3: # !=
return not (x == y)
raise NotImplemented('comparison not implemented for HypergraphNode')
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