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Fix set_features on tagger
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0c7520dbb7
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52d2702782
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@ -1,13 +1,13 @@
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.extra.eg cimport Example
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from thinc.structs cimport ExampleC
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from thinc.structs cimport ExampleC, FeatureC
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from .structs cimport TokenC
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from .vocab cimport Vocab
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cdef class TaggerModel(AveragedPerceptron):
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cdef void set_featuresC(self, ExampleC* eg, const void* _token) nogil
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cdef int set_featuresC(self, FeatureC* feats, const void* _token) nogil
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cdef class Tagger:
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@ -71,15 +71,17 @@ cpdef enum:
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cdef class TaggerModel(AveragedPerceptron):
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cdef void set_featuresC(self, ExampleC* eg, const void* _token) nogil:
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cdef int set_featuresC(self, FeatureC* features, const void* _token) nogil:
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cdef atom_t[N_CONTEXT_FIELDS] context
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memset(context, 0, sizeof(context))
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token = <const TokenC*>_token
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_fill_from_token(&eg.atoms[P2_orth], token - 2)
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_fill_from_token(&eg.atoms[P1_orth], token - 1)
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_fill_from_token(&eg.atoms[W_orth], token)
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_fill_from_token(&eg.atoms[N1_orth], token + 1)
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_fill_from_token(&eg.atoms[N2_orth], token + 2)
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_fill_from_token(&context[P2_orth], token - 2)
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_fill_from_token(&context[P1_orth], token - 1)
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_fill_from_token(&context[W_orth], token)
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_fill_from_token(&context[N1_orth], token + 1)
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_fill_from_token(&context[N2_orth], token + 2)
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eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
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return self.extracter.set_features(features, context)
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cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
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@ -202,7 +204,7 @@ cdef class Tagger:
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nr_feat=self.model.nr_feat)
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for i in range(tokens.length):
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if tokens.c[i].pos == 0:
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self.model.set_featuresC(eg.c, &tokens.c[i])
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eg.c.nr_feat = self.model.set_featuresC(eg.c.features, &tokens.c[i])
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self.model.set_scoresC(eg.c.scores,
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eg.c.features, eg.c.nr_feat)
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guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
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@ -232,7 +234,7 @@ cdef class Tagger:
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nr_class=self.vocab.morphology.n_tags,
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nr_feat=self.model.nr_feat)
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for i in range(tokens.length):
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self.model.set_featuresC(eg.c, &tokens.c[i])
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eg.c.nr_feat = self.model.set_featuresC(eg.c.features, &tokens.c[i])
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eg.costs = [ 1 if golds[i] not in (c, -1) else 0 for c in xrange(eg.nr_class) ]
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self.model.set_scoresC(eg.c.scores,
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eg.c.features, eg.c.nr_feat)
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