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https://github.com/explosion/spaCy.git
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737a1408d9
Follow-ups to the parser efficiency fix. * Avoid introducing new counter for number of pushes * Base cut on number of transitions, keeping it more even * Reintroduce the randomization we had in v2.
55 lines
1.4 KiB
Cython
55 lines
1.4 KiB
Cython
# cython: infer_types=True
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import numpy
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from ...tokens.doc cimport Doc
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cdef class StateClass:
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def __init__(self, Doc doc=None, int offset=0):
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cdef Pool mem = Pool()
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self.mem = mem
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self._borrowed = 0
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if doc is not None:
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self.c = new StateC(doc.c, doc.length)
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self.c.offset = offset
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def __dealloc__(self):
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if self._borrowed != 1:
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del self.c
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@property
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def stack(self):
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return {self.S(i) for i in range(self.c._s_i)}
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@property
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def queue(self):
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return {self.B(i) for i in range(self.c.buffer_length())}
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@property
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def token_vector_lenth(self):
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return self.doc.tensor.shape[1]
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@property
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def history(self):
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hist = numpy.ndarray((8,), dtype='i')
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for i in range(8):
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hist[i] = self.c.get_hist(i+1)
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return hist
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def is_final(self):
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return self.c.is_final()
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def copy(self):
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cdef StateClass new_state = StateClass.init(self.c._sent, self.c.length)
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new_state.c.clone(self.c)
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return new_state
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def print_state(self, words):
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words = list(words) + ['_']
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top = f"{words[self.S(0)]}_{self.S_(0).head}"
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second = f"{words[self.S(1)]}_{self.S_(1).head}"
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third = f"{words[self.S(2)]}_{self.S_(2).head}"
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n0 = words[self.B(0)]
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n1 = words[self.B(1)]
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return ' '.join((third, second, top, '|', n0, n1))
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