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* Have Tokens return proper numpy arrays, not Cython views.
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@ -1,7 +1,7 @@
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from libc.stdint cimport uint32_t
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from numpy cimport ndarray
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cimport numpy
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cimport numpy as np
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from cymem.cymem cimport Pool
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from thinc.typedefs cimport atom_t
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@ -47,7 +47,7 @@ cdef class Tokens:
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cdef int push_back(self, int i, LexemeOrToken lex_or_tok) except -1
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cpdef long[:,:] to_array(self, object features)
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cpdef np.ndarray to_array(self, object features)
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cdef int set_parse(self, const TokenC* parsed) except -1
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@ -18,7 +18,9 @@ from .structs cimport UniStr
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from unidecode import unidecode
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cimport numpy
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cimport numpy as np
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np.import_array()
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import numpy
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cimport cython
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@ -207,7 +209,7 @@ cdef class Tokens:
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return idx + t.lex.length
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@cython.boundscheck(False)
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cpdef long[:,:] to_array(self, object py_attr_ids):
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cpdef np.ndarray to_array(self, object py_attr_ids):
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"""Given a list of M attribute IDs, export the tokens to a numpy ndarray
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of shape N*M, where N is the length of the sentence.
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@ -221,10 +223,10 @@ cdef class Tokens:
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"""
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cdef int i, j
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cdef attr_id_t feature
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cdef numpy.ndarray[long, ndim=2] output
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cdef np.ndarray[long, ndim=2] output
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# Make an array from the attributes --- otherwise our inner loop is Python
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# dict iteration.
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cdef numpy.ndarray[long, ndim=1] attr_ids = numpy.asarray(py_attr_ids)
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cdef np.ndarray[long, ndim=1] attr_ids = numpy.asarray(py_attr_ids)
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output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int)
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for i in range(self.length):
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for j, feature in enumerate(attr_ids):
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@ -464,7 +466,9 @@ cdef class Token:
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property repvec:
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def __get__(self):
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return numpy.asarray(<float[:self.vocab.repvec_length,]> self.c.lex.repvec)
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cdef int length = self.vocab.repvec_length
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repvec_view = <float[:length,]>self.c.lex.repvec
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return numpy.asarray(repvec_view)
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property n_lefts:
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def __get__(self):
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