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60 lines
1.8 KiB
Cython
60 lines
1.8 KiB
Cython
# cython: embedsignature=True
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from cpython.ref cimport Py_INCREF
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from cymem.cymem cimport Pool
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from murmurhash.mrmr cimport hash64
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from libc.string cimport memset
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from .orth cimport word_shape
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from .typedefs cimport attr_t, flags_t
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import numpy
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memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
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cdef int set_lex_struct_props(LexemeC* lex, dict props, StringStore string_store,
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const float* empty_vec) except -1:
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lex.length = props['length']
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lex.orth = string_store[props['orth']]
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lex.lower = string_store[props['lower']]
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lex.norm = string_store[props['norm']]
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lex.shape = string_store[props['shape']]
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lex.prefix = string_store[props['prefix']]
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lex.suffix = string_store[props['suffix']]
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lex.cluster = props['cluster']
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lex.prob = props['prob']
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lex.sentiment = props['sentiment']
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lex.flags = props['flags']
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cdef flags_t sense_id = 0
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cdef flags_t one = 1
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lex.senses = 0
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for _sense_id in props.get('senses', []):
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sense_id = _sense_id
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lex.senses |= one << sense_id
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lex.repvec = empty_vec
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cdef class Lexeme:
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"""An entry in the vocabulary. A Lexeme has no string context --- it's a
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word-type, as opposed to a word token. It therefore has no part-of-speech
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tag, dependency parse, or lemma (lemmatization depends on the part-of-speech
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tag).
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"""
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def __cinit__(self, int vec_size):
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self.repvec = numpy.ndarray(shape=(vec_size,), dtype=numpy.float32)
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@property
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def has_repvec(self):
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return self.l2_norm != 0
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cpdef bint check(self, attr_id_t flag_id) except -1:
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cdef flags_t one = 1
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return self.flags & (one << flag_id)
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cpdef bint has_sense(self, flags_t flag_id) except -1:
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cdef flags_t one = 1
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return self.senses & (one << flag_id)
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