spaCy/spacy/syntax/stateclass.pyx

88 lines
2.6 KiB
Cython

# coding: utf-8
# cython: infer_types=True
from __future__ import unicode_literals
from libc.string cimport memcpy, memset
from libc.stdint cimport uint32_t, uint64_t
from ..vocab cimport EMPTY_LEXEME
from ..structs cimport Entity
from ..lexeme cimport Lexeme
from ..symbols cimport punct
from ..attrs cimport IS_SPACE
from ..attrs cimport attr_id_t
from ..tokens.token cimport Token
cdef class StateClass:
def __init__(self, int length):
cdef Pool mem = Pool()
self.mem = mem
def __dealloc__(self):
del self.c
@property
def stack(self):
return {self.S(i) for i in range(self.c._s_i)}
@property
def queue(self):
return {self.B(i) for i in range(self.c.buffer_length())}
@property
def token_vector_lenth(self):
return self.doc.tensor.shape[1]
def py_is_final(self):
return self.c.is_final()
def print_state(self, words):
words = list(words) + ['_']
top = words[self.S(0)] + '_%d' % self.S_(0).head
second = words[self.S(1)] + '_%d' % self.S_(1).head
third = words[self.S(2)] + '_%d' % self.S_(2).head
n0 = words[self.B(0)]
n1 = words[self.B(1)]
return ' '.join((third, second, top, '|', n0, n1))
@classmethod
def nr_context_tokens(cls, int nF, int nB, int nS, int nL, int nR):
return 13
def set_context_tokens(self, int[:] output, nF=1, nB=0, nS=2,
nL=2, nR=2):
output[0] = self.B(0)
output[1] = self.B(1)
output[2] = self.S(0)
output[3] = self.S(1)
output[4] = self.S(2)
output[5] = self.L(self.S(0), 1)
output[6] = self.L(self.S(0), 2)
output[6] = self.R(self.S(0), 1)
output[7] = self.L(self.B(0), 1)
output[8] = self.R(self.S(0), 2)
output[9] = self.L(self.S(1), 1)
output[10] = self.L(self.S(1), 2)
output[11] = self.R(self.S(1), 1)
output[12] = self.R(self.S(1), 2)
def set_attributes(self, uint64_t[:, :] vals, int[:] tokens, int[:] names):
cdef int i, j, tok_i
for i in range(tokens.shape[0]):
tok_i = tokens[i]
if tok_i >= 0:
token = &self.c._sent[tok_i]
for j in range(names.shape[0]):
vals[i, j] = Token.get_struct_attr(token, <attr_id_t>names[j])
else:
vals[i] = 0
def set_token_vectors(self, tokvecs,
all_tokvecs, int[:] indices):
for i in range(indices.shape[0]):
if indices[i] >= 0:
tokvecs[i] = all_tokvecs[indices[i]]
else:
tokvecs[i] = 0