本文整理汇总了Python中composes.semantic_space.space.Space.get_row方法的典型用法代码示例。如果您正苦于以下问题：Python Space.get_row方法的具体用法？Python Space.get_row怎么用？Python Space.get_row使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类composes.semantic_space.space.Space的用法示例。
在下文中一共展示了Space.get_row方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: to_dissect_core_space
# 需要导入模块: from composes.semantic_space.space import Space [as 别名]
# 或者: from composes.semantic_space.space.Space import get_row [as 别名]
def to_dissect_core_space(self):
"""
Converts this object to a composes.semantic_space.space.Space
:rtype: composes.semantic_space.space.Space
"""
from composes.matrix.sparse_matrix import SparseMatrix
from composes.semantic_space.space import Space
mat, cols, rows = self.to_sparse_matrix()
mat = SparseMatrix(mat)
s = Space(mat, rows, cols)
# test that the mapping from string to its vector has not been messed up
for i in range(min(10, len(self))):
s1 = s.get_row(rows[i]).mat
s2 = self.v.transform(dict(self[rows[i]]))
# sparse matrices do not currently support equality testing
assert abs(s1 - s2).nnz == 0
return s示例2: LexicalFunction
# 需要导入模块: from composes.semantic_space.space import Space [as 别名]
# 或者: from composes.semantic_space.space.Space import get_row [as 别名]
#.........这里部分代码省略.........
self._has_intercept = self._regression_learner.has_intercept()
if not isinstance(arg_space, Space):
raise ValueError("expected one input spaces!")
result_mats = []
train_data = sorted(train_data, key=lambda tup: tup[0])
function_word_list, arg_list, phrase_list = self.valid_data_to_lists(train_data,
(None,
arg_space.row2id,
phrase_space.row2id))
#partitions the sorted input data
keys, key_ranges = get_partitions(function_word_list, self._MIN_SAMPLES)
if not keys:
raise ValueError("No valid training data found!")
assert(len(arg_space.element_shape) == 1)
if self._has_intercept:
new_element_shape = phrase_space.element_shape + (arg_space.element_shape[0] + 1,)
else:
new_element_shape = phrase_space.element_shape + (arg_space.element_shape[0],)
for i in xrange(len(key_ranges)):
idx_beg, idx_end = key_ranges[i]
print ("Training lexical function...%s with %d samples"
% (keys[i], idx_end - idx_beg))
arg_mat = arg_space.get_rows(arg_list[idx_beg:idx_end])
phrase_mat = phrase_space.get_rows(phrase_list[idx_beg:idx_end])
#convert them to the same type
matrix_type = get_type_of_largest([arg_mat, phrase_mat])
[arg_mat, phrase_mat] = resolve_type_conflict([arg_mat, phrase_mat],
matrix_type)
result_mat = self._regression_learner.train(arg_mat, phrase_mat).transpose()
result_mat.reshape((1, np.prod(new_element_shape)))
result_mats.append(result_mat)
new_space_mat = arg_mat.nary_vstack(result_mats)
self.composed_id2column = phrase_space.id2column
self._function_space = Space(new_space_mat, keys, [],
element_shape=new_element_shape)
log.print_composition_model_info(logger, self, 1, "\nTrained composition model:")
log.print_info(logger, 3, "Trained: %s lexical functions" % len(keys))
log.print_info(logger, 3, "With total data points:%s" % len(function_word_list))
log.print_matrix_info(logger, arg_space.cooccurrence_matrix, 3,
"Semantic space of arguments:")
log.print_info(logger, 3, "Shape of lexical functions learned:%s"
% (new_element_shape,))
log.print_matrix_info(logger, new_space_mat, 3,
"Semantic space of lexical functions:")
log.print_time_info(logger, time.time(), start, 2)
def compose(self, data, arg_space):