def setUp(self):
super(TestOnDenseGrid, self).setUp()
sparse_grid_context = nql_test_lib.make_grid()
context = nql.NeuralQueryContext()
# copy the grid but densify some of it
context.declare_relation('n', 'place_t', 'place_t')
context.declare_relation('s', 'place_t', 'place_t')
context.declare_relation('e', 'place_t', 'place_t')
context.declare_relation('w', 'place_t', 'place_t')
context.declare_relation('color', 'place_t', 'color_t', dense=True)
context.declare_relation('distance_to', 'place_t', 'corner_t')
# copy the type definitions
for type_name in sparse_grid_context.get_type_names():
entity_list = [
sparse_grid_context.get_entity_name(i, type_name)
for i in range(sparse_grid_context.get_max_id(type_name))
]
context.extend_type(type_name, entity_list)
# copy the data over
for r in sparse_grid_context.get_relation_names():
m = sparse_grid_context.get_initial_value(r)
if context.is_dense(r):
context.set_initial_value(r, m.todense())
else:
context.set_initial_value(r, m)
self.context = context
self.session = tf.Session()
def setUp(self): super(TestOnGrid, self).setUp() self.context = nql_test_lib.make_grid() self.session = tf.Session()