def _create_data_specs(self): ws = (self.window_size * 2 + 1) space = CompositeSpace((IndexSequenceSpace(max_labels=self.vocab_size, dim=ws), IndexSequenceSpace(max_labels=self.total_feats, dim=self.feat_num), VectorSequenceSpace(dim=self.n_classes))) source = ('words', 'features', 'targets') self.data_specs = (space, source)
def test_np_format_as_indexsequence2indexsequence(): index_sequence_space1 = IndexSequenceSpace(max_labels=6, dim=1, dtype='int16') index_sequence_space2 = IndexSequenceSpace(max_labels=6, dim=1, dtype='int32') data = np.random.randint(low=0, high=5, size=(10, 1)) rval = index_sequence_space1.np_format_as(data, index_sequence_space2) assert np.all(rval == data)
def test_np_format_as_indexsequence2vectorsequence(): index_sequence_space = IndexSequenceSpace(max_labels=6, dim=1) vector_sequence_space = VectorSequenceSpace(dim=6) data = np.array([[0], [1], [4], [3]]) rval = index_sequence_space.np_format_as(data, vector_sequence_space) true_val = np.array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0]]) assert np.all(rval == true_val)
def test_np_format_as_sequence2other(): vector_sequence_space = VectorSequenceSpace(dim=3) vector_space = VectorSpace(dim=3) data = np.random.uniform(low=0.0, high=1.0, size=(10, 3)) np.testing.assert_raises(ValueError, vector_sequence_space.np_format_as, data, vector_space) index_sequence_space = IndexSequenceSpace(max_labels=6, dim=1) index_space = IndexSpace(max_labels=6, dim=1) data = np.random.randint(low=0, high=5, size=(10, 1)) np.testing.assert_raises(ValueError, index_sequence_space.np_format_as, data, index_space)