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src/p/y/pylearn2-HEAD/pylearn2/utils/iteration.py   pylearn2(Download)
from pylearn2.space import CompositeSpace
from pylearn2.utils import safe_izip, wraps
from pylearn2.utils.data_specs import is_flat_specs
from pylearn2.utils.rng import make_np_rng
 
        # Keep only the needed sources in self._raw_data.
        # Remember what source they correspond to in self._source
        assert is_flat_specs(data_specs)
 
        dataset_space, dataset_source = self._dataset.get_data_specs()
        assert is_flat_specs((dataset_space, dataset_source))

src/p/y/pylearn2-HEAD/pylearn2/datasets/vector_spaces_dataset.py   pylearn2(Download)
 
from pylearn2.datasets.dataset import Dataset
from pylearn2.utils.data_specs import is_flat_specs
from pylearn2.utils.rng import make_np_rng
 
    def __init__(self, data=None, data_specs=None, rng=_default_seed,
                 preprocessor=None, fit_preprocessor=False):
        # data_specs should be flat, and there should be no
        # duplicates in source, as we keep only one version
        assert is_flat_specs(data_specs)

src/p/y/pylearn2-HEAD/pylearn2/datasets/transformer_dataset.py   pylearn2(Download)
from pylearn2.datasets.dataset import Dataset
from pylearn2.space import CompositeSpace
from pylearn2.utils.data_specs import is_flat_specs
 
 
        # Build the right data_specs to query self.raw
        if data_specs is not None:
            assert is_flat_specs(data_specs)
            space, source = data_specs
            if not isinstance(source, tuple):