def __init__(self, cardinalities, seed=None):
Classifier.__init__(self)
self.multitask = True
self.K = cardinalities
if seed is None:
seed = np.random.randint(1e6)
self._set_seed(seed)
def __init__(self, K, config):
Classifier.__init__(self, None, config)
self.multitask = True
self.K = K
def _stack_batches(X):
"""Given a list of batches, each consisting of a T-len list of
np.ndarrays, stack along the first (batch) axis, returning a T-len list
of np.ndarrays."""
return [Classifier._stack_batches(Xt) for Xt in zip(*X)]
def _to_numpy(Z):
"""Converts a None, list, np.ndarray, or torch.Tensor to np.ndarray"""
if isinstance(Z, list):
return [Classifier._to_numpy(z) for z in Z]
else:
return Classifier._to_numpy(Z)
def _to_torch(Z, dtype=None):
"""Converts a None, list, np.ndarray, or torch.Tensor to torch.Tensor"""
if isinstance(Z, list):
return [Classifier._to_torch(z, dtype=dtype) for z in Z]
else:
return Classifier._to_torch(Z)