def zeros(cls,
indices: Sequence[Index],
dtype: Optional[Type[np.number]] = None) -> "BlockSparseTensor":
"""
Initialize a symmetric tensor with zeros.
Args:
indices: List of `Index` objects, one for each leg.
dtype: An optional numpy dtype. The dtype of the tensor
Returns:
BlockSparseTensor
"""
data, charges, flows, order = _data_initializer(np.zeros,
compute_num_nonzero,
indices,
dtype=dtype)
return cls(data=data,
charges=charges,
flows=flows,
order=order,
check_consistency=False)
def randn(cls,
indices: Sequence[Index],
dtype: Optional[Type[np.number]] = None) -> "BlockSparseTensor":
"""
Initialize a random symmetric tensor from a random normal distribution
with mean 0 and variance 1.
Args:
indices: List of `Index` objects, one for each leg.
dtype: An optional numpy dtype. The dtype of the tensor
Returns:
BlockSparseTensor
"""
data, charges, flows, order = _data_initializer(_randn,
compute_num_nonzero,
indices,
dtype=dtype)
return cls(data=data,
charges=charges,
flows=flows,
order=order,
check_consistency=False)
def random(cls,
indices: Union[Tuple[Index], List[Index]],
boundaries: Optional[Tuple[float, float]] = (0.0, 1.0),
dtype: Optional[Type[np.number]] = None) -> "ChargeArray":
"""
Initialize a random ChargeArray object with data from a random
uniform distribution.
Args:
indices: List of `Index` objects.
boundaries: Tuple of interval boundaries for the random uniform
distribution.
dtype: An optional numpy dtype. The dtype of the ChargeArray
Returns:
ChargeArray
"""
data, charges, flows, order = _data_initializer(
_random, lambda charges, flows: np.prod([c.dim for c in charges]),
indices, dtype=dtype)
return cls(data=data, charges=charges, flows=flows, order=order)
def random(cls,
indices: Sequence[Index],
boundaries: Optional[Tuple[float, float]] = (0.0, 1.0),
dtype: Optional[Type[np.number]] = None) -> "BlockSparseTensor":
"""
Initialize a random symmetric tensor from random uniform distribution.
Args:
indices: List of `Index` objects, one for each leg.
boundaries: Tuple of interval boundaries for the random uniform
distribution.
dtype: An optional numpy dtype. The dtype of the tensor
Returns:
BlockSparseTensor
"""
data, charges, flows, order = _data_initializer(_random,
compute_num_nonzero,
indices,
dtype=dtype,
boundaries=boundaries)
return cls(data=data,
charges=charges,
flows=flows,
order=order,
check_consistency=False)