Skip to content

Tensor.random

Create a tensor with all admissible blocks filled with random values.

random classmethod 

random(
    indices: Sequence[Index],
    dtype: dtype = torch.float64,
    seed: Optional[int] = None,
    itags: Optional[Sequence[str]] = None,
    device: Optional[Union[str, device]] = None,
    requires_grad: bool = False,
) -> Tensor

Create a tensor filled with random values for each admissible block.

Parameters:

Name Type Description Default
indices Sequence[Index]

Sequence of Index objects defining the tensor structure

 required
dtype dtype

Data type for the tensor blocks (default: torch.float64)

 float64
seed int

Random seed for reproducibility

 None
itags Sequence[str]

Tags for each index (default: "init" for all)

 None
device str or device

Device to place tensors on (default: current default device)

 None
requires_grad bool

If True, enables gradient tracking for this tensor (default: False)

 False
Notes

Gradient tracking follows PyTorch's default behavior. Set requires_grad=True to enable autograd for this tensor. Use torch.no_grad() context to temporarily disable gradient computation during operations.

MPS (Apple Silicon) doesn't support float64/complex128. If creating on MPS with these dtypes, they will be automatically downgraded to float32/complex64.

For generic symmetry groups (e.g., SU2), intertwiners (intw) are automatically populated with Bridge objects containing default Clebsch-Gordan specifications.

Description

Creates a new tensor with random values in all charge-conserving blocks. Random values are drawn from a standard normal distribution. Use the seed parameter for reproducibility.

See Also

Notes

  • Random values from standard normal: mean=0, std=1
  • Use seed parameter for reproducible results
  • Only admissible blocks are created and filled