pyttb.hosvd

Higher Order SVD Implementation.

pyttb.hosvd.hosvd(input_tensor: tensor, tol: float, verbosity: float = 1, dimorder: int | float | Iterable[int] | Iterable[float] | ndarray | None = None, sequential: bool = True, ranks: int | float | Iterable[int] | Iterable[float] | ndarray | None = None) → ttensor[source]

Compute sequentially-truncated higher-order SVD (Tucker).

Computes a Tucker decomposition with relative error specified by tol, i.e., it computes a ttensor T such that ||X-T||/||X|| <= tol.

Parameters:

  • input_tensor – Tensor to factor

  • tol – Relative error to stop at

  • verbosity – Print level

  • dimorder – Order to loop through dimensions

  • sequential – Use sequentially-truncated version

  • ranks – Specify ranks to consider rather than computing

Example

>>> data = np.array([[29, 39.0], [63.0, 85.0]])
>>> tol = 1e-4
>>> disable_printing = -1
>>> tensorInstance = ttb.tensor(data)
>>> result = hosvd(tensorInstance, tol, verbosity=disable_printing)
>>> ((result.full() - tensorInstance).norm() / tensorInstance.norm()) < tol
True