legendre_decomp#

Submodules#

Functions#

`LD`(X[, B, order, n_iter, lr, eps, error_tol, ngd, ...])	Compute many-body tensor approximation.
`LD_MBA`(X[, I, order, n_iter, lr, eps, error_tol, ...])	Compute many-body tensor approximation.

Package Contents#

legendre_decomp.LD(X, B=None, order=2, n_iter=10, lr=1.0, eps=1e-05, error_tol=1e-05, ngd=True, ngd_lstsq=False, verbose=True, gpu=True, exit_abs=False, dtype=None)#

Compute many-body tensor approximation.

Parameters:

X (numpy.typing.NDArray[numpy.float64]) – Input tensor.
B (numpy.typing.NDArray[numpy.intp] | list[tuple[int, Ellipsis]] | None) – B tensor.
order (int) – Order of default tensor B, if not provided.
n_iter (int) – Maximum number of iteration.
lr (float) – Learning rate.
eps (float) – (see paper).
error_tol (float) – KL divergence tolerance for the iteration.
ngd (bool) – Use natural gradient.
ngd_lstsq (bool) – Use natural gradient conputed by lstsq to avoid singular matrix.
verbose (bool) – Print debug messages.
gpu (bool) – Use GPU (CUDA or ROCm depending on the installed CuPy version).
exit_abs (bool) – Previous implementation (wrongly?) uses kl- kl_prev as iteration exit criterion. Use abs(kl - kl_prev) instead.
dtype (numpy.dtype | None) – By default, the data-type is inferred from the input data.

Returns:

KL divergence history. scaleX: Scaled X tensor. Q: Q tensor. theta: Theta.

Return type:

all_history_kl

legendre_decomp.LD_MBA(X, I=None, order=2, n_iter=100, lr=1.0, eps=1e-05, error_tol=1e-05, init_theta=None, init_theta_mask=None, ngd=True, ngd_lstsq=True, verbose=True, gpu=True, dtype=None)#