pyttb.gcp.fg_est

Evaluate Functions And Gradients based on Subsamples.

pyttb.gcp.fg_est.estimate(model: ttb.ktensor, data_subs: numpy.ndarray, data_vals: numpy.ndarray, weights: numpy.ndarray, function_handle: Literal[None], gradient_handle: function_type, lambda_check: bool = True, crng: numpy.ndarray | None = None) → list[numpy.ndarray][source]
pyttb.gcp.fg_est.estimate(model: ttb.ktensor, data_subs: numpy.ndarray, data_vals: numpy.ndarray, weights: numpy.ndarray, function_handle: function_type, gradient_handle: Literal[None] = None, lambda_check: bool = False, crng: numpy.ndarray | None = None) → float
pyttb.gcp.fg_est.estimate(model: ttb.ktensor, data_subs: numpy.ndarray, data_vals: numpy.ndarray, weights: numpy.ndarray, function_handle: function_type, gradient_handle: function_type, lambda_check: bool, crng: numpy.ndarray | None) → tuple[float, list[numpy.ndarray]]
pyttb.gcp.fg_est.estimate(model: ttb.ktensor, data_subs: np.ndarray, data_vals: np.ndarray, weights: np.ndarray, function_handle: function_type | None = None, gradient_handle: function_type | None = None, lambda_check: bool = True, crng: np.ndarray | None = None) → float | list[np.ndarray] | tuple[float, list[np.ndarray]]

Estimate the GCP function and gradient with a subsample.

Parameters:

  • model – Current decomposition.

  • data_subs – Subscripts of data sample.

  • data_vals – Values of data sample.

  • function_handle – Handle to evaluate objective function.

  • gradient_handle – Handle to evaluate gradient of objective function.

  • lambda_check – Whether or not to check decomposition weights are all ones. (Which is assumed in implementation details)

  • crng – Range of indices for correct/adjustment when zeros are sampled accidentally.

Returns:

  • Estimated objective function value and/or estimated gradient value with

  • respect to the model.

pyttb.gcp.fg_est.estimate_helper(factors: list[ndarray], subs: ndarray) → tuple[ndarray, list[ndarray]][source]

Extract model values at sample locations and exploded Zk’s.

Parameters:

  • factors – Factor matrices from model.

  • subs – Subscripts to extract from model.

Returns:

Model values at subs and exploded Zk’s