pyttb.gcp.fg

Evaluate Function And Gradient Handles.

pyttb.gcp.fg.evaluate(model: pyttb.ktensor.ktensor, data: pyttb.tensor.tensor | pyttb.sptensor.sptensor, weights: numpy.ndarray | None, function_handle: Literal[None], gradient_handle: function_type) → list[numpy.ndarray]

pyttb.gcp.fg.evaluate(model: pyttb.ktensor.ktensor, data: pyttb.tensor.tensor | pyttb.sptensor.sptensor, weights: numpy.ndarray | None, function_handle: function_type, gradient_handle: Literal[None]) → float

pyttb.gcp.fg.evaluate(model: pyttb.ktensor.ktensor, data: pyttb.tensor.tensor | pyttb.sptensor.sptensor, weights: numpy.ndarray | None, function_handle: function_type, gradient_handle: function_type) → tuple[float, list[numpy.ndarray]]

pyttb.gcp.fg.evaluate(model: ttb.ktensor, data: ttb.tensor | ttb.sptensor, weights: np.ndarray | None = None, function_handle: function_type | None = None, gradient_handle: function_type | None = None) → float | list[np.ndarray] | tuple[float, list[np.ndarray]]

Evaluate an objective function and/or gradient function.

Parameters:

• model – Current decomposition.

• data – Source tensor to decompose.

• weights – Weighted values for returned tensor. Can be used as a mask.

• function_handle – Objective function.

• gradient_handle – Gradient definition.

Returns:

Objective function value and/or gradient function value with respect to model.