LangermannFunction#

class LangermannFunction(objective: str = 'minimize', modifiers: List[BaseModifier] | None = None, memory: bool = False, collect_data: bool = True, callbacks: Callable | List[Callable] | None = None, catch_errors: Dict[type, float] | None = None)[source]#

Langermann two-dimensional test function.

A multimodal function with many unevenly distributed local minima.

Parameters:

metric (str, default="score") – Either “loss” (minimize) or “score” (maximize).
modifiers (list of BaseModifier, optional) – List of modifiers to apply to function evaluations.

n_dim[source]#

Number of dimensions (always 2).

Type:: int

c[source]#

Coefficient vector.

Type:: ndarray

m[source]#

Number of terms in the summation.

Type:: int

A[source]#

Matrix of center coordinates.

Type:: ndarray

Examples

>>> from surfaces.test_functions import LangermannFunction
>>> func = LangermannFunction()
>>> result = func({"x0": 0.0, "x1": 0.0})

__call__(params: Dict[str, Any] | ndarray | list | tuple | None = None, **kwargs)[source]#

Evaluate the objective function.

Args:: params: Parameter values as dict, array, list, or tuple **kwargs: Parameters as keyword arguments (only with dict input)
Returns:: The objective function value

batch(X: ArrayLike) → ArrayLike[source]#

Evaluate multiple parameter sets in a single call.

Parameters:

X (ArrayLike) – 2D array of shape (n_points, n_dim) where each row is a parameter set.

Returns:

1D array of shape (n_points,) with evaluation results.

Return type:

ArrayLike

Raises:

NotImplementedError – If the function does not implement _batch_objective.
ValueError – If X has wrong number of dimensions or wrong n_dim.

property callbacks[source]#: Callback management (CallbackAccessor).

property data[source]#: Evaluation data (DataAccessor).

property errors[source]#: Error handler management (ErrorAccessor).

property memory[source]#: Memory cache management (MemoryAccessor).

property meta[source]#: Function metadata (MetaAccessor).

property modifiers[source]#: Modifier management (ModifierAccessor).

property plot[source]#: Access plotting methods for this function.

pure(params: Dict[str, Any] | ndarray | list | tuple | None = None, **kwargs)[source]#

Evaluate the function without modifiers.

Returns the true (deterministic) function value, bypassing any configured modifiers. Does not update search_data, n_evaluations, or callbacks. Ignores memory caching.

Parameters:

params (dict, array, list, or tuple) – Parameter values to evaluate.
**kwargs (dict) – Parameters as keyword arguments.

Returns:

The true function value without modifiers, with direction applied.

Return type:

float or np.ndarray

reset() → None[source]#: Reset all state including collected data and memory cache.

property search_space: Dict[str, Any][source]#: Search space for this function (read-only public API).

property spec[source]#: Function characteristics (SpecAccessor).