SimionescuFunction#

class SimionescuFunction(A=0.1, r_T=1, r_S=0.2, n=8, objective='minimize', modifiers: List[BaseModifier] | None = None, memory=False, collect_data=True, callbacks=None, catch_errors=None)[source]#

Simionescu two-dimensional constrained test function.

A function with a bumpy constraint boundary. Points outside the constraint region return NaN.

The function is defined as:

\[f(x, y) = Axy\]

Subject to:

\[x^2 + y^2 \le [r_T + r_S \cos(n \arctan(x/y))]^2\]

where \(A = 0.1\), \(r_T = 1\), \(r_S = 0.2\), and \(n = 8\) by default.

The global minimum is \(f(\pm 0.84852813, \mp 0.84852813) = -0.072\).

Parameters:

A (float, default=0.1) – Amplitude scaling parameter.
r_T (float, default=1) – Constraint radius parameter.
r_S (float, default=0.2) – Constraint wave amplitude.
n (int, default=8) – Number of bumps in the constraint boundary.
objective (str, default="minimize") – Either “minimize” or “maximize”.
modifiers (list of BaseModifier, optional) – List of modifiers to apply to function evaluations.

n_dim[source]#

Number of dimensions (always 2).

Type:: int

Examples

>>> from surfaces.test_functions import SimionescuFunction
>>> func = SimionescuFunction()
>>> result = func({"x0": 0.84852813, "x1": -0.84852813})

Evaluate the objective function.

Args:

params: Parameter values as dict, array, list, or tuple fidelity: Optional fidelity level in (0, 1]. Controls evaluation

cost vs accuracy trade-off for multi-fidelity optimization (e.g. Hyperband, BOHB). None means full-fidelity evaluation. Only supported by ML test functions; ignored by algebraic functions.

**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: MetaSpec[source]#

Instance display/identity metadata (a frozen MetaSpec).

Metadata is fully static today, so this returns the class-level MetaSpec resolved at class-definition time.

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

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

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.
fidelity (float or None) – Fidelity level in (0, 1] for multi-fidelity evaluation.
**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: FunctionSpec[source]#

Instance-resolved function specification (a frozen FunctionSpec).

type(self)._spec is the static class-level template. This property overlays the fields that genuinely vary per instance (n_dim, n_objectives, f_global, x_global) by lifting them off the instance, so that func.spec.n_dim reflects this instance’s value. It is resolved on every access rather than cached, because some functions (e.g. BBOB) read spec during __init__ before the optimum has been computed, and a cached early value would go stale.