RotatedDiscus#

class RotatedDiscus(n_dim: int = 10, 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]#

F3: Rotated Discus Function.

A unimodal function where one variable has much larger contribution than others. Also known as the Rotated Tablet function.

Properties: - Unimodal - Non-separable (due to rotation) - Scalable - Condition number: 10^6

Parameters:

n_dim (int, default=10) – Number of dimensions. Supported: 10, 20, 30, 50, 100.
objective (str, default="minimize") – Either “minimize” or “maximize”.
sleep (float, default=0) – Artificial delay in seconds.

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 f_global: float[source]#

Global optimum value for this function.

CEC 2014 formula: f* = func_id * 100

property func_id: int | None[source]#

Function ID within the CEC suite.

Read from the static class-level spec, not self.spec: func_id is constant per class, and self.spec resolution lifts f_global, which (for CEC) is derived from func_id, so routing through it would recurse.

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.

property x_global: ndarray | None[source]#: Global optimum location (the shift vector).