metaheuristic_designer.parameter_schedules.noisy_schedule module#
Schedule that applies gaussian noise to a subschedule.
- class NoisySchedule(subschedule, noise_level=0.01, rng=None)[source]#
Bases:
SchedulableParameterSchedule that applies gaussian noise to a subschedule.
- Parameters:
- subschedule: SchedulableParameter
Parameter schedule to modify the parameter each iterations iterations.
- noise_levelfloat, optional
Standard deviation of the gaussian noise applied to the parameter value
- Parameters:
subschedule (SchedulableParameter)
noise_level (float)
rng (int | Generator | None)
Methods
__call__(progress)Shorthand for
evaluate().evaluate(progress)Return the parameter value at the given progress.
- evaluate(progress)[source]#
Return the parameter value at the given progress.
- Return type:
float- Parameters:
- progressfloat
Current progress, a number between 0 (start) and 1 (end).
- Returns:
- Any
The parameter value at this stage of the optimization.
- Parameters:
progress (float)
Notes
The return value is not restricted to numbers. You can return: * a float (e.g., a linearly decaying mutation strength), * an int (e.g., a discrete number of mutated components), * a bool (e.g., switching on/off a feature after a threshold), * a string (e.g., switching between strategies), or * any other object that the consuming component expects.
This makes schedules suitable for changing discrete algorithm choices as well as continuous numerical parameters.