TargetMaskScenario
Training scenarios · Implementation: mlsysim/engine/scenario_definitions/training.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
One serialized training example by segment, and the responses kept as context only.
Notes
Equation: Composition of mlsysim.solvers.TargetMaskModel.solve.
Assumptions: Inputs are routed to TargetMaskModel.solve; their documented regimes and units apply to this configuration.
Excludes: Additional mechanisms beyond the linked solver contracts; the scenario does not collect measurements.
References
Derivation: This scenario binds named inputs to TargetMaskModel. The linked solver contracts own equations, sources, and result-unit definitions.
Examples
Evaluate the named configuration and display a modeled result.
>>> from mlsysim import Scenarios
>>> case = Scenarios.RecoveryRecordTargetMask
>>> value = case.result.targets_without_rationale
>>> print(value)
800Attributes
| Name | Description |
|---|---|
| observation | |
| prompt | |
| rationale | |
| response | |
| result | Evaluate the stated target mask case through TargetMaskModel.solve; returns its typed result and quantity units. |
| task | |
| unselected_response |
prompt
Type: int.
Declared default: required.
task
Type: int.
Declared default: required.
rationale
Type: int.
Declared default: required.
response
Type: int.
Declared default: required.
observation
Type: int.
Declared default: required.
unselected_response
Type: int.
Declared default: required.
result
engine.scenario_types.TargetMaskScenario.result
Evaluate the stated target mask case through TargetMaskModel.solve; returns its typed result and quantity units.
Calculation: mlsysim.solvers.TargetMaskModel.solve.