ShieldCoverageBillScenario
Robotics scenarios · Implementation: mlsysim/engine/scenario_definitions/robotics/site_limits.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
A fleet’s zero-event proposal log and a shield’s tested coverage against a hazard target.
Notes
Equation: The result property delegates to the named solver documented below; it converts declared scenario units before passing the inputs.
Assumptions: Zero-event exposure and tested shield coverage use the declared statistical model and common-cause rate.
Excludes: Certification of a hazard target or evidence that the optional hazardous_proposals field was incorporated into solve.
References
Derivation: the scenario composes mlsysim.solvers.ShieldCoverageBillModel. Their contracts supply the underlying equations; property equations show the remaining unit conversions and accounting.
Examples
>>> from mlsysim import Scenarios
>>> scenario = Scenarios.ShieldCoverageBill
>>> value = scenario.result
>>> type(value).__name__
'ShieldCoverageBillResult'Attributes
| Name | Description |
|---|---|
| common_cause_rate | |
| confidence | |
| coverage | |
| cycle_days | |
| fleet_units | |
| hazardous_proposals | |
| hours_per_day | |
| result | Return the typed accounting result from ShieldCoverageBillModel using this |
| target_rate |
fleet_units
Type: int.
Declared default: required.
cycle_days
Type: Quantity.
Declared default: required.
hours_per_day
Type: Quantity.
Declared default: required.
coverage
Type: float.
Declared default: required.
common_cause_rate
Type: Quantity.
Declared default: required.
target_rate
Type: Quantity.
Declared default: required.
confidence
Type: float.
Declared default: required.
hazardous_proposals
Type: int.
Declared default: 0.
result
engine.scenario_types.ShieldCoverageBillScenario.result
Return the typed accounting result from ShieldCoverageBillModel using this scenario’s declared inputs; units and exclusions follow that solver contract.
Equation: ShieldCoverageBillModel().solve(fleet_units, cycle_days, hours_per_day, coverage, common_cause_rate, target_rate, confidence).