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InjectionExposureScenario

Agents scenarios · Implementation: mlsysim/engine/scenario_definitions/agents.py.

Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.

Workloads reading reads untrusted items each, a fraction payload_fraction carrying a payload, with and without a screen that misses miss_rate of payloads.

Notes

Equation: The result property composes mlsysim.solvers.InjectionExposureModel.solve with the stored fields and any derived properties described below.

Assumptions: Inputs use the units and validity ranges of mlsysim.solvers.InjectionExposureModel.solve. The named scenario supplies a fixed configuration rather than observations of a new run.

Excludes: Additional runtime effects or statistical guarantees beyond the cited solver contracts; no measurement is performed by this wrapper.

References

Derivation: The result property composes mlsysim.solvers.InjectionExposureModel.solve with the stored fields and any derived properties described below. Individual property contracts identify the exact accounting expression or reusable calculation. Registry entries supply the scenario inputs; this class does not turn them into measured outcomes.

Examples

Evaluate a named scenario using its declared inputs.

>>> from mlsysim import Scenarios
>>> scenario = Scenarios.InjectionExposureByReadVolume
>>> round(float(scenario.result.exposure['repair']), 4)
0.1821

Attributes

Name Description
cohort
miss_rate
payload_fraction
reads
result Return the modeled InjectionExposure result from

payload_fraction

Type: float.

Declared default: required.

reads

Type: Dict[str, int].

Declared default: required.

miss_rate

Type: float.

Declared default: required.

cohort

Type: int.

Declared default: required.

result

engine.scenario_types.InjectionExposureScenario.result

Return the modeled InjectionExposure result from mlsysim.solvers.InjectionExposureModel.solve.

Equation: the scenario supplies its named fields to that solver. Returned quantity units, validity conditions, and omissions follow its calculation contract; this property adds no empirical calibration.

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