| Overview | Group | Tree | Graph | Index | Concepts |
IlcProbabilisticCriticalityCalculatorI is the implementation
class that implements the probabilistic texture criticality calculator.
Example
The probabilistic criticality calculation assumes that the aggregate demand and variance can be represented by a normal distribution parameterized by the expected demand (EX, in the following figure) and variance. The criticality for a minimum constraint, m, is calculated as the fraction of the area under the curve to the left of the m-value. Similarly, the criticality for a maximum constraint, M, is the fraction of the area under the curve to the right of M.
For more information, see Texture Measurements.
See Also:
IlcResourceTexture, IlcTextureCriticalityCalculator, IlcTextureCriticalityCalculatorI, IlcRelativeDemandCriticalityCalculatorI
| Constructor and Destructor Summary | |
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public | IlcProbabilisticCriticalityCalculatorI() |
| Method Summary | |
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public virtual IlcFloat | calculateCriticalityGreaterThan(IlcFloat expectedDemand, IlcFloat constraintVal, IlcFloat expectedVariance) const |
public virtual IlcFloat | calculateCriticalityLessThan(IlcFloat expectedDemand, IlcFloat constraintVal, IlcFloat expectedVariance) const |
Inherited Methods from IlcTextureCriticalityCalculatorI |
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calculateCriticalityGreaterThan, calculateCriticalityLessThan |
| Constructor and Destructor Detail |
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This constructor creates an instance of
IlcProbabilisticCriticalityCalculatorI.
| Method Detail |
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This method calculates the criticality of a maximum constraint
at one time point based on a probabilistic estimation. This
estimation assumes that the aggregate demand at a time point can be
represented by a normal distribution with expected demand, demand
and with variance, variance. The fraction of this distribution that
lies to the right of constraintVal, is used as the criticality.
This calculation is illustrated in the previous figure.
This method calculates the criticality of a minimum constraint at one time point based on a probabilistic estimation. This estimation assumes that the aggregate demand at a time point can be represented by a normal distribution with expected demand, demand and with variance, variance. The fraction of this distribution that lies to the left of constraintVal, is used as the criticality. This calculation is illustrated in the previous figure.