gemseo.algos.doe.doe_quality module#
DOE assessor.
- class DOEMeasures(discrepancy, mindist, phip)[source]#
Bases:
NamedTuple
The quality measures of a DOE.
Namely \(\phi^p\), minimum-distance and discrepancy measures, accessible with the attributes
discrepancy
,mindist
andphip
.The smaller the quality measures, the better, except for the minimum-distance criterion for which the larger it is the better.
Create new instance of DOEMeasures(discrepancy, mindist, phip)
- class DOEQuality(samples, power=50, discrepancy_type_name='CD', **discrepancy_options)[source]#
Bases:
object
The quality of a DOE.
- Parameters:
samples (RealArray) -- The input samples of the DOE.
power (int) --
The power \(p\) of the \(\phi^p\) criterion.
By default it is set to 50.
discrepancy_type_name (DiscrepancyTypeNameType) --
The type of discrepancy.
By default it is set to "CD".
**discrepancy_options (Any) -- The options passed to
scipy.stats.qmc.discrepancy
.
- measures: DOEMeasures#
The quality measures of the DOE.
- compute_discrepancy(samples, type_name='CD', **options)[source]#
Compute the discrepancy of a sample set (the smaller, the better).
- Parameters:
samples (RealArray) -- The data samples.
type_name (DiscrepancyTypeNameType) --
The type of discrepancy.
By default it is set to "CD".
**options (Any) -- The options passed to
scipy.stats.qmc.discrepancy()
.
- Returns:
The discrepancy.
- Return type:
- compute_mindist_criterion(samples)[source]#
Compute the minimum-distance criterion of a sample set (the higher, the better).
This criterion is also called mindist.
- Parameters:
samples (RealArray) -- The data samples.
- Returns:
The minimum-distance criterion.
- Return type:
- compute_phip_criterion(samples, power=50)[source]#
Compute the math:phi^p criterion of a sample set (the smaller, the better).
See [MM95].
- Parameters:
samples (RealArray) -- The data samples.
power (float) --
The power \(p\) of the \(\phi^p\) criterion.
By default it is set to 50.
- Returns:
phi^p criterion.
- Return type:
The math