AnalyzeRe.Metrics.ConditionalDistributionMetrics Class Reference

The metrics computed from a distribution filtered by some conditions. For example, in the co-tail-metrics response, this is the distribution of losses from the component that are also part of the tail distribution of the primary. More...

Public Member Functions
virtual T	ShallowCopy< T > ()
	Create a shallow copy of this object. See also ExtensionMethods.DeepCopy<T> for a serializer-based copy method.

Protected Member Functions
virtual void	AfterMembersCloned (APIType originalResource)
	Invoked following construction if the current instance has been created using a member-wise clone of some other instance. Override if your derived APIType class contains some members that should not be cloned.

Properties
double	correlation = Double.NaN [get, set]
	The correlation between the two distributions.
double	covariance = Double.NaN [get, set]
	The covariance is a measure of the joint variability between the two distributions used to produce these conditional distribution metrics.
double	mean = Double.NaN [get, set]
	The contribution to the mean value of the primary distribution.
double	min = Double.NaN [get, set]
	The contribution to the minimum value in the primary distribution.

Detailed Description

The metrics computed from a distribution filtered by some conditions. For example, in the co-tail-metrics response, this is the distribution of losses from the component that are also part of the tail distribution of the primary.

See also

IAPIResourceView.co_metrics<T>(ProbabilityWindow,IReference<T>,CoMetricsOptions,Int32?)

Definition at line 11 of file ConditionalDistributionMetrics.cs.

Inheritance diagram for AnalyzeRe.Metrics.ConditionalDistributionMetrics:

Member Function Documentation

AfterMembersCloned()

virtual void AnalyzeRe.APIType.AfterMembersCloned ( APIType  originalResource )

inlineprotectedvirtualinherited

Invoked following construction if the current instance has been created using a member-wise clone of some other instance. Override if your derived APIType class contains some members that should not be cloned.

Overriding implementations should be sure to invoke base.AfterMembersCloned().

A sane question for a code reviewer to ask might be: "Why not avoid copying those members in the first place?" The answer is that there is no framework-supported method of excluding members from a MemberwiseClone. The only officially supported solution is to not use the object.MemberwiseClone method at all and instead have each class implement it's own Copy method. In our case, most objects have no need to specialize their copy implementation (even though they could - the ShallowCopy<T> method is marked virtual). It's simpler to simply "correct" any special-case members after the fact, and requires less error-prone code than if the code were responsible for ensuring no members were missed in a copy. It's also faster than any reflection-based approach, even though such an approach could benefit from custom attributes meant to exclude certain members from copying.

Reimplemented in AnalyzeRe.APITypes.APIResource_WithDataEndpoint, AnalyzeRe.APIResourceView.BaseAPIResourceView, AnalyzeRe.Distributions.CustomDistribution, AnalyzeRe.LossSets.LossSet_WithData, AnalyzeRe.Optimization.Candidate, AnalyzeRe.Optimization.OptimizationView, and AnalyzeRe.StaticSimulation.

Definition at line 37 of file APIType.cs.

ShallowCopy< T >()

virtual T AnalyzeRe.APIType.ShallowCopy< T > ( )

inlinevirtualinherited

Create a shallow copy of this object.

See also

ExtensionMethods.DeepCopy<T>

for a serializer-based copy method.

Returns

A shallow copy of this object.

If this object contains any members that reference the current object (this), the class should override this method to avoid cloning a reference to the old class.

Implements AnalyzeRe.IAPIType.

Type Constraints

T

:

IAPIType

Definition at line 14 of file APIType.cs.

Property Documentation

correlation

double AnalyzeRe.Metrics.ConditionalDistributionMetrics.correlation = Double.NaN

getset

The correlation between the two distributions.

Definition at line 30 of file ConditionalDistributionMetrics.cs.

covariance

double AnalyzeRe.Metrics.ConditionalDistributionMetrics.covariance = Double.NaN

getset

The covariance is a measure of the joint variability between the two distributions used to produce these conditional distribution metrics.

Definition at line 26 of file ConditionalDistributionMetrics.cs.

mean

double AnalyzeRe.Metrics.ConditionalDistributionMetrics.mean = Double.NaN

getset

The contribution to the mean value of the primary distribution.

Definition at line 21 of file ConditionalDistributionMetrics.cs.

min

double AnalyzeRe.Metrics.ConditionalDistributionMetrics.min = Double.NaN

getset

The contribution to the minimum value in the primary distribution.

Definition at line 16 of file ConditionalDistributionMetrics.cs.

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The documentation for this class was generated from the following file:

• ConditionalDistributionMetrics.cs