Uses of Interface
microsim.statistics.IDoubleSource
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Packages that use IDoubleSource Package Description microsim.statistics microsim.statistics.functions microsim.statistics.reflectors microsim.statistics.regression microsim.statistics.weighted.functions -
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Uses of IDoubleSource in microsim.statistics
Methods in microsim.statistics with parameters of type IDoubleSource Modifier and Type Method Description void
TimeSeries. addSeries(java.lang.String name, IDoubleSource source, java.lang.Enum<?> valueID)
Add a new series.Constructors in microsim.statistics with parameters of type IDoubleSource Constructor Description Double(IDoubleSource source)
Create a statistic probe on a collection of IDoubleSource objects.Double(IDoubleSource source, java.lang.Enum<?> valueID)
Create a statistic probe on a collection of IDoubleSource objects. -
Uses of IDoubleSource in microsim.statistics.functions
Classes in microsim.statistics.functions that implement IDoubleSource Modifier and Type Class Description class
CountArrayFunction
This class computes the number of values in an array taken from a data source.class
MaxArrayFunction
This class computes the maximum value in an array of source values.static class
MaxArrayFunction.Double
MaxFunction operating on double source values.static class
MaxArrayFunction.Float
MaxFunction operating on float source values.static class
MaxArrayFunction.Integer
MaxFunction operating on integer source values.static class
MaxArrayFunction.Long
MaxFunction operating on long source values.class
MaxTraceFunction
A MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static class
MaxTraceFunction.Double
An implementation of the MemorylessSeries class, which manages double type data sources.static class
MaxTraceFunction.Float
An implementation of the MemorylessSeries class, which manages float type data sources.static class
MaxTraceFunction.Integer
An implementation of the MemorylessSeries class, which manages integer type data sources.static class
MaxTraceFunction.Long
An implementation of the MemorylessSeries class, which manages long type data sources.class
MeanArrayFunction
This class computes the average value of an array of values taken from a data source.class
MeanVarianceArrayFunction
This class computes the average and variance value of an array of values taken from a data source.class
MinArrayFunction
This class computes the minimum value in an array of source values.static class
MinArrayFunction.Double
MinFunction operating on double source values.static class
MinArrayFunction.Float
MinFunction operating on float source values.static class
MinArrayFunction.Integer
MinFunction operating on integer source values.static class
MinArrayFunction.Long
MinFunction operating on long source values.class
MinTraceFunction
A MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static class
MinTraceFunction.Double
An implementation of the MemorylessSeries class, which manages double type data sources.static class
MinTraceFunction.Float
An implementation of the MemorylessSeries class, which manages float type data sources.static class
MinTraceFunction.Integer
An implementation of the MemorylessSeries class, which manages integer type data sources.static class
MinTraceFunction.Long
An implementation of the MemorylessSeries class, which manages long type data sources.class
MovingAverageArrayFunction
This class computes the average of the last given number of values in an array taken from a data source.class
MovingAverageTraceFunction
This class computes the average of the last values collected from a data source.class
MultiTraceFunction
A MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static class
MultiTraceFunction.Double
An implementation of the MemorylessSeries class, which manages double type data sources.static class
MultiTraceFunction.Float
An implementation of the MemorylessSeries class, which manages float type data sources.static class
MultiTraceFunction.Integer
An implementation of the MemorylessSeries class, which manages integer type data sources.static class
MultiTraceFunction.Long
An implementation of the MemorylessSeries class, which manages long type data sources.class
SumArrayFunction
This class computes the sum of an array of source values.static class
SumArrayFunction.Double
SumFunction operating on double source values.static class
SumArrayFunction.Float
SumFunction operating on float source values.static class
SumArrayFunction.Integer
SumFunction operating on integer source values.static class
SumArrayFunction.Long
SumFunction operating on long source values.Constructors in microsim.statistics.functions with parameters of type IDoubleSource Constructor Description Double(IDoubleSource source, java.lang.Enum<?> valueID)
Create a basic statistic probe on a IDblSource object.Double(IDoubleSource source, java.lang.Enum<?> valueID)
Create a basic statistic probe on a IDblSource object.Double(IDoubleSource source, java.lang.Enum<?> valueID)
Create a basic statistic probe on a IDblSource object.MovingAverageTraceFunction(IDoubleSource source, java.lang.Enum<?> valueID, int windowSize)
Create a basic statistic probe on a IDoubleSource object. -
Uses of IDoubleSource in microsim.statistics.reflectors
Classes in microsim.statistics.reflectors that implement IDoubleSource Modifier and Type Class Description class
DoubleInvoker
Not of interest for users. -
Uses of IDoubleSource in microsim.statistics.regression
Methods in microsim.statistics.regression with parameters of type IDoubleSource Modifier and Type Method Description static <T extends java.lang.Enum<T>>
doubleLinearRegression. computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, java.lang.Class<T> enumType)
Uses reflection to obtain information from the iDblSrc object, so it is possibly slow.static <T extends java.lang.Enum<T>>
doubleLinearRegression. computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, java.lang.Class<T> enumType, boolean singleKeyCoefficients)
Use this method when the underlying agent does not have any additional conditioning regression keys (such as the gender or civil status) to determine the appropriate regression co-efficients, i.e.static <T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
doubleLinearRegression. computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDouble, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObject)
Requires the implementation of the IObjectSource to ascertain whether any additional conditioning regression keys are used (e.g.<T extends java.lang.Enum<T>>
booleanLogitRegression. event(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
booleanLogitRegression. event(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDbl, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObj)
<T extends java.lang.Enum<T>>
booleanProbitRegression. event(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
booleanProbitRegression. event(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDbl, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObj)
<E extends java.lang.Enum<E>>
TMultiLogitRegression. eventType(IDoubleSource iDblSrc, java.lang.Class<E> Regressors, java.lang.Class<T> enumType)
<E extends java.lang.Enum<E>>
doubleMultiLogitRegression. getLogitTransformOfScore(T event, IDoubleSource iDblSrc, java.lang.Class<E> Regressors)
<T extends java.lang.Enum<T>>
doubleLogitRegression. getProbability(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
doubleLogitRegression. getProbability(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDbl, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObj)
<T extends java.lang.Enum<T>>
doubleProbitRegression. getProbability(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
doubleProbitRegression. getProbability(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDbl, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObj)
<T extends java.lang.Enum<T>>
doubleILinearRegression. getScore(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
doubleILinearRegression. getScore(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDouble, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObject)
<T extends java.lang.Enum<T>>
doubleLinearRegression. getScore(IDoubleSource iDblSrc, java.lang.Class<T> enumType)
<T extends java.lang.Enum<T>,U extends java.lang.Enum<U>>
doubleLinearRegression. getScore(IDoubleSource iDblSrc, java.lang.Class<T> enumTypeDouble, IObjectSource iObjSrc, java.lang.Class<U> enumTypeObject)
Requires the implementation of the IObjectSource to ascertain whether any additional conditioning regression keys are used (e.g. -
Uses of IDoubleSource in microsim.statistics.weighted.functions
Classes in microsim.statistics.weighted.functions that implement IDoubleSource Modifier and Type Class Description class
Weighted_MeanArrayFunction
This class computes the (weighted) average (mean) value of an array of values taken from a data source, weighted by corresponding weights: weighted mean = sum (values * weights) / sum (weights) Note that the array of weights must have the same length as the array of values, otherwise an exception will be thrown.class
Weighted_SumArrayFunction
This class computes the sum of an array of source values, with each element of the array multiplied by the weight of the source (the source must implement the Weight interface).static class
Weighted_SumArrayFunction.Double
SumFunction operating on weighted double source values.static class
Weighted_SumArrayFunction.Float
SumFunction operating on weighted float source values.static class
Weighted_SumArrayFunction.Integer
SumFunction operating on weighted integer source values.static class
Weighted_SumArrayFunction.Long
SumFunction operating on weighted long source values.
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