Uses of Interface
microsim.statistics.IDoubleSource

Packages that use IDoubleSource

Package	Description
microsim.statistics
microsim.statistics.functions
microsim.statistics.reflectors
microsim.statistics.regression
microsim.statistics.weighted.functions

Uses of IDoubleSource in microsim.statistics

Fields in microsim.statistics declared as IDoubleSource

Modifier and Type	Field	Description
protected IDoubleSource	Series.Double.target

Methods in microsim.statistics with parameters of type IDoubleSource

Modifier and Type	Method	Description
void	TimeSeries.addSeries(String name, IDoubleSource source, Enum<?> valueID)	Add a new series.

Constructors in microsim.statistics with parameters of type IDoubleSource

Modifier	Constructor	Description
	Double(IDoubleSource source)	Create a statistic probe on a collection of IDoubleSource objects.
	Double(IDoubleSource source, 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	PercentileArrayFunction	This function calculates percentiles (p1,p5,p10-p90,p95,p99) for a given cross section of data.
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.

Fields in microsim.statistics.functions declared as IDoubleSource

Modifier and Type	Field	Description
protected IDoubleSource	MovingAverageTraceFunction.dblSource
protected IDoubleSource	MaxTraceFunction.Double.target
protected IDoubleSource	MinTraceFunction.Double.target
protected IDoubleSource	MultiTraceFunction.Double.target

Constructors in microsim.statistics.functions with parameters of type IDoubleSource

Modifier	Constructor	Description
	Double(IDoubleSource source, Enum<?> valueID)	Create a basic statistic probe on a IDblSource object.
	Double(IDoubleSource source, Enum<?> valueID)	Create a basic statistic probe on a IDblSource object.
	Double(IDoubleSource source, Enum<?> valueID)	Create a basic statistic probe on a IDblSource object.
	MovingAverageTraceFunction(IDoubleSource source, 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 Enum<T>> double	LinearRegression.computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, Class<T> enumType)	Uses reflection to obtain information from the iDblSrc object, so it is possibly slow.
static <T extends Enum<T>> double	LinearRegression.computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, 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. the regression co-efficients do not depend on any properties of the underlying model.
static <T extends Enum<T>, U extends Enum<U>> double	LinearRegression.computeScore(MultiKeyCoefficientMap coeffMultiMap, IDoubleSource iDblSrc, Class<T> enumTypeDouble, IObjectSource iObjSrc, Class<U> enumTypeObject)	Requires the implementation of the IObjectSource to ascertain whether any additional conditioning regression keys are used (e.g. whether the underlying agent is female, married etc., where the regression co-efficients are conditioned on additional keys of gender and civil status, for example).
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> Map<E,Double>	BinomialRegression.getProbabilities(IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> Map<E,Double>	GeneralisedOrderedRegression.getProbabilities(IDoubleSource iDblSrc, Class<E2> Regressors)
<T extends Enum<T> & IntegerValuedEnum, E extends Enum<E>> Map<T,Double>	IDiscreteChoiceModel.getProbabilities(IDoubleSource iDblSrc, Class<E> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> Map<E,Double>	MultinomialRegression.getProbabilities(IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> Map<E,Double>	OrderedRegression.getProbabilities(IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> double	BinomialRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors)
<E2 extends Enum<E2>> double	BinomialRegression.getProbability(IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> double	GeneralisedOrderedRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors)
<T extends Enum<T> & IntegerValuedEnum, E extends Enum<E>> double	IDiscreteChoiceModel.getProbability(T event, IDoubleSource iDblSrc, Class<E> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> double	MultinomialRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E> & IntegerValuedEnum, E2 extends Enum<E2>> double	OrderedRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors)
<E extends Enum<E>> double	ProbabilityCalculator.getProbability(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors)
<E extends Enum<E>> double	ProbabilityCalculator.getProbability(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors, double adjust)
<E2 extends Enum<E2>> double	BinomialRegression.getScore(IDoubleSource iDblSrc, Class<E2> Regressors)
<T extends Enum<T>> double	ILinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumType)
<T extends Enum<T>, U extends Enum<U>> double	ILinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumTypeDouble, IObjectSource iObjSrc, Class<U> enumTypeObject)
<T extends Enum<T>> double	LinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumType)
<T extends Enum<T>, U extends Enum<U>> double	LinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumTypeDouble, IObjectSource iObjSrc, Class<U> enumTypeObject)	Requires the implementation of the IObjectSource to ascertain whether any additional conditioning regression keys are used (e.g. whether the underlying agent is female, married etc., where the regression co-efficients are conditioned on additional keys of gender and civil status, for example).
<E extends Enum<E>> double	ProbabilityCalculator.getScore(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors)

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.