Uses of Interface
microsim.statistics.IDoubleSource
Packages that use IDoubleSource
Package
Description
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Uses of IDoubleSource in microsim.statistics
Fields in microsim.statistics declared as IDoubleSourceMethods in microsim.statistics with parameters of type IDoubleSourceModifier and TypeMethodDescriptionvoidTimeSeries.addSeries(String name, IDoubleSource source, Enum<?> valueID) Add a new series.Constructors in microsim.statistics with parameters of type IDoubleSourceModifierConstructorDescriptionDouble(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 IDoubleSourceModifier and TypeClassDescriptionclassThis class computes the number of values in an array taken from a data source.classThis class computes the maximum value in an array of source values.static classMaxFunction operating on double source values.static classMaxFunction operating on float source values.static classMaxFunction operating on integer source values.static classMaxFunction operating on long source values.classA MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static classAn implementation of the MemorylessSeries class, which manages double type data sources.static classAn implementation of the MemorylessSeries class, which manages float type data sources.static classAn implementation of the MemorylessSeries class, which manages integer type data sources.static classAn implementation of the MemorylessSeries class, which manages long type data sources.classThis class computes the average value of an array of values taken from a data source.classThis class computes the average and variance value of an array of values taken from a data source.classThis class computes the minimum value in an array of source values.static classMinFunction operating on double source values.static classMinFunction operating on float source values.static classMinFunction operating on integer source values.static classMinFunction operating on long source values.classA MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static classAn implementation of the MemorylessSeries class, which manages double type data sources.static classAn implementation of the MemorylessSeries class, which manages float type data sources.static classAn implementation of the MemorylessSeries class, which manages integer type data sources.static classAn implementation of the MemorylessSeries class, which manages long type data sources.classThis class computes the average of the last given number of values in an array taken from a data source.classThis class computes the average of the last values collected from a data source.classA MixFunction object is to collect data over time, computing some statistics on the fly, without storing the data in memory.static classAn implementation of the MemorylessSeries class, which manages double type data sources.static classAn implementation of the MemorylessSeries class, which manages float type data sources.static classAn implementation of the MemorylessSeries class, which manages integer type data sources.static classAn implementation of the MemorylessSeries class, which manages long type data sources.classThis function calculates percentiles (p1,p5,p10-p90,p95,p99) for a given cross section of data.classThis class computes the sum of an array of source values.static classSumFunction operating on double source values.static classSumFunction operating on float source values.static classSumFunction operating on integer source values.static classSumFunction operating on long source values.Fields in microsim.statistics.functions declared as IDoubleSourceModifier and TypeFieldDescriptionprotected IDoubleSourceMovingAverageTraceFunction.dblSourceprotected IDoubleSourceMaxTraceFunction.Double.targetprotected IDoubleSourceMinTraceFunction.Double.targetprotected IDoubleSourceMultiTraceFunction.Double.targetConstructors in microsim.statistics.functions with parameters of type IDoubleSourceModifierConstructorDescriptionDouble(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 -
Uses of IDoubleSource in microsim.statistics.regression
Methods in microsim.statistics.regression with parameters of type IDoubleSourceModifier and TypeMethodDescriptionstatic <T extends Enum<T>>
doubleLinearRegression.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>>
doubleLinearRegression.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.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>>
doubleBinomialRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors) <E2 extends Enum<E2>>
doubleBinomialRegression.getProbability(IDoubleSource iDblSrc, Class<E2> Regressors) <E extends Enum<E> & IntegerValuedEnum,E2 extends Enum<E2>>
doubleGeneralisedOrderedRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors) <T extends Enum<T> & IntegerValuedEnum,E extends Enum<E>>
doubleIDiscreteChoiceModel.getProbability(T event, IDoubleSource iDblSrc, Class<E> Regressors) <E extends Enum<E> & IntegerValuedEnum,E2 extends Enum<E2>>
doubleMultinomialRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors) <E extends Enum<E> & IntegerValuedEnum,E2 extends Enum<E2>>
doubleOrderedRegression.getProbability(E event, IDoubleSource iDblSrc, Class<E2> Regressors) <E extends Enum<E>>
doubleProbabilityCalculator.getProbability(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors) <E extends Enum<E>>
doubleProbabilityCalculator.getProbability(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors, double adjust) <E2 extends Enum<E2>>
doubleBinomialRegression.getScore(IDoubleSource iDblSrc, Class<E2> Regressors) <T extends Enum<T>>
doubleILinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumType) ILinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumTypeDouble, IObjectSource iObjSrc, Class<U> enumTypeObject) <T extends Enum<T>>
doubleLinearRegression.getScore(IDoubleSource iDblSrc, Class<T> enumType) 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>>
doubleProbabilityCalculator.getScore(MultiKeyCoefficientMap map, IDoubleSource iDblSrc, Class<E> Regressors) -
Uses of IDoubleSource in microsim.statistics.weighted.functions
Classes in microsim.statistics.weighted.functions that implement IDoubleSourceModifier and TypeClassDescriptionclassThis 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.classThis 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 classSumFunction operating on weighted double source values.static classSumFunction operating on weighted float source values.static classSumFunction operating on weighted integer source values.static classSumFunction operating on weighted long source values.