SharpEntropyDoc.xml

<?xml version="1.0"?>
<doc>
    <assembly>
        <name>SharpEntropy</name>
    </assembly>
    <members>
        <member name="T:SharpEntropy.AbstractDataIndexer">
            <summary>
            Abstract base for DataIndexer implementations.
            </summary>
            <author>
            Tom Morton
            </author>
            <author>
            Richard J. Northedge
            </author>
        </member>
        <member name="T:SharpEntropy.ITrainingDataIndexer">
            <summary>
            Object that compresses events in memory and performs feature selection.
            </summary>
        </member>
        <member name="M:SharpEntropy.ITrainingDataIndexer.GetContexts">
            <summary>
            Gets an array of context data calculated from the training data.
            </summary>
            <returns>
            Array of integer arrays, each containing the context data for an event.
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingDataIndexer.GetNumTimesEventsSeen">
            <summary>
            Gets an array indicating how many times each event is seen.
            </summary>
            <returns>
            Integer array with event frequencies.
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingDataIndexer.GetOutcomeList">
            <summary>
            Gets an outcome list.
            </summary>
            <returns>
            Integer array of outcomes.
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingDataIndexer.GetPredicateLabels">
            <summary>
            Gets an array of predicate labels.
            </summary>
            <returns>
            Array of predicate labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingDataIndexer.GetOutcomeLabels">
            <summary>
            Gets an array of outcome labels.
            </summary>
            <returns>
            Array of outcome labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.GetContexts">
            <summary>
            Gets an array of context data calculated from the training data.
            </summary>
            <returns>
            Array of integer arrays, each containing the context data for an event.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SetContexts(System.Int32[][])">
            <summary>
            Sets the array of context data calculated from the training data.
            </summary>
            <param name="newContexts">
            Array of integer arrays, each containing the context data for an event.
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.GetNumTimesEventsSeen">
            <summary>
            Gets an array indicating how many times each event is seen.
            </summary>
            <returns>
            Integer array with event frequencies.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SetNumTimesEventsSeen(System.Int32[])">
            <summary>
            Sets an array indicating how many times each event is seen.
            </summary>
            <param name="newNumTimesEventsSeen">
            Integer array with event frequencies.
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.GetOutcomeList">
            <summary>
            Gets an outcome list.
            </summary>
            <returns>
            Integer array of outcomes.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SetOutcomeList(System.Int32[])">
            <summary>
            Sets an outcome list.
            </summary>
            <param name="newOutcomeList">
            Integer array of outcomes.
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.GetPredicateLabels">
            <summary>
            Gets an array of predicate labels.
            </summary>
            <returns>
            Array of predicate labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SetPredicateLabels(System.String[])">
            <summary>
            Sets an array of predicate labels.
            </summary>
            <param name="newPredicateLabels">
            Array of predicate labels.
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.GetOutcomeLabels">
            <summary>
            Gets an array of outcome labels.
            </summary>
            <returns>
            Array of outcome labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SetOutcomeLabels(System.String[])">
            <summary>
            Sets an array of outcome labels.
            </summary>
            <param name="newOutcomeLabels">
            Array of outcome labels.
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.SortAndMerge(System.Collections.Generic.List{SharpEntropy.ComparableEvent})">
            <summary>
            Sorts and uniques the array of comparable events.  This method
            will alter the eventsToCompare array -- it does an in place
            sort, followed by an in place edit to remove duplicates.
            </summary>
            <param name="eventsToCompare">
            a List of <code>ComparableEvent</code> values
            </param>
        </member>
        <member name="M:SharpEntropy.AbstractDataIndexer.ToIndexedStringArray(System.Collections.Generic.Dictionary{System.String,System.Int32})">
            <summary>
            Utility method for creating a string[] array from a dictionary whose
            keys are labels (strings) to be stored in the array and whose
            values are the indices (integers) at which the corresponding
            labels should be inserted.
            </summary>
            <param name="labelToIndexMap">
            a <code>Dictionary</code> value
            </param>
            <returns>
            a <code>string[]</code> value
            </returns>
        </member>
        <member name="T:SharpEntropy.BasicContextGenerator">
            <summary>
            Generate contexts for maxent decisions, assuming that the input
            given to the GetContext() method is a String containing contextual
            predicates separated by spaces, e.g:
            <p>
            cp_1 cp_2 ... cp_n
            </p>
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>based on BasicContextGenerator.java, $Revision: 1.2 $, $Date: 2002/04/30 08:48:35 $
            </version>
        </member>
        <member name="T:SharpEntropy.IContextGenerator`1">
            <summary> 
            Generate contexts for maximum entropy decisions.
            </summary>
        </member>
        <member name="M:SharpEntropy.IContextGenerator`1.GetContext(`0)">
            <summary>
            Builds up the list of contextual predicates given an object of type T.
            </summary>
        </member>
        <member name="M:SharpEntropy.BasicContextGenerator.GetContext(System.String)">
            <summary>
            Builds up the list of contextual predicates given a string.
            </summary>
            <param name="input">
            String with contextual predicates separated by spaces.
            </param>
            <returns>String array of contextual predicates.</returns>
        </member>
        <member name="T:SharpEntropy.BasicEventReader">
            <summary>
            An object which can deliver a stream of training events assuming
            that each event is represented as a space separated list containing
            all the contextual predicates, with the last item being the
            outcome, e.g.: 
            
            <p> cp_1 cp_2 ... cp_n outcome</p>
            </summary>
        </member>
        <member name="T:SharpEntropy.ITrainingEventReader">
            <summary>
            An object which can deliver a stream of training events for the GIS
            procedure (or others such as IIS if and when they are implemented).
            TrainingEventReaders don't need to use SharpEntropy.ITrainingDataReader, but doing so
            would provide greater flexibility for producing events from data stored in
            different formats.
            </summary>
        </member>
        <member name="M:SharpEntropy.ITrainingEventReader.ReadNextEvent">
            <summary> 
            Returns the next TrainingEvent object held in this TrainingEventReader.
            </summary>
            <returns>
            the TrainingEvent object which is next in this TrainingEventReader
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingEventReader.HasNext">
            <summary> 
            Test whether there are any TrainingEvents remaining in this TrainingEventReader.
            </summary>
            <returns>
            true if this TrainingEventReader has more TrainingEvents
            </returns>
        </member>
        <member name="M:SharpEntropy.BasicEventReader.#ctor(SharpEntropy.ITrainingDataReader{System.String})">
            <summary>
            Constructor sets up the training event reader based on a stream of training data.
            </summary>
            <param name="dataReader">
            Stream of training data.
            </param>
        </member>
        <member name="M:SharpEntropy.BasicEventReader.ReadNextEvent">
            <summary> 
            Returns the next Event object held in this EventReader.  Each call to ReadNextEvent advances the EventReader.
            </summary>
            <returns>
            the Event object which is next in this EventReader
            </returns>
        </member>
        <member name="M:SharpEntropy.BasicEventReader.HasNext">
            <summary> 
            Test whether there are any Events remaining in this EventReader.
            </summary>
            <returns>
            true if this EventReader has more Events
            </returns>
        </member>
        <member name="T:SharpEntropy.ComparableEvent">
            <summary>
            A Maximum Entropy event representation which we can use to sort based on the
            predicates indexes contained in the events.
            </summary>
            <author>
             Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version> 
            based on ComparableEvent.java, $Revision: 1.2 $, $Date: 2001/12/27 19:20:26 $
            </version>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.GetPredicateIndexes">
            <summary>
            Returns an array containing the indexes of the predicates in this event.
            </summary>
            <returns>
            Integer array of predicate indexes.
            </returns>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.SetPredicateIndexes(System.Int32[])">
            <summary>
            Sets the array containing the indices of the predicates in this event.
            </summary>
            <param name="predicateIndexes">
            Integer array of predicate indexes.
            </param>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.#ctor(System.Int32,System.Int32[])">
            <summary>
            Constructor for the ComparableEvent.
            </summary>
            <param name="outcome">
            The ID of the outcome for this event.
            </param>
            <param name="predicateIndexes">
            Array of indexes for the predicates in this event.
            </param>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.CompareTo(SharpEntropy.ComparableEvent)">
            <summary>
            Implementation of the IComparable interface.
            </summary>
            <param name="eventToCompare">
            ComparableEvent to compare this event to.
            </param>
            <returns>
            A value indicating if the compared object is smaller, greater or the same as this event.
            </returns>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.Equals(System.Object)">
            <summary>
            Tests if this event is equal to another object.
            </summary>
            <param name="o">
            Object to test against.
            </param>
            <returns>
            True if the objects are equal.
            </returns>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.GetHashCode">
            <summary>
            Provides a hashcode for storing events in a dictionary or hashtable.
            </summary>
            <returns>
            A hashcode value.
            </returns>
        </member>
        <member name="M:SharpEntropy.ComparableEvent.ToString">
            <summary>
            Override to provide a succint summary of the ComparableEvent object.
            </summary>
            <returns>
            String representation of the ComparableEvent object.
            </returns>
        </member>
        <member name="P:SharpEntropy.ComparableEvent.Outcome">
            <summary>
            The outcome ID of this event.
            </summary>
        </member>
        <member name="P:SharpEntropy.ComparableEvent.SeenCount">
            <summary>
            The number of times this event
            has been seen.
            </summary>
        </member>
        <member name="T:SharpEntropy.GisModel">
            <summary>
            A maximum entropy model which has been trained using the Generalized
            Iterative Scaling procedure.
            </summary>
            <author>
            Tom Morton and Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on GISModel.java, $Revision: 1.13 $, $Date: 2004/06/11 20:51:44 $
            </version>
        </member>
        <member name="T:SharpEntropy.IMaximumEntropyModel">
            <summary>
            Interface for maximum entropy models.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>    
            based on MaxentModel.java, $Revision: 1.4 $, $Date: 2003/12/09 23:13:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.Evaluate(System.String[])">
            <summary> 
            Evaluates a context.
            </summary>
            <param name="context">
            A list of string names of the contextual predicates
            which are to be evaluated together.
            </param>
            <returns>
            An array of the probabilities for each of the different
            outcomes, all of which sum to 1.
            </returns>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.Evaluate(System.String[],System.Double[])">
            <summary>
            Evaluates a context.
            </summary>
            <param name="context">
            A list of string names of the contextual predicates
            which are to be evaluated together.
            </param>
            <param name="probabilities">
            An array which is populated with the probabilities for each of the different
            outcomes, all of which sum to 1.
            </param>
            <returns>
            an array of the probabilities for each of the different
            outcomes, all of which sum to 1.  The <code>probabilities</code> array is returned if it is appropiately sized. 
            </returns>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.GetBestOutcome(System.Double[])">
            <summary>
            Simple function to return the outcome associated with the index
            containing the highest probability in the double[].
            </summary>
            <param name="outcomes">
            A <code>double[]</code> as returned by the
            <code>Evaluate(string[] context)</code>
            method.
            </param>
            <returns> 
            the string name of the best outcome
            </returns>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.GetAllOutcomes(System.Double[])">
            <summary>
            Return a string matching all the outcome names with all the
            probabilities produced by the <code>eval(String[]
            context)</code> method.
            </summary>
            <param name="outcomes">
            A <code>double[]</code> as returned by the
            <code>eval(String[] context)</code>
            method.
            </param>
            <returns>
            String containing outcome names paired with the normalized
            probability (contained in the <code>double[] ocs</code>)
            for each one.
            </returns>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.GetOutcomeName(System.Int32)">
            <summary>
            Gets the String name of the outcome associated with the supplied index
            </summary>
            <param name="index">
            the index for which the name of the associated outcome is desired.
            </param>
            <returns> 
            the String name of the outcome
            </returns>
        </member>
        <member name="M:SharpEntropy.IMaximumEntropyModel.GetOutcomeIndex(System.String)">
            <summary>
            Gets the index associated with the string name of the given
            outcome.
            </summary>
            <param name="outcome">
            the String name of the outcome for which the
            index is desired
            </param>
            <returns>
            the index if the given outcome label exists for this
            model, -1 if it does not.
            </returns>
        </member>
        <member name="P:SharpEntropy.IMaximumEntropyModel.OutcomeCount">
            <summary>
            Returns the number of outcomes for this model.
            </summary>
            <returns>
            The number of outcomes.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.#ctor(SharpEntropy.IO.IGisModelReader)">
            <summary>
            Constructor for a maximum entropy model trained using the
            Generalized Iterative Scaling procedure.
            </summary>
            <param name="reader">
            A reader providing the data for the model.
            </param>
        </member>
        <member name="M:SharpEntropy.GisModel.Evaluate(System.String[])">
            <summary> 
            Evaluates a context.
            </summary>
            <param name="context">
            A list of string names of the contextual predicates
            which are to be evaluated together.
            </param>
            <returns>
            An array of the probabilities for each of the different
            outcomes, all of which sum to 1.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.Evaluate(System.String[],System.Double[])">
            <summary>
            Use this model to evaluate a context and return an array of the
            likelihood of each outcome given that context.
            </summary>
            <param name="context">
            The names of the predicates which have been observed at
            the present decision point.
            </param>
            <param name="outcomeSums">
            This is where the distribution is stored.
            </param>
            <returns>
            The normalized probabilities for the outcomes given the
            context. The indexes of the double[] are the outcome
            ids, and the actual string representation of the
            outcomes can be obtained from the method
            GetOutcome(int outcomeIndex).
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetBestOutcome(System.Double[])">
            <summary>
            Return the name of the outcome corresponding to the highest likelihood
            in the parameter outcomes.
            </summary>
            <param name="outcomes">
            A double[] as returned by the Evaluate(string[] context)
            method.
            </param>
            <returns>
            The name of the most likely outcome.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetAllOutcomes(System.Double[])">
            <summary>
            Return a string matching all the outcome names with all the
            probabilities produced by the <code>Evaluate(string[] context)</code>
            method.
            </summary>
            <param name="outcomes">
            A <code>double[]</code> as returned by the
            <code>eval(String[] context)</code>
            method.
            </param>
            <returns>
            String containing outcome names paired with the normalized
            probability (contained in the <code>double[] outcomes</code>)
            for each one.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetOutcomeName(System.Int32)">
            <summary>
            Return the name of an outcome corresponding to an integer ID value.
            </summary>
            <param name="outcomeIndex">
            An outcome ID.
            </param>
            <returns>
            The name of the outcome associated with that ID.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetOutcomeIndex(System.String)">
            <summary> 
            Gets the index associated with the string name of the given outcome.
            </summary>
            <param name="outcome">
            the string name of the outcome for which the
            index is desired
            </param>
            <returns>
            the index if the given outcome label exists for this
            model, -1 if it does not.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetPredicates">
            <summary>
            Provides the predicates data structure which is part of the encoding of the maxent model
            information.  This method will usually only be needed by
            GisModelWriters.
            </summary>
            <returns>
            Dictionary containing PatternedPredicate objects.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetOutcomePatterns">
            <summary>
            Provides the list of outcome patterns used by the predicates.  This method will usually
            only be needed by GisModelWriters.
            </summary>
            <returns>
            Array of outcome patterns.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisModel.GetOutcomeNames">
            <summary>
            Provides the outcome names data structure which is part of the encoding of the maxent model
            information.  This method will usually only be needed by
            GisModelWriters.
            </summary>
            <returns>
            Array containing the outcome names.
            </returns>
        </member>
        <member name="P:SharpEntropy.GisModel.OutcomeCount">
            <summary>
            Returns the number of outcomes for this model.
            </summary>
            <returns>
            The number of outcomes.
            </returns>
        </member>
        <member name="P:SharpEntropy.GisModel.CorrectionConstant">
            <summary>
            Provides the model's correction constant.  This property will usually only be needed by
            GisModelWriters.
            </summary>
        </member>
        <member name="P:SharpEntropy.GisModel.CorrectionParameter">
            <summary>
            Provides the model's correction parameter.  This property will usually only be needed by
            GisModelWriters.
            </summary>
        </member>
        <member name="T:SharpEntropy.GisTrainer">
            <summary>
            An implementation of Generalized Iterative Scaling.  The reference paper
            for this implementation was Adwait Ratnaparkhi's tech report at the
            University of Pennsylvania's Institute for Research in Cognitive Science,
            and is available at <a href ="ftp://ftp.cis.upenn.edu/pub/ircs/tr/97-08.ps.Z"><code>ftp://ftp.cis.upenn.edu/pub/ircs/tr/97-08.ps.Z</code></a>. 
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
             Richard J, Northedge
            </author>
            <version>
            based on GISTrainer.java, $Revision: 1.15 $, $Date: 2004/06/14 20:52:41 $
            </version>
        </member>
        <member name="T:SharpEntropy.IO.IGisModelReader">
            <summary> 
            Interface for readers of GIS models.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.IGisModelReader.GetOutcomeLabels">
            <summary>
            Returns the model's outcome labels as a String array.  This method should
            usually only be accessed by GIS model writer classes via the GisModel class.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.IGisModelReader.GetOutcomePatterns">
            <summary>
            Returns the model's outcome patterns.  This method should
            usually only be accessed by GIS model writer classes via the GisModel class.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.IGisModelReader.GetPredicates">
            <summary>
            Returns the model's predicates.  This method should
            usually only be accessed by GIS model writer classes via the GisModel class.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.IGisModelReader.GetPredicateData(System.String,System.Int32[],System.Double[])">
            <summary>
            Returns model information for a predicate, given the predicate label.
            </summary>
            <param name="predicateLabel">
            The predicate label to fetch information for.
            </param>
            <param name="featureCounts">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method increments the count of each outcome that is active in the specified
            predicate.
            </param>
            <param name="outcomeSums">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method adds the parameter values for each of the active outcomes in the
            predicate.
            </param>
        </member>
        <member name="P:SharpEntropy.IO.IGisModelReader.CorrectionConstant">
            <summary>
            Returns the value of the model's correction constant.  This property should
            usually only be accessed by GIS model writer classes via the GisModel class.
            </summary>
        </member>
        <member name="P:SharpEntropy.IO.IGisModelReader.CorrectionParameter">
            <summary>
            Returns the value of the model's correction constant parameter.  This property should
            usually only be accessed by GIS model writer classes via the GisModel class.
            </summary>
        </member>
        <member name="M:SharpEntropy.GisTrainer.OnTrainingProgress(SharpEntropy.TrainingProgressEventArgs)">
            <summary>
            Used to raise events providing messages with information
            about training progress.
            </summary>
            <param name="e">
            Contains the message with information about the progress of 
            the training algorithm.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.#ctor">
            <summary>
            Creates a new <code>GisTrainer</code> instance.
            </summary>
        </member>
        <member name="M:SharpEntropy.GisTrainer.#ctor(System.Boolean)">
            <summary>
            Creates a new <code>GisTrainer</code> instance.
            </summary>
            <param name="useSlackParameter">
            Sets whether this trainer will use slack parameters while training the model.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.#ctor(System.Double)">
            <summary>
            Creates a new <code>GisTrainer</code> instance.
            </summary>
            <param name="smoothingObservation">
            If smoothing is in use, this value indicates the "number" of
            times we want the trainer to imagine that it saw a feature that it
            actually didn't see.  Defaulted to 0.1.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.#ctor(System.Boolean,System.Double)">
            <summary>
            Creates a new <code>GisTrainer</code> instance.
            </summary>
            <param name="useSlackParameter">
            Sets whether this trainer will use slack parameters while training the model.
            </param>
            <param name="smoothingObservation">
            If smoothing is in use, this value indicates the "number" of
            times we want the trainer to imagine that it saw a feature that it
            actually didn't see.  Defaulted to 0.1.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.TrainModel(SharpEntropy.ITrainingEventReader)">
            <summary>
            Train a model using the GIS algorithm.
            </summary>
            <param name="eventReader">
            The ITrainingEventReader holding the data on which this model
            will be trained.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.TrainModel(SharpEntropy.ITrainingEventReader,System.Int32,System.Int32)">
            <summary>
            Train a model using the GIS algorithm.
            </summary>
            <param name="eventReader">
            The ITrainingEventReader holding the data on which this model
            will be trained.
            </param>
            <param name="iterations">
            The number of GIS iterations to perform.
            </param>
            <param name="cutoff">
            The number of times a predicate must be seen in order
            to be relevant for training.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.TrainModel(System.Int32,SharpEntropy.ITrainingDataIndexer)">
            <summary>
            Train a model using the GIS algorithm.
            </summary>
            <param name="iterations">
            The number of GIS iterations to perform.
            </param>
            <param name="dataIndexer">
            The data indexer used to compress events in memory.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.FindParameters(System.Int32)">
            <summary>
            Estimate and return the model parameters.
            </summary>
            <param name="iterations">
            Number of iterations to run through.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.Evaluate(System.Int32[],System.Double[])">
            <summary>
            Use this model to evaluate a context and return an array of the
            likelihood of each outcome given that context.
            </summary>
            <param name="context">
            The integers of the predicates which have been
            observed at the present decision point.
            </param>
            <param name="outcomeSums">
            The normalized probabilities for the outcomes given the
            context. The indexes of the double[] are the outcome
            ids.
            </param>
        </member>
        <member name="M:SharpEntropy.GisTrainer.NextIteration">
            <summary>
            Compute one iteration of GIS and retutn log-likelihood.
            </summary>
            <returns>The log-likelihood.</returns>
        </member>
        <member name="M:SharpEntropy.GisTrainer.ConvertPredicates">
            <summary>
            Convert the predicate data into the outcome pattern / patterned predicate format used by the GIS models.
            </summary>
        </member>
        <member name="M:SharpEntropy.GisTrainer.GetOutcomeLabels">
            <summary>
            Obtains the outcome labels for the model produced as a result of training.
            </summary>
            <returns>
            Array of outcome labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisTrainer.GetOutcomePatterns">
            <summary>
            Obtains the outcome patterns for the model produced as a result of training.
            </summary>
            <returns>
            Array of outcome patterns.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisTrainer.GetPredicates">
            <summary>
            Obtains the predicate data for the model produced as a result of training.
            </summary>
            <returns>
            Dictionary containing PatternedPredicate objects.
            </returns>
        </member>
        <member name="M:SharpEntropy.GisTrainer.GetPredicateData(System.String,System.Int32[],System.Double[])">
            <summary>
            Returns trained model information for a predicate, given the predicate label.
            </summary>
            <param name="predicateLabel">
            The predicate label to fetch information for.
            </param>
            <param name="featureCounts">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method increments the count of each outcome that is active in the specified
            predicate.
            </param>
            <param name="outcomeSums">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method adds the parameter values for each of the active outcomes in the
            predicate.
            </param>
        </member>
        <member name="E:SharpEntropy.GisTrainer.TrainingProgress">
            <summary>
            Used to provide informational messages regarding the
            progress of the training algorithm.
            </summary>
        </member>
        <member name="P:SharpEntropy.GisTrainer.Smoothing">
            <summary>
            Sets whether this trainer will use smoothing while training the model.
            This can improve model accuracy, though training will potentially take
            longer and use more memory.  Model size will also be larger.
            </summary>
            <remarks>
            Initial testing indicates improvements for models built on small data sets and
            few outcomes, but performance degradation for those with large data
            sets and lots of outcomes.
            </remarks>
        </member>
        <member name="P:SharpEntropy.GisTrainer.UseSlackParameter">
            <summary>
            Sets whether this trainer will use slack parameters while training the model.
            </summary>
        </member>
        <member name="P:SharpEntropy.GisTrainer.SmoothingObservation">
            <summary>
            If smoothing is in use, this value indicates the "number" of
            times we want the trainer to imagine that it saw a feature that it
            actually didn't see.  Defaulted to 0.1.
            </summary>
        </member>
        <member name="P:SharpEntropy.GisTrainer.CorrectionConstant">
            <summary>
            The correction constant for the model produced as a result of training.
            </summary>
        </member>
        <member name="P:SharpEntropy.GisTrainer.CorrectionParameter">
            <summary>
            The correction parameter for the model produced as a result of training.
            </summary>
        </member>
        <member name="M:SharpEntropy.GisTrainer.OutcomePatternComparer.Compare(System.Int32[],System.Int32[])">
            <summary>
            Compare two outcome patterns and determines which comes first,
            based on the outcome ids (lower outcome ids first)
            </summary>
            <param name="firstPattern">
            First outcome pattern to compare.
            </param>
            <param name="secondPattern">
            Second outcome pattern to compare.
            </param>
            <returns></returns>
        </member>
        <member name="T:SharpEntropy.TrainingProgressEventArgs">
            <summary>
            Event arguments class for training progress events.
            </summary>
        </member>
        <member name="M:SharpEntropy.TrainingProgressEventArgs.#ctor(System.String)">
            <summary>
            Constructor for the training progress event arguments.
            </summary>
            <param name="message">
            Information message about the progress of training.
            </param>
        </member>
        <member name="P:SharpEntropy.TrainingProgressEventArgs.Message">
            <summary>
            Information message about the progress of training.
            </summary>
        </member>
        <member name="T:SharpEntropy.TrainingProgressEventHandler">
            <summary>
            Event handler delegate for the training progress event.
            </summary>
        </member>
        <member name="T:SharpEntropy.IContextGenerator">
            <summary> 
            Generate contexts for maximum entropy decisions.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on ContextGenerator.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IContextGenerator.GetContext(System.Object)">
            <summary>
            Builds up the list of contextual predicates given an object.
            </summary>
        </member>
        <member name="T:SharpEntropy.IO.BinaryGisModelReader">
            <summary>
            A reader for GIS models stored in a binary format.  This format is not the one
            used by the <see cref="T:SharpEntropy.IO.JavaBinaryGisModelReader">java version of MaxEnt</see>.
            It has two main differences, designed for performance when loading the data
            from file: first, it uses big endian data values, which is native for C#, and secondly it
            encodes the outcome patterns and values in a more efficient manner.
            </summary>
            <author> 
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on BinaryGISModelReader.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="T:SharpEntropy.IO.GisModelReader">
            <summary>
            Abstract parent class for readers of GIS models.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on GISModelReader.java, $Revision: 1.5 $, $Date: 2004/06/11 20:51:36 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadModel">
            <summary>
            Retrieve a model from disk.
            
            <p>This method delegates to worker methods for each part of this 
            sequence.  If you are creating a reader that conforms largely to this
            sequence but varies at one or more points, override the relevant worker
            method(s) to achieve the required format.</p>
            
            <p>If you are creating a reader for a format which does not follow this
            sequence at all, override this method and ignore the
            other ReadX methods provided in this abstract class.</p>
            </summary>
            <remarks>
            Thie method assumes that models are saved in the
            following sequence:
            
            <p>GIS (model type identifier)</p>
            <p>1. the correction constant (int)</p>
            <p>2. the correction constant parameter (double)</p>
            <p>3. outcomes</p>
            <p>3a. number of outcomes (int)</p>
            <p>3b. outcome names (string array - length specified in 3a)</p>
            <p>4. predicates</p>
            <p>4a. outcome patterns</p>
            <p>4ai. number of outcome patterns (int)</p>
            <p>4aii. outcome pattern values (each stored in a space delimited string)</p>
            <p>4b. predicate labels</p>
            <p>4bi. number of predicates (int)</p>
            <p>4bii. predicate names (string array - length specified in 4bi)</p>
            <p>4c. predicate parameters (double values)</p>
            </remarks>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.CheckModelType">
            <summary>
            Checks the model file being read from begins with the sequence of characters
            "GIS".
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadCorrectionConstant">
            <summary>
            Reads the correction constant from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadCorrectionParameter">
            <summary>
            Reads the correction constant parameter from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadOutcomes">
            <summary>
            Reads the outcome names from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadPredicates(System.Int32[][]@,System.Collections.Generic.Dictionary{System.String,SharpEntropy.PatternedPredicate}@)">
            <summary>
            Reads the predicate information from the model file, placing the data in two
            structures - an array of outcome patterns, and a Dictionary of predicates
            keyed by predicate name.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadOutcomePatterns">
            <summary>
            Reads the outcome pattern information from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadPredicateLabels">
            <summary>
            Reads the outcome labels from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadParameters(System.Int32[][],System.String[])">
            <summary>
            Reads the predicate parameter information from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadInt32">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadDouble">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.ReadString">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.GetOutcomeLabels">
            <summary>
            Returns the labels for all the outcomes in the model.
            </summary>
            <returns>
            String array containing outcome labels.
            </returns>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.GetOutcomePatterns">
            <summary>
            Returns the outcome patterns in the model.
            </summary>
            <returns>
            Array of integer arrays containing the information for
            each outcome pattern in the model.
            </returns>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.GetPredicates">
            <summary>
            Returns the predicates in the model.
            </summary>
            <returns>
            Dictionary containing PatternedPredicate objects keyed
            by predicate label.
            </returns>
        </member>
        <member name="M:SharpEntropy.IO.GisModelReader.GetPredicateData(System.String,System.Int32[],System.Double[])">
            <summary>
            Returns model information for a predicate, given the predicate label.
            </summary>
            <param name="predicateLabel">
            The predicate label to fetch information for.
            </param>
            <param name="featureCounts">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method increments the count of each outcome that is active in the specified
            predicate.
            </param>
            <param name="outcomeSums">
            Array to be passed in to the method; it should have a length equal to the number of outcomes
            in the model.  The method adds the parameter values for each of the active outcomes in the
            predicate.
            </param>
        </member>
        <member name="P:SharpEntropy.IO.GisModelReader.PredicateCount">
            <summary>
            The number of predicates contained in the model.
            </summary>
        </member>
        <member name="P:SharpEntropy.IO.GisModelReader.CorrectionConstant">
            <summary>
            The model's correction constant.
            </summary>
        </member>
        <member name="P:SharpEntropy.IO.GisModelReader.CorrectionParameter">
            <summary>
            The model's correction constant parameter.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.#ctor(System.IO.Stream)">
            <summary>
            Constructor which directly instantiates the Stream containing
            the model contents.
            </summary>
            <param name="dataInputStream">
            The Stream containing the model information.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.#ctor(System.String)">
            <summary>
            Constructor which takes a filename and creates a reader for it. 
            </summary>
            <param name="fileName">
            The full path and name of the file in which the model is stored.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.ReadInt32">
            <summary>
            Reads a 32-bit signed integer from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.ReadDouble">
            <summary>
            Reads a double-precision floating point number from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.ReadString">
            <summary>
            Reads a UTF-8 encoded string from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelReader.ReadPredicates(System.Int32[][]@,System.Collections.Generic.Dictionary{System.String,SharpEntropy.PatternedPredicate}@)">
            <summary>
            Reads the predicate data from the file in a more efficient format to that implemented by
            GisModelReader.
            </summary>
            <param name="outcomePatterns">
            Jagged 2-dimensional array of integers that will contain the outcome patterns for the model
            after this method is called.
            </param>
            <param name="predicates">
            Dictionary that will contain the predicate information for the model
            after this method is called.
            </param>
        </member>
        <member name="T:SharpEntropy.IO.BinaryGisModelWriter">
            <summary>
            A writer for GIS models that saves models in a binary format. This format is not the one
            used by the <see cref="T:SharpEntropy.IO.JavaBinaryGisModelWriter">java version of MaxEnt</see>.
            It has two main differences, designed for performance when loading the data
            from file: first, it uses big endian data values, which is native for C#, and secondly it
            encodes the outcome patterns and values in a more efficient manner.
            </summary>
            <author> 
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on BinaryGISModelWriter.java $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="T:SharpEntropy.IO.GisModelWriter">
            <summary> Abstract parent class for GIS model writers that save data to a single
            file.  It provides the persist method which takes care of the structure of a stored 
            document, and requires an extending class to define precisely how the data should
             be stored.
            </summary>
            <author>
             Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on GISModelWriter.java, $Revision: 1.5 $, $Date: 2004/06/11 20:51:36 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteString(System.String)">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
            <param name="data">
            String data to be written to storage.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteInt32(System.Int32)">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
            <param name="data">
            Integer data to be written to storage.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteDouble(System.Double)">
            <summary>
            Implement as needed for the format the model is stored in.
            </summary>
            <param name="data">
            Double precision floating point data to be written to storage.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.GetPredicates">
            <summary>
            Obtains a list of the predicates in the model to be written to storage.
            </summary>
            <returns>
            Array of PatternedPredicate objects containing the predicate data for the model.
            </returns>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.SetPredicates(SharpEntropy.PatternedPredicate[])">
            <summary>
            Sets the list of predicates to be written to storage.
            </summary>
            <param name="predicates">
            Array of PatternedPredicate objects to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.Persist(SharpEntropy.GisModel)">
            <summary>
            Writes the model to persistent storage, using the <code>writeX()</code> methods
            provided by extending classes.
            
            <p>This method delegates to worker methods for each part of this 
            sequence.  If you are creating a writer that conforms largely to this
            sequence but varies at one or more points, override the relevant worker
            method(s) to achieve the required format.</p>
            
            <p>If you are creating a writer for a format which does not follow this
            sequence at all, override this method and ignore the
            other WriteX methods provided in this abstract class.</p>  
            </summary>
            <param name="model">
            GIS model whose data is to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.Initialize(SharpEntropy.GisModel)">
             <summary>
             Organises the data available in the GIS model into a structure that is easier to
             persist from.
             </summary>
             <param name="model">
             The GIS model to be persisted. 
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteModelType(System.String)">
            <summary>
            Writes the model type identifier at the beginning of the file.
            </summary>
            <param name="modelType">String identifying the model type.</param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteCorrectionConstant(System.Int32)">
            <summary>
            Writes the value of the correction constant
            </summary>
            <param name="correctionConstant">the model's correction constant value.</param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteCorrectionParameter(System.Double)">
            <summary>
            Writes the value of the correction constant parameter.
            </summary>
            <param name="correctionParameter">the model's correction constant parameter.</param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteOutcomes(System.String[])">
            <summary>
            Writes the outcome labels to the file.
            </summary>
            <param name="outcomeLabels">String array of outcome labels.</param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WritePredicates(SharpEntropy.GisModel)">
            <summary>
            Writes the predicate information to the model file.
            </summary>
            <param name="model">The GIS model to write the data from.</param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteOutcomePatterns(System.Int32[][])">
            <summary>
            Writes the outcome pattern data to the file.
            </summary>
            <param name="outcomePatterns">
            Array of outcome patterns, each an integer array containing
            the number of predicates using the pattern, and then the list of
            outcome IDs in the pattern.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WritePredicateNames">
            <summary>
            Write the names of the predicates to the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.WriteParameters">
            <summary>
            Writes out the parameter values for all the predicates to the model file.
            </summary>
        </member>
        <member name="T:SharpEntropy.IO.GisModelWriter.OutcomePatternIndexComparer">
            <summary>
            Class to enable sorting PatternedPredicates into order based on the
            outcome pattern index.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.OutcomePatternIndexComparer.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.GisModelWriter.OutcomePatternIndexComparer.Compare(SharpEntropy.PatternedPredicate,SharpEntropy.PatternedPredicate)">
            <summary>
            Implementation of the IComparer interface.
            Compares two PatternedPredicate objects and returns a value indicating whether
            one is less than, equal to or greater than the other.
            </summary>
            <param name="firstPredicate">
            First object to compare.
            </param>
            <param name="secondPredicate">
            Second object to compare.
            </param>
            <returns>
            -1 if the first PatternedPredicate has a lower outcome pattern index;
            1 if the second PatternedPredicate has a lower outcome pattern index;
            0 if they both have the same outcome pattern index.
            </returns>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.Persist(SharpEntropy.GisModel,System.String)">
            <summary>
            Takes a GIS model and a file and
            writes the model to that file.
            </summary>
            <param name="model">
            The GisModel which is to be persisted.
            </param>
            <param name="fileName">
            The full path and name of the file in which the model is to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.Persist(SharpEntropy.GisModel,System.IO.Stream)">
            <summary>
            Takes a GIS model and a Stream and
            writes the model to that Stream.
            </summary>
            <param name="model">
            The GIS model which is to be persisted.
            </param>
            <param name="dataOutputStream">
            The Stream which will be used to persist the model.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.WriteString(System.String)">
            <summary>
            Writes a UTF-8 encoded string to the model file.
            </summary>
            <param name="data">
            The string data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.WriteInt32(System.Int32)">
            <summary>
            Writes a 32-bit signed integer to the model file.
            </summary>
            <param name="data">
            The integer data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.WriteDouble(System.Double)">
            <summary>
            Writes a double-precision floating point number to the model file.
            </summary>
            <param name="data">
            The floating point data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.BinaryGisModelWriter.WritePredicates(SharpEntropy.GisModel)">
            <summary>
            Writes the predicate data to the file in a more efficient format to that implemented by
            GisModelWriter.
            </summary>
            <param name="model">
            The GIS model containing the predicate data to be persisted.
            </param>
        </member>
        <member name="T:SharpEntropy.IO.JavaBinaryGisModelReader">
            <summary>
            A reader for GIS models stored in the binary format produced by the java version
            of MaxEnt.  This binary format stores data using big-endian values, which means
            that the C# version must reverse the byte order of each value in turn, making it
            less efficient.  Use only for compatibility with the java MaxEnt library.
            </summary>
            <author> 
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on BinaryGISModelReader.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelReader.#ctor(System.IO.Stream)">
            <summary>
            Constructor which directly instantiates the Stream containing
            the model contents.
            </summary>
            <param name="dataInputStream">The Stream containing the model information.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelReader.#ctor(System.String)">
            <summary>
            Constructor which takes a filename and creates a reader for it.
            </summary>
            <param name="fileName">The full path and name of the file in which the model is stored.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelReader.ReadInt32">
            <summary>
            Reads a 32-bit signed integer from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelReader.ReadDouble">
            <summary>
            Reads a double-precision floating point number from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelReader.ReadString">
            <summary>
            Reads a UTF-8 encoded string from the model file.
            </summary>
        </member>
        <member name="T:SharpEntropy.IO.JavaBinaryGisModelWriter">
            <summary>
            A writer for GIS models that saves models in the binary format used by the java 
            version of MaxEnt.  This binary format stores data using big-endian values, which means
            that the C# version must reverse the byte order of each value in turn, making it
            less efficient.  Use only for compatibility with the java MaxEnt library.
            </summary>
            <author> 
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on BinaryGISModelWriter.java $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.Persist(SharpEntropy.GisModel,System.String)">
            <summary> Takes a GisModel and a File and
            writes the model to that file.
            </summary>
            <param name="model">The GisModel which is to be persisted.
            </param>
            <param name="fileName">The name of the file in which the model is to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.Persist(SharpEntropy.GisModel,System.IO.Stream)">
            <summary>
            Takes a GisModel and a Stream and writes the model to that stream.
            </summary>
            <param name="model">
            The GIS model which is to be persisted.
            </param>
            <param name="dataOutputStream">
            The Stream which will be used to persist the model.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.WriteString(System.String)">
            <summary>
            Writes a UTF-8 encoded string to the model file.
            </summary>
            /// <param name="data">
            The string data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.WriteInt32(System.Int32)">
            <summary>
            Writes a 32-bit signed integer to the model file.
            </summary>
            /// <param name="data">
            The integer data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.JavaBinaryGisModelWriter.WriteDouble(System.Double)">
            <summary>
            Writes a double-precision floating point number to the model file.
            </summary>
            /// <param name="data">
            The floating point data to be persisted.
            </param>
        </member>
        <member name="T:SharpEntropy.IO.PlainTextGisModelReader">
            <summary>
            A reader for GIS models stored in plain text format.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on PlainTextGISModelReader.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelReader.#ctor(System.IO.StreamReader)">
            <summary>
            Constructor which directly instantiates the StreamReader containing
            the model contents.
            </summary>
            <param name="reader">
            The StreamReader containing the model information.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelReader.#ctor(System.String)">
            <summary>
            Constructor which takes a file and creates a reader for it. 
            </summary>
            <param name="fileName">
            The full path and file name in which the model is stored.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelReader.ReadInt32">
            <summary>
            Reads a 32-bit signed integer from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelReader.ReadDouble">
            <summary>
            Reads a double-precision floating point number from the model file.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelReader.ReadString">
            <summary>
            Reads a string from the model file.
            </summary>
        </member>
        <member name="T:SharpEntropy.IO.PlainTextGisModelWriter">
            <summary> 
            Model writer that saves models in plain text format.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on PlainTextGISModelWriter.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.#ctor">
            <summary>
            Default constructor.
            </summary>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.Persist(SharpEntropy.GisModel,System.String)">
            <summary>
            Takes a GIS model and a file and writes the model to that file.
            </summary>
            <param name="model">
            The GisModel which is to be persisted.
            </param>
            <param name="fileName">
            The name of the file in which the model is to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.Persist(SharpEntropy.GisModel,System.IO.StreamWriter)">
            <summary>
            Takes a GisModel and a stream and writes the model to that stream.
            </summary>
            <param name="model">
            The GisModel which is to be persisted.
            </param>
            <param name="writer">
            The StreamWriter which will be used to persist the model.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.WriteString(System.String)">
            <summary>
            Writes a string to the model file.
            </summary>
            /// <param name="data">
            The string data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.WriteInt32(System.Int32)">
            <summary>
            Writes a 32-bit signed integer to the model file.
            </summary>
            <param name="data">
            The integer data to be persisted.
            </param>
        </member>
        <member name="M:SharpEntropy.IO.PlainTextGisModelWriter.WriteDouble(System.Double)">
            <summary>
            Writes a double-precision floating point number to the model file.
            </summary>
            <param name="data">
            The floating point data to be persisted.
            </param>
        </member>
        <member name="T:SharpEntropy.ITrainingDataReader`1">
            <summary>
            A interface for objects which can deliver a stream of training data to be
            supplied to an ITrainingEventReader. It is not necessary to use a ITrainingDataReader in a
            SharpEntropy application, but it can be used to support a wider variety of formats
            in which your training data can be held.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on DataStream.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.ITrainingDataReader`1.NextToken">
            <summary> 
            Returns the next slice of data held in this ITrainingDataReader.
            </summary>
            <returns>
            the object representing the data which is next in this
            ITrainingDataReader
            </returns>
        </member>
        <member name="M:SharpEntropy.ITrainingDataReader`1.HasNext">
            <summary> 
            Test whether there are any training data items remaining in this ITrainingDataReader.
            </summary>
            <returns>
            true if this ITrainingDataReader has more data tokens
            </returns>
        </member>
        <member name="T:SharpEntropy.OnePassDataIndexer">
            <summary>
            An indexer for maxent model data which handles cutoffs for uncommon
            contextual predicates and provides a unique integer index for each of the
            predicates.  The data structures built in the constructor of this class are
            used by the GIS trainer.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on OnePassDataIndexer.java, $Revision: 1.1 $, $Date: 2003/12/13 16:41:29 $
            </version>
        </member>
        <member name="M:SharpEntropy.OnePassDataIndexer.#ctor(SharpEntropy.ITrainingEventReader)">
            <summary>
            One argument constructor for OnePassDataIndexer which calls the two argument
            constructor assuming no cutoff.
            </summary>
            <param name="eventReader">
            An ITrainingEventReader which contains the a list of all the Events
            seen in the training data.
            </param>
        </member>
        <member name="M:SharpEntropy.OnePassDataIndexer.#ctor(SharpEntropy.ITrainingEventReader,System.Int32)">
            <summary> 
            Two argument constructor for OnePassDataIndexer.
            </summary>
            <param name="eventReader">
            An ITrainingEventReader which contains the a list of all the Events
            seen in the training data.
            </param>
            <param name="cutoff">
            The minimum number of times a predicate must have been
            observed in order to be included in the model.
            </param>
        </member>
        <member name="M:SharpEntropy.OnePassDataIndexer.ComputeEventCounts(SharpEntropy.ITrainingEventReader,System.Collections.Generic.Dictionary{System.String,System.Int32},System.Int32)">
            <summary>
            Reads events from <tt>eventReader</tt> into a List&lt;TrainingEvent&gt;.  The
            predicates associated with each event are counted and any which
            occur at least <tt>cutoff</tt> times are added to the
            <tt>predicatesInOut</tt> dictionary along with a unique integer index.
            </summary>
            <param name="eventReader">
            an <code>ITrainingEventReader</code> value
            </param>
            <param name="predicatesInOut">
            a <code>Dictionary</code> value
            </param>
            <param name="cutoff">
            an <code>int</code> value
            </param>
            <returns>
            an <code>List of TrainingEvents</code> value
            </returns>
        </member>
        <member name="T:SharpEntropy.PatternedPredicate">
            <summary>
            Object containing predicate data, where the parameters are matched to
            the outcomes in an outcome pattern.
            </summary>
            <author>
            Richard J. Northedge
            </author>
        </member>
        <member name="M:SharpEntropy.PatternedPredicate.#ctor(System.Int32,System.Double[])">
            <summary>
            Creates a PatternedPredicate object.
            </summary>
            <param name="outcomePattern">
            Index into the outcome pattern array, specifying which outcome pattern relates to
            this predicate.
            </param>
            <param name="parameters">
            Array of parameters for this predicate.
            </param>
        </member>
        <member name="M:SharpEntropy.PatternedPredicate.#ctor(System.String,System.Double[])">
            <summary>
            Creates a PatternedPredicate object.
            </summary>
            <param name="name">
            The predicate name.
            </param>
            <param name="parameters">
            Array of parameters for this predicate.
            </param>
        </member>
        <member name="M:SharpEntropy.PatternedPredicate.GetParameter(System.Int32)">
            <summary>
            Gets the value of a parameter from this predicate.
            </summary>
            <param name="index">
            index into the parameter array.
            </param>
            <returns></returns>
        </member>
        <member name="P:SharpEntropy.PatternedPredicate.OutcomePattern">
            <summary>
            Index into array of outcome patterns.
            </summary>
        </member>
        <member name="P:SharpEntropy.PatternedPredicate.ParameterCount">
            <summary>
            Number of parameters associated with this predicate.
            </summary>
        </member>
        <member name="P:SharpEntropy.PatternedPredicate.Name">
            <summary>
            Name of the predicate.
            </summary>
        </member>
        <member name="T:SharpEntropy.PlainTextByLineDataReader">
            <summary>
            This ITrainingDataReader implementation will take care of reading a plain text file
            and returning the strings between each new line character, which is what
            many SharpEntropy applications need in order to create ITrainingEventReaders.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on PlainTextByLineDataStream.java, $Revision: 1.1.1.1 $, $Date: 2001/10/23 14:06:53 $
            </version>
        </member>
        <member name="M:SharpEntropy.PlainTextByLineDataReader.#ctor(System.IO.StreamReader)">
            <summary>
            Creates a training data reader for reading text lines from a file or other text stream.
            </summary>
            <param name="dataSource">
            StreamReader containing the source of the training data.
            </param>
        </member>
        <member name="M:SharpEntropy.PlainTextByLineDataReader.NextToken">
            <summary>
            Gets the next text line from the training data.
            </summary>
            <returns>
            Next text line from the training data.
            </returns>
        </member>
        <member name="M:SharpEntropy.PlainTextByLineDataReader.HasNext">
            <summary>
            Checks if there is any more training data.
            </summary>
            <returns>
            true if there is more training data to be read.
            </returns>
        </member>
        <member name="T:SharpEntropy.TrainingEvent">
            <summary>
            The context of a decision point during training.  This includes
            contextual predicates and an outcome.
            </summary>
            <author>
            Jason Baldridge
            </author>
            <author>
            Richard J. Northedge
            </author>
            <version>
            based on Event.java, $Revision: 1.3 $, $Date: 2003/12/09 23:13:08 $
            </version>
        </member>
        <member name="M:SharpEntropy.TrainingEvent.GetContext">
            <summary>
            Gets the context for this training event.
            </summary>
            <returns>
            A string array of context values for this training event.
            </returns>
        </member>
        <member name="M:SharpEntropy.TrainingEvent.#ctor(System.String,System.String[])">
            <summary>
            Constructor for a training event.
            </summary>
            <param name="outcome">
            the outcome label
            </param>
            <param name="context">
            array containing context values
            </param>
        </member>
        <member name="M:SharpEntropy.TrainingEvent.ToString">
            <summary>
            Override providing text summary of the training event.
            </summary>
            <returns>
            Summary of the training event.
            </returns>
        </member>
        <member name="P:SharpEntropy.TrainingEvent.Outcome">
            <summary>
            The outcome label for this training event.
            </summary>
        </member>
        <member name="T:SharpEntropy.TwoPassDataIndexer">
            <summary>
            Collecting event and context counts by making two passes over the events.  The
            first pass determines which contexts will be used by the model, and the
            second pass creates the events in memory containing only the contexts which 
            will be used.  This greatly reduces the amount of memory required for storing
            the events.  During the first pass a temporary event file is created which
            is read during the second pass.
            </summary>
            /// <author>  
            Tom Morton
            </author>
            /// /// <author>  
            Richard J. Northedge
            </author>
        </member>
        <member name="M:SharpEntropy.TwoPassDataIndexer.#ctor(SharpEntropy.ITrainingEventReader)">
            <summary>
            One argument constructor for DataIndexer which calls the two argument
            constructor assuming no cutoff.
            </summary>
            <param name="eventReader">
            An ITrainingEventReader which contains the list of all the events
            seen in the training data.
            </param>
        </member>
        <member name="M:SharpEntropy.TwoPassDataIndexer.#ctor(SharpEntropy.ITrainingEventReader,System.Int32)">
            <summary> 
            Two argument constructor for TwoPassDataIndexer.
            </summary>
            <param name="eventReader">
            An ITrainingEventReader which contains the a list of all the events
            seen in the training data.
            </param>
            <param name="cutoff">
            The minimum number of times a predicate must have been
            observed in order to be included in the model.
            </param>
        </member>
        <member name="M:SharpEntropy.TwoPassDataIndexer.ComputeEventCounts(SharpEntropy.ITrainingEventReader,System.String,System.Collections.Generic.Dictionary{System.String,System.Int32},System.Int32)">
            <summary>
            Reads events from <tt>eventStream</tt> into a dictionary.  The
            predicates associated with each event are counted and any which
            occur at least <tt>cutoff</tt> times are added to the
            <tt>predicatesInOut</tt> map along with a unique integer index.
            </summary>
            <param name="eventReader">
            an <code>ITrainingEventReader</code> value
            </param>
            <param name="eventStoreFile">
            a file name to which the events are written to for later processing.
            </param>
            <param name="predicatesInOut">
            a <code>Dictionary</code> value
            </param>
            <param name="cutoff">
            an <code>int</code> value
            </param>
        </member>
    </members>
</doc>