svm.xml

SVM的一个源程序

            <returns>The Range transform for the problem</returns>
        </member>
        <member name="M:SVM.Scaling.Scale(SVM.Problem,SVM.IRangeTransform)">
            <summary>
            Scales a problem using the provided range.  This will not affect the parameter.
            </summary>
            <param name="prob">The problem to scale</param>
            <param name="range">The Range transform to use in scaling</param>
            <returns>The Scaled problem</returns>
        </member>
        <member name="T:SVM.GaussianTransform">
            <summary>
            A transform which learns the mean and variance of a sample set and uses these to transform new data
            so that it has zero mean and unit variance.
            </summary>
        </member>
        <member name="M:SVM.GaussianTransform.#ctor(System.Int32)">
            <summary>
            Constructor.
            </summary>
            <param name="maxIndex">The maximum index of the vectors to be transformed</param>
        </member>
        <member name="M:SVM.GaussianTransform.Add(SVM.Node[])">
            <summary>
            Adds a sample to the data.  No computation is performed.  The maximum index of the
            sample must be less than MaxIndex.
            </summary>
            <param name="sample">The sample to add</param>
        </member>
        <member name="M:SVM.GaussianTransform.ComputeStatistics">
            <summary>
            Computes the statistics for the samples which have been obtained so far.
            </summary>
        </member>
        <member name="M:SVM.GaussianTransform.Write(System.IO.Stream,SVM.GaussianTransform)">
            <summary>
            Saves the transform to the disk.  The samples are not stored, only the 
            statistics.
            </summary>
            <param name="stream">The destination stream</param>
            <param name="transform">The transform</param>
        </member>
        <member name="M:SVM.GaussianTransform.Read(System.IO.Stream)">
            <summary>
            Reads a GaussianTransform from the provided stream.
            </summary>
            <param name="stream">The source stream</param>
            <returns>The transform</returns>
        </member>
        <member name="M:SVM.GaussianTransform.Write(System.String,SVM.GaussianTransform)">
            <summary>
            Saves the transform to the disk.  The samples are not stored, only the 
            statistics.
            </summary>
            <param name="filename">The destination filename</param>
            <param name="transform">The transform</param>
        </member>
        <member name="M:SVM.GaussianTransform.Read(System.String)">
            <summary>
            Reads a GaussianTransform from the provided stream.
            </summary>
            <param name="filename">The source filename</param>
            <returns>The transform</returns>
        </member>
        <member name="M:SVM.GaussianTransform.Transform(System.Double,System.Int32)">
            <summary>
            Transform the input value using the transform stored for the provided index.
            <see cref="M:SVM.GaussianTransform.ComputeStatistics"/> must be called first, or the transform must
            have been read from the disk.
            </summary>
            <param name="input">Input value</param>
            <param name="index">Index of the transform to use</param>
            <returns>The transformed value</returns>
        </member>
        <member name="M:SVM.GaussianTransform.Transform(SVM.Node[])">
            <summary>
            Transforms the input array.  <see cref="M:SVM.GaussianTransform.ComputeStatistics"/> must be called 
            first, or the transform must have been read from the disk.
            </summary>
            <param name="input">The array to transform</param>
            <returns>The transformed array</returns>
        </member>
        <member name="T:SVM.ParameterSelection">
            <summary>
            This class contains routines which perform parameter selection for a model which uses C-SVC and
            an RBF kernel.
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.NFOLD">
            <summary>
            Default number of times to divide the data.
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.MIN_C">
            <summary>
            Default minimum power of 2 for the C value (-5)
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.MAX_C">
            <summary>
            Default maximum power of 2 for the C value (15)
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.C_STEP">
            <summary>
            Default power iteration step for the C value (2)
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.MIN_G">
            <summary>
            Default minimum power of 2 for the Gamma value (-15)
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.MAX_G">
            <summary>
            Default maximum power of 2 for the Gamma Value (3)
            </summary>
        </member>
        <member name="F:SVM.ParameterSelection.G_STEP">
            <summary>
            Default power iteration step for the Gamma value (2)
            </summary>
        </member>
        <member name="M:SVM.ParameterSelection.GetList(System.Double,System.Double,System.Double)">
            <summary>
            Returns a logarithmic list of values from minimum power of 2 to the maximum power of 2 using the provided iteration size.
            </summary>
            <param name="minPower">The minimum power of 2</param>
            <param name="maxPower">The maximum power of 2</param>
            <param name="iteration">The iteration size to use in powers</param>
            <returns></returns>
        </member>
        <member name="M:SVM.ParameterSelection.Grid(SVM.Problem,SVM.Parameter,System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  The default ranges of C and Gamma values are used.  Use this method if there is no validation data available, and it will
            divide it 5 times to allow 5-fold validation (training on 4/5 and validating on 1/5, 5 times).
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="C">The optimal C value will be put into this variable</param>
            <param name="Gamma">The optimal Gamma value will be put into this variable</param>
        </member>
        <member name="M:SVM.ParameterSelection.Grid(SVM.Problem,SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Use this method if there is no validation data available, and it will
            divide it 5 times to allow 5-fold validation (training on 4/5 and validating on 1/5, 5 times).
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The set of C values to use</param>
            <param name="GammaValues">The set of Gamma values to use</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="C">The optimal C value will be put into this variable</param>
            <param name="Gamma">The optimal Gamma value will be put into this variable</param>
        </member>
        <member name="M:SVM.ParameterSelection.Grid(SVM.Problem,SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Int32,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Use this method if validation data isn't available, as it will
            divide the training data and train on a portion of it and test on the rest.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The set of C values to use</param>
            <param name="GammaValues">The set of Gamma values to use</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="nrfold">The number of times the data should be divided for validation</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="M:SVM.ParameterSelection.Grid(SVM.Problem,SVM.Problem,SVM.Parameter,System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Uses the default values of C and Gamma.
            </summary>
            <param name="problem">The training data</param>
            <param name="validation">The validation data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="outputFile">The output file for the parameter results</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="M:SVM.ParameterSelection.Grid(SVM.Problem,SVM.Problem,SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.
            </summary>
            <param name="problem">The training data</param>
            <param name="validation">The validation data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The C values to use</param>
            <param name="GammaValues">The Gamma values to use</param>
            <param name="outputFile">The output file for the parameter results</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="T:SVM.Prediction">
            <summary>
            Class containing the routines to perform class membership prediction using a trained SVM.
            </summary>
        </member>
        <member name="M:SVM.Prediction.Predict(SVM.Problem,System.String,SVM.Model,System.Boolean)">
            <summary>
            Predicts the class memberships of all the vectors in the problem.
            </summary>
            <param name="problem">The SVM Problem to solve</param>
            <param name="outputFile">File for result output</param>
            <param name="model">The Model to use</param>
            <param name="predict_probability">Whether to output a distribution over the classes</param>
            <returns>Percentage correctly labelled</returns>
        </member>
        <member name="M:SVM.Prediction.Predict(SVM.Model,SVM.Node[])">
            <summary>
            Predict the class for a single input vector.
            </summary>
            <param name="model">The Model to use for prediction</param>
            <param name="x">The vector for which to predict class</param>
            <returns>The result</returns>
        </member>
        <member name="M:SVM.Prediction.PredictProbability(SVM.Model,SVM.Node[])">
            <summary>
            Predicts a class distribution for the single input vector.
            </summary>
            <param name="model">Model to use for prediction</param>
            <param name="x">The vector for which to predict the class distribution</param>
            <returns>A probability distribtion over classes</returns>
        </member>
        <member name="M:SVM.Prediction.Predict(System.String[])">
            <summary>
            Legacy method, provided to allow usage as though this were the command line version of libsvm.
            </summary>
            <param name="args">Standard arguments passed to the svm_predict exectutable.  See libsvm documentation for details.</param>
        </member>
    </members>
</doc>