Uses of Class
com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException

Package
Description
Statistical methods.
  • Uses of DiscriminantAnalysis.SumOfWeightsNegException in com.imsl.stat

    Modifier and Type
    Method
    Description
    void
    DiscriminantAnalysis.classify(double[][] x)
    Classify a set of observations using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.classify(double[][] x, int[] varIndex)
    Classify a set of observations using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.classify(double[][] x, int[] frequencies, double[] weights)
    Classify a set of observations and associated frequencies and weights using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.classify(double[][] x, int[] group, int[] varIndex)
    Classify a set of observations and compare against known groups using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.classify(double[][] x, int[] varIndex, int[] frequencies, double[] weights)
    Classify a set of observations and associated frequencies and weights using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.classify(double[][] x, int[] group, int[] varIndex, int[] frequencies, double[] weights)
    Classify a set of observations, associated frequencies and weights, and compare against known groups using the linear or quadratic discriminant functions generated during the training process.
    void
    DiscriminantAnalysis.downdate(double[][] x, int[] group)
    Removes a set of observations from the discriminant functions.
    void
    DiscriminantAnalysis.downdate(double[][] x, int[] group, int[] varIndex)
    Removes a set of observations from the discriminant functions.
    void
    DiscriminantAnalysis.downdate(double[][] x, int[] group, int[] frequencies, double[] weights)
    Removes a set of observations and associated frequencies and weights from the discriminant functions.
    void
    DiscriminantAnalysis.downdate(double[][] x, int[] group, int[] varIndex, int[] frequencies, double[] weights)
    Removes a set of observations and associated frequencies and weights from the discriminant functions.
    void
    DiscriminantAnalysis.update(double[][] x)
    void
    DiscriminantAnalysis.update(double[][] x, double[] frequencies, double[] weights)
    void
    DiscriminantAnalysis.update(double[][] x, int groupIndex)
    void
    DiscriminantAnalysis.update(double[][] x, int[] group)
    Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
    void
    DiscriminantAnalysis.update(double[][] x, int[] varIndex, double[] frequencies, double[] weights)
    void
    DiscriminantAnalysis.update(double[][] x, int[] group, int[] varIndex)
    Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
    void
    DiscriminantAnalysis.update(double[][] x, int[] group, int[] frequencies, double[] weights)
    Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
    void
    DiscriminantAnalysis.update(double[][] x, int[] group, int[] varIndex, int[] frequencies, double[] weights)
    Trains a set of observations and associated frequencies and weights by performing a linear or quadratic discriminant function analysis among several known groups.
    void
    DiscriminantAnalysis.update(double[][] x, int groupIndex, double[] frequencies, double[] weights)
    void
    DiscriminantAnalysis.update(double[][] x, int groupIndex, int[] varIndex)
    void
    DiscriminantAnalysis.update(double[][] x, int groupIndex, int[] varIndex, double[] frequencies, double[] weights)