Class Classifier1D

java.lang.Object
org.openjump.core.attributeoperations.Classifier1D
public class Classifier1D extends Object

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static String
    EQUAL_NUMBER
     
    static String
    EQUAL_RANGE
     
    static String
    JENKS_BREAKS
     
    static String
    KMEANS_OPTIMIZE
     
    static String
    MAX_BREAKS
     
    static String
    MEAN_STDEV
     

  • Constructor Summary

    Constructors
    Constructor
    Description
    Classifier1D()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    static double[]
    adjustLimitsKMeans(double[] data, double[] oldLimits)
    Moves the limits, by assigning data points to the closest class mean value.
    static double[]
    calcClassMeans(double[] data, int[] classes, int numClasses)
     
    static double
    calcGVF(double[] data, double[] limits, double SDAM)
    GVF (goodness of variance fit): see B.D.
    static double
    calcGVF(double SDAM, double SDCM)
    GVF (goodness of variance fit): see B.D.
    static double
    calcSDAM(double[] data)
    SDAM (squared deviation [from] array mean): see B.D.
    static double
    calcSDCM(double[] data, int[] classes, double[] classMeans, int numClasses)
    SDCM (squared deviations [from] class means): see B.D.
    static int[]
    classifyData(double[] data, double[] limits)
    Classifies the given data according to the given limits.
    static double[]
    classifyEqualNumber(double[] data, int numberClasses)
    calculates class limits with equal number, which is euqal to the "quantiles" method.
    static double[]
    classifyEqualRange(double[] data, int numberClasses)
    calculates class limits with equal range
    static double[]
    classifyKMeansOnExistingBreaks(double[] data, int numberClasses, int initialLimitAlgorithm)
    calculates class limits using optimal breaks method (see e.g.
    static double[]
    classifyMaxBreaks(double[] data, int numberClasses)
    calculates class limits using Maximum Breaks method (see e.g.
    static double[]
    classifyMeanStandardDeviation(double[] data, int numberClasses)
    calculates class limits using mean value and standard deviation, i.e. for 5 classes: c1: values < m- 2std, c2: m - 2std < values < m - 1std, c3: m - 1std < values < m + 1std, c4: m + 1std < values < m + 2std c5: values > m- 2std
    static double[]
    classifyNaturalBreaks(double[] data, int numberClasses)
    calculates class limits using Jenks's Optimisation Method(Natural Break)
    static List
    getAvailableClassificationMethods()
     
    static boolean
    isInClass(double val, double lowerBound, double upperBound)
    Checks if value is within limits.

    Methods inherited from class Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Field Details

    • EQUAL_RANGE

      public static String EQUAL_RANGE

    • EQUAL_NUMBER

      public static String EQUAL_NUMBER

    • MEAN_STDEV

      public static String MEAN_STDEV

    • MAX_BREAKS

      public static String MAX_BREAKS

    • JENKS_BREAKS

      public static String JENKS_BREAKS

    • KMEANS_OPTIMIZE

      public static String KMEANS_OPTIMIZE

  • Constructor Details

    • Classifier1D

      public Classifier1D()

  • Method Details

    • getAvailableClassificationMethods

      public static List getAvailableClassificationMethods()

    • classifyEqualRange

      public static double[] classifyEqualRange(double[] data, int numberClasses)
      calculates class limits with equal range
      Parameters:
      data - input data
      numberClasses - number of classes
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • classifyEqualNumber

      public static double[] classifyEqualNumber(double[] data, int numberClasses)
      calculates class limits with equal number, which is euqal to the "quantiles" method. Note that differences in the items per classes occure, if items have same values and need to be grouped into the same class.
      Parameters:
      data - input data
      numberClasses - number of classes
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • classifyMeanStandardDeviation

      public static double[] classifyMeanStandardDeviation(double[] data, int numberClasses)
      calculates class limits using mean value and standard deviation, i.e. for 5 classes: c1: values < m- 2std, c2: m - 2std < values < m - 1std, c3: m - 1std < values < m + 1std, c4: m + 1std < values < m + 2std c5: values > m- 2std
      Parameters:
      data - input data
      numberClasses - number of classes
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • classifyMaxBreaks

      public static double[] classifyMaxBreaks(double[] data, int numberClasses)
      calculates class limits using Maximum Breaks method (see e.g. T. A. Slocum: "Thematic Cartography and Visualization", 1999)
      Parameters:
      data - input data
      numberClasses - number of classes
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • classifyNaturalBreaks

      public static double[] classifyNaturalBreaks(double[] data, int numberClasses)
      calculates class limits using Jenks's Optimisation Method(Natural Break)
      Parameters:
      data - input data
      numberClasses - number of classes
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • classifyKMeansOnExistingBreaks

      public static double[] classifyKMeansOnExistingBreaks(double[] data, int numberClasses, int initialLimitAlgorithm)
      calculates class limits using optimal breaks method (see e.g. T. A. Slocum: "Thematic Cartography and Visualization", 1999, p.73) or B.D. Dent: "Cartography: Thematic Map Design", 1999, p.146).

      Note: limits should not be equal to values. Since values that are equal to bounds can be classified into 2 classes.

      Parameters:
      data - input data
      numberClasses - number of classes
      initialLimitAlgorithm - 1: maxBreaks, 2: equalRange, 3: quantiles, 4: MeanStd-Dev 5: Jenks
      Returns:
      break values for classes. E.g. for 4 ranges 3 breaks are returned. Min and Max Values are not returned.

    • adjustLimitsKMeans

      public static double[] adjustLimitsKMeans(double[] data, double[] oldLimits)
      Moves the limits, by assigning data points to the closest class mean value.

      This approach is equal to the k-means procedure (see e.g. Duda, Hart and Stork 2000, p. 526).

      Parameters:
      data - (sortedData from min to max, e.g. use jmathtools DoubleArray.sort())
      oldLimits - old limits array
      Returns:
      a double array of adjusted limits

    • classifyData

      public static int[] classifyData(double[] data, double[] limits)
      Classifies the given data according to the given limits.
      Parameters:
      data - input data
      limits - The break/decision values between the classes. Highest and lowest values are not delivered. Example Limits are for instance delivered by the Classifier1D.classifyEqualNumber() method.
      Returns:
      array containg a class ID for every item.

    • isInClass

      public static boolean isInClass(double val, double lowerBound, double upperBound)
      Checks if value is within limits.

      Note: values equal to the bound values return "true". (qery: lowerlimit <= val <= upperlimit)

      Parameters:
      val - the value to test
      lowerBound - the lower bound
      upperBound - the upper bound
      Returns:
      true if val is included between lowerBound (included) and upperBound (included)

    • calcSDAM

      public static double calcSDAM(double[] data)
      SDAM (squared deviation [from] array mean): see B.D. Dent (1999, p. 148) alternatively look for T.A. Slocum (1999, p. 73).

      Used for Optimal Breaks Method.

      Parameters:
      data - input data
      Returns:
      the squared deviation from double array mean

    • calcSDCM

      public static double calcSDCM(double[] data, int[] classes, double[] classMeans, int numClasses)
      SDCM (squared deviations [from] class means): see B.D. Dent (1999, p. 148) alternatively look for T.A. Slocum (1999, p. 73). \n Used for Optimal Breaks Method. TODO : definition of SDCM (relative to SDAM)
      Parameters:
      data - input data
      classes - the classes for every item of the data array
      classMeans - class means
      numClasses - number of classes
      Returns:
      squared deviations from class means

    • calcGVF

      public static double calcGVF(double SDAM, double SDCM)
      GVF (goodness of variance fit): see B.D. Dent (1999, p. 148) alternatively look for T.A. Slocum (1999, p. 73). \n Used for Optimal Breaks Method.
      Parameters:
      SDAM - squared deviation [from] array mean
      SDCM - squared deviation [from] class mean
      Returns:
      the Goodness of Variant Fit for a particular SDAM and SDCM

    • calcGVF

      public static double calcGVF(double[] data, double[] limits, double SDAM)
      GVF (goodness of variance fit): see B.D. Dent (1999, p. 148) alternatively look for T.A. Slocum (1999, p. 73). \n Used for Optimal Breaks Method.
      Parameters:
      data - input data
      limits - The break/decision values between the classes. Highest and lowest values are not delivered. Example Limits are for instance delivered by the Classifier1D.classifyEqualNumber() method.
      SDAM - squared deviation [from] array mean
      Returns:
      goodness of variance fit

    • calcClassMeans

      public static double[] calcClassMeans(double[] data, int[] classes, int numClasses)
      Parameters:
      data - input data
      classes - the vector containing the information on the class for an item
      numClasses - the number of classes
      Returns:
      class means