Packages

class DecisionTreeC45 extends ClassifierInt

The DecisionTreeC45 class implements a Decision Tree classifier using the C4.5 algorithm. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered '0, ..., k-1'. Each column in the matrix represents a feature (e.g., Humidity). The 'vc' array gives the number of distinct values per feature (e.g., 2 for Humidity).

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  1. DecisionTreeC45
  2. ClassifierInt
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Instance Constructors

  1. new DecisionTreeC45(x: MatriI, y: VectoI, fn: Array[String], isCont: Array[Boolean], k: Int, cn: Array[String], vc: VectoI = null)

    x

    the data vectors stored as rows of a matrix

    y

    the class array, where y_i = class for row i of the matrix x

    fn

    the names for all features/variables

    isCont

    Boolean value to indicate whether according feature is continuous

    k

    the number of classes

    cn

    the names for all classes

    vc

    the value count array indicating number of distinct values per feature

Type Members

  1. class Node extends AnyRef

    Class that contains information for a tree node.

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def buildTree(opt: (Int, Double)): Unit

    Given the next most distinguishing feature/attribute, extend the decision tree.

    Given the next most distinguishing feature/attribute, extend the decision tree.

    opt

    the optimal feature and its gain

  6. def calThreshold(f: Int): Unit

    Given a continuous feature, adjust its threshold to improve gain.

    Given a continuous feature, adjust its threshold to improve gain.

    f

    the feature index to consider

  7. def calcCorrelation: MatriD

    Calculate the correlation matrix for the feature vectors 'fea'.

    Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.

    Definition Classes
    ClassifierInt
  8. def calcCorrelation2(zrg: Range, xrg: Range): MatriD

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.

    zrg

    the range of Z-columns

    xrg

    the range of X-columns

    Definition Classes
    ClassifierInt
  9. def classify(z: VectoD): (Int, String, Double)

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf.

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf. Return the best class, it ane and FIX.

    z

    the data vector to classify (some continuous features)

    Definition Classes
    DecisionTreeC45ClassifierIntClassifier
  10. def classify(z: VectoI): (Int, String, Double)

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf.

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf. Return the best class, its name and its FIX.

    z

    the data vector to classify (purely discrete features)

    Definition Classes
    DecisionTreeC45Classifier
  11. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. def crossValidate(nx: Int = 5): Double

    Test the accuracy of the classified results by cross-validation, returning the accuracy.

    Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.

    nx

    the number of crosses and cross-validations (defaults to 5x).

    Definition Classes
    Classifier
  13. def crossValidateRand(nx: Int = 5): Double

    Test the accuracy of the classified results by cross-validation, returning the accuracy.

    Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.

    nx

    the number of crosses and cross-validations (defaults to 5x).

    Definition Classes
    Classifier
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. final def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  18. def frequency(fCol: VectoI, value: Int, cont: Boolean = false, thres: Double = 0): (Double, VectorD)

    Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of occurrence the value for each classification (e.g., 0 (no), 1 (yes)) to estimate k probabilities.

    Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of occurrence the value for each classification (e.g., 0 (no), 1 (yes)) to estimate k probabilities. Also, determine the fraction of training cases where the feature has this value (e.g., fraction where Humidity is High = 7/14).

    fCol

    a feature column to consider (e.g., Humidity)

    value

    one of the possible values for this feature (e.g., 1 (High))

    cont

    indicates whether is calculating continuous feature

    thres

    threshold for continuous feature

  19. def gain(f: Int): Double

    Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.g., how well does Humidity with its values Normal and High indicate whether one will play tennis).

    Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.g., how well does Humidity with its values Normal and High indicate whether one will play tennis).

    f

    the feature to consider (e.g., 2 (Humidity))

  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  21. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. val m: Int

    the number of data vectors in training/test-set (# rows)

    the number of data vectors in training/test-set (# rows)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  24. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  25. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  26. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. def nextXY(f: Int, value: Int): (MatrixI, VectorI)

    Return new x matrix and y array for next step of constructing decision tree.

    Return new x matrix and y array for next step of constructing decision tree.

    f

    the feature index

    value

    one of the features values

  29. final def notify(): Unit
    Definition Classes
    AnyRef
  30. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  31. def printTree(): Unit

    Print out the decision tree using Breadth First Search (BFS).

  32. def reset(): Unit

    Reset or re-initialize the frequency tables and the probability tables.

    Reset or re-initialize the frequency tables and the probability tables.

    Definition Classes
    DecisionTreeC45Classifier
  33. def shiftToZero(): Unit

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Definition Classes
    ClassifierInt
  34. def size: Int

    Return the number of data vectors in training/test-set (# rows).

    Return the number of data vectors in training/test-set (# rows).

    Definition Classes
    ClassifierIntClassifier
  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  36. def test(itest: VectorI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    itest

    indices of the instances considered test data

    Definition Classes
    ClassifierIntClassifier
  37. def test(xx: MatrixI, yy: VectorI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    xx

    the integer-valued test vectors stored as rows of a matrix

    yy

    the test classification vector, where 'yy_i = class' for row 'i' of 'xx'

    Definition Classes
    ClassifierInt
  38. def test(testStart: Int, testEnd: Int): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    testStart

    beginning of test region (inclusive)

    testEnd

    end of test region (exclusive)

    Definition Classes
    ClassifierIntClassifier
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. def train(testStart: Int, testEnd: Int): Unit

    Train the classifier, i.e., determine which feature provides the most information gain and select it as the root of the decision tree.

    Train the classifier, i.e., determine which feature provides the most information gain and select it as the root of the decision tree.

    testStart

    starting index of test region (inclusive) used in cross-validation.

    testEnd

    ending index of test region (exclusive) used in cross-validation.

    Definition Classes
    DecisionTreeC45Classifier
  41. def train(): Unit

    Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions.

    Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions. Assumes that conditional densities follow the Normal (Gaussian) distribution.

    Definition Classes
    Classifier
  42. def train(itrain: Array[Int]): Unit

    Given a set of data vectors and their classifications, build a classifier.

    Given a set of data vectors and their classifications, build a classifier.

    itrain

    the indices of the instances considered train data

    Definition Classes
    Classifier
  43. def vc_default: VectorI

    Return default values for binary input data (value count 'vc' set to 2).

    Return default values for binary input data (value count 'vc' set to 2).

    Definition Classes
    ClassifierInt
  44. def vc_fromData: VectorI

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    Definition Classes
    ClassifierInt
  45. def vc_fromData2(rg: Range): VectorI

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    rg

    the range of columns to be considered

    Definition Classes
    ClassifierInt
  46. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  48. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  49. val x: MatriI
  50. val y: VectoI

Inherited from ClassifierInt

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

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