Packages

class SelNaiveBayes extends BayesClassifier

The SelNaiveBayes class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. The version is "selective", since features whose impact is small are ignored. ----------------------------------------------------------------------------

Linear Supertypes
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. SelNaiveBayes
  2. BayesClassifier
  3. BayesMetrics
  4. ClassifierInt
  5. Error
  6. Classifier
  7. AnyRef
  8. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new SelNaiveBayes(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], me: Int = 3, fset: ListBuffer[Int] = null, vc: VectoI = null)

    x

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

    y

    the class vector, where y(l) = class for row l of the matrix x, x(l)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    me

    use m-estimates (me == 0 => regular MLE estimates)

    fset

    the array of selected features

    vc

    the value count (number of distinct values) for each feature

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. def aic(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Int = me_default): Double

    Compute the 'AIC' for the given Bayesian Network structure and data.

    Compute the 'AIC' for the given Bayesian Network structure and data.

    vc

    the value count

    vcp1

    the value count for parent 1

    vcp2

    the value count for parent 2

    popX

    the population counts

    k

    the number of classes

    me

    the m-estimate value

    Definition Classes
    BayesMetrics
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def buildModel(testStart: Int = 0, testEnd: Int = 0): (Array[Boolean], DAG)

    Select the features and save them into fset.

    Select the features and save them into fset.

    testStart

    beginning of test region (inclusive)

    testEnd

    end of test region (exclusive)

    Definition Classes
    SelNaiveBayesBayesClassifier
  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: VectoI): (Int, String, Double)

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its relative probability.

    z

    the data vector to classify

    Definition Classes
    SelNaiveBayesClassifier
  10. def classify(z: VectoD): (Int, String, Double)

    Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector.

    Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector. Return the best class, its name and its relative probability

    z

    the vector to classify

    Definition Classes
    ClassifierIntClassifier
  11. def classifyHelper(z: VectoI, tprobC: VectorD, tprobX: HMatrix3[Double], tfset: ListBuffer[Int]): (Int, String, Double)

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.

    z

    the data vector to classify

    tprobC

    probabilities for classes 0, ..., k-1

    tprobX

    conditional probabilities for variable/feature j

    tfset

    the array of selected features

  12. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  13. def condMutualInformation(pz: VectorD, ptz: HMatrix3[Double], pxyz: HMatrix5[Double]): MatrixD

    Compute conditional mutual information for XY given Z from frequency counts

    Compute conditional mutual information for XY given Z from frequency counts

    pz

    the probability of Z

    ptz

    the probability of X given Z, or Y given Z

    pxyz

    the probability of Y and Y given Z

    Definition Classes
    BayesClassifier
    See also

    http://www.cs.technion.ac.il/~dang/journal_papers/friedman1997Bayesian.pdf, p.12

  14. def crossValidate(nx: Int, tfset: ListBuffer[Int]): 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

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

    tfset

    the array of selected features

  15. 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
  16. 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
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  19. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. 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
  21. var fset: ListBuffer[Int]
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  23. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  24. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  25. def logLikelihood(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Int = me_default): Double

    Compute the Log-Likelihood for the given Bayesian Network structure and data.

    Compute the Log-Likelihood for the given Bayesian Network structure and data.

    vc

    the value count

    vcp1

    the value count for parent 1

    vcp2

    the value count for parent 2

    popX

    the population counts

    k

    the number of classes

    me

    the m-estimate value

    Definition Classes
    BayesMetrics
  26. 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
  27. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  28. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  29. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. def nn: Int

    Return the current size of the feature set (it varies as different features are selected).

    Return the current size of the feature set (it varies as different features are selected). If all features are selected 'nn = n'.

  32. final def notify(): Unit
    Definition Classes
    AnyRef
  33. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  34. def reset(): Unit

    Reset or re-initialize all the population and probability vectors to 0 and the hypermatrices to null.

    Reset or re-initialize all the population and probability vectors to 0 and the hypermatrices to null.

    Definition Classes
    SelNaiveBayesClassifier
  35. def resetHelper(tpopC: VectorI, tprobC: VectorD): Unit

    Reset or re-initialize all the population and probability vectors to 0 and the hypermatrices to null.

    Reset or re-initialize all the population and probability vectors to 0 and the hypermatrices to null.

    tpopC

    frequency counts for classes 0, ..., k-1

    tprobC

    probabilities for classes 0, ..., k-1

  36. def setFset(fset2: ListBuffer[Int]): Unit

    Set/change the feature set to the given feature set.

    Set/change the feature set to the given feature set.

    fset2

    feature set to be changed

  37. 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
  38. 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
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def test(testStart: Int, testEnd: Int, tprobC: VectorD, tprobX: HMatrix3[Double], tfset: ListBuffer[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)

    tprobC

    probabilities for classes 0, ..., k-1

    tprobX

    conditional probabilities for variable/feature j

    tfset

    the array of selected features

  41. 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
  42. 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
  43. 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
  44. def toString(): String
    Definition Classes
    AnyRef → Any
  45. def train(testStart: Int = 0, testEnd: Int = 0): Unit

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    testStart

    beginning of test region (inclusive)

    testEnd

    end of test region (exclusive)

    Definition Classes
    SelNaiveBayesClassifier
  46. 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
  47. 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
  48. def trainHelper(testStart: Int = 0, testEnd: Int = 0, tpopC: VectorI, tprobC: VectorD, tfset: ListBuffer[Int]): (HMatrix3[Int], HMatrix3[Double])

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    testStart

    beginning of test region (inclusive)

    testEnd

    end of test region (exclusive)

    tpopC

    frequency counts for classes 0, ..., k-1

    tprobC

    probabilities for classes 0, ..., k-1

    tfset

    the array of selected features

  49. 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
  50. 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
  51. 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
  52. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  53. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  54. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from BayesClassifier

Inherited from BayesMetrics

Inherited from ClassifierInt

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped