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class BayesNetwork2 extends ClassifierInt with BayesMetrics

The BayesNetwork2 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data. Each node is limited to have at most 2 parents, and hence the "2" in the class name BayesNetwork2. 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 supports limited dependency between features/variables. ------------------------------------------------------------------------------

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  1. BayesNetwork2
  2. BayesMetrics
  3. ClassifierInt
  4. Error
  5. Classifier
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Instance Constructors

  1. new BayesNetwork2(xy: MatriI, fn: Array[String], k: Int, cn: Array[String])

    Compute the parent of each feature based on the correlation matrix.

    Compute the parent of each feature based on the correlation matrix. Feature 'x_i' is only a possible candidate for parent of feature 'x_j' if

    xy

    the data vectors along with their classifications stored as rows of a matrix

    fn

    the names of the features

    k

    the number of classes

    cn

    the names for all classes

  2. new BayesNetwork2(x: MatriI, y: VectoI, fn: Array[String], vc: VectoI = null, k: Int, cn: Array[String], thres: Double = 0.3, me: Int = 3)

    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(l)

    fn

    the names for all features/variables

    vc

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

    k

    the number of classes

    cn

    the names for all classes

    thres

    the correlation threshold between 2 features for possible parent-child relationship

    me

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

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): Unit

    Build the Bayes Networks2 classier model by using the 'AIC' criterion.

    Build the Bayes Networks2 classier model by using the 'AIC' criterion. Limited dependencies between variables/features are also supported. Maximum number of parents for each feature is 2.

    testStart

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

    testEnd

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

  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
    BayesNetwork2Classifier
  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 clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. def computeParent(parent: MatrixI, cor: MatriD, featureOrder: VectorI): MatrixI

    Compute the parent of each feature based on the correlation matrix.

    Compute the parent of each feature based on the correlation matrix. Feature 'x_i' is only a possible candidate for parent of feature 'x_j' if 'x_i' appears before 'x_j' in 'featureOrder'.

    parent

    vector holding the parent for each feature/variable

    cor

    feature correlation matrix

    featureOrder

    keep the order of the features

  13. def computeVcp(vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorI, VectorI)

    Compute the value counts of each parent feature based on the parent matrix.

    Compute the value counts of each parent feature based on the parent matrix.

    vcp1

    value count for parent1

    vcp2

    value count for parent2

    parent

    vector holding the parent for each feature/variable

  14. 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
  15. 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
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. 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
  20. val g_AIC: Array[Double]
  21. val g_optimalFeatureOrder: Array[VectorI]
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  23. def getFeatureOrder: VectoI

    Return the feature order.

  24. def getParent: MatrixI

    Return the parent.

  25. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  26. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  27. 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
  28. 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
  29. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  30. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  31. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  32. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. final def notify(): Unit
    Definition Classes
    AnyRef
  34. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  35. 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
    BayesNetwork2Classifier
  36. def resetHelper(parent: MatrixI, cor: MatriD, featureOrder: VectorI, vcp1: VectorI, vcp2: VectorI, popC: VectorI, probC: VectorD, popX: HMatrix5[Int], probX: HMatrix5[Double]): Unit

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

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

    parent

    vector holding the parent for each feature/variable

    cor

    feature correlation matrix

    featureOrder

    keep the order of the features

    vcp1

    value count for parent1

    vcp2

    value count for parent2

    popC

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

    probC

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

    popX

    conditional frequency counts for variable/feature j: xj

    probX

    conditional probabilities for variable/feature j: xj

  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. val t_parent: MatrixI
  41. val t_vcp1: VectorI
  42. val t_vcp2: VectorI
  43. 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
  44. 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
  45. 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
  46. def toString(): String

    Convert 'this' object to a string.

    Convert 'this' object to a string. FIX - implement

    Definition Classes
    BayesNetwork2 → AnyRef → Any
  47. 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

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

    testEnd

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

    Definition Classes
    BayesNetwork2Classifier
  48. def train(itrain: Array[Int]): 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.

    itrain

    indices of the instances considered train data

    Definition Classes
    BayesNetwork2Classifier
  49. 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
  50. def train4order(itrain: Array[Int], popC: VectorI, popX: HMatrix5[Int], probC: VectorD, probX: HMatrix5[Double], vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorD, HMatrix5[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.

    itrain

    indices of the instances considered train data

    popC

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

    popX

    conditional frequency counts for variable/feature j: xj

    probC

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

    probX

    conditional probabilities for variable/feature j: xj

    vcp1

    value count for parent1

    vcp2

    value count for parent2

    parent

    vector holding the parent for each feature/variable

  51. def train4order(testStart: Int = 0, testEnd: Int = 0, popC: VectorI, popX: HMatrix5[Int], probC: VectorD, probX: HMatrix5[Double], vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorD, HMatrix5[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

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

    testEnd

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

    popC

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

    popX

    conditional frequency counts for variable/feature j: xj

    probC

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

    probX

    conditional probabilities for variable/feature j: xj

    vcp1

    value count for parent1

    vcp2

    value count for parent2

    parent

    vector holding the parent for each feature/variable

  52. 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
  53. 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
  54. 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
  55. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  56. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  57. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from BayesMetrics

Inherited from ClassifierInt

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped