Packages

class OneBAN0 extends BayesClassifier

The OneBAN class implements an Integer-Based One-parent BN Augmented 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 supports limited dependency between features/variables.

This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------

Linear Supertypes
Known Subclasses
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. OneBAN0
  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 OneBAN0(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI = null, me: Float = me_default, thres: Double = 0.0)

    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

    k

    the number of classes

    cn

    the names for all classes

    vc

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

    me

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

    thres

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

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. val N0: Double
    Attributes
    protected
    Definition Classes
    BayesClassifier
  5. var additive: Boolean
    Attributes
    protected
    Definition Classes
    BayesClassifier
  6. def aic(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = 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
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def calcCMI(idx: IndexedSeq[Int], vca: Array[Int]): MatrixD

    Compute the conditional mutual information matrix

    Compute the conditional mutual information matrix

    idx

    indicies of either training or testing region

    vca

    array of value counts

    Definition Classes
    BayesClassifier
  9. 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
  10. 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
  11. 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
    OneBAN0Classifier
  12. 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
  13. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  14. def cmiJoint(p_C: VectorD, p_CX: HMatrix3[Double], p_CXZ: HMatrix5[Double]): MatrixD

    Compute conditional mutual information matrix given the marginal probability of C and joint probabilities of CXZ and CX, where C is the class (parent), and X & Z are features.

    Compute conditional mutual information matrix given the marginal probability of C and joint probabilities of CXZ and CX, where C is the class (parent), and X & Z are features.

    p_C

    the marginal probability of C

    p_CX

    the joint probability of C and X

    p_CXZ

    the joint probability of C, X, and Z

    Definition Classes
    BayesClassifier
    See also

    en.wikipedia.org/wiki/Conditional_mutual_information

  15. def computeParent(idx: IndexedSeq[Int]): Unit

    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 i < j.

    idx

    indicies of either training or testing region

  16. def computeVcp(): Unit

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

  17. def crossValidate(nx: Int = 10): 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
  18. def crossValidateRand(nx: Int = 10): 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. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency.

    nx

    number of crosses and cross-validations (defaults to 10x).

    Definition Classes
    Classifier
  19. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  21. val f_C: VectorI
    Attributes
    protected
    Definition Classes
    BayesClassifier
  22. var f_CX: HMatrix3[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  23. val f_CXP: HMatrix4[Int]
    Attributes
    protected
  24. var f_CXZ: HMatrix5[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  25. var f_X: HMatrix2[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  26. var featureOrder: VectorI
    Attributes
    protected
  27. def featureSelection(TOL: Double = 0.01): Unit

    Perform feature selection on the classifier.

    Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.

    TOL

    tolerance indicating negligible accuracy loss when removing features

    Definition Classes
    ClassifierInt
  28. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  29. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  30. val fset: Array[Boolean]

    the set of features to turn on or off.

    the set of features to turn on or off. All features are on by default. Used for feature selection.

    Attributes
    protected
    Definition Classes
    ClassifierInt
  31. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  32. def getParent: VectorI

    Return the parent.

    Return the parent.

    Definition Classes
    OneBAN0BayesClassifier
  33. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  34. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  35. def logLikelihood(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = 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
  36. 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
  37. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  38. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  39. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  40. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  41. final def notify(): Unit
    Definition Classes
    AnyRef
  42. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  43. var p_C: VectorD
    Attributes
    protected
    Definition Classes
    BayesClassifier
  44. val p_X_CP: HMatrix4[Double]
    Attributes
    protected
  45. val parent: VectorI
    Attributes
    protected
  46. val permutedVec: PermutedVecI
    Attributes
    protected
  47. def randomizeFeatureOrder(): Unit

    Randomize the feature order and re-compute parent and vcp.

  48. 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
    OneBAN0Classifier
  49. 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
  50. 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
  51. var smooth: Boolean
    Attributes
    protected
    Definition Classes
    BayesClassifier
  52. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  53. 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
  54. 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
  55. 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
  56. val tiny: Double
    Attributes
    protected
    Definition Classes
    BayesClassifier
  57. def toString(): String
    Definition Classes
    AnyRef → Any
  58. def toggleSmooth(): Unit

    Toggle the value of the 'smooth' property.

    Toggle the value of the 'smooth' property.

    Definition Classes
    BayesClassifier
  59. def train(itest: IndexedSeq[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. This is the quick version that uses the "subtraction" method to achieve efficiency.

    itest

    indices of the instances considered testing data

    Definition Classes
    OneBAN0Classifier
  60. def train(testStart: Int, testEnd: 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.

    testStart

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

    testEnd

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

    Definition Classes
    OneBAN0Classifier
  61. def train(): 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.

    Definition Classes
    Classifier
  62. def updateFreq(i: Int): Unit

    Increment frequency counters used in CMI calculations based on the 'i'th row of the data matrix.

    Increment frequency counters used in CMI calculations based on the 'i'th row of the data matrix.

    i

    the index for current data row

    Attributes
    protected
    Definition Classes
    OneBAN0BayesClassifier
  63. 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
  64. 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
  65. 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
  66. val vca: Array[Int]
    Attributes
    protected
  67. val vcp: VectorI
    Attributes
    protected
  68. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  69. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  70. 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