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class TANBayes extends TANBayes0

The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------

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  1. TANBayes
  2. TANBayes0
  3. BayesClassifier
  4. BayesMetrics
  5. ClassifierInt
  6. Error
  7. Classifier
  8. AnyRef
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Instance Constructors

  1. new TANBayes(x: MatriI, y: VectoI, fn: Array[String] = null, k: Int = 2, cn: Array[String] = Array ("no", "yes"), me: Double = me_default, vc_: Array[Int] = 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(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)

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: Array[Int], 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
    TANBayes0Classifier
  12. def classify(xx: MatriI): VectoI

    Classify all of the row vectors in matrix 'xx'.

    Classify all of the row vectors in matrix 'xx'.

    xx

    the row vectors to classify

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

    Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.

    Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability. Override in classes that require precise real values for classification.

    z

    the real vector to classify

    Definition Classes
    ClassifierIntClassifier
  14. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  15. def cmiJoint(p_C: VectoD, 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

  16. 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

    Definition Classes
    TANBayes0
  17. def computeVcp(): Unit

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

    Compute the value counts of each parent feature based on the parent vector. Let 1 be the default value count when there is no parent.

    Definition Classes
    TANBayes0
  18. def crossValidate(nx: Int = 10, show: Boolean = false): 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'. FIX - should return a StatVector

    nx

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

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  19. def crossValidateRand(nx: Int = 10, show: Boolean = false): 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. FIX - are the comments correct? FIX - should return a StatVector

    nx

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

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. var f_CX: HMatrix3[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  23. val f_CXP: HMatrix4[Int]
    Attributes
    protected
    Definition Classes
    TANBayes0
  24. var f_CXZ: HMatrix5[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  25. var f_X: HMatrix2[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  26. 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
  27. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  28. def fit(y: VectoI, yp: VectoI, k: Int = 2): VectoD

    Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'.

    Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'. Override to add more quality of fit measures.

    y

    the actual class labels

    yp

    the precicted class labels

    k

    the number of class labels

    Definition Classes
    Classifier
    See also

    ConfusionMat

    medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b

  29. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Classifier
  30. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  31. def frequenciesAll(): Unit

    Compute frequency counts using the entire data matrix

  32. 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
  33. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  34. def getParent: VectoI

    Return the parent.

    Return the parent.

    Definition Classes
    TANBayes0BayesClassifier
  35. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  36. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  37. def logLikelihood(vc: Array[Int], 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
  38. 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
  39. def maxSpanningTree(ch: Array[Set[Int]], elabel: Map[Pair, Double]): MinSpanningTree

    Create MaxSpanningTree from conditional mutual information.

    Create MaxSpanningTree from conditional mutual information.

    ch

    the adjacency set

    elabel

    the edge labels/weights

    Definition Classes
    TANBayes0
  40. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  41. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  42. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  43. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  44. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  45. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  46. val nu_y: VectorI
    Attributes
    protected
    Definition Classes
    BayesClassifier
  47. var p_C: VectorD
    Attributes
    protected
    Definition Classes
    BayesClassifier
  48. val p_X_CP: HMatrix4[Double]
    Attributes
    protected
    Definition Classes
    TANBayes0
  49. var parent: VectorI
    Attributes
    protected
    Definition Classes
    TANBayes0
  50. def reset(): Unit

    Reset or re-initialize the frequency tables from the global frequencies.

    Reset or re-initialize the frequency tables from the global frequencies.

    Definition Classes
    TANBayesTANBayes0Classifier
  51. 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
  52. def size: Int

    Return the number of data vectors/points in the entire dataset (training + testing),

    Return the number of data vectors/points in the entire dataset (training + testing),

    Definition Classes
    ClassifierIntClassifier
  53. var smooth: Boolean
    Attributes
    protected
    Definition Classes
    BayesClassifier
  54. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  55. def test(xx: MatriI, yy: VectoI): 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
  56. def test(itest: IndexedSeq[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.

    itest

    indices of the instances considered test data

    Definition Classes
    ClassifierIntClassifier
  57. def test(testStart: Int, testEnd: Int): Double

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

    Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

    Definition Classes
    Classifier
  58. val tiny: Double
    Attributes
    protected
    Definition Classes
    BayesClassifier
  59. def toString(): String
    Definition Classes
    AnyRef → Any
  60. def toggleSmooth(): Unit

    Toggle the value of the 'smooth' property.

    Toggle the value of the 'smooth' property.

    Definition Classes
    BayesClassifier
  61. def train(itest: IndexedSeq[Int]): TANBayes0

    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.

    itest

    indices of the instances considered as testing data

    Definition Classes
    TANBayes0Classifier
  62. def train(): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.

    Definition Classes
    Classifier
  63. def train(testStart: Int, testEnd: Int): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

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

    testEnd

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

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

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

    Decrement 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
    TANBayesTANBayes0BayesClassifier
  65. var vc: Array[Int]
    Attributes
    protected
    Definition Classes
    TANBayes0
  66. def vc_default: Array[Int]

    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
  67. def vc_fromData: Array[Int]

    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
  68. def vc_fromData2(rg: Range): Array[Int]

    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
  69. val vcp: Array[Int]
    Attributes
    protected
    Definition Classes
    TANBayes0
  70. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  71. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  72. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from TANBayes0

Inherited from BayesClassifier

Inherited from BayesMetrics

Inherited from ClassifierInt

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped