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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
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Instance Constructors

  1. new TANBayes(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], me: Double = me_default, vc_: Array[Int] = null, PARALLELISM: Int = ...)

    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)

    PARALLELISM

    the level of parallelism, the number of threads to use

Value Members

  1. 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
  2. def calcCMI(idx: IndexedSeq[Int], vca: Array[Int]): MatrixD

    Compute the conditional mutual information matrix

    Compute the conditional mutual information matrix

    Definition Classes
    BayesClassifier
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. def cmiJoint(p_y: VectorD, p_Xy: HMatrix3[Double], p_XyZ: 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_y

    the marginal probability of y

    p_Xy

    the joint probability of X and y

    p_XyZ

    the joint probability of C, X, and Z

    Definition Classes
    BayesClassifier
    See also

    en.wikipedia.org/wiki/Conditional_mutual_information

  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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 predicted 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

  15. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Classifier
  16. def fitMap(y: VectoI, yp: VectoI, k: Int = 2): Map[String, String]

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered). Override to add more quality of fit measures.

    y

    the actual class labels

    yp

    the predicted class labels

    k

    the number of class labels

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

    Compute frequency counts using the entire data matrix

  19. def getParent: VectoI

    Return the parent.

    Return the parent.

    Definition Classes
    TANBayes0
  20. 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
  21. def maxSpanningTree(ch: Array[Set[Int]], elabel: Map[(Int, Int), Double]): MinSpanningTree

    Create MaxSpanningTree from conditional mutual information

    Create MaxSpanningTree from conditional mutual information

    Definition Classes
    TANBayes0
  22. def reset(): Unit

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

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

    Definition Classes
    TANBayesTANBayes0Classifier
  23. 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
  24. 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
  25. def split(indices: IndexedSeq[Int], k: Int): Array[IndexedSeq[Int]]

    Split 'indices' into 'k' arrays of equal sizes (perhaps except for the last one)

    Split 'indices' into 'k' arrays of equal sizes (perhaps except for the last one)

    indices

    the ParSeq to be splitted

    k

    the number of pieces the vector is to be splitted

    Definition Classes
    BayesClassifier
  26. def test(itest: Array[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
    BayesClassifier
  27. 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
  28. 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
  29. 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
  30. def toggleSmooth(): Unit

    Toggle the value of the 'smooth' property.

    Toggle the value of the 'smooth' property.

    Definition Classes
    BayesClassifier
  31. 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 testing data

    Definition Classes
    TANBayes0Classifier
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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