Packages

class BayesClfML extends Classifier

The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification matrix '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.

See also

www.aia-i.com/ijai/sample/vol3/no2/173-188.pdf -----------------------------------------------------------------------------

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Instance Constructors

  1. new BayesClfML(bayesBuilder: (Int) ⇒ BayesClassifier, nLabels: Int, nFeatures: Int)

    bayesBuilder

    the function mapping an integer to a regular Bayes classifier

    nLabels

    the number of labels/class variables

    nFeatures

    the number of feature variables

Value Members

  1. def buildModel(testStart: Int, testEnd: Int): (Array[Boolean], DAG)

    Build a model.

    Build a model.

    testStart

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

    testEnd

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

  2. def classify(z: VectoD): (Int, String, Double)

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

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

    z

    the data vector to classify

    Definition Classes
    BayesClfML → Classifier
  3. 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, returning the class, its name and its relative probability.

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

    z

    the data vector to classify

    Definition Classes
    BayesClfML → Classifier
  4. 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
  5. 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
  6. def reset(): Unit

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

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

    Definition Classes
    BayesClfML → Classifier
  7. 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
    BayesClfML → Classifier
  8. 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
    BayesClfML → Classifier
  9. 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

    the indices of the instances considered test data

    Definition Classes
    Classifier
  10. def train(itrain: 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.

    itrain

    indices of the instances considered train data

    Definition Classes
    BayesClfML → Classifier
  11. 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
    BayesClfML → Classifier
  12. 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