Packages

trait Classifier extends AnyRef

The Classifier trait provides a common framework for several classifiers. A classifier is for bounded responses. When the number of distinct responses cannot be bounded by some integer 'k', a predictor should be used.

Linear Supertypes
AnyRef, Any
Known Subclasses
BayesClassifier, BayesClfML, BayesNetwork, ClassifierInt, ClassifierReal, DecisionTreeC45, DecisionTreeID3, DynBayesNetwork, GMM, HiddenMarkov, KNN_Classifier, LDA, LogisticRegression, NaiveBayes, NaiveBayes0, NaiveBayesR, OneBAN, OneBAN0, PGMHD3, PGMHD3cp, PGMHD3fl, SupportVectorMachine, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, BayesClassifier, NaiveBayes, NaiveBayes0, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Classifier
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Abstract Value Members

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

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

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

    z

    the vector to classify

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

    Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    z

    the vector to classify

  3. abstract def reset(): Unit

    Reset the frequency and probability tables.

  4. abstract def size: Int

    Return the size of the feature set.

  5. abstract 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

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

  6. abstract def train(testStart: Int, testEnd: Int): 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.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

Concrete Value Members

  1. 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).

  2. 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).

  3. 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

  4. def train(): Unit

    Given a set of data vectors and their classifications, build a classifier.

  5. def train(itest: IndexedSeq[Int]): 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.

    itest

    the indices of the instances considered as testing data