Packages

class LDA extends ClassifierReal

The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.

See also

en.wikipedia.org/wiki/Linear_discriminant_analysis

Linear Supertypes
ClassifierReal, Error, Classifier, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LDA
  2. ClassifierReal
  3. Error
  4. Classifier
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new LDA(x: MatrixD, y: VectoI, fn: Array[String])

    x

    the real-valued training/test data vectors stored as rows of a matrix

    y

    the training/test classification vector, where y_i = class for row i of the matrix x

    fn

    the names for all features/variables

Value Members

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

    z

    the vector to classify

    Definition Classes
    LDA → Classifier
  3. def classify(z: VectoI): (Int, String, Double)

    Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles.

    Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability

    z

    the vector to classify

    Definition Classes
    ClassifierReal → 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. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  7. def reset(): Unit

    Definition Classes
    LDA → Classifier
  8. def setCutoff(thres: Double): Unit

  9. 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
    ClassifierReal → Classifier
  10. def test(xx: MatrixD, 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 real-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
    ClassifierReal
  11. 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
    ClassifierReal → Classifier
  12. 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
  13. def train(testStart: Int, testEnd: Int): Unit

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

    Definition Classes
    LDA → Classifier
  14. 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
  15. 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

    Definition Classes
    Classifier