Packages

class KNN_Classifier extends ClassifierReal

The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes. It works by finding its 'kappa' nearest neighbors. These neighbors essentially vote according to their classification. The class with most votes is selected as the classification of 'z'. Using a distance metric, the 'kappa' vectors nearest to 'z' are found in the training data, which is stored row-wise in the data matrix 'x'. The corresponding classifications are given in the vector 'y', such that the classification for vector 'x(i)' is given by 'y(i)'.

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ClassifierReal, Error, Classifier, AnyRef, Any
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  1. KNN_Classifier
  2. ClassifierReal
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Instance Constructors

  1. new KNN_Classifier(x: MatriD, y: VectoI, fn: Array[String], k: Int, cn: Array[String], kappa: Int = 3)

    x

    the vectors/points of classified data stored as rows of a matrix

    y

    the classification of each vector in x

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    kappa

    the number of nearest neighbors to consider

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. def actualVpredicted(y: VectoI, yp: VectoI): Map[String, Double]

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.

    y

    the actual class labels

    yp

    the precicted class labels

    Definition Classes
    Classifier
    See also

    www.dataschool.io/simple-guide-to-confusion-matrix-terminology

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. 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
  7. 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
    ClassifierReal
  8. def classify(z: VectoD): (Int, String, Double)

    Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'kappa' nearest neighbors.

    Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'kappa' nearest neighbors. Return the best class, its name and its votes

    z

    the vector to classify

    Definition Classes
    KNN_ClassifierClassifier
  9. 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
    ClassifierRealClassifier
  10. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  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 distance(x: VectoD, z: VectoD): Double

    Compute a distance metric between vectors/points 'x' and 'z'.

    Compute a distance metric between vectors/points 'x' and 'z'. The squared Euclidean norm used for efficiency, but may use other norms.

    x

    the first vector/point

    z

    the second vector/point

  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. 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
    ClassifierReal
  17. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  19. 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
    ClassifierReal
  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. def kNearest(z: VectoD): Unit

    Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array.

    Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array. Break ties by flipping a fair coin.

    z

    the vector to be classified

  24. val m: Int

    the number of data vectors in training-set (# rows)

    the number of data vectors in training-set (# rows)

    Attributes
    protected
    Definition Classes
    ClassifierReal
  25. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierReal
  26. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierReal
  27. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierReal
  28. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  29. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  30. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  31. def reset(): Unit

    Reset or re-initialize 'topK' and counters.

    Reset or re-initialize 'topK' and counters.

    Definition Classes
    KNN_ClassifierClassifier
  32. 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
    ClassifierRealClassifier
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def test(xx: MatriD, 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 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
  35. 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
    ClassifierRealClassifier
  36. 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
  37. def toString(): String
    Definition Classes
    AnyRef → Any
  38. def train(itest: IndexedSeq[Int]): KNN_Classifier

    Training involves resetting the data structures before each classification.

    Training involves resetting the data structures before each classification. It uses lazy training, so most of it is done during classification.

    itest

    the indices of the test data

    Definition Classes
    KNN_ClassifierClassifier
  39. 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
  40. 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
  41. 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). Also may be used for binning into two categories.

    Definition Classes
    ClassifierReal
  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from ClassifierReal

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

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