Packages

class GMM extends Classifier

The GMM class is used for univariate Gaussian Mixture Models. Given a sample, thought to be generated according to 'k' Normal distributions, estimate the values for the 'mu' and 'sig2' parameters for the Normal distributions. Given a new value, determine which class (0, ..., k-1) it is most likely to have come from. FIX: need a class for multivariate Gaussian Mixture Models. FIX: need to adapt for clustering. -----------------------------------------------------------------------------

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Classifier, Model, Error, AnyRef, Any
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Instance Constructors

  1. new GMM(x: VectoD, k: Int = 3)

    x

    the data vector

    k

    the number of components in the mixture

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def classify(z: VectoI): (Int, String, Double)

    Classify the first point in vector 'z'.

    Classify the first point in vector 'z'.

    z

    the vector to be classified.

    Definition Classes
    GMMClassifier
  6. def classify(z: VectoD): (Int, String, Double)

    Classify the first point in vector 'z'.

    Classify the first point in vector 'z'.

    z

    the vector to be classified.

    Definition Classes
    GMMClassifier
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  8. def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]

    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 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    GMMClassifier
  9. def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.

    nx

    number of crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    GMMClassifier
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def eval(xx: MatriD, yy: VectoD): GMM

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model).

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.

    Definition Classes
    GMMModel
  13. def exp_step(): Unit

    Execute the Expectation (E) Step in the EM algoithm.

  14. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  17. def hparameter: HyperParameter

    Return the model hyper-parameters (if none, return null).

    Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

    Definition Classes
    GMMModel
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. def max_step(): Unit

    Execute the Maximumization (M) Step in the EM algoithm.

  20. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  21. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  22. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  24. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  25. def parameter: VectoD

    Return the vector of model parameter/coefficient values.

    Return the vector of model parameter/coefficient values.

    Definition Classes
    GMMModel
  26. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    GMMModel
    See also

    'summary' method for more details

  27. def reset(): Unit

    Reset ...

    Reset ... FIX

    Definition Classes
    GMMClassifier
  28. def setStream(str: Int = 0): Unit

    Set the random number 'stream' to 'str'.

    Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.

    str

    the new fixed random number stream

    Definition Classes
    Classifier
  29. def size: Int

    Return the size of the feature set.

    Return the size of the feature set.

    Definition Classes
    GMMClassifier
  30. val stream: Int

    the random number stream {0, 1, ..., 999} to be used

    the random number stream {0, 1, ..., 999} to be used

    Attributes
    protected
    Definition Classes
    Classifier
  31. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  32. def test(itest: Ints): Double

    Test ...

    Test ...

    itest

    the indices of test data

    Definition Classes
    GMMClassifier
  33. 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
  34. def toString(): String
    Definition Classes
    AnyRef → Any
  35. def train(itest: Ints): GMM

    Train the model to determine values for the parameter vectors 'mu' and 'sig2'.

    Train the model to determine values for the parameter vectors 'mu' and 'sig2'.

    itest

    the indices of test data

    Definition Classes
    GMMClassifier
  36. def train(xx: MatriD = null, yy: VectoD = null): 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.

    xx

    the data/input matrix (impl. classes should ignore or default xx to x)

    yy

    the response/classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    ClassifierModel
  37. 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
  38. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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