Packages

trait Predictor extends AnyRef

The Predictor trait provides a common framework for several predictors. A predictor is for potentially unbounded responses (real or integer). When the number of distinct responses is bounded by some relatively small integer 'k', a classifier is likdely more appropriate. Note, the 'train' method must be called first followed by 'eval'.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Predictor
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Abstract Value Members

  1. abstract def eval(yy: VectoD): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

  2. abstract def predict(z: VectoD): Double

    Given a new continuous data vector z, predict the y-value of f(z).

    Given a new continuous data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

  3. abstract def train(yy: VectoD): Predictor

    Given a set of data vectors 'x's and their corresponding responses 'yy's, train the prediction function 'yy = f(x)' by fitting its parameters.

    Given a set of data vectors 'x's and their corresponding responses 'yy's, train the prediction function 'yy = f(x)' by fitting its parameters. The 'x' values must be provided by the implementing class.

    yy

    the response vector

Concrete 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. val b: VectoD
    Attributes
    protected
  6. def build(x: MatriD, y: VectoD): Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the predictor.

    Compute diagostics for the predictor. Override to add more diagostics. Note, for 'mse' and 'rmse', 'sse' is divided by the number of instances 'm' rather than the degrees of freedom.

    yy

    the response vector

    Attributes
    protected
    See also

    en.wikipedia.org/wiki/Mean_squared_error

  10. val e: VectoD
    Attributes
    protected
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def fit: VectoD

    Return the quality of fit including 'sst', 'sse', 'mae', rmse' and 'rSq'.

    Return the quality of fit including 'sst', 'sse', 'mae', rmse' and 'rSq'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the last or number 5 measure. Override to add more quality of fit measures.

  15. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. val index_rSq: Int
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. val mae: Double
    Attributes
    protected
  21. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

  22. val mse: Double
    Attributes
    protected
  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  26. def predict(z: VectoI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

  27. val rSq: Double
    Attributes
    protected
  28. def residual: VectoD

    Return the vector of residuals/errors.

  29. val rmse: Double
    Attributes
    protected
  30. val sse: Double
    Attributes
    protected
  31. val ssr: Double
    Attributes
    protected
  32. val sst: Double
    Attributes
    protected
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def toString(): String
    Definition Classes
    AnyRef → Any
  35. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped