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trait Forecaster extends AnyRef

The Forecaster 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'.

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Abstract Value Members

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

    Compute the error and useful diagnostics for the entire dataset.

    Compute the error and useful diagnostics for the entire dataset.

    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): Forecaster

    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
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  6. def diagnose(yy: VectoD, ee: 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

    ee

    the error/residual vector

    Attributes
    protected
    See also

    en.wikipedia.org/wiki/Mean_squared_error

  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  9. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector FIX - implement in classes

  10. def finalize(): Unit
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  11. 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.

  12. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

  13. final def getClass(): Class[_]
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  14. def hashCode(): Int
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  15. val index_rSq: Int
  16. final def isInstanceOf[T0]: Boolean
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  17. val mae: Double
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    protected
  18. def metrics: Map[String, Any]

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

  19. val mse: Double
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  20. final def ne(arg0: AnyRef): Boolean
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  21. final def notify(): Unit
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  22. final def notifyAll(): Unit
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  23. 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

  24. val rSq: Double
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  25. val rmse: Double
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  26. val sse: Double
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  27. val ssr: Double
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  28. val sst: Double
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  29. final def synchronized[T0](arg0: ⇒ T0): T0
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  31. final def wait(): Unit
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  32. final def wait(arg0: Long, arg1: Int): Unit
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  33. final def wait(arg0: Long): Unit
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