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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.

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Known Subclasses
ANCOVA, ANOVA, ARIMA, ExpRegression, ExpSmoothing, LassoRegression, NeuralNet, NonLinRegression, Perceptron, PoissonRegression, PolyRegression, Regression, Regression_WLS, ResponseSurface, RidgeRegression, SimpleRegression, TranRegression, TrigRegression, ANCOVA, PolyRegression, Regression, ResponseSurface, RidgeRegression, TranRegression, TrigRegression, NLS_ODE
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Abstract Value Members

  1. 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

  2. abstract def train(): Unit

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

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

    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. Also, 'train' must call 'diagnose'.

    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. val b: VectoD
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  6. def clone(): AnyRef
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    protected[java.lang]
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    @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

  8. def diagnose(yy: VectoD): Unit

    Compute diagostics for the predictor.

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

    yy

    the response vector

    See also

    en.wikipedia.org/wiki/Mean_squared_error

  9. val e: VectoD
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  10. final def eq(arg0: AnyRef): Boolean
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  11. def equals(arg0: Any): Boolean
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  12. def finalize(): Unit
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  13. def fit: VectoD

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

    Return the quality of fit including 'sse', 'mae', rmse' and 'rSq'. Override to add more quality of fit measures.

  14. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

  15. final def getClass(): Class[_]
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  16. def hashCode(): Int
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  17. final def isInstanceOf[T0]: Boolean
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  18. val mae: Double
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  19. final def ne(arg0: AnyRef): Boolean
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  20. final def notify(): Unit
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  21. final def notifyAll(): Unit
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  22. 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

  23. val rSq: Double
    Attributes
    protected
  24. def residual: VectoD

    Return the vector of residuals/errors.

  25. val rmse: Double
    Attributes
    protected
  26. val sse: Double
    Attributes
    protected
  27. val ssr: Double
    Attributes
    protected
  28. val sst: Double
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  29. final def synchronized[T0](arg0: ⇒ T0): T0
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  30. def toString(): String
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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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