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
Known Subclasses
ANCOVA, ANOVA1, ExpRegression, LassoRegression, NeuralNet, NeuralNet_2L, NeuralNet_3L, NeuralNet_XL, NonLinRegression, NullModel, Perceptron, PoissonRegression, PolyRegression, PredictorMat, PredictorVec, Regression, Regression_WLS, ResponseSurface, RidgeRegression, SimpleRegression, SimplerRegression, TranRegression, TrigRegression, ANCOVA, PolyRegression, Regression, RidgeRegression, NLS_ODE
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Abstract Value Members

  1. abstract def eval(): Unit

    Compute the error and useful diagnostics for the entire dataset.

  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. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

  2. 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

  3. 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

  4. def residual: VectoD

    Return the vector of residuals/errors.