Packages

c

scalation.analytics.par

PredictorVec

abstract class PredictorVec extends Predictor with Error

The PredictorVec class supports term expanded regression (work is delegated to the Regression class). Fit the parameter vector 'b' in the regression equation. Use Least-Squares (minimizing the residuals) to solve for the parameter vector 'b' using the Normal Equations:

x.t * x * b = x.t * y b = fac.solve (.)

Linear Supertypes
Predictor, Model, Error, AnyRef, Any
Known Subclasses
PolyRegression
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Inherited
  1. PredictorVec
  2. Predictor
  3. Model
  4. Error
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PredictorVec(t: VectoD, y: VectoD, ord: Int)

    t

    the input vector: t_i expands to x_i = vector

    y

    the response vector

    ord

    the order of the expansion

Abstract Value Members

  1. abstract def crossVal(k: Int = 10, ord: Int = 10, rando: Boolean = true): Unit

    The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.

    The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method. The 'algor' parameter may be specified as a lambda function to create the prediction algorithm.

    k

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

    ord

    the given order

    rando

    whether to use randomized cross-validation

  2. abstract def expand(t: Double): VectoD

    Expand the scalar 't' into a vector of terms/columns.

    Expand the scalar 't' into a vector of terms/columns.

    t

    the scalar to expand into the vector

  3. abstract def predict(z: Double): Double

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.

    z

    the new scalar to predict

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. def analyze(x_r: MatriD = null, y_r: VectoD = y, x_e: MatriD = null, y_e: VectoD = y): PredictorVec

    Analyze a dataset using this model using ordinary training with the 'train' method.

    Analyze a dataset using this model using ordinary training with the 'train' method.

    x_r

    the training/full data/input matrix

    y_r

    the training/full response/output vector

    x_e

    the test/full data/input matrix

    y_e

    the test/full response/output vector

    Definition Classes
    PredictorVec → Predictor
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def backwardElim(cols: Set[Int], index_q: Int = index_rSqBar, first: Int = 1): (Int, PredictorMat)

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit.

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

    index_q

    index of Quality of Fit (QoF) to use for comparing quality

    first

    first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)

    See also

    Fit for index of QoF measures.

  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  8. def corrMatrix(xx: MatriD): MatriD

    Return the correlation matrix for the columns in data matrix 'xx'.

    Return the correlation matrix for the columns in data matrix 'xx'.

    xx

    the data matrix shose correlation matrix is sought

    Definition Classes
    Predictor
  9. def crossValidate(algor: (VectoD, VectoD, Int) ⇒ PredictorVec, k: Int = 10, rando: Boolean = true): Array[Statistic]
    Attributes
    protected
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def eval(yy: VectoD): Regression

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    yy

    the test response vector

  13. def eval(xx: MatriD, yy: VectoD = y): Regression

    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

    Definition Classes
    PredictorVec → Model
  14. def eval(tt: VectoD, yy: VectoD): Regression

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    tt

    the test data vector (unexpanded)

    yy

    the test response vector

  15. def expand(t: VectoD): MatriD

    Expand the vector 't' into a matrix.

    Expand the vector 't' into a matrix.

    t

    the vector to expand into the matrix

  16. def fit: VectoD

    Return the quality of fit measures including 'rSq'.

  17. def fitLabel: Seq[String]

    Return the labels for the fit.

  18. def fitMap: Map[String, String]

    Build a map of quality of fit measures.

  19. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  20. def forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, PredictorMat)

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit.

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

    index_q

    index of Quality of Fit (QoF) to use for comparing quality

    Definition Classes
    PredictorVec → Predictor
    See also

    Fit for index of QoF measures.

  21. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  22. def getX: MatriD

    Return the 'used' data matrix 'x'.

    Return the 'used' data matrix 'x'. Mainly for derived classes where 'x' is expanded from the given columns in 'x_', e.g., QuadRegression add squared columns.

    Definition Classes
    PredictorVec → Predictor
  23. def getY: VectoD

    Return the 'used' response vector 'y'.

    Return the 'used' response vector 'y'. Mainly for derived classes where 'y' is transformed, e.g., TranRegression, Regression4TS.

    Definition Classes
    PredictorVec → Predictor
  24. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  25. def hparameter: HyperParameter

    Return the hyper-parameters.

    Return the hyper-parameters.

    Definition Classes
    PredictorVec → Model
  26. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  27. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  28. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  29. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  31. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  32. def parameter: VectoD

    Return the vector of parameters/coefficients.

    Return the vector of parameters/coefficients.

    Definition Classes
    PredictorVec → Model
  33. def predict(z: MatriD = rg.getX): VectoD

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.

    z

    the new matrix to predict

    Definition Classes
    PredictorVec → Predictor
  34. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new expanded/orhogonalized vector to predict

    Definition Classes
    PredictorVec → Predictor
  35. def predict(z: VectoI): Double

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

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

    z

    the vector to use for prediction

    Definition Classes
    Predictor
  36. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    PredictorVec → Model
    See also

    'summary' method for more details

  37. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    PredictorVec → Predictor
  38. var rg: Regression
    Attributes
    protected
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def test(modelName: String, doPlot: Boolean = true): Unit

    Test the model on the full dataset (i.e., train and evaluate on full dataset).

    Test the model on the full dataset (i.e., train and evaluate on full dataset).

    modelName

    the name of the model being tested

    doPlot

    whether to plot the actual vs. predicted response

    Definition Classes
    Predictor
  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. def train(xx: MatriD = MatrixD (Seq (t)), yy: VectoD = y): Regression

    Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.

    Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.

    xx

    the data/input single column matrix (unexpanded)

    yy

    the response/output vector

    Definition Classes
    PredictorVec → Model
  43. def vif(skip: Int = 1): VectoD

    Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'x_j' against the rest of the variables.

    Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'x_j' against the rest of the variables. A VIF over 10 indicates that over 90% of the variance of 'x_j' can be predicted from the other variables, so 'x_j' may be a candidate for removal from the model.

    skip

    the number of columns of x at the beginning to skip in computing VIF

  44. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  46. 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

Inherited from Predictor

Inherited from Model

Inherited from Error

Inherited from AnyRef

Inherited from Any

Ungrouped