Packages

c

scalation.analytics.fda

Regression_F2S

class Regression_F2S extends Predictor

The Regression_F2S class performs functional linear regression with scaler response and functional covariates.

y = a + <b(t),x(t)> + ε

where <b, x> denotes the inner product of b and x.

Linear Supertypes
Predictor, Model, Error, AnyRef, Any
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  1. Regression_F2S
  2. Predictor
  3. Model
  4. Error
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Visibility
  1. Public
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Instance Constructors

  1. new Regression_F2S(x: MatriD, y: VectoD, t: VectoD, bf: DBasisFunction, technique: RegTechnique.RegTechnique = Cholesky, lambda: Double = 1E-4)

    x

    the covariate matrix - treated as functional

    y

    the response vector

    t

    the time vector

    bf

    the basis function object with derivatives support

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. val _1: VectorD
  5. def analyze(x_r: MatriD = x, y_r: VectoD = y, x_e: MatriD = x, y_e: VectoD = y): Regression_F2S

    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 data/input matrix (training/full)

    y_r

    the response/output vector (training/full)

    x_e

    the data/input matrix (testing/full)

    y_e

    the response/output vector (testing/full)

    Definition Classes
    Regression_F2S → Predictor
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  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(k: Int): Double

    Test the accuracy of the predicted results by cross-validation, returning the sse.

    Test the accuracy of the predicted results by cross-validation, returning the sse.

    k

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

  10. val e: VectorD
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def eval(yy: VectoD): PredictorMat

    Compute the error and useful diagnostics for the entire dataset.

    Compute the error and useful diagnostics for the entire dataset.

    yy

    the test response/output vector

  14. def eval(xx: MatriD = x, yy: VectoD = y): PredictorMat

    Compute the error and useful diagnostics for the entire dataset.

    Compute the error and useful diagnostics for the entire dataset.

    xx

    the test data/input matrix

    yy

    the test response/output vector

    Definition Classes
    Regression_F2S → Model
  15. def fitMap: Map[String, String]

    Return the quality of fit map.

  16. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  17. def forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, Regression_F2S)

    Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model.

    Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.

    cols

    the columns of matrix x currently included in the existing model

    index_q

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

    Definition Classes
    Regression_F2S → Predictor
    See also

    Fit for index of QoF measures.

  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  19. 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
    Regression_F2S → Predictor
  20. 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
    Regression_F2S → Predictor
  21. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  22. def hparameter: HyperParameter

    Return the hyper-parameters.

    Return the hyper-parameters.

    Definition Classes
    Regression_F2S → Model
  23. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  24. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  25. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  29. def parameter: VectoD

    Return the vector of parameter/coefficient values.

    Return the vector of parameter/coefficient values.

    Definition Classes
    Regression_F2S → Model
  30. def predict(z: MatriD = x): 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
    Regression_F2S → Predictor
  31. def predict(z: VectoD): 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

    Definition Classes
    Regression_F2S → Predictor
  32. def predict(xf: FunctionS2S): Double

    Predict the y-value at time point 'tt'.

    Predict the y-value at time point 'tt'.

    xf

    the given function of time

  33. 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
  34. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    Regression_F2S → Model
    See also

    'summary' method for more details

  35. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Regression_F2S → Predictor
  36. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  37. val t0: Double
  38. def test(itest: VectoI): Unit

    Test the model on the testing set for cross-validation purposes

    Test the model on the testing set for cross-validation purposes

    itest

    indicies of the testing set

  39. 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
  40. val tn: Double
  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. def train(itest: IndexedSeq[Int]): Unit

    Train the model on the training set for cross-validation purposes

    Train the model on the training set for cross-validation purposes

    itest

    indicies of the testing set, to be excluded for training

  43. def train(xx: MatriD = x, yy: VectoD = y): Regression_F2S

    Train the model using the smoothed data to find the regression coefficients 'b'.

    Train the model using the smoothed data to find the regression coefficients 'b'.

    xx

    the data/input matrix

    yy

    the response.output vector

    Definition Classes
    Regression_F2S → Model
  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( ... )
  47. def x_i(i: Int): VectorD

    The inner product of phi and a smoothed function

    The inner product of phi and a smoothed function

    i

    the i-th smoothed function function

  48. val xmoo: IndexedSeq[Smoothing_F]
  49. val z: MatrixD

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

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