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abstract class PredictorMat2 extends Predictor

The PredictorMat2 abstract class provides the basic structure and API for a variety of modeling techniques with multiple outputs/responses, e.g., Neural Networks.

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Predictor, Model, Error, AnyRef, Any
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  1. PredictorMat2
  2. Predictor
  3. Model
  4. Error
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Instance Constructors

  1. new PredictorMat2(x: MatriD, y: MatriD, fname: Strings, hparam: HyperParameter)

    x

    the m-by-nx data/input matrix (data consisting of m input vectors)

    y

    the m-by-ny response/output matrix (data consisting of m output vectors)

    fname

    the feature/variable names (if null, use x_j's)

    hparam

    the hyper-parameters for the model/network

Abstract Value Members

  1. abstract def buildModel(x_cols: MatriD): PredictorMat2

    Build a sub-model that is restricted to the given columns of the data matrix.

    Build a sub-model that is restricted to the given columns of the data matrix.

    x_cols

    the columns that the new model is restricted to

  2. abstract def parameters: NetParams

    Return the all parameters (weights and biases).

  3. abstract def predictV(z: MatriD = x): MatriD

    Given a new input matrix 'z', predict the output/response matrix 'f(z)'.

    Given a new input matrix 'z', predict the output/response matrix 'f(z)'.

    z

    the new input matrix

  4. abstract def predictV(z: VectoD): VectoD

    Given a new input vector 'z', predict the output/response vector 'f(z)'.

    Given a new input vector 'z', predict the output/response vector 'f(z)'.

    z

    the new input vector

  5. abstract def train(x_: MatriD = x, y_: MatriD = y): PredictorMat2

    Given data/input matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).

    Given data/input matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).

    x_

    the training/full data/input matrix

    y_

    the training/full response/output matrix

  6. abstract def train0(x_: MatriD = x, y_: MatriD = y): PredictorMat2

    Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases) using a simple, easy to follow algorithm.

    Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases) using a simple, easy to follow algorithm.

    x_

    the training/full data/input matrix

    y_

    the training/full response/output matrix

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. val _1: VectorD
    Attributes
    protected
  5. def analyze(x_: MatriD = x, y_: VectoD = y(0), x_e: MatriD = x, y_e: VectoD = y(0)): PredictorMat2

    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. Only uses the first output variable's value.

    x_

    the data/input matrix (training/full)

    y_

    the response/output vector (training/full)

    x_e

    the data/input matrix (testing/full)

    y_e

    the response/output vector (testing/full)

    Definition Classes
    PredictorMat2Predictor
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def backwardElim(cols: Set[Int], index_q: Int = index_rSqBar, first: Int = 1): (Int, PredictorMat2)

    Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector and the new Quality of Fit (QoF).

    Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector and the new Quality of Fit (QoF). 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

    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.

  8. def backwardElimAll(index_q: Int = index_rSqBar, first: Int = 1, cross: Boolean = true): (Set[Int], MatriD)

    Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.

    Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.

    index_q

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

    first

    first variable to consider for elimination

    cross

    whether to include the cross-validation QoF measure

    See also

    Fit for index of QoF measures.

  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  10. 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
  11. def crossValidate(k: Int = 10, rando: Boolean = true): Array[Statistic]
  12. var ee: MatriD
    Attributes
    protected
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. var eta: Double
    Attributes
    protected
  16. def eval(ym: Double, y_e: VectoD, yp: VectoD): PredictorMat2

    Compute the error (difference between actual and predicted) and useful diagnostics for the test dataset.

    Compute the error (difference between actual and predicted) and useful diagnostics for the test dataset. Requires predicted responses to be passed in.

    ym

    the training/full mean actual response/output vector

    y_e

    the test/full actual response/output vector

    yp

    the test/full predicted response/output vector

  17. def eval(x_e: MatriD, y_e: MatriD): PredictorMat2

    Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset.

    Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset. Considers all the response/output variables/columns.

    x_e

    the test/full data/input data matrix

    y_e

    the test/full response/output response matrix

  18. def eval(x_e: MatriD = x, y_e: VectoD = y.col(0)): PredictorMat2

    Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset.

    Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset. Only considers the first response/output variable/column.

    x_e

    the test/full data/input matrix

    y_e

    the test/full response/output vector (first column only)

    Definition Classes
    PredictorMat2Model
  19. val fitA: Array[Fit]
  20. def fitLabel: Seq[String]

    Return the labels for the quality of fit measures.

  21. def fitMap: IndexedSeq[Map[String, String]]

    Return 'fitMap' results for each y-column and print the overall 'rSq' average over all y-columns.

  22. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  23. var fname: Strings
    Attributes
    protected
  24. def forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, PredictorMat2)

    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
    PredictorMat2Predictor
    See also

    Fit for index of QoF measures.

  25. def forwardSelAll(index_q: Int = index_rSqBar, cross: Boolean = true): (Set[Int], MatriD)

    Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.

    Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.

    index_q

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

    cross

    whether to include the cross-validation QoF measure

    See also

    Fit for index of QoF measures.

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

    Return the data matrix 'x'.

    Return the 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
    PredictorMat2Predictor
  28. def getY: VectoD

    Return the first response vector 'y.col(0)'.

    Return the first response vector 'y.col(0)'. Mainly for derived classes where 'y' is transformed.

    Definition Classes
    PredictorMat2Predictor
  29. def getYY: MatriD

    Return the response matrix 'y'.

    Return the response matrix 'y'. Mainly for derived classes where 'y' is transformed.

  30. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  31. def hparameter: HyperParameter

    Return the hyper-parameters.

    Return the hyper-parameters.

    Definition Classes
    PredictorMat2Model
  32. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  33. val m: Int
    Attributes
    protected
  34. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  35. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  36. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  37. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  38. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  39. val nx: Int
    Attributes
    protected
  40. val ny: Int
    Attributes
    protected
  41. def parameter: VectoD

    Return the parameter/weight vector (first layer, first output).

    Return the parameter/weight vector (first layer, first output).

    Definition Classes
    PredictorMat2Model
  42. def predict(z: MatriD = x): VectoD

    Given a new input matrix 'z', predict the output/response matrix 'f(z)'.

    Given a new input matrix 'z', predict the output/response matrix 'f(z)'. Return only the first output variable's value.

    z

    the new input matrix

    Definition Classes
    PredictorMat2Predictor
  43. def predict(z: VectoD): Double

    Given a new input vector 'z', predict the output/response value 'f(z)'.

    Given a new input vector 'z', predict the output/response value 'f(z)'. Return only the first output variable's value.

    z

    the new input vector

    Definition Classes
    PredictorMat2Predictor
  44. 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
  45. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    PredictorMat2Model
    See also

    'summary' method for more details

  46. def reset(eta_: Double): Unit

    Reset the learning rate 'eta'.

    Reset the learning rate 'eta'. Since this hyper-parameter needs frequent tuning, this method is provided to facilitate that.

    eta_

    the learning rate

  47. def resetDF(df_update: PairD): Unit

    Reset the degrees of freedom to the new updated values.

    Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built. Caveat: only applies to the first response/output variable.

    df_update

    the updated degrees of freedom (model, error)

  48. def residual: VectoD

    Return the vector of residuals/errors for first response/output variable/column.

    Return the vector of residuals/errors for first response/output variable/column.

    Definition Classes
    PredictorMat2Predictor
  49. def residuals: MatriD

    Return the matrix of residuals/errors.

  50. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  51. 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
  52. def toString(): String
    Definition Classes
    AnyRef → Any
  53. def train(x_: MatriD, y_: VectoD): PredictorMat2

    Given data matrix 'x_' and response vector 'y_', fit the parameter 'b' (weights and biases).

    Given data matrix 'x_' and response vector 'y_', fit the parameter 'b' (weights and biases).

    x_

    the training/full data/input matrix

    y_

    the training/full response/output vector, e.g., for the first variable/column

    Definition Classes
    PredictorMat2Model
  54. def train2(x_: MatriD = x, y_: MatriD = y): PredictorMat2

    Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).

    Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases). Overriding implementations (if needed) of this method should optimize hyper-parameters (e.g., the learning rate 'eta').

    x_

    the training/full data/input matrix

    y_

    the training/full response/output matrix

  55. def trainSwitch(which: Int, x_: MatriD = x, y_: MatriD = y): PredictorMat2

    Switch between 'train' methods: simple (0), regular (1) and hyper-parameter optimizing (2).

    Switch between 'train' methods: simple (0), regular (1) and hyper-parameter optimizing (2).

    which

    the kind of 'train' method to use

    x_

    the training/full data/input matrix

    y_

    the training/full response/output matrix

  56. 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. Note: override this method to use a superior regression technique.

    skip

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

  57. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  58. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  59. 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

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