Class/Object

regressors

BayesRegressor

Related Docs: object BayesRegressor | package regressors

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class BayesRegressor extends Regressor

Bayes regressor

following https://stats.stackexchange.com/questions/252577/bayes-regression-how-is-it-done-in-comparison-to-standard-regression

The priors can be set using the priorPars parameter. If left empty, random values will be used. e.g. List(List(2), List(0, 1), List(1, 3)) for a posterior width 2 and Gaussian priors for the intercept (mean 0, sigma 1) and the other weights (mean 1, sigma 3)

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Instance Constructors

  1. new BayesRegressor(json: JsValue)

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  2. new BayesRegressor(degree: Int = BayesRegressor.degree, model: String = BayesRegressor.model, priorPars: List[List[Double]] = BayesRegressor.priorPars, randInit: Boolean = BayesRegressor.randInit, savePlots: Boolean = BayesRegressor.savePlots)

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    degree

    Order of polynomial features to add to the instances (1 for no addition)

    model

    The shape of the prior function

    priorPars

    Parameters of the prior probability density functions

    randInit

    If set to true, initialize the prior pdf parameters randomly

    savePlots

    If set to true, save plots of algorithm performance

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def _predict(X: List[List[Double]]): List[Double]

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  5. def _train(X: List[List[Double]], y: List[Double]): Unit

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  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    AnyRef
    Annotations
    @throws( ... )

  8. def diagnostics(): Map[String, List[(Double, Double)]]

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    Provides meta-information on the regressor

    Provides meta-information on the regressor

    returns

    Map object of metric names and metric values

    Definition Classes
    Regressor

  9. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  15. def logToProb(logs: List[Double]): List[Double]

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    Converts a list of logarithmic likelihoods to probabilities

  16. val name: String

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    The name of the regressor

    The name of the regressor

    Definition Classes
    BayesRegressor → Regressor

  17. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  18. final def notify(): Unit

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    Definition Classes
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  19. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  20. def predict(X: List[List[Double]]): List[Double]

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    Applies the trained regressor to a dataset

    Applies the trained regressor to a dataset

    X

    List of data instances

    returns

    List of predictions

    Definition Classes
    BayesRegressor → Regressor

  21. def priorFunc(model: String, x: Double, params: List[Double]): Double

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    The probability distribution for the weight priors

  22. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
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  23. def toString(): String

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    Definition Classes
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  24. def train(X: List[List[Double]], y: List[Double]): Unit

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    Performs the training of the regressor

    Performs the training of the regressor

    X

    List of training instances

    y

    List of training labels

    Definition Classes
    BayesRegressor → Regressor

  25. final def wait(): Unit

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    @throws( ... )

  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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  28. var weight: List[Double]

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    The weight vector to be determined by the training

  29. var width: Double

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    The posterior width to be determined by the training

Inherited from Regressor

Inherited from AnyRef

Inherited from Any

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