Class

com.linkedin.photon.ml.hyperparameter.estimators

GaussianProcessModel

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class GaussianProcessModel extends AnyRef

Gaussian Process regression model that predicts mean and variance of response for new observations

See also

Gaussian Processes for Machine Learning (GPML), http://www.gaussianprocess.org/gpml/, Chapter 2

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Instance Constructors

  1. new GaussianProcessModel(xTrain: DenseMatrix[Double], yTrain: DenseVector[Double], yMean: Double, kernels: Seq[Kernel], predictionTransformation: Option[PredictionTransformation])

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    xTrain

    the observed training features

    yTrain

    the observed training labels

    yMean

    the mean of the observed labels

    kernels

    the sampled kernels

    predictionTransformation

    optional transformation function that will be applied to the predicted response for each sampled kernel

    Attributes
    protected[com.linkedin.photon.ml.hyperparameter.estimators]

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  8. val featureDimension: Int

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  9. def finalize(): Unit

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  16. def predict(x: DenseMatrix[Double]): (DenseVector[Double], DenseVector[Double])

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    Predicts mean and variance of response for new observations

    Predicts mean and variance of response for new observations

    x

    the observed features

    returns

    predicted mean and variance of response

  17. def predictTransformed(x: DenseMatrix[Double]): DenseVector[Double]

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    Predicts and transforms the response for the new observations

    Predicts and transforms the response for the new observations

    x

    the observed features

    returns

    the transformed response prediction

  18. def predictWithKernel(x: DenseMatrix[Double], kernel: Kernel): (DenseVector[Double], DenseVector[Double])

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    Computes the predicted mean and variance of response for the new observation, given a single kernel

    Computes the predicted mean and variance of response for the new observation, given a single kernel

    x

    the observed features

    kernel

    the covariance kernel

    returns

    predicted mean and variance of response

    Attributes
    protected[com.linkedin.photon.ml.hyperparameter.estimators]

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