# gpr

smile.regression.`package`.gpr
object gpr

Gaussian Process for Regression.

Graph
Supertypes
class Object
trait Matchable
class Any
Self type
gpr.type

## Members list

### Value members

#### Concrete methods

def apply[T <: AnyRef](x: Array[T], y: Array[Double], kernel: MercerKernel[T], noise: Double, normalize: Boolean, tol: Double, maxIter: Int): GaussianProcessRegression[T]

Gaussian Process for Regression. A Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution.

Gaussian Process for Regression. A Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution.

A Gaussian process can be used as a prior probability distribution over functions in Bayesian inference. Given any set of N points in the desired domain of your functions, take a multivariate Gaussian whose covariance matrix parameter is the Gram matrix of N points with some desired kernel, and sample from that Gaussian. Inference of continuous values with a Gaussian process prior is known as Gaussian process regression.

The fitting is performed in the reproducing kernel Hilbert space with the "kernel trick". The loss function is squared-error. This also arises as the kriging estimate of a Gaussian random field in spatial statistics.

A significant problem with Gaussian process prediction is that it typically scales as O(n3). For large problems (e.g. n > 10,000) both storing the Gram matrix and solving the associated linear systems are prohibitive on modern workstations. An extensive range of proposals have been suggested to deal with this problem. A popular approach is the reduced-rank Approximations of the Gram Matrix, known as Nystrom approximation. Greedy approximation is another popular approach that uses an active set of training points of size m selected from the training set of size n > m. We assume that it is impossible to search for the optimal subset of size m due to combinatorics. The points in the active set could be selected randomly, but in general we might expect better performance if the points are selected greedily w.r.t. some criterion. Recently, researchers had proposed relaxing the constraint that the inducing variables must be a subset of training/test cases, turning the discrete selection problem into one of continuous optimization.

This method fits a regular Gaussian process model.

====References:====

• Carl Edward Rasmussen and Chris Williams. Gaussian Processes for Machine Learning, 2006.
• Joaquin Quinonero-candela, Carl Edward Ramussen, Christopher K. I. Williams. Approximation Methods for Gaussian Process Regression. 2007.
• T. Poggio and F. Girosi. Networks for approximation and learning. Proc. IEEE 78(9):1484-1487, 1990.
• Kai Zhang and James T. Kwok. Clustered Nystrom Method for Large Scale Manifold Learning and Dimension Reduction. IEEE Transactions on Neural Networks, 2010.

## Value parameters

kernel

the Mercer kernel.

maxIter

the maximum number of iterations for HPO. No HPO if maxIter <= 0.

noise

the noise variance, which also works as a regularization parameter.

normalize

the option to normalize the response variable.

tol

the stopping tolerance for HPO.

x

the training dataset.

y

the response variable.

## Attributes

def approx[T <: AnyRef](x: Array[T], y: Array[Double], t: Array[T], kernel: MercerKernel[T], noise: Double, normalize: Boolean): GaussianProcessRegression[T]

Fits an approximate Gaussian process model with a subset of regressors.

Fits an approximate Gaussian process model with a subset of regressors.

## Value parameters

kernel

the Mercer kernel.

noise

the noise variance, which also works as a regularization parameter.

normalize

the option to normalize the response variable.

t

the inducing input, which are pre-selected or inducing samples acting as active set of regressors. In simple case, these can be chosen randomly from the training set or as the centers of k-means clustering.

x

the training dataset.

y

the response variable.

## Attributes

def nystrom[T <: AnyRef](x: Array[T], y: Array[Double], t: Array[T], kernel: MercerKernel[T], noise: Double, normalize: Boolean): GaussianProcessRegression[T]

Fits an approximate Gaussian process model with Nystrom approximation of kernel matrix.

Fits an approximate Gaussian process model with Nystrom approximation of kernel matrix.

## Value parameters

kernel

the Mercer kernel.

noise

the noise variance, which also works as a regularization parameter.

normalize

the option to normalize the response variable.

t

the inducing input, which are pre-selected or inducing samples acting as active set of regressors. In simple case, these can be chosen randomly from the training set or as the centers of k-means clustering.

x

the training dataset.

y

the response variable.