rectscale

rectunscale

Scale data lying in an arbitrary rectangle to lie in
 the unit rectangle, and back again

 utilities

Sequential Monte Carlo (SMC) inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL) following Gramacy & Polson (2011) <arXiv:0909.5262>.
The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics. Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
provide for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos.

Robert Gramacy

plgp

Particle Learning of Gaussian Processes

rectscale function

<dl> <dt>X</dt>
<dd>a <code>matrix</code> or <code>data.frame</code> of real-valued covariates</dd> <dt>rect</dt>
<dd>a <code>matrix</code> describing a bounding rectangle for <code>X</code>
 with 2 columns and <code>ncol(X)</code> rows</dd></dl>

Arguments

Robert B. Gramacy, <a href="/link/rbg%40vt.edu?package=plgp&version=1.1-12" data-mini-rdoc="plgp::rbg@vt.edu">rbg@vt.edu</a>

Author

Un/Scale data in a bounding rectangle — rectscale

<dl>

 <dt>X</dt>
<dd>a <code>matrix</code> or <code>data.frame</code> of real-valued covariates</dd>

 <dt>rect</dt>
<dd>a <code>matrix</code> describing a bounding rectangle for <code>X</code>
 with 2 columns and <code>ncol(X)</code> rows</dd>

</dl>

Robert B. Gramacy, <a href='mailto:rbg@vt.edu'>rbg@vt.edu</a>

rectscale: Un/Scale data in a bounding rectangle

Description

Usage

Value

Arguments

Author

References

Examples