exhaustivePlot: Plot of the LML function by exhaustive search.

In alkalait/gprege: Gaussian Process Ranking and Estimation of Gene Expression time-series

Description

Exhaustively searches the hyperparameter space by a grid, whose resolution is passed as an argument, and plots the LML function for every point in the space.

Usage

 exhaustivePlot(y, x, xstar, options, maxwidth, res, nlevels)

Arguments

y	the target (output) data.
x	the input data matrix.
xstar	the points to predict function values.
options	options structure as defined by gpOptions.m.
maxwidth	maximum lengthscale to search for.
res	The search resolution. Number of points to plot for in the search range.
nlevels	Number of contour levels.

Value

area	Area under the ROC curve of method-A.

See Also

rocStats

Examples

  noiseLevel <- 0.2
  noiseVar <- noiseLevel^2
  options <- gpOptions()
  options$kern$comp <- list('rbf','white')
  ## Create data set
  l <- 9; x <- matrix(seq(0,240,by=20), ncol=1)
  trueKern <- kernCreate(x, 'rbf')
  trueKern$inverseWidth <- 1/(20^2) ## Characteristic inverse-width.
  K <- kernCompute(trueKern, x) + diag(dim(x)[1])*noiseVar
  ## Sample some true function values.
  y <- gaussSamp(Sigma=K, numSamps=1)
  xTest <- as.matrix(seq(0, 240, length=200))
  graphics.off(); dev.new(); plot.new(); dev.new(); plot.new()
  exhaustivePlot(y, x, xTest, options=options, maxwidth=100, res=50, nlevels=75)

alkalait/gprege documentation built on May 10, 2019, 9:18 a.m.