gputools (version 0.1-1)

gpuMi: B spline based mutual information

Description

This function estimates the mutual information for permutations of pairs of columns of a matrix using a B spline approach on a GPU device. Please note, the data must be values from the interval [0.0, 1.0].

Usage

gpuMi(x, y = NULL, bins = 2, splineOrder = 1)

Arguments

x

a matrix of floating point numbers from the interval [0.0, 1.0]. Each column represents a list of samples of a random variable. The mutual information between each column of x and each column of y will be computed. If y is NULL then each pair of c

y

a matrix of floating point numbers from the interval [0.0, 1.0]. Each column represents a list of samples of a random variable. The mutual information between each column of x and each column of y will be computed. If y is NULL then each pair of c

bins

a single integer value representing the number of equal intervals that [0.0, 1.0] will be divided into in order to determine the bins in which to place each value of the columns of x and y. In the case of splineOrder = 1, this determines the histog

splineOrder

a single interver value giving the degree of the spline polynomials used to define both the number of bins a single value will be placed in and the weight of membership given to the value.

Value

• a matrix of single precision floating point values of order ncol(y) by ncol(x). Entry $(i, j)$ of this matrix represents the mutual information calculation for $(y_i, x_j)$.

References

Carten O. Daub, Ralf Steuer, Joachim Selbig, and Sebastian Kloska. 2004. Estimating mutual information using B-spline functions -- an improved similarity measure for analysing gene expression data. BMC Bioinformatics. 5:118. Available from http://www.biomedcentral.com/1471-2105/5/118

Examples

# get 10 random variables each with 100 samples
	x <- matrix(runif(1000), 100, 10)
	y <- matrix(runif(1000), 100, 10)
	# do something interesting
	y[,2] <- 3.0 * (x[,8] + x[,9])
	z <- gpuMi(x, y, bins = 10, splineOrder = 3)
	print(z)