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B spline based mutual information

gpuMi.Rd

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,
    precision = getOption("gputools.precision", "single"))

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 columns of x will be compared.

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 columns of x will be compared.

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 histogram for traditional mutual information. For splineOrder > 1, a single value may be placed in multiple adjoining bins with varying weights on membership.

splineOrder

a single integer 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.

precision

"single" or "double": the floating point precision the GPU computes in. Single precision is the default, set with options(gputools.precision = "double"); it runs many times faster on most consumer GPUs. Results are returned as doubles either way.

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 doi:10.1186/1471-2105-5-118

Examples

if (FALSE) { # gpuAvailable()
  # get 3 random variables each with 20 samples
  x <- matrix(runif(60), 20, 3)
  y <- matrix(runif(60), 20, 3)
  # do something interesting
  y[,2] <- 3.0 * (x[,1] + x[,3])
  z <- gpuMi(x, y, bins = 10, splineOrder = 3)
  print(z)
}