hilbertProjection: Project a Cut Reference Matrix to a Different Space through...

In yannabraham/hilbertSimilarity: Hilbert Similarity Index for High Dimensional Data

Description

Starting from a Hilbert Index generated in a high dimensional space, returns a set of coordinates in a new (lower) dimensional space

Usage

hilbertProjection(hc, target = 2)

Arguments

hc	the hilbert index returned by do.hilbert
target	the number of dimensions in the target space (defaults to 2)

Details

Based on the maximum index and the targeted number of dimensions the number of target bins is computed and used to generate a reference matrix and a reference index. The reference matrix is returned, ordered by the reference index.

Value

a matrix with target columns, corresponding to the projection of each Hilbert index to target dimensions

Author(s)

Marilisa Neri

Yann Abraham

John Skilling (for the original C function)

Examples

# generate a random matrix
ncols <- 5
mat <- matrix(rnorm(ncols*5000),ncol=ncols)
dimnames(mat)[[2]] <- LETTERS[seq(ncols)]

# generate 4 bins with a minimum bin size of 5
horder <- 4
cuts <- make.cut(mat,n=horder+1,count.lim=5)

# Generate the cuts and compute the Hilbert index
cut.mat <- do.cut(mat,cuts,type='fixed')
hc <- do.hilbert(cut.mat,horder)
chc <- table(hc)
idx <- as.numeric(names(chc))

# project the matrix to 2 dimensions
proj <- hilbertProjection(hc)

# visualize the result
img <- matrix(0,ncol=max(proj[,2])+1,nrow = max(proj[,1])+1)
img[proj[idx,]+1] <- chc
image(img)

yannabraham/hilbertSimilarity documentation built on Dec. 4, 2019, 3:05 p.m.