Fast computation of squared mahalanobis distance between all rows of X and the vector mu with respect to sigma.
Fast computation of squared mahalanobis distance between all rows of X and the vector mu with respect to sigma.
maha(X, mu, sigma, ncores = 1, isChol = FALSE)
X |
matrix n by d where each row is a d dimensional random vector. Alternatively |
mu |
vector of length d, representing the central position. |
sigma |
covariance matrix (d x d). Alternatively is can be the cholesky decomposition
of the covariance. In that case |
ncores |
Number of cores used. The parallelization will take place only if OpenMP is supported. |
isChol |
boolean set to true is |
a vector of length n where the i-the entry contains the square mahalanobis distance i-th random vector.
Matteo Fasiolo <matteo.fasiolo@gmail.com>
N <- 100
d <- 5
mu <- 1:d
X <- t(t(matrix(rnorm(N*d), N, d)) + mu)
tmp <- matrix(rnorm(d^2), d, d)
mcov <- tcrossprod(tmp, tmp)
myChol <- chol(mcov)
rbind(head(maha(X, mu, mcov), 10),
head(maha(X, mu, myChol, isChol = TRUE), 10),
head(mahalanobis(X, mu, mcov), 10))
## Not run:
# Performance comparison: microbenchmark does not work on all
# platforms, hence we need to check whether it is installed
if( "microbenchmark" %in% rownames(installed.packages()) ){
library(microbenchmark)
a <- cbind(
maha(X, mu, mcov),
maha(X, mu, myChol, isChol = TRUE),
mahalanobis(X, mu, mcov))
# Same output as mahalanobis
a[ , 1] / a[, 3]
a[ , 2] / a[, 3]
microbenchmark(maha(X, mu, mcov),
maha(X, mu, myChol, isChol = TRUE),
mahalanobis(X, mu, mcov))
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.