fdist {collapse}

Fast and Flexible Distance Computations

Description

A fast and flexible replacement for dist, to compute euclidean distances.

Usage

fdist(x, v = NULL, ..., method = "euclidean", nthreads = .op[["nthreads"]])

Arguments

x

a numeric vector or matrix. Data frames/lists can be passed but will be converted to matrix using qM. Non-numeric (double) inputs will be coerced.

v

an (optional) numeric (double) vector such that length(v) == NCOL(x), to compute distances with (the rows of) x. Other vector types will be coerced.

...

not used. A placeholder for possible future arguments.

method

an integer or character string indicating the method of computing distances.

Int.		String		Description
1		"euclidean"		euclidean distance
2		"euclidean_squared"		squared euclidean distance (more efficient)

nthreads

integer. The number of threads to use. If v = NULL (full distance matrix), multithreading is along the distance matrix columns (decreasing thread loads as matrix is lower triangular). If v is supplied, multithreading is at the sub-column level (across elements).

Value

If v = NULL, a full lower-triangular distance matrix between the rows of x is computed and returned as a 'dist' object (all methods apply, see dist). Otherwise, a numeric vector of distances of each row of x with v is returned. See Examples.

Note

fdist does not check for missing values, so NA's will result in NA distances.

kit::topn is a suitable complimentary function to find nearest neighbors. It is very efficient and skips missing values by default.

See Also

flm, Fast Statistical Functions, Collapse Overview

Examples

# Distance matrix
m = as.matrix(mtcars)
str(fdist(m)) # Same as dist(m)

# Distance with vector
d = fdist(m, fmean(m))
kit::topn(d, 5)  # Index of 5 nearest neighbours

# Mahalanobis distance
m_mahal = t(forwardsolve(t(chol(cov(m))), t(m)))
fdist(m_mahal, fmean(m_mahal))
sqrt(unattrib(mahalanobis(m, fmean(m), cov(m))))

# Distance of two vectors
x <- rnorm(1e6)
y <- rnorm(1e6)
microbenchmark::microbenchmark(
  fdist(x, y),
  fdist(x, y, nthreads = 2),
  sqrt(sum((x-y)^2))
)