Generic weighted squared distance

Symbolic weighted squared distance binary operation for LazyTensor objects.

Usage

weightedsqdist(x, y, s)

Arguments

x

A vector of numeric values or a scalar value.

y

a LazyTensor, a ComplexLazyTensor, a vector of numeric values, or a scalar value.

s

a LazyTensor, a ComplexLazyTensor, a vector of numeric values, or a scalar value.

Value

An object of class LazyTensor.

Details

weightedsqdist(x, y, s) returns a LazyTensor that encodes, symbolically, the weighted squared distance of a vector x with weights stored in the LazyTensor s, same as weightedsqnorm(x - y, s).

Note: x, y and s should all have the same inner dimension.

Note: Run browseVignettes("rkeops") to access the vignettes and find details about this function in the "RKeOps LazyTensor" vignette, at section "Simple vector operations".

See also

weightedsqnorm(), sqdist()

Author

Chloe Serre-Combe, Amelie Vernay

Examples

if (FALSE) {
x <- matrix(runif(100 * 3), 100, 3) # arbitrary R matrix, 100 rows, 3 columns
y <- matrix(runif(100 * 3), 100, 3) # arbitrary R matrix, 100 rows, 3 columns
s <- matrix(runif(100 * 3), 100, 3) # arbitrary R matrix, 100 rows, 3 columns

x_i <- LazyTensor(x, index = 'i')   # creating LazyTensor from matrix x, 
                                    # indexed by 'i'
y_j <- LazyTensor(y, index = 'j')   # creating LazyTensor from matrix y, 
                                    # indexed by 'j'
s_i <- LazyTensor(s, index = 'i')   # creating LazyTensor from matrix s, 
                                    # indexed by 'i'

wsqd_xy <- weightedsqdist(x_i, y_j, s_i)    # symbolic matrix
}