local/tests/runit.euclidean.R

In eddelbuettel/rcppannoy: 'Rcpp' Bindings for 'Annoy', a Library for Approximate Nearest Neighbors

.setUp <- function() {
    suppressMessages(library(RcppAnnoy))
}

test01getNNsByVector <- function() {
    f <- 2
    a <- new(AnnoyEuclidean, f)

    a$addItem(0, c(2, 2))
    a$addItem(1, c(3, 2))
    a$addItem(2, c(3, 3))

    a$build(10)

    checkEquals(a$getNNsByVector(c(4,4), 3), c(2,1,0), msg="getNNsByVector check 1")
    checkEquals(a$getNNsByVector(c(1,1), 3), c(0,1,2), msg="getNNsByVector check 1")
    checkEquals(a$getNNsByVector(c(4,2), 3), c(1,2,0), msg="getNNsByVector check 1")
}

test02getNNsByItem <- function() {
    f <- 2
    a <- new(AnnoyEuclidean, f)

    a$addItem(0, c(2, 2))
    a$addItem(1, c(3, 2))
    a$addItem(2, c(3, 3))

    a$build(10)

    checkEquals(a$getNNsByItem(0, 3), c(0, 1, 2), msg="getNNsByItem check 1")
    checkEquals(a$getNNsByItem(2, 3), c(2, 1, 0), msg="getNNsByItem check 2")
}

test03dist <- function() {
    f <- 2
    a <- new(AnnoyEuclidean, f)
    a$addItem(0, c(0, 1))
    a$addItem(1, c(1, 1))
    a$addItem(2, c(0, 0))

    checkEquals(a$getDistance(0, 1), 1.0^0.5, msg="distance 1")
    checkEquals(a$getDistance(1, 2), 2.0^0.5, msg="distance 2", tolerance=1e-6)

}

test04largeIndex <- function() {
    ## Generate pairs of random points where the pair is super close
    f <- 10
    #q <- rnorm(f, 0, 10)
    a <- new(AnnoyEuclidean, f)
    set.seed(123)
    for (j in seq(0, 10000, by=2)) {
        p <- rnorm(f)
        x <- 1 + p + rnorm(f, 0, 1.0e-2)
        y <- 1 + p + rnorm(f, 0, 1.0e-2)
        a$addItem(j, x)
        a$addItem(j+1, y)
    }
    a$build(10)
    for (j in seq(0, 10000, by=2)) {
        checkEquals(a$getNNsByItem(j,   2), c(j,   j+1), msg="getNNsByItem check1")
        checkEquals(a$getNNsByItem(j+1, 2), c(j+1, j),   msg="getNNsByItem check1")
    }
}

test05precision <- function() {

    precision <- function(n, nTrees=10, nPoints=10000, nRounds=3) {
        found <- 0
        for (r in 1:nRounds) {
            ## create random points at distance x
            f <- 10
            a <- new(AnnoyEuclidean, f)

            for (j in seq(nPoints)) {
                p <- rnorm(f, 0, 1)
                nrm <- sqrt(sum(p^2))
                x <- p / nrm * j
                a$addItem(j, x)
            }
            a$build(nTrees)

            nns <- a$getNNsByVector(rep(0, f), n)
            checkEquals(nns, nns[order(nns)], msg="checking precision order")  # should be in order
            ## The number of gaps should be equal to the last item minus n-1
            found <- found + length(nns[ nns <= n])
        }
        return(1.0 * found / (n * nRounds))
    }

    checkTrue(precision(1)    >= 0.98, msg="precision at 1")
    checkTrue(precision(10)   >= 0.98, msg="precision at 10")
    checkTrue(precision(100)  >= 0.98, msg="precision at 100")
    checkTrue(precision(1000) >= 0.98, msg="precision at 1000")

}