findNN {protViz} | R Documentation |
Given a vector of sorted double values vec
of size
n
and a vector of m
query objects q
.
findNN
determines for each element q[i]
in q
the nearest neighbor index o
so that the following remains true:
there is no element k
with 1
≤ k
≤ n
and k
is not o
so that
abs(vec[k] - q[i])
< abs(vec[o] - q[i])
.
findNN(q, vec, check) findNN_(q, vec, check)
q |
a double vector which can be considered as query objects. |
vec |
a sorted double vector which can be considered as a data base. |
check |
boolean enables test if |
The internal algorithm of findNN
is implemented as binary search.
findNN
has O(m * log_2(n)) time complexity where n is defined as
length(vec)
and m is length(m)
.
findNN
is implemented using C library function - bsearch(),
while findNN_
uses C++11 STL function lower_bound().
Christian Panse 2007, 2008, 2009, 2010, 2012 , 2015 based on the C++ STL lower_bound method.
(NNidx <- findNN(q <- c(1, 1.0001, 1.24, 1.26), DB <- seq(1, 5 , by = 0.25))) (NNidx == c(1, 1, 2, 2)) DB <- sort(rnorm(100, mean=100, sd = 10)) # should be 0 unique(DB[findNN(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN(q,DB) hist(DB[findNN(q,DB)] - q) # definition of findNN holds i <- 1:5 findNN(3.5, i) i <- 1:6 findNN(3.5, i) # compare ANSI-C binary search with C++ std::lower_bound DB <- c(rep(1.0, 3), rep(2.0, 3)) q <- c(-1, 1.0, 1.01, 1.5, 1.9) abs(DB[findNN(q, DB)] - q) abs(DB[findNN_(q, DB)] - q) DB <- sort(rnorm(100, mean=100, sd=10)) # should be 0 unique(DB[findNN_(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN_(q, DB) hist(DB[findNN_(q, DB)] - q) # definition of findNN_ holds i <- 1:5 findNN_(3.5, i) i <- 1:6 findNN_(3.5, i)