'update'

[epclust.git] / epclust / src / computeMedoidsIndices.cpp

diff --git a/epclust/src/computeMedoidsIndices.cpp b/epclust/src/computeMedoidsIndices.cpp
deleted file mode 100644 (file)
index 895031a..0000000
--- a/epclust/src/computeMedoidsIndices.cpp
+++ /dev/null
@@ -1,57 +0,0 @@
-#include <Rcpp.h>
-
-// [[Rcpp::depends(BH, bigmemory)]]
-#include <bigmemory/MatrixAccessor.hpp>
-
-#include <numeric>
-#include <cfloat>
-
-using namespace Rcpp;
-
-//' computeMedoidsIndices
-//'
-//' For each column of the 'series' matrix input, search for the closest medoid
-//' (euclidian distance) and store its index
-//'
-//' @param pMedoids External pointer (a big.matrix 'address' slot in R)
-//' @param series (reference) series, a matrix of size Lxn
-//'
-//' @return An integer vector of the closest medoids indices, for each (column) serie
-// [[Rcpp::export]]
-IntegerVector computeMedoidsIndices(SEXP pMedoids, NumericMatrix series)
-{
-       // Turn SEXP external pointer into BigMatrix (description) object
-       XPtr<BigMatrix> pMed(pMedoids);
-       // medoids: access to the content of the BigMatrix object
-       MatrixAccessor<double> medoids = MatrixAccessor<double>(*pMed);
-
-       int nb_series = series.ncol(),
-               K = pMed->ncol(),
-               L = pMed->nrow();
-       IntegerVector mi(nb_series);
-
-       for (int i=0; i<nb_series ; i++) //column index in series
-       {
-               // In R: mi[i] <- which.min( rowSums( sweep(medoids, 2, series[i,], '-')^2 ) )
-               // In C(++), computations must be unrolled
-               mi[i] = 0;
-               double best_dist = DBL_MAX;
-               for (int j=0; j<K; j++) //column index in medoids
-               {
-                       double dist_ij = 0.;
-                       for (int k=0; k<L; k++) //common row index (medoids, series)
-                       {
-                               // Accessing values for a big matrix is a bit weird; see Rcpp gallery/bigmemory
-                               double delta = series(k,i) - *(medoids[j]+k);
-                               dist_ij += delta * delta;
-                       }
-                       if (dist_ij < best_dist)
-                       {
-                               mi[i] = j+1; //R indices start at 1
-                               best_dist = dist_ij;
-                       }
-               }
-       }
-
-       return mi;
-}