throw away old code, prepare tests

[epclust.git] / old_C_code / stage1 / src / Algorithm / get_dissimilarities.c

diff --git a/old_C_code/stage1/src/Algorithm/get_dissimilarities.c b/old_C_code/stage1/src/Algorithm/get_dissimilarities.c
deleted file mode 100644 (file)
index 5dc8ea2..0000000
--- a/old_C_code/stage1/src/Algorithm/get_dissimilarities.c
+++ /dev/null
@@ -1,47 +0,0 @@
-#include "Util/types.h"
-#include <stdlib.h>
-#include <math.h>
-
-// compute L^p dissimilarities for a nxm matrix
-float* get_dissimilarities_intra(float* samples, uint32_t nbSamples, uint32_t nbValues, uint32_t p)
-{
-       float* dissimilarities = (float*) malloc(nbSamples*nbSamples*sizeof(float));
-       for (uint32_t i=0; i<nbSamples; i++)
-       {
-               dissimilarities[i*nbSamples+i] = 0.0;
-               for (uint32_t j=0; j<i; j++)
-               {
-                       // dissimilarities[i*nbSamples+j] = L^p distance between reduced rows i and j
-                       double dissim = 0.0;
-                       for (uint32_t m=0; m<nbValues; m++)
-                       {
-                               double delta = fabs(samples[i*nbValues+m] - samples[j*nbValues+m]);
-                               dissim += pow(delta, p);
-                       }
-                       dissimilarities[i*nbSamples+j] = pow(dissim, 1.0/p);
-                       dissimilarities[j*nbSamples+i] = dissimilarities[i*nbSamples+j];
-               }
-       }
-       return dissimilarities;
-}
-
-// compute L^p dissimilarities between rows of 2 matrices
-float* get_dissimilarities_inter(float* mat1, uint32_t n1, float* mat2, uint32_t n2, 
-       uint32_t nbValues, uint32_t p)
-{
-       float* dissimilarities = (float*) malloc(n1*n2*sizeof(float));
-       for (uint32_t i=0; i<n1; i++)
-       {
-               for (uint32_t j=0; j<n2; j++)
-               {
-                       double dissim = 0.0;
-                       for (uint32_t m=0; m<nbValues; m++)
-                       {
-                               double delta = fabs(mat1[i*nbValues+m] - mat2[j*nbValues+m]);
-                               dissim += pow(delta, p);
-                       }
-                       dissimilarities[i*n2+j] = pow(dissim, 1.0/p);
-               }
-       }
-       return dissimilarities;
-}