throw away old code, prepare tests

[epclust.git] / old_C_code / stage1 / src / Classification / getClass.c

diff --git a/old_C_code/stage1/src/Classification/getClass.c b/old_C_code/stage1/src/Classification/getClass.c
deleted file mode 100644 (file)
index acec167..0000000
--- a/old_C_code/stage1/src/Classification/getClass.c
+++ /dev/null
@@ -1,46 +0,0 @@
-#include "Algorithm/get_dissimilarities.h"
-#include "TimeSeries/deserialize.h"
-#include <math.h>
-#include "Algorithm/compute_coefficients.h"
-#include <string.h>
-#include "Util/utils.h"
-
-uint32_t* get_class(PowerCurve* data, uint32_t nbSeries, PowerCurve* medoids, 
-       uint32_t nbClusters, uint32_t nbValues, uint32_t p_for_dissims, double* DISTOR)
-{
-       // nbReducedCoordinates = smallest power of 2 which is above nbValues
-       uint32_t nbReducedCoordinates = (uint32_t)ceil(log2(nbValues));
-
-       // Preprocessing to reduce dimension of both data and medoids
-       float* reducedCoordinates_data = (float*) malloc(nbSeries * nbReducedCoordinates * sizeof(float));
-       compute_coefficients(data, nbSeries, nbValues, 
-               reducedCoordinates_data, 0, nbReducedCoordinates);
-       float* reducedCoordinates_medoids = (float*) malloc(nbClusters * nbReducedCoordinates * sizeof(float));
-       compute_coefficients(medoids, nbClusters, nbValues, 
-               reducedCoordinates_medoids, 0, nbReducedCoordinates);
-       
-       float* dissimilarities = get_dissimilarities_inter(reducedCoordinates_data, nbSeries, 
-               reducedCoordinates_medoids, nbClusters, nbReducedCoordinates, p_for_dissims);
-       free(reducedCoordinates_data);
-       free(reducedCoordinates_medoids);
-       
-       // 3] Finally, assign each row to the least dissimilar center
-       uint32_t* result = (uint32_t*) malloc(nbSeries*sizeof(uint32_t));
-       for (uint32_t i=0; i<nbSeries; i++)
-       {
-               uint32_t minIndex = 0;
-               float minDissim = dissimilarities[i*nbClusters + 0];
-               for (uint32_t j=1; j<nbClusters; j++)
-               {
-                       if (dissimilarities[i*nbClusters + j] < minDissim)
-                       {
-                               minDissim = dissimilarities[i*nbClusters + j];
-                               minIndex = j;
-                       }
-               }
-               result[i] = minIndex + 1;
-               (*DISTOR) += minDissim;
-       }
-       free(dissimilarities);
-       return result;
-}