/* This function reads in a discription of a graph and loads it into
* a graph discription structure. The file format is asci were the
* first token is the number of vericies followed by verticy labels
* and guess x, y coordinates. This is followed by the number of edges
* and the indexes of the vericies that they connect.
*/
void saveGraphDiscription(const char* filename, GraphDiscription gd){
FILE* file;
int i;
file = fopen(filename,"w");
DEBUG_CHECK_1ARG(file,"Error opening file: %s", filename);
/* Read the number of vericies in the file */
fprintf(file,"%d\n",(gd->numVert));
/* allocate space for vericies and load discriptions */
for(i = 0; i < gd->numVert; i++){
fprintf(file, "%s %f %f\n", gd->vertLabels[i], (gd->verts[i].x), (gd->verts[i].y));
}
/* Read the number of edges in the file */
fprintf(file,"%d\n",(gd->numEdge));
/* allocate space for vericies and load discriptions */
for(i = 0; i < gd->numEdge; i++){
fprintf(file, "%d %d\n",(gd->edges[i].vert1), (gd->edges[i].vert2));
}
fclose(file);
}
/* This function reads in a discription of a graph and loads it into
* a graph discription structure. The file format is asci were the
* first token is the number of vericies followed by verticy labels
* and guess x, y coordinates. This is followed by the number of edges
* and the indexes of the vericies that they connect.
*/
GraphDiscription readGraphDiscription(const char* filename){
FILE* file;
int i;
GraphDiscription gd = ALLOCATE(graph_discription,1);
DEBUG_CHECK(gd,"Error allocating memory");
file = fopen(filename,"r");
DEBUG_CHECK_1ARG(file,"Error opening file: %s", filename);
/* Read the number of vericies in the file */
DEBUG_CHECK_1ARG(fscanf(file,"%d",&(gd->numVert)) == 1, "Error: could not determin the number of verticies in file %s", filename);
/* allocate space for vericies and load discriptions */
gd->verts = ALLOCATE(Vert,gd->numVert);
gd->vertLabels = ALLOCATE(char*,gd->numVert);
gd->bunch = ALLOCATE(JetBunch,gd->numVert);
for(i = 0; i < gd->numVert; i++){
gd->vertLabels[i] = ALLOCATE(char,LABELMAX);
DEBUG_CHECK_2ARG(fscanf(file, "%s %lf %lf", gd->vertLabels[i], &(gd->verts[i].x), &(gd->verts[i].y)) == 3,
"Error Parsing vertex %d in file: %s", i, filename);
gd->bunch[i] = makeJetBunch();
}
/* Read the number of edges in the file */
DEBUG_CHECK_1ARG(fscanf(file,"%d",&(gd->numEdge)) == 1, "Error: could not determin the number of edges in file %s", filename);
/* allocate space for vericies and load discriptions */
gd->edges = ALLOCATE(Edge,gd->numEdge);
for(i = 0; i < gd->numEdge; i++){
DEBUG_CHECK_2ARG(fscanf(file, "%d %d",&(gd->edges[i].vert1), &(gd->edges[i].vert2)) == 2,
"Error Parsing vertex %d in file: %s", i, filename);
}
fclose(file);
return gd;
}
void
sortSubjectsBySimilarityToProbe (char *probe, ListOfStrings subjects, char *distanceMatrix, int *indices)
{
int i, j, nDistances;
DistanceMeasure *distances = NULL;
DistanceMeasure *toSort = NULL;
int nSubjects = countStrings (subjects);
/* If we are using a distance matrix, then we load up the
* list of distances from the probe image. This allows us
* to lookup the distances from the probe quickly
*/
if (distanceMatrix)
{
Tokenizer tok;
FILE *f = fopen (makePath (distanceMatrix, probe), "r");
void *distanceList = NULL;
DEBUG_CHECK_1ARG (f, "Unable to open file %s in scores directory", makePath (distanceMatrix, probe));
tokenizerInit (&tok, tokenizerStreamReader, f);
while (!tokenizerEndOfFile (&tok))
{
DistanceMeasure m;
m.subject = strdup (tokenizerGetWord (&tok));
m.distance = atof (tokenizerGetWord (&tok));
listAccumulate (&distanceList, &m, sizeof (DistanceMeasure));
}
fclose (f);
distances = (DistanceMeasure*) listToArray (&distanceList, sizeof (DistanceMeasure), (size_t*)&nDistances);
}
/* Copy the list of names into the intermediate data structure that allows us to sort the
* subjects by distance to the probe.
*/
toSort = (DistanceMeasure*) malloc (nSubjects * sizeof (DistanceMeasure));
for (j = 0; j < nSubjects; j++)
{
toSort[j].subject = subjects[j];
toSort[j].distance = 0.0;
toSort[j].index = j;
}
/* Read distances between probe and every other image in the subject list.
* As a special case, a subject is said to be infinitely far away from him/herself.
* Random scores are used when a distanceMatrix is not provided */
for (j = 0; j < nSubjects; j++)
{
if (strcmp (subjects[j], probe) == 0) {
/* Probe and subject are the same. Say they are far apart since
* we don't want to treat an image and itself as being two replicates
* of a person */
toSort[j].distance = HUGE;
} else if (distanceMatrix != NULL) {
/* Look for the subject in the list of distances and return assign the
* score */
toSort[j].distance = HUGE;
for (i = 0; i < nDistances; ++i)
if (strcmp (distances[i].subject, toSort[j].subject) == 0)
toSort[j].distance = distances[i].distance;
} else {
/* If we are not using a distance matrix, then choose
* a random value */
toSort[j].distance = ((double) rand()) / RAND_MAX;
}
}
/* Now sort the list by similarity to the probe */
qsort (toSort, nSubjects, sizeof (DistanceMeasure), distanceMeasureComparator);
/* Copy the data back into the subject list or return the permuted indices */
if (indices == NULL)
for (j = 0; j < nSubjects; j++) {
subjects[j] = toSort[j].subject;
}
else
for (j = 0; j < nSubjects; j++) {
indices[j] = toSort[j].index;
}
/* Clean up */
if (distanceMatrix)
{
for (i = 0; i < nDistances; ++i)
free (distances[i].subject);
free (distances);
}
free (toSort);
}
