int main(int argc, char *argv[]){
char inputFile[256], outputFile[256], confFile[256];
int inputFlag = 0, outputFlag = 0, confFlag = 0;
FILE* input=NULL;
FILE* output=NULL;
FILE* config=NULL;
srand(time(NULL));
int confNums[5];
int r = 365;
int choice;
while (1)
{
static struct option long_options[] =
{
/* Use flags like so:
{"verbose", no_argument, &verbose_flag, 'V'}*/
/* Argument styles: no_argument, required_argument, optional_argument */
{"version", no_argument, 0, 'v'},
{"help", no_argument, 0, 'h'},
{"inputFile", required_argument, 0, 'i'},
{"conf", required_argument, 0, 'c'},
{"output", required_argument, 0, 'o'},
{"rSize", required_argument, 0, 'r'},
{"default", no_argument, 0, 'd'},
{0,0,0,0}
};
int option_index = 0;
/* Argument parameters:
no_argument: " "
required_argument: ":"
optional_argument: "::" */
choice = getopt_long( argc, argv, "vhi:c:o:r:d",
long_options, &option_index);
if (choice == -1)
break;
switch( choice )
{
case 'v':
printf("beta version\n");
break;
case 'h':
printf("ussage <executable> –d <inputFile file> –c <configuration file> -o <output file> -I <init fun> -A <assign fun> -U <update fun> --complete\n");
break;
case 'd':
strcpy(inputFile, "./inputFiles/bio_small_input.dat");
strcpy(outputFile, "./inputFiles/conform.dat");
strcpy(confFile,"./inputFiles/cluster_5.conf" );
inputFlag = 1;
outputFlag = 1;
confFlag = 1;
break;
case 'i':
strcpy(inputFile, optarg);
inputFlag = 1;
break;
case 'c':
strcpy(confFile, optarg);
confFlag = 1;
break;
case 'o':
strcpy(outputFile, optarg);
outputFlag = 1;
break;
case 'r':
r = atoi(optarg);;
break;
case '?':
/* getopt_long will have already printed an error */
break;
default:
/* Not sure how to get here... */
return EXIT_FAILURE;
}
}
/* Deal with non-option arguments here */
if ( optind < argc )
{
while ( optind < argc )
{
}
}
if ( !inputFlag )
{
printf("Give me an input file\n");
scanf("%s", inputFile);
}
if ( !confFlag )
{
printf("Give me an conf file\n");
scanf("%s", confFile);
}
if ( !outputFlag )
{
printf("Give me an output file\n");
scanf("%s", outputFile);
}
printf("inputFile %s, output %s, confingure %s,r = %d \n",inputFile, outputFile, confFile, r );
if((input=fopen(inputFile,"r")) == NULL){
perror(inputFile);
exit(-1);
}
if((output=fopen(outputFile,"w")) == NULL){
perror(outputFile);
exit(-1);
}
if((config=fopen(confFile,"r")) == NULL){
perror(confFile);
exit(-1);
}
//ReadDataMolecule(NULL, input);
SpecifyDatasetMolConf("molecule");
ParseConfig(config,confNums);
parseData(input, r);
void (*init)() = KMedoidPlusPlusInit;
double (*assigment)() = PamAssign;
int (*update)() = ClaransUpdate;
int kCluster = 19;
//int j = 0;
int j = 0;
init(kCluster);
FirstAssignment();
assigment(confNums[1],confNums[2]);
j = 0;
while(update(assigment,confNums)){
fprintf(output,"Update loop #%d\n",j);
printf("Update loop #%d\n",j);
j++;
}
PrintClusters(output);
double silhouette=Silhouette(output);
fprintf(output, "%d\n%f\n",kCluster,silhouette );
PrintConform(output);
data.destroyInput();
DestroyData();
printf("Silhouette: %f\n",silhouette);
fclose(input);
fclose(output);
fclose(config);
return 0;
}
/** Ester, Kriegel, Sander, Xu; Proceedings of 2nd International Conference
* on Knowledge Discovery and Data Mining (KDD-96); pp 226-231.
*/
int Cluster_DBSCAN::Cluster() {
std::vector<int> NeighborPts;
std::vector<int> Npts2; // Will hold neighbors of a neighbor
std::vector<int> FramesToCluster;
ClusterDist::Cframes cluster_frames;
// First determine which frames are being clustered.
// FIXME: Just use sieved array?
for (int frame = 0; frame < (int)FrameDistances_.Nframes(); ++frame)
if (!FrameDistances_.IgnoringRow( frame ))
FramesToCluster.push_back( frame );
// Calculate Kdist function
if (!kdist_.Empty()) {
if (kdist_.Size() == 1)
ComputeKdist( kdist_.Front(), FramesToCluster );
else
ComputeKdistMap( kdist_, FramesToCluster );
return 0;
}
// Set up array to keep track of points that have been visited.
// Make it the size of FrameDistances so we can index into it. May
// waste memory during sieving but makes code easier.
std::vector<bool> Visited( FrameDistances_.Nframes(), false );
// Set up array to keep track of whether points are noise or in a cluster.
Status_.assign( FrameDistances_.Nframes(), UNASSIGNED);
mprintf("\tStarting DBSCAN Clustering:\n");
ProgressBar cluster_progress(FramesToCluster.size());
int iteration = 0;
for (std::vector<int>::iterator point = FramesToCluster.begin();
point != FramesToCluster.end(); ++point)
{
if (!Visited[*point]) {
// Mark this point as visited
Visited[*point] = true;
// Determine how many other points are near this point
RegionQuery( NeighborPts, FramesToCluster, *point );
if (debug_ > 0) {
mprintf("\tPoint %i\n", *point + 1);
mprintf("\t\t%u neighbors:", NeighborPts.size());
}
// If # of neighbors less than cutoff, noise; otherwise cluster
if ((int)NeighborPts.size() < minPoints_) {
if (debug_ > 0) mprintf(" NOISE\n");
Status_[*point] = NOISE;
} else {
// Expand cluster
cluster_frames.clear();
cluster_frames.push_back( *point );
// NOTE: Use index instead of iterator since NeighborPts may be
// modified inside this loop.
unsigned int endidx = NeighborPts.size();
for (unsigned int idx = 0; idx < endidx; ++idx) {
int neighbor_pt = NeighborPts[idx];
if (!Visited[neighbor_pt]) {
if (debug_ > 0) mprintf(" %i", neighbor_pt + 1);
// Mark this neighbor as visited
Visited[neighbor_pt] = true;
// Determine how many other points are near this neighbor
RegionQuery( Npts2, FramesToCluster, neighbor_pt );
if ((int)Npts2.size() >= minPoints_) {
// Add other points to current neighbor list
NeighborPts.insert( NeighborPts.end(), Npts2.begin(), Npts2.end() );
endidx = NeighborPts.size();
}
}
// If neighbor is not yet part of a cluster, add it to this one.
if (Status_[neighbor_pt] != INCLUSTER) {
cluster_frames.push_back( neighbor_pt );
Status_[neighbor_pt] = INCLUSTER;
}
}
// Remove duplicate frames
// TODO: Take care of this in Renumber?
std::sort(cluster_frames.begin(), cluster_frames.end());
ClusterDist::Cframes::iterator it = std::unique(cluster_frames.begin(),
cluster_frames.end());
cluster_frames.resize( std::distance(cluster_frames.begin(),it) );
// Add cluster to the list
AddCluster( cluster_frames );
if (debug_ > 0) {
mprintf("\n");
PrintClusters();
}
}
}
cluster_progress.Update(iteration++);
} // END loop over FramesToCluster
// Calculate the distances between each cluster based on centroids
CalcClusterDistances();
return 0;
}
