bool fit(DataVec& data, DataVec& centroids) {
bool converged(true);
// assign points to closest centroid
for (DataVec::iterator it1 = data.begin(); it1!=data.end(); ++it1) {
double min_dist = std::numeric_limits<double>::max();
int min_clust = -1;
for (DataVec::iterator it2 = centroids.begin(); it2!=centroids.end(); ++it2) {
double d = dist(*it1,*it2);
if (d < min_dist) {
min_dist = d;
min_clust = it2-centroids.begin();
}
}
//std::cout << "Point " << *it1 << "\n";
//std::cout << "min_dist=" << min_dist << " min_clust=" << min_clust << "\n";
it1->cluster_ = min_clust;
}
// re-estimate centroids
for (size_t i=0;i<K;++i) {
std::cout << "Centroid at " << i << " was " << centroids[i] << "\n";
bool centroidUpdated = getCentroid(data,i,centroids[i]);
if (centroidUpdated) converged = false;
std::cout << "Centroid at " << i << " is now " << centroids[i] << "\n";
}
return converged;
}
int main(int argc, char** argv) {
// seed random generator
srand(time(NULL));
ifstream infile("../iris.data");
string line;
DataVec data;
double fac = 4.0;
while (std::getline(infile, line))
{
std::vector<std::string> fields;
boost::split(fields,line, boost::is_any_of(","));
assert(fields.size() == 5);
double x1 = atof(fields[0].c_str())*fac;
double x2 = atof(fields[1].c_str())*fac;
double x3 = atof(fields[2].c_str())*fac;
double x4 = atof(fields[3].c_str())*fac;
Point p(x1,x2,x3,x4);
//std::cout << p << std::endl;
data.push_back(p);
}
std::cout << "Collected " << data.size() << " points. " << std::endl;
assert(data.size()>K);
// init centroids to random points
DataVec centroids;
//centroids.reserve(K);
DataVec::iterator dataBegin = random_unique(data.begin(),data.end(),K);
std::cout << K << " random points " << std::endl;
for (size_t i=0;i<K;++i) {
std::cout << data[i] << "\n";
centroids.push_back(data[i]);
}
std::cout << centroids.size() << " centroids " << std::endl;
// Lloyd's algorithm to iteratively fit the cluster centroids.
bool done = fit(data,centroids);
while(!done) {
done = fit(data,centroids);
std::cout << done << "\n";
}
double idx = dunnIndex(data,centroids);
cout << "Dunn Index for this clustering " << idx << "\n";
// write clustering to file
ofstream of("clusters.dat");
for (DataVec::iterator it = data.begin(); it!=data.end(); ++it) {
of << *it << std::endl;
}
of.close();
return EXIT_SUCCESS;
}
bool getCentroid(const DataVec& data, const int cluster, Point& centroid) {
size_t num(0);
Point new_centroid(0.0,0.0,0.0,0.0);
for (DataVec::const_iterator ct=data.begin();ct!=data.end();++ct) {
if (ct->cluster_ == cluster) {
new_centroid.x1_ += ct->x1_;
new_centroid.x2_ += ct->x2_;
new_centroid.x3_ += ct->x3_;
new_centroid.x4_ += ct->x4_;
++new_centroid.cluster_;
++num;
}
}
if (num==0) {
std::cout << "Cluster unchanged \n";
return true;
}
new_centroid.x1_ /= num;
new_centroid.x2_ /= num;
new_centroid.x3_ /= num;
new_centroid.x4_ /= num;
double d = dist(centroid,new_centroid);
std::cout << "getCentroid: d=" << d << "\n";
std::cout << "num=" << num << "\n";
bool changed = d>0.05 ? true : false;
centroid = new_centroid;
return changed;
}
double dunnIndex(DataVec& data, DataVec& centroids) {
double res = 0.0;
// compute max cluster diameter
double max_clust_diam = 0.0;
for (size_t i=0;i<K;++i) {
double clust_diam = 0.0;
double n = 0.0;
for (DataVec::const_iterator ct=data.begin();ct!=data.end();++ct) {
if (ct->cluster_ == i) {
double d = dist(*ct,centroids[K]);
clust_diam += d;
++n;
}
}
std::cout << "clust_diam = " << clust_diam << "\n";
if (n>0) clust_diam /= n;
std::cout << "clust_diam = " << clust_diam << "\n";
if (clust_diam > max_clust_diam) max_clust_diam = clust_diam;
}
// compute min intercluster distance
double min_clust_dist = std::numeric_limits<double>::max();
for (size_t i=0;i<K;++i) {
double d = 0.0;
for (size_t j=(i+1);j<K;++j) {
d = dist(centroids[i],centroids[j]);
//std::cout << "distance btw " << i << " " << j << " d= " << d << "\n";
}
if (d>0 && d<min_clust_dist) min_clust_dist = d;
}
std::cout << "min_clust_dist = " << min_clust_dist << "\n";
std::cout << "max_clust_diam = " << max_clust_diam << "\n";
if (max_clust_diam > 0) res = min_clust_dist / max_clust_diam;
return res;
}
int main(int argc, const char * argv[]) {
parseCommandLineArgs(argc,argv);
vector<uint32_t> msgFreqVec;
msgFreqVec.push_back(50);
msgFreqVec.push_back(100);
msgFreqVec.push_back(200);
msgFreqVec.push_back(300);
msgFreqVec.push_back(400);
msgFreqVec.push_back(500);
map <uint,float> fairnessIndexMap;
map <uint,uint32_t> collisionsIndexMap;
map <uint,float> utilizationMap;
map <uint,uint32_t> thruputMap;
DataVec dataMap;
for (uint32_t testNum =1; testNum < g_uSimNum; testNum++)
{
uint jjj(1);
if(testNum%10 == 0)
cout << "Test: " << testNum << endl;
for (uint hiddenNode = 0; hiddenNode <= 1; hiddenNode++ )
{
for (uint useVcs = 0; useVcs <= 1; useVcs++ )
{
for (uint nodeAFreqScale = 1; nodeAFreqScale <= 2; nodeAFreqScale++)
{
for (vector<uint32_t>::iterator it = msgFreqVec.begin();
it != msgFreqVec.end(); ++it )
{
//cout << "sim: " << jjj << endl;
//init simulation
Simulation *sim = new Simulation(simDuration);
Channel *channel = new Channel();
if (! sim || ! channel) {
cout << "failed to allocate memory for simulation and channel"<< endl;
exit(1);
}
//init nodes
uint32_t nodeAFreq = *it*nodeAFreqScale;
uint32_t nodeCFreq = (*it);
//init nodes
RxNode *nodeB = new RxNode(2,sim,channel,ACK_RTS_CTS_SND_DUR,SLOT_DUR);
RxNode *nodeD = new RxNode(4,sim,channel,ACK_RTS_CTS_SND_DUR,SLOT_DUR);
TxNode *nodeA = new TxNode(0,sim,channel,nodeB,nodeAFreq,DIFS,SIFS,PACKET_SEND_DUR,ACK_RTS_CTS_SND_DUR,SLOT_DUR,useVcs,hiddenNode);
TxNode *nodeC = new TxNode(1,sim,channel,nodeD,nodeCFreq,DIFS,SIFS,PACKET_SEND_DUR,ACK_RTS_CTS_SND_DUR,SLOT_DUR,useVcs,hiddenNode);
if (! nodeA ||!nodeB || ! nodeC || !nodeD) {
cout << "failed to allocate memory for nodes"<< endl;
exit(1);
}
//seed starting events
nodeA->schedulePacketReady(random_distro::exponential(nodeAFreq,random_distro::TEN_USECS));
nodeC->schedulePacketReady(random_distro::exponential(nodeCFreq,random_distro::TEN_USECS));
//run simulation
sim->Run();
uint32_t aThruput(0),cThruput(0);
float aUtil(0.0f),cUtil(0.0f);
if ( useVcs)
{
aThruput = nodeA->SuccessfulSends() * (PACKET_SIZE_BYTES + 3 * ACK_SIZE_BYTES);
aUtil = (((float)nodeA->SuccessfulSends()*VCS_RTT)/(float)simDuration) * 100.0f;
cThruput = nodeC->SuccessfulSends() * (PACKET_SIZE_BYTES + 3* ACK_SIZE_BYTES);
cUtil = (((float)nodeC->SuccessfulSends()*VCS_RTT)/(float)simDuration) * 100.0f;
}
else
{
aThruput = nodeA->SuccessfulSends() * (PACKET_SIZE_BYTES + ACK_SIZE_BYTES);
aUtil = (((float)nodeA->SuccessfulSends()*RTT)/(float)simDuration) * 100.0f;
cThruput = nodeC->SuccessfulSends() * (PACKET_SIZE_BYTES + ACK_SIZE_BYTES);
cUtil = (((float)nodeC->SuccessfulSends()*RTT)/(float)simDuration) * 100.0f;
}
dataMap.insert(Data(jjj, nodeAFreq, nodeCFreq, aThruput, cThruput, aUtil, cUtil, nodeC->TotalCollisions(), nodeC->IsHiddenNode(), nodeC->UsesVCS(), aThruput + cThruput, aUtil + cUtil, aUtil/cUtil));
//sim->PrintData();
// destroy objects
nodeA = NULL;
nodeC = NULL;
channel = NULL;
sim = NULL;
delete nodeA;
delete nodeC;
delete channel;
delete sim;
jjj++;
} // for lamdas
} //for nodeA Freq scale
} //for use Vcs
} // hiddenNode
} // testNum
dataMap.print();
stringstream ss;
ss<< g_uSimNum;
string strNumOfTests("");
ss >>strNumOfTests;
dataMap.OutputToFile("data_"+ strNumOfTests+".csv");
if (g_bShowEnhancedStats)
{
cout <<endl;
cout << "Row 1: Parallel, No VCS" << endl;
cout << "Row 2: Parallel, VCS" << endl;
cout << "Row 3: Hidden Node, No VCS" << endl;
cout << "Row 4: Hidden Node, VCS" << endl;
cout << endl<<"Throughput A:" << endl;
for ( map<uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(10) << std::setprecision( 3 ) << fixed <<it2->second->thruput_a << "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
cout << endl<<"Throughput C:" << endl;
for ( map<uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(10) << std::setprecision( 3 ) << fixed << it2->second->thruput_c << "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
cout << endl<<"Throughput:" << endl;
for ( map<uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(10) << std::setprecision( 3 ) << fixed <<it2->second->thruput_tot << "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
cout <<endl<< "Utilization:" << endl;
for ( map <uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(5) << std::setprecision( 3 ) <<fixed<< it2->second->util_tot << "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
cout <<endl<< "Fairness Indeces:" << endl;
for ( map <uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(5) << std::setprecision( 3 ) <<fixed<< it2->second->fairness<< "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
cout << endl<<"Collisions:" << endl;
for ( map<uint32_t, Data*>::iterator it2 = dataMap.begin();
it2 != dataMap.end(); ++it2 )
{
cout << left << setw(2) <<it2->first << ":"
<<setw(10) << std::setprecision( 3 ) <<fixed<< it2->second->colls << "\t";
if (it2->first && it2->first % 12 == 0)
cout << endl;
}
}
return 0;
}
int main (int argc, char *argv[]) {
// handle cmd args
int batch_size, maxiter;
std::string datadir;
std::string output_file;
if ( argc > 5 || argc < 2 ) {
printf( "Usage: ./logistic_mpi <data_directory> <batch_size> "
"<max_iterations> <model_output_file>\n");
MPI_Finalize();
exit( 0 );
} else if ( argc == 5 ) {
datadir = argv[1];
batch_size = atoi( argv[2] ); // mini-batch processing
if ( batch_size == -1 ) { batch_size = INT_MIN; }
maxiter = atoi( argv[3] );
output_file = argv[4];
} else if ( argc == 4 ) {
datadir = argv[1];
batch_size = atoi( argv[2] ); // mini-batch processing
if ( batch_size == -1 ) { batch_size = INT_MIN; }
maxiter = atoi( argv[3] );
output_file = "logistic.model";
} else if ( argc == 4 ) {
datadir = argv[1];
batch_size = atoi( argv[2] ); // mini-batch processing
if ( batch_size == -1 ) { batch_size = INT_MIN; }
maxiter = 100;
output_file = "logistic.model";
} else {
datadir = argv[1];
batch_size = INT_MIN; // batch processing
maxiter = 100;
output_file = "logistic.model";
}
// initialize/populate mpi specific vars local to each node
double t1,t2; // elapsed time computation
int numtasks, taskid, len;
char hostname[MPI_MAX_PROCESSOR_NAME];
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &numtasks);
MPI_Comm_rank(MPI_COMM_WORLD,&taskid);
MPI_Get_processor_name(hostname, &len);
MPI_Op op;
/* DATA PREPROCESSING */
if ( taskid == MASTER ) {
printf( "\nLoading and Preprocessing Data\n" );
}
t1 = MPI_Wtime();
// determine number of instances
DataVec datavec;
mlu::count_instances( datadir, datavec, num_inst );
// determine number of features
mlu::count_features( datavec[0], n );
/* DATA INITIALIZATION */
// randomize instances
std::random_shuffle( datavec.begin(), datavec.end() );
// partition data based on taskid
size_t div = datavec.size() / numtasks;
ProbSize limit = ( taskid == numtasks - 1 ) ? num_inst : div * ( taskid + 1 );
m = limit - div * taskid;
// danamically allocate data
Mat X( m, n );
Vec labels( m );
// load data partition
double feat_val, label;
ProbSize i = 0;
for ( ProbSize idx = taskid * div; idx < limit; ++idx ) {
std::ifstream data( datavec[idx] );
for ( ProbSize j=0; j<n; ++j ) {
data >> feat_val;
X(i,j) = feat_val;
}
data >> label;
labels[i] = label;
i++;
}
// perform feature scaling (optional)
if ( scaling ) {
// Allreduce to find global min
Vec X_min_tmp = X.colwise().minCoeff();
X_min_data = X_min_tmp.data();
Vec X_min = Vec( X_min_tmp.size() );
MPI_Allreduce( X_min_tmp.data(), X_min.data(), X_min_tmp.size(), MPI_DOUBLE, MPI_MIN, MPI_COMM_WORLD );
// Allreduce to find global max
Vec X_max_tmp = X.colwise().maxCoeff();
X_max_data = X_max_tmp.data();
Vec X_max = Vec( X_max_tmp.size() );
MPI_Allreduce( X_max_tmp.data(), X_max.data(), X_max_tmp.size(), MPI_DOUBLE, MPI_MAX, MPI_COMM_WORLD );
// scale features using global min and max
mlu::scale_features( X, X_min, X_max, 1, 0 );
}
/* FORMAT LABELS */
// get unique labels
mlu::get_unique_labels( labels, classmap );
// allreduce to obtain maximum label set size
int local_size = classmap.size();
int max_size = 0;
MPI_Allreduce( &local_size, &max_size, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD );
// allreduce to obtain global unique label set
int unique_labels[max_size];
int global_unique_labels[max_size];
for ( int i=0; i<max_size; ++i ) {
unique_labels[i] = -1;
global_unique_labels[i] = -1;
}
int idx = 0;
for ( auto& kv : classmap ) {
unique_labels[idx++] = kv.first;
}
MPI_Op_create( (MPI_User_function *)reduce_unique_labels, 1, &op );
MPI_Allreduce( unique_labels, global_unique_labels, max_size, MPI_INT, op, MPI_COMM_WORLD );
MPI_Op_free( &op );
// update local classmap
std::sort( global_unique_labels, global_unique_labels + max_size );
classmap.clear();
int labeltmp;
idx=0;
for ( int i=0; i<max_size; ++i ) {
labeltmp = global_unique_labels[i];
if ( labeltmp != -1 ) {
classmap.emplace( labeltmp, idx++ );
}
}
// format the local label set into a matrix based on global class map
Mat y = mlu::format_labels( labels, classmap );
numlabels = (LayerSize) classmap.size();
// output total data loading time for each task
MPI_Barrier( MPI_COMM_WORLD );
t2 = MPI_Wtime();
printf( "--- task %d loading time %lf\n", taskid, t2 - t1 );
/* INIT LOCAL CLASSIFIER */
LogisticRegression logistic_layer( n, numlabels, true );
/* OPTIMIZATION */
if ( taskid == MASTER ) {
printf( "\nPerforming Gradient Descent\n" );
}
int update_size; // stores the number of instances read for each update
double grad_mag; // stores the magnitude of the gradient for each update
int delta_size = logistic_layer.get_theta_size();
Vec delta_update = Vec::Zero( delta_size );
int global_update_size;
if ( taskid == MASTER ) {
printf( "iteration : elapsed time : magnitude\n" );
}
for ( int i=0; i<maxiter; ++i ) {
// compute gradient update
t1 = MPI_Wtime();
logistic_layer.compute_gradient( X, y, batch_size, update_size );
delta_data = logistic_layer.get_delta().data();
// sum updates across all partitions
MPI_Allreduce(
delta_data,
delta_update.data(),
delta_size,
MPI_DOUBLE,
MPI_SUM,
MPI_COMM_WORLD
);
logistic_layer.set_delta( delta_update );
// sum the update sizes
MPI_Allreduce( &update_size, &global_update_size, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD );
// normalize + regularize gradient update
logistic_layer.normalize_gradient( global_update_size );
logistic_layer.regularize_gradient( global_update_size );
// update logistic_layer parameters
t2 = MPI_Wtime();
if ( logistic_layer.converged( grad_mag ) ) { break; }
if ( taskid == MASTER ) {
printf( "%d : %lf : %lf\n", i+1, t2 - t1, grad_mag );
}
logistic_layer.update_theta();
}
/* MODEL STORAGE */
if (taskid == MASTER) {
FILE *output;
output = fopen ( output_file.c_str(), "w" );
int idx;
Vec theta = logistic_layer.get_theta();
printf( "\nWriting Model to File: %s\n\n", output_file.c_str() );
fprintf( output, "%lu\n", theta.size() );
for ( idx=0; idx<theta.size()-1; ++idx ) {
fprintf( output, "%lf\t", theta[idx] );
}
fprintf( output, "%lf\n", theta[idx] );
fclose( output );
}
MPI_Finalize();
return 0;
}