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#include <mpe.h>
#include <mpi.h>
#include <Eigen/Dense>
#include <algorithm> // std::min
#include <iostream>
#include <fstream>
#include <sstream>
using namespace Eigen;
class KMeans {
private:
int numClusters_, dimension_;
int numTrainingPoints_;
MatrixXd trainingSet_;
MatrixXd means_;
MatrixXd partialMeans_;
VectorXi classCount_;
public:
KMeans(int nc, int dim) : numClusters_(nc), dimension_(dim) { initTheta(); }
void train(MatrixXd tset, int firstPoint, int lastPoint, int numSteps = 3000) {
numTrainingPoints_ = tset.cols();
trainingSet_ = tset;
int rp_eid_begin, rp_eid_end, ms_eid_begin, ms_eid_end;
MPE_Log_get_state_eventIDs(&rp_eid_begin, &rp_eid_end);
MPE_Log_get_state_eventIDs(&ms_eid_begin, &ms_eid_end);
MPE_Describe_state(rp_eid_begin, rp_eid_end, "reassignPoints", "blue");
MPE_Describe_state(ms_eid_begin, ms_eid_end, "meanSummation", "red");
int err;
for (int i = 0; i < numSteps; ++i) {
MPE_Log_event(rp_eid_begin, 0, "reassigning");
reassignPoints(firstPoint, lastPoint);
MPE_Log_event(rp_eid_end, 0, "reassigned");
VectorXi newCount = VectorXi::Zero(numClusters_);
MPI_Allreduce(classCount_.data(), newCount.data(), numClusters_,
MPI_INT, MPI_SUM, MPI_COMM_WORLD);
MPI_Allreduce(partialMeans_.data(), means_.data(), partialMeans_.size(),
MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
MPE_Log_event(ms_eid_begin, 0, "summing");
means_ = means_.array().rowwise() / newCount.transpose().array().cast<double>();
MPE_Log_event(ms_eid_end, 0, "summed");
}
MPI_Barrier(MPI_COMM_WORLD);
}
void printMeans() { std::cout << means_ << std::endl; }
private:
void initTheta() { means_ = MatrixXd::Random(dimension_, numClusters_); }
bool reassignPoints(int firstPoint, int lastPoint) {
int newCluster;
partialMeans_ = MatrixXd::Zero(dimension_, numClusters_);
classCount_ = VectorXi::Zero(numClusters_);
for (int i = firstPoint; i < lastPoint; ++i) {
(means_.colwise() - trainingSet_.col(i)).colwise().squaredNorm().minCoeff(&newCluster);
partialMeans_.col(newCluster) += trainingSet_.col(i);
classCount_[newCluster]++;
}
}
};
int main(int argc, char *argv[]) {
double component;
std::string line;
int numTrainingPoints, numClusters, dimension;
// Initialize MPI stuff.
int nprocs, myRank;
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &nprocs);
MPI_Comm_rank(MPI_COMM_WORLD, &myRank);
// Seed the RNG.
srand(time(NULL));
// Open the training file.
std::ifstream trainingData(argv[1]);
// Read dimensionality information.
getline(trainingData, line);
std::istringstream iss(line, std::istringstream::in);
iss >> numTrainingPoints >> numClusters >> dimension;
// Parse training point data.
MatrixXd trainingPoints(dimension, numTrainingPoints);
for (int n = 0; n < numTrainingPoints; ++n) {
// Read next line (one point per line).
getline(trainingData, line);
std::istringstream iss(line, std::istringstream::in);
for (int d = 0; d < dimension; ++d) {
iss >> component;
trainingPoints(d,n) = component;
}
}
trainingData.close();
// Decide how many points each rank is responsible for.
int pointsPerRank = numTrainingPoints / nprocs;
int firstPoint = myRank * pointsPerRank;
int lastPoint = std::min(firstPoint + pointsPerRank, numTrainingPoints);
// Initialize model and train with the data read above.
int train_eid_begin, train_eid_end, init_eid_begin, init_eid_end;
MPE_Log_get_state_eventIDs(&train_eid_begin, &train_eid_end);
MPE_Describe_state(train_eid_begin, train_eid_end, "training", "green");
KMeans g(numClusters, dimension);
MPE_Log_event(train_eid_begin, 0, "training");
g.train(trainingPoints, firstPoint, lastPoint);
MPE_Log_event(train_eid_end, 0, "trained");
// Print the good stuff.
if (myRank == 0) {
std::cout << "means: " << std::endl; g.printMeans();
}
MPI_Finalize();
return 0;
}
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