void Print(const SparseMatrix<T>& M)
{
// Print a SparseMatrix to the screen.
const unsigned int* col_buf = M.LockedColBuffer();
const unsigned int* row_buf = M.LockedRowBuffer();
const T* buf = M.LockedDataBuffer();
if (0 == M.Size())
{
std::cout << "Matrix is empty." << std::endl;
return;
}
for (unsigned int c=0; c != M.Width(); ++c)
{
unsigned int start = col_buf[c];
unsigned int end = col_buf[c+1];
for (unsigned int offset=start; offset != end; ++offset)
{
assert(offset >= 0);
assert(offset < M.Size());
unsigned int row_index = row_buf[offset];
T data = buf[offset];
std::cout << "(" << row_index << ", " << c << "): " << data << std::endl;
}
}
std::cout << "Col indices: "; std::cout.flush();
for (unsigned int i=0; i != M.Width(); ++i)
std::cout << col_buf[i] << ", ";
std::cout << col_buf[M.Width()] << std::endl;
std::cout << "Row indices: "; std::cout.flush();
for (unsigned int i=0; i != M.Size(); ++i)
std::cout << row_buf[i] << ", ";
std::cout << std::endl;
std::cout << "Data: "; std::cout.flush();
for (unsigned int i=0; i != M.Size(); ++i)
std::cout << buf[i] << ", ";
std::cout << std::endl;
}
T FrobeniusNorm(const SparseMatrix<T>& A)
{
// compute the sum of the absolute value squared of each element
const T* data_a = A.LockedDataBuffer();
const unsigned int size_a = A.Size();
T sum = T(0);
for (unsigned int i=0; i != size_a; ++i)
{
T val = fabs(data_a[i]);
sum += val*val;
}
return sqrt(sum);
}
T MaxNorm(const SparseMatrix<T>& A)
{
// find max( |A_ij| )
const T* data_a = A.LockedDataBuffer();
const unsigned int size_a = A.Size();
T max_norm = T(0);
for (unsigned int i=0; i != size_a; ++i)
{
T val = fabs(data_a[i]);
if (val > max_norm)
max_norm = val;
}
return max_norm;
}
bool WriteMatrixMarketFile(const std::string& file_path,
const SparseMatrix<T>& A,
const unsigned int precision)
{
// Write a MatrixMarket file with no comments. Note that the
// MatrixMarket format uses 1-based indexing for rows and columns.
std::ofstream outfile(file_path);
if (!outfile)
return false;
unsigned int height = A.Height();
unsigned int width = A.Width();
unsigned int nnz = A.Size();
// write the 'banner'
outfile << MM_BANNER << " matrix coordinate real general" << std::endl;
// write matrix dimensions and number of nonzeros
outfile << height << " " << width << " " << nnz << std::endl;
outfile << std::fixed;
outfile.precision(precision);
const unsigned int* cols_a = A.LockedColBuffer();
const unsigned int* rows_a = A.LockedRowBuffer();
const T* data_a = A.LockedDataBuffer();
unsigned int width_a = A.Width();
for (unsigned int c=0; c != width_a; ++c)
{
unsigned int start = cols_a[c];
unsigned int end = cols_a[c+1];
for (unsigned int offset=start; offset != end; ++offset)
{
unsigned int r = rows_a[offset];
T val = data_a[offset];
outfile << r+1 << " " << c+1 << " " << val << std::endl;
}
}
outfile.close();
return true;
}
//-----------------------------------------------------------------------------
void Nmf(const unsigned int kval,
const Algorithm algorithm,
const std::string& csv_file_w,
const std::string& csv_file_h)
{
if (!matrix_loaded)
throw std::logic_error("smallk error (NMF): no matrix has been loaded.");
if (max_iter < min_iter)
throw std::logic_error("smallk error (NMF): min_iterations exceeds max_iterations.");
if (0 == kval)
throw std::logic_error("smallk error (NMF): k must be greater than 0.");
// Check the sizes of matrix W(m, k) and matrix H(k, n) and make sure
// they don't overflow Elemental's default signed int index type.
if (!SizeCheck<int>(m, kval))
throw std::logic_error("smallk error (Nmf): mxk matrix W is too large.");
if (!SizeCheck<int>(kval, n))
throw std::logic_error("smallk error (Nmf): kxn matrix H is too large.");
k = kval;
// convert to the 'NmfAlgorithm' type in nmf.hpp
switch (algorithm)
{
case Algorithm::MU:
nmf_opts.algorithm = NmfAlgorithm::MU;
break;
case Algorithm::HALS:
nmf_opts.algorithm = NmfAlgorithm::HALS;
break;
case Algorithm::RANK2:
nmf_opts.algorithm = NmfAlgorithm::RANK2;
break;
case Algorithm::BPP:
nmf_opts.algorithm = NmfAlgorithm::BPP;
break;
default:
throw std::logic_error("smallk error (NMF): unknown NMF algorithm.");
}
// set k == 2 for Rank2 algorithm
if (NmfAlgorithm::RANK2 == nmf_opts.algorithm)
k = 2;
ldim_w = m;
ldim_h = k;
if (buf_w.size() < m*k)
buf_w.resize(m*k);
if (buf_h.size() < k*n)
buf_h.resize(k*n);
// initialize matrices W and H
bool ok;
unsigned int height_w = m, width_w = k, height_h = k, width_h = n;
cout << "Initializing matrix W..." << endl;
if (csv_file_w.empty())
ok = RandomMatrix(&buf_w[0], ldim_w, m, k, rng);
else
ok = LoadDelimitedFile(buf_w, height_w, width_w, csv_file_w);
if (!ok)
{
std::ostringstream msg;
msg << "smallk error (Nmf): load failed for file ";
msg << "\"" << csv_file_w << "\"";
throw std::runtime_error(msg.str());
}
if ( (height_w != m) || (width_w != k))
{
cerr << "\tdimensions of matrix W are " << height_w
<< " x " << width_w << endl;
cerr << "\texpected " << m << " x " << k << endl;
throw std::logic_error("smallk error (Nmf): non-conformant matrix W.");
}
cout << "Initializing matrix H..." << endl;
if (csv_file_h.empty())
ok = RandomMatrix(&buf_h[0], ldim_h, k, n, rng);
else
ok = LoadDelimitedFile(buf_h, height_h, width_h, csv_file_h);
if (!ok)
{
std::ostringstream msg;
msg << "smallk error (Nmf): load failed for file ";
msg << "\"" << csv_file_h << "\"";
throw std::runtime_error(msg.str());
}
if ( (height_h != k) || (width_h != n))
{
cerr << "\tdimensions of matrix H are " << height_h
<< " x " << width_h << endl;
cerr << "\texpected " << k << " x " << n << endl;
throw std::logic_error("smallk error (Nmf): non-conformant matrix H.");
}
// The ratio of projected gradient norms doesn't seem to work very well
// with MU. We frequently observe a 'leveling off' behavior and the
// convergence is even slower than usual. So for MU use the relative
// change in the Frobenius norm of W as the stopping criterion, which
// always seems to behave well, even though it is on shaky theoretical
// ground.
if (NmfAlgorithm::MU == nmf_opts.algorithm)
nmf_opts.prog_est_algorithm = NmfProgressAlgorithm::DELTA_FNORM;
else
nmf_opts.prog_est_algorithm = NmfProgressAlgorithm::PG_RATIO;
nmf_opts.tol = nmf_tolerance;
nmf_opts.height = m;
nmf_opts.width = n;
nmf_opts.k = k;
nmf_opts.min_iter = min_iter;
nmf_opts.max_iter = max_iter;
nmf_opts.tolcount = 1;
nmf_opts.max_threads = max_threads;
nmf_opts.verbose = true;
nmf_opts.normalize = true;
// display all params to user
PrintNmfOpts(nmf_opts);
NmfStats stats;
Result result;
if (is_sparse)
{
result = NmfSparse(nmf_opts,
A.Height(), A.Width(), A.Size(),
A.LockedColBuffer(),
A.LockedRowBuffer(),
A.LockedDataBuffer(),
&buf_w[0], ldim_w,
&buf_h[0], ldim_h,
stats);
}
else
{
result = Nmf(nmf_opts,
&buf_a[0], ldim_a,
&buf_w[0], ldim_w,
&buf_h[0], ldim_h,
stats);
}
cout << "Elapsed wall clock time: ";
cout << ElapsedTime(stats.elapsed_us) << endl;
cout << endl;
if (Result::OK != result)
throw std::runtime_error("smallk error (Nmf): NMF solver failure.");
// write the computed W and H factors to disk
std::string outfile_w, outfile_h;
if (outdir.empty())
{
outfile_w = DEFAULT_FILENAME_W;
outfile_h = DEFAULT_FILENAME_H;
}
else
{
outfile_w = outdir + DEFAULT_FILENAME_W;
outfile_h = outdir + DEFAULT_FILENAME_H;
}
cout << "Writing output files..." << endl;
if (!WriteDelimitedFile(&buf_w[0], ldim_w, m, k, outfile_w, outprecision))
throw std::runtime_error("smallk error (Nmf): could not write W result.");
if (!WriteDelimitedFile(&buf_h[0], ldim_h, k, n, outfile_h, outprecision))
throw std::runtime_error("smallk error (Nmf): could not write H result.");
}
//-----------------------------------------------------------------------------
int main(int argc, char* argv[])
{
Timer timer;
Random rng;
rng.SeedFromTime();
//rng.SeedFromInt(78);
CommandLineOptions opts;
if (!ParseCommandLine(argc, argv, opts))
{
if (opts.show_help)
{
ShowHelp(argv[0]);
return 0;
}
else
{
// command line error
return -1;
}
}
// validate command line options
if (!IsValid(opts))
return -1;
NmfInitialize(argc, argv);
bool ok = true;
unsigned int m, n, nnz, ldim_a, num_clusters;
//-------------------------------------------------------------------------
//
// load the dictionary file
//
//-------------------------------------------------------------------------
if (opts.clust_opts.verbose)
cout << "loading dictionary..." << endl;
std::vector<std::string> dictionary;
if (!LoadStringsFromFile(opts.dictfile, dictionary))
{
cerr << "\ncould not load dictionary file " << opts.dictfile << endl;
NmfFinalize();
return -1;
}
//-------------------------------------------------------------------------
//
// load matrix A, the data matrix
//
//-------------------------------------------------------------------------
if (opts.clust_opts.verbose)
cout << "loading matrix..." << endl;
SparseMatrix<R> A;
std::vector<R> buf_a;
if (IsSparse(opts.infile_A))
{
if (!LoadSparseMatrix(opts.infile_A, A, m, n, nnz))
{
cerr << "\nload failed for file " << opts.infile_A << endl;
NmfFinalize();
return -1;
}
}
else if (IsDense(opts.infile_A))
{
ok = LoadDenseMatrix(opts.infile_A, buf_a, m, n);
if (!ok || (buf_a.size() < m*n))
{
cerr << "\nload failed for file " << opts.infile_A << endl;
NmfFinalize();
return -1;
}
}
else
{
cerr << "\nunsupported file type: " << opts.infile_A << endl;
NmfFinalize();
return -1;
}
num_clusters = opts.clust_opts.num_clusters;
// HierNMF2 requires W and H initializer matrices of dimensions
//
// W: m x 2
// H: 2 x n
//
// Elemental uses a default signed 32-bit integer for its index type,
// and it computes offsets into the data buffer with this same type.
// The following lines check to see that the W and H matrices fit
// within these limits.
// check W matrix (2*m elements required)
uint64_t required_size = static_cast<uint64_t>(m);
required_size *= 2u;
if (!FitsWithin<int>(required_size))
{
cerr << "W matrix size too large" << endl;
NmfFinalize();
return -1;
}
// check H matrix (2*n elements required)
required_size = static_cast<uint64_t>(n);
required_size *= 2u;
if (!FitsWithin<int>(required_size))
{
cerr << "H matrix size too large" << endl;
NmfFinalize();
return -1;
}
opts.clust_opts.nmf_opts.height = m;
opts.clust_opts.nmf_opts.width = n;
opts.clust_opts.nmf_opts.k = 2;
// leading dimensions for dense matrix A data buffer
ldim_a = m;
// print a summary of all options
if (opts.clust_opts.verbose)
PrintOpts(opts);
//-------------------------------------------------------------------------
//
// run the selected clustering algorithm
//
//-------------------------------------------------------------------------
// W and H buffer for flat clustering
std::vector<R> buf_w(m*num_clusters);
std::vector<R> buf_h(num_clusters*n);
Tree<R> tree;
ClustStats stats;
std::vector<float> probabilities;
std::vector<unsigned int> assignments_flat;
std::vector<int> term_indices(opts.clust_opts.maxterms * num_clusters);
Result result = Result::OK;
timer.Start();
if (A.Size() > 0)
{
result = ClustSparse(opts.clust_opts, A,
&buf_w[0], &buf_h[0], tree, stats, rng);
}
else
{
result = Clust(opts.clust_opts, &buf_a[0], ldim_a,
&buf_w[0], &buf_h[0], tree, stats, rng);
}
if (opts.clust_opts.flat)
{
// compute flat clustering assignments and top terms
unsigned int k = num_clusters;
ComputeFuzzyAssignments(probabilities, &buf_h[0], k, k, n);
ComputeAssignments(assignments_flat, &buf_h[0], k, k, n);
TopTerms(opts.clust_opts.maxterms, &buf_w[0], m, m, k, term_indices);
}
timer.Stop();
double elapsed = timer.ReportMilliseconds();
cout << "\nElapsed wall clock time: ";
if (elapsed < 1000.0)
cout << elapsed << " ms." << endl;
else
cout << elapsed*0.001 << " s." << endl;
int num_converged = stats.nmf_count - stats.max_count;
cout << num_converged << "/" << stats.nmf_count << " factorizations"
<< " converged." << endl << endl;
//-------------------------------------------------------------------------
//
// write results
//
//-------------------------------------------------------------------------
if (Result::FAILURE == result)
{
cerr << "\nHierarchical clustering fatal error." << endl;
}
else
{
if (opts.clust_opts.verbose)
cout << "Writing output files..." << endl;
if (!tree.WriteAssignments(opts.assignfile))
cerr << "\terror writing assignments file" << endl;
IHierclustWriter* writer = CreateHierclustWriter(opts.format);
if (!tree.WriteTree(writer, opts.treefile, dictionary))
cerr << "\terror writing factorization file" << endl;
if (opts.clust_opts.flat && Result::FLATCLUST_FAILURE != result)
{
FlatClustWriteResults(opts.outdir, assignments_flat, probabilities,
dictionary, term_indices, opts.format,
opts.clust_opts.maxterms, n,
opts.clust_opts.num_clusters);
}
delete writer;
}
NmfFinalize();
return 0;
}
