GURLS_EXPORT void svd(const gMat2D<float>& A, gMat2D<float>& U, gVec<float>& W, gMat2D<float>& Vt) {
char jobu = 'S', jobvt = 'S';
int m = A.rows();
int n = A.cols();
int k = std::min<int>(m, n);
if ((int)W.getSize() < k) {
throw gException("The length of vector W must be at least equal to the minimum dimension of the input matrix A");
}
if ((int)U.rows() < m || (int)U.cols() < k) {
throw gException("Please check the dimensions of the matrix U where to store the singular vectors");
}
if ((int)Vt.rows() < k || (int)Vt.cols() < n) {
throw gException("Please check the dimensions of the matrix Vt where to store the rigth singular vectors");
}
int lda = A.cols();
int ldu = U.cols();
int ldvt = Vt.cols();
int info, lwork = std::max<int>(3*k+std::max<int>(m,n), 5*k);
float* work = new float[lwork];
float* copy = new float[m*n];
A.asarray(copy, m*n);
sgesvd_(&jobu, &jobvt, &n, &m, copy, &lda, W.getData(), Vt.getData(), &ldvt, U.getData(), &ldu, work, &lwork, &info);
delete[] work;
delete[] copy;
}
GURLS_EXPORT void dot(const gMat2D<float>& A, const gMat2D<float>& B, gMat2D<float>& C) {
dot(A.getData(), B.getData(), C.getData(),
A.rows(), A.cols(),
B.rows(), B.cols(),
C.rows(), C.cols(),
CblasNoTrans, CblasNoTrans, CblasRowMajor);
}
GURLS_EXPORT void dot(const gMat2D<double>& A, const gMat2D<double>& B, gMat2D<double>& C)
{
dot(A.getData(), B.getData(), C.getData(),
A.rows(), A.cols(),
B.rows(), B.cols(),
C.rows(), C.cols(),
CblasNoTrans, CblasNoTrans, CblasColMajor);
}
void BigArray<T>::getMatrix(unsigned long startingRow, unsigned long startingCol, gMat2D<T>&result) const
{
if(startingRow >= this->numrows || startingCol >= this->numcols)
throw gException(Exception_Index_Out_of_Bound);
if(startingRow+result.rows() > this->numrows || startingCol+result.cols() > this->numcols)
throw gException(Exception_Index_Out_of_Bound);
getMatrix(startingRow, startingCol, result.rows(), result.cols(), result.getData());
}
GURLS_EXPORT void lu(gMat2D<float>& A, gVec<int>& pv) {
unsigned int k = std::min<unsigned int>(A.cols(), A.rows());
if (pv.getSize() != k) {
throw gException("The lenghth of pv must be equal to the minimun dimension of A");
}
int info;
int m = A.rows();
int n = A.cols();
int lda = A.cols();
sgetrf_(&m, &n, A.getData(), &lda, pv.getData(), &info);
}
GURLS_EXPORT void svd(const gMat2D<float>& A, gMat2D<float>& U, gVec<float>& W, gMat2D<float>& Vt)
{
float* Ubuf;
float* Sbuf;
float* Vtbuf;
int Urows, Ucols;
int Slen;
int Vtrows, Vtcols;
gurls::svd(A.getData(), Ubuf, Sbuf, Vtbuf,
A.rows(), A.cols(),
Urows, Ucols, Slen, Vtrows, Vtcols);
U.resize(Urows, Ucols);
copy(U.getData(), Ubuf, U.getSize());
W.resize(Slen);
copy(W.getData(), Sbuf, Slen);
Vt.resize(Vtrows, Vtcols);
copy(Vt.getData(), Vtbuf, Vt.getSize());
delete [] Ubuf;
delete [] Sbuf;
delete [] Vtbuf;
}
GURLS_EXPORT void eig(const gMat2D<float>& A, gVec<float>& Wr, gVec<float>& Wi)
{
if (A.cols() != A.rows())
throw gException("The input matrix A must be squared");
float* Atmp = new float[A.getSize()];
copy(Atmp, A.getData(), A.getSize());
char jobvl = 'N', jobvr = 'N';
int n = A.cols(), lda = A.cols(), ldvl = 1, ldvr = 1;
int info, lwork = 4*n;
float* work = new float[lwork];
sgeev_(&jobvl, &jobvr, &n, Atmp, &lda, Wr.getData(), Wi.getData(), NULL, &ldvl, NULL, &ldvr, work, &lwork, &info);
delete[] Atmp;
delete[] work;
if(info != 0)
{
std::stringstream str;
str << "Eigenvalues/eigenVectors computation failed, error code " << info << ";" << std::endl;
throw gException(str.str());
}
}
float RLSlinear::predictModel(gMat2D<float> &X, bool newGURLS)
{
if (modelLinearRLS==NULL)
{
cout << "Error: train model first!" << endl;
return 0.0;
}
if (newGURLS==false)
{
gMat2D<float> empty;
RLS.run(X,empty,*modelLinearRLS,"test");
}
else
{
unsigned long nTest = X.rows();
unsigned long d = X.cols();
gMat2D<float> empty(nTest, 1);
unsigned long t = empty.cols();
float* perfBuffer = new float[empty.cols()];
float* predBuffer = new float[empty.cols()*nTest];
test(*modelLinearRLS, X.getData(), empty.getData(), predBuffer, perfBuffer, nTest, d, t, "auto");
}
// Retrieve prediction
gMat2D<float>& pred = modelLinearRLS->getOptValue<OptMatrix<gMat2D<float> > >("pred");
return pred(0,0);
}
GURLS_EXPORT void inv(const gMat2D<float>& A, gMat2D<float>& Ainv, InversionAlgorithm alg){
Ainv = A;
int k = std::min<int>(Ainv.cols(), Ainv.rows());
int info;
int* ipiv = new int[k];
int m = Ainv.rows();
int n = Ainv.cols();
int lda = Ainv.cols();
float* work = new float[n];
sgetrf_(&m, &n, Ainv.getData(), &lda, ipiv, &info);
sgetri_(&m, Ainv.getData(), &lda, ipiv, work, &n, &info);
delete[] ipiv;
delete[] work;
}
BigArray<T>::BigArray(std::wstring fileName, const gMat2D<T>& mat)
{
std::string fName = std::string(fileName.begin(), fileName.end());
init(fName, mat.rows(), mat.cols());
MPI_Barrier(MPI_COMM_WORLD);
}
void RecursiveRLSWrapper<T>::train(const gMat2D<T> &X, const gMat2D<T> &y)
{
this->opt->removeOpt("split");
this->opt->removeOpt("paramsel");
this->opt->removeOpt("optimizer");
this->opt->removeOpt("kernel");
SplitHo<T> splitTask;
GurlsOptionsList* split = splitTask.execute(X, y, *(this->opt));
this->opt->addOpt("split", split);
const gMat2D<unsigned long>& split_indices = split->getOptValue<OptMatrix<gMat2D<unsigned long> > >("indices");
const gMat2D<unsigned long>& split_lasts = split->getOptValue<OptMatrix<gMat2D<unsigned long> > >("lasts");
const unsigned long n = X.rows();
const unsigned long d = X.cols();
const unsigned long t = y.cols();
const unsigned long last = split_lasts.getData()[0];
const unsigned long nva = n-last;
unsigned long* va = new unsigned long[nva];
copy(va, split_indices.getData()+last, nva);
gMat2D<T>* Xva = new gMat2D<T>(nva, d);
gMat2D<T>* yva = new gMat2D<T>(nva, t);
subMatrixFromRows(X.getData(), n, d, va, nva, Xva->getData());
subMatrixFromRows(y.getData(), n, t, va, nva, yva->getData());
gMat2D<T>* XtX = new gMat2D<T>(d, d);
gMat2D<T>* Xty = new gMat2D<T>(d, t);
dot(X.getData(), X.getData(), XtX->getData(), n, d, n, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);
dot(X.getData(), y.getData(), Xty->getData(), n, d, n, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);
GurlsOptionsList* kernel = new GurlsOptionsList("kernel");
kernel->addOpt("XtX", new OptMatrix<gMat2D<T> >(*XtX));
kernel->addOpt("Xty", new OptMatrix<gMat2D<T> >(*Xty));
kernel->addOpt("Xva", new OptMatrix<gMat2D<T> >(*Xva));
kernel->addOpt("yva", new OptMatrix<gMat2D<T> >(*yva));
nTot = n;
this->opt->addOpt("kernel", kernel);
ParamSelHoPrimal<T> paramselTask;
this->opt->addOpt("paramsel", paramselTask.execute(X, y, *(this->opt)));
RLSPrimalRecInit<T> optimizerTask;
this->opt->addOpt("optimizer", optimizerTask.execute(X, y, *(this->opt)));
}
GURLS_EXPORT void cholesky(const gMat2D<float>& A, gMat2D<float>& L, bool upper)
{
float* chol = cholesky<float>(A.getData(), A.rows(), A.cols(), upper);
copy(L.getData(), chol, A.getSize());
delete [] chol;
}
GURLS_EXPORT void pinv(const gMat2D<float>& A, gMat2D<float>& Ainv, float RCOND)
{
int r, c;
float* inv = pinv(A.getData(), A.rows(), A.cols(), r, c, &RCOND);
Ainv.resize(r, c);
gurls::copy(Ainv.getData(), inv, r*c);
delete[] inv;
}
GURLS_EXPORT void dot(const gMat2D<float>& A, const gVec<float>& x, gVec<float>& y)
{
if ( (A.cols() != x.getSize()) || (A.rows() != y.getSize()))
throw gException(Exception_Inconsistent_Size);
// y = alpha*A*x + beta*y
float alpha = 1.0f;
float beta = 0.0f;
char transA = 'N';
int m = A.rows();
int n = A.cols();
int lda = m;
int inc = 1;
sgemv_(&transA, &m, &n, &alpha, const_cast<float*>(A.getData()), &lda,
const_cast<float*>(x.getData()), &inc, &beta, y.getData(), &inc);
}
GURLS_EXPORT void eig(const gMat2D<float>& A, gVec<float>& Wr, gVec<float>& Wi) {
if (A.cols() != A.rows()) {
throw gException("The input matrix A must be squared");
}
char jobvl = 'N', jobvr = 'N';
int n = A.cols(), lda = A.cols(), ldvl = 1, ldvr = 1;
int info, lwork = 4*n;
float* work = new float[lwork];
sgeev_(&jobvl, &jobvr, &n, const_cast<gMat2D<float>&>(A).getData(), &lda, Wr.getData(), Wi.getData(), NULL, &ldvl, NULL, &ldvr, work, &lwork, &info);
delete[] work;
}
GURLS_EXPORT void dot(const gMat2D<double>& A, const gVec<double>& x, gVec<double>& y){
if ( (A.cols() != x.getSize()) || (A.rows() != y.getSize()))
throw gException(Exception_Inconsistent_Size);
// y = alpha*A*x + beta*y
double alpha = 1.0f;
double beta = 0.0f;
char transA = 'T'; // row major matrix
int m = A.cols(); // row major matrix
int n = A.rows(); // row major matrix
int lda = m; // row major matrix
int inc = 1;
dgemv_(&transA, &m, &n, &alpha, const_cast<double*>(A.getData()), &lda,
const_cast<double*>(x.getData()), &inc, &beta,
y.getData(), &inc);
}
GURLS_EXPORT void cholesky(const gMat2D<float>& A, gMat2D<float>& L, bool upper){
typedef float T;
L = A;
int LDA = A.rows();
int n = A.cols();
char UPLO = upper? 'U' : 'L';
int info;
spotrf_(&UPLO,&n, L.getData(),&LDA,&info);
// This is required because we adopted a column major order to store the
// data into matrices
gMat2D<T> tmp(L.rows(), L.cols());
if (!upper){
L.uppertriangular(tmp);
} else {
L.lowertriangular(tmp);
}
tmp.transpose(L);
}
GURLS_EXPORT void eig(const gMat2D<float>& A, gMat2D<float>& V, gVec<float>& Wr, gVec<float>& Wi) {
if (A.cols() != A.rows()) {
throw gException("The input matrix A must be squared");
}
char jobvl = 'N', jobvr = 'V';
int n = A.cols(), lda = A.cols(), ldvl = 1, ldvr = A.cols();
int info, lwork = 4*n;
float* work = new float[lwork];
gMat2D<float> Atmp = A;
gMat2D<float> Vtmp = V;
sgeev_(&jobvl, &jobvr, &n, Atmp.getData(), &lda, Wr.getData(), Wi.getData(), NULL, &ldvl, Vtmp.getData(), &ldvr, work, &lwork, &info);
Vtmp.transpose(V);
delete[] work;
}
void RLSlinear::trainModel(gMat2D<float> &X, gMat2D<float> &Y, bool newGURLS)
{
if(modelLinearRLS!=NULL)
delete modelLinearRLS;
if (newGURLS==false)
{
OptTaskSequence *seq = new OptTaskSequence();
OptProcess* process_train = new OptProcess();
OptProcess* process_predict = new OptProcess();
*seq << "kernel:linear" << "split:ho" << "paramsel:hodual";
*process_train << GURLS::computeNsave << GURLS::compute << GURLS::computeNsave;
*process_predict << GURLS::load << GURLS::ignore << GURLS::load;
*seq<< "optimizer:rlsdual"<< "pred:dual";
*process_train<<GURLS::computeNsave<<GURLS::ignore;
*process_predict<<GURLS::load<<GURLS::computeNsave;
GurlsOptionsList * processes = new GurlsOptionsList("processes", false);
processes->addOpt("train",process_train);
processes->addOpt("test",process_predict);
modelLinearRLS = new GurlsOptionsList(className, true);
modelLinearRLS->addOpt("seq", seq);
modelLinearRLS->addOpt("processes", processes);
RLS.run(X,Y,*modelLinearRLS,"train");
}
else
{
unsigned long n = X.rows();
unsigned long d = X.cols();
unsigned long t = Y.cols(); // should be 1
modelLinearRLS = train(X.getData(), Y.getData(), n, d, t, "krls", "linear");
}
}
BigArray<T>::BigArray(std::string fileName, const gMat2D<T>& mat)
{
init(fileName, mat.rows(), mat.cols());
MPI_Barrier(MPI_COMM_WORLD);
}
void BigArray<T>::setMatrix(unsigned long startingRow, unsigned long startingCol, const gMat2D<T>&value)
{
setMatrix(startingRow, startingCol, value.getData(), value.rows(), value.cols());
}
GURLS_EXPORT void pinv(const gMat2D<float>& A, gMat2D<float>& Ainv, float RCOND){
/*
subroutine SGELSS ( INTEGER M,
INTEGER N,
INTEGER NRHS,
REAL,dimension( lda, * ) A,
INTEGER LDA,
REAL,dimension( ldb, * ) B,
INTEGER LDB,
REAL,dimension( * ) S,
REAL RCOND,
INTEGER RANK,
REAL,dimension( * ) WORK,
INTEGER LWORK,
INTEGER INFO
)
*/
int M = A.rows();
int N = A.cols();
// The following step is required because we are currently storing
// the matrices using a column-major order while LAPACK's
// routines require row-major ordering
float* a = new float[M*N];
const float* ptr_A = A.getData();
float* ptr_a = a;
for (int j = 0; j < N ; j++){
for (int i = 0; i < M ; i++){
*ptr_a++ = *(ptr_A+i*N+j);
}
}
int LDA = M;
int LDB = std::max(M, N);
int NRHS = LDB;
float *b = new float[LDB*NRHS], *b_ptr = b;
for (int i = 0; i < LDB*NRHS; i++){
*b_ptr++=0.f;
}
b_ptr = b;
for (int i = 0; i < std::min(LDB, NRHS); i++, b_ptr+=(NRHS+1)){
*b_ptr = 1.f;
}
float* S = new float[std::min(M,N)];
float condnum = 0.f; // The condition number of A in the 2-norm = S(1)/S(min(m,n)).
if (RCOND < 0){
RCOND = 0.f;
}
int RANK = -1; // std::min(M,N);
int LWORK = -1; //2 * (3*LDB + std::max( 2*std::min(M,N), LDB));
float* WORK = new float[1];
/*
INFO:
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value.
> 0: the algorithm for computing the SVD failed to converge;
if INFO = i, i off-diagonal elements of an intermediate
bidiagonal form did not converge to zero.
*/
int INFO;
/* Query and allocate the optimal workspace */
/*int res = */sgelss_( &M, &N, &NRHS, a, &LDA, b, &LDB, S, &RCOND, &RANK, WORK, &LWORK, &INFO);
LWORK = static_cast<int>(WORK[0]);
delete [] WORK;
WORK = new float[LWORK];
/*res = */sgelss_( &M, &N, &NRHS, a, &LDA, b, &LDB, S, &RCOND, &RANK, WORK, &LWORK, &INFO);
// TODO: check INFO on exit
condnum = S[0]/(S[std::min(M, N)]-1);
// gMat2D<float> *tmp = new gMat2D<float>(b, LDB, LDB, false);
float *ainv = new float[N*M];
float* ptr_b = ainv;
float* ptr_B = b;
for (int i = 0; i < N ; i++){
for (int j = 0; j < M ; j++){
*(ptr_b+i*M+j) = *(ptr_B+j*NRHS+i);
}
}
Ainv = * new gMat2D<float>(ainv, N, M, true);
// gMat2D<float> *tmp = new gMat2D<float>(b, LDB, NRHS, false);
// gMat2D<float> *tmp1 = new gMat2D<float>(NRHS, LDB);
// tmp->transpose(*tmp1);
// Ainv = * new gMat2D<float>(tmp1->getData(), N, M, true);
// std::cout << "A = " << std::endl << A << std::endl;
// std::cout << "pinv(A) = " << std::endl << Ainv << std::endl;
delete [] S;
delete [] WORK;
delete [] a;
// delete tmp, tmp1;
delete [] b;
}
GURLS_EXPORT void cholesky(const gMat2D<float>& A, gMat2D<float>& L, bool upper)
{
cholesky<float>(A.getData(), A.rows(), A.cols(), L.getData(), upper);
}
bool configure(ResourceFinder &rf)
{
string name=rf.find("name").asString().c_str();
setName(name.c_str());
// Set verbosity
verbose = rf.check("verbose",Value(0)).asInt();
// Set dimensionalities
d = rf.check("d",Value(0)).asInt();
t = rf.check("t",Value(0)).asInt();
if (d <= 0 || t <= 0 )
{
printf("Error: Inconsistent feature or output dimensionalities!\n");
return false;
}
// Set perf type WARNING: perf types should be defined as separate sister classes
perfType = rf.check("perf",Value("RMSE")).asString();
if ( perfType != "MSE" && perfType != "RMSE" && perfType != "nMSE" )
{
printf("Error: Inconsistent performance measure! Set to RMSE.\n");
perfType = "RMSE";
}
// Set number of saved performance measurements
numPred = rf.check("numPred",Value("-1")).asInt();
// Set number of saved performance measurements
savedPerfNum = rf.check("savedPerfNum",Value("0")).asInt();
if (savedPerfNum > numPred)
{
savedPerfNum = numPred;
cout << "Warning: savedPerfNum > numPred, setting savedPerfNum = numPred" << endl;
}
//experimentCount = rf.check("experimentCount",Value("0")).asInt();
experimentCount = rf.find("experimentCount").asInt();
// Print Configuration
cout << endl << "-------------------------" << endl;
cout << "Configuration parameters:" << endl << endl;
cout << "experimentCount = " << experimentCount << endl;
cout << "d = " << d << endl;
cout << "t = " << t << endl;
cout << "perf = " << perfType << endl;
cout << "-------------------------" << endl << endl;
// Open ports
string fwslash="/";
inVec.open((fwslash+name+"/vec:i").c_str());
printf("inVec opened\n");
pred.open((fwslash+name+"/pred:o").c_str());
printf("pred opened\n");
perf.open((fwslash+name+"/perf:o").c_str());
printf("perf opened\n");
rpcPort.open((fwslash+name+"/rpc:i").c_str());
printf("rpcPort opened\n");
// Attach rpcPort to the respond() method
attach(rpcPort);
// Initialize random number generator
srand(static_cast<unsigned int>(time(NULL)));
// Initialize error structures
error.resize(1,t);
error = gMat2D<T>::zeros(1, t); //
if (savedPerfNum > 0)
{
storedError.resize(savedPerfNum,t);
storedError = gMat2D<T>::zeros(savedPerfNum, t); //MSE
}
updateCount = 0;
//------------------------------------------
// Pre-training
//------------------------------------------
if ( pretrain == 1 )
{
if ( pretr_type == "fromFile" )
{
//------------------------------------------
// Pre-training from file
//------------------------------------------
string trainFilePath = rf.getContextPath() + "/data/" + pretrainFile;
try
{
// Load data files
cout << "Loading data file..." << endl;
trainSet.readCSV(trainFilePath);
cout << "File " + trainFilePath + " successfully read!" << endl;
cout << "trainSet: " << trainSet << endl;
cout << "n_pretr = " << n_pretr << endl;
cout << "d = " << d << endl;
//WARNING: Add matrix dimensionality check!
// Resize Xtr
Xtr.resize( n_pretr , d );
// Initialize Xtr
//Xtr.submatrix(trainSet , n_pretr , d);
Xtr.submatrix(trainSet , 0 , 0);
cout << "Xtr initialized!" << endl << Xtr << endl;
// Resize ytr
ytr.resize( n_pretr , t );
cout << "ytr resized!" << endl;
// Initialize ytr
gVec<T> tmpCol(trainSet.rows());
cout << "tmpCol" << tmpCol << endl;
for ( int i = 0 ; i < t ; ++i )
{
cout << "trainSet(d + i): " << trainSet(d + i) << endl;
tmpCol = trainSet(d + i);
gVec<T> tmpCol1(n_pretr);
//cout << tmpCol.subvec( (unsigned int) n_pretr , (unsigned int) 0); // WARNING: Fixed in latest GURLS version
gVec<T> locs(n_pretr);
for (int j = 0 ; j < n_pretr ; ++j)
locs[j] = j;
cout << "locs" << locs << endl;
gVec<T>& tmpCol2 = tmpCol.copyLocations(locs);
cout << "tmpCol2" << tmpCol2 << endl;
//tmpCol1 = tmpCol.subvec( (unsigned int) n_pretr );
//cout << "tmpCol1: " << tmpCol1 << endl;
ytr.setColumn( tmpCol2 , (long unsigned int) i);
}
cout << "ytr initialized!" << endl;
// Compute variance for each output on the training set
gMat2D<T> varCols = gMat2D<T>::zeros(1,t);
gVec<T>* sumCols_v = ytr.sum(COLUMNWISE); // Vector containing the column-wise sum
gMat2D<T> meanCols(sumCols_v->getData(), 1, t, 1); // Matrix containing the column-wise sum
meanCols /= n_pretr; // Matrix containing the column-wise mean
if (verbose) cout << "Mean of the output columns: " << endl << meanCols << endl;
for (int i = 0; i < n_pretr; i++)
{
gMat2D<T> ytri(ytr[i].getData(), 1, t, 1);
varCols += (ytri - meanCols) * (ytri - meanCols); // NOTE: Temporary assignment
}
varCols /= n_pretr; // Compute variance
if (verbose) cout << "Variance of the output columns: " << endl << varCols << endl;
// Initialize model
cout << "Batch pretraining the RLS model with " << n_pretr << " samples." << endl;
estimator.train(Xtr, ytr);
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return false; // Terminate program. NOTE: May be worth to set up specific error return values
}
}
else if ( pretr_type == "fromStream" )
{
//------------------------------------------
// Pre-training from stream
//------------------------------------------
try
{
cout << "Pretraining from stream started. Listening on port vec:i." << n_pretr << " samples expected." << endl;
// Resize Xtr
Xtr.resize( n_pretr , d );
// Resize ytr
ytr.resize( n_pretr , t );
// Initialize Xtr
for (int j = 0 ; j < n_pretr ; ++j)
{
// Wait for input feature vector
if(verbose) cout << "Expecting input vector # " << j+1 << endl;
Bottle *bin = inVec.read(); // blocking call
if (bin != 0)
{
if(verbose) cout << "Got it!" << endl << bin->toString() << endl;
//Store the received sample in gMat2D format for it to be compatible with gurls++
for (int i = 0 ; i < bin->size() ; ++i)
{
if ( i < d )
{
Xtr(j,i) = bin->get(i).asDouble();
}
else if ( (i>=d) && (i<d+t) )
{
ytr(j, i - d ) = bin->get(i).asDouble();
}
}
if(verbose) cout << "Xtr[j]:" << endl << Xtr[j] << endl << "ytr[j]:" << endl << ytr[j] << endl;
}
else
--j; // WARNING: bug while closing with ctrl-c
}
cout << "Xtr initialized!" << endl;
cout << "ytr initialized!" << endl;
// Compute variance for each output on the training set
gMat2D<T> varCols = gMat2D<T>::zeros(1,t);
gVec<T>* sumCols_v = ytr.sum(COLUMNWISE); // Vector containing the column-wise sum
gMat2D<T> meanCols(sumCols_v->getData(), 1, t, 1); // Matrix containing the column-wise sum
meanCols /= n_pretr; // Matrix containing the column-wise mean
if (verbose) cout << "Mean of the output columns: " << endl << meanCols << endl;
for (int i = 0; i < n_pretr; i++)
{
gMat2D<T> ytri(ytr[i].getData(), 1, t, 1);
varCols += (ytri - meanCols) * (ytri - meanCols); // NOTE: Temporary assignment
}
varCols /= n_pretr; // Compute variance
if (verbose) cout << "Variance of the output columns: " << endl << varCols << endl;
// Initialize model
cout << "Batch pretraining the RLS model with " << n_pretr << " samples." << endl;
estimator.train(Xtr, ytr);
}
catch (gException& e)
{
cout << e.getMessage() << endl;
return false; // Terminate program. NOTE: May be worth to set up specific error return values
}
}
// Print detailed pretraining information
if (verbose)
estimator.getOpt().printAll();
}
return true;
}
GURLS_EXPORT void lu(gMat2D<float>& A)
{
gVec<int> pv(std::min(A.cols(), A.rows()));
lu(A, pv);
}