void CMT::WhiteningTransform::initialize(const ArrayXXd& input, int dimOut) {
if(input.cols() < input.rows())
throw Exception("Too few inputs to compute whitening transform.");
mMeanIn = input.rowwise().mean();
// compute covariances
MatrixXd covXX = covariance(input);
// input whitening
SelfAdjointEigenSolver<MatrixXd> eigenSolver;
eigenSolver.compute(covXX);
Array<double, 1, Dynamic> eigenvalues = eigenSolver.eigenvalues();
MatrixXd eigenvectors = eigenSolver.eigenvectors();
// don't whiten directions with near-zero variance
for(int i = 0; i < eigenvalues.size(); ++i)
if(eigenvalues[i] < 1e-7)
eigenvalues[i] = 1.;
mPreIn = (eigenvectors.array().rowwise() * eigenvalues.sqrt().cwiseInverse()).matrix()
* eigenvectors.transpose();
mPreInInv = (eigenvectors.array().rowwise() * eigenvalues.sqrt()).matrix()
* eigenvectors.transpose();
mMeanOut = VectorXd::Zero(dimOut);
mPreOut = MatrixXd::Identity(dimOut, dimOut);
mPreOutInv = MatrixXd::Identity(dimOut, dimOut);
mPredictor = MatrixXd::Zero(dimOut, input.rows());
mGradTransform = MatrixXd::Zero(dimOut, input.rows());
mLogJacobian = 1.;
}
void ctBdG:: Initialize_Euabv(){
SelfAdjointEigenSolver<MatrixXcd> ces;
distribution(_NK2);
int nkx, nky, nk;
for (int ig = 0; ig < _size; ++ig) {
if (ig==_rank) {
_bdg_E.resize(recvcount,4);
_bdg_u.resize(recvcount,4);
_bdg_a.resize(recvcount,4);
_bdg_b.resize(recvcount,4);
_bdg_v.resize(recvcount,4);
local_Delta_k_r = new double[recvcount];
local_Delta_k_i = new double[recvcount];
for (int i = 0; i < recvcount; ++i) {
nk = recvbuf[i];
nkx = nk%_NK;
nky = int(nk/_NK);
construct_BdG(_gauss_k[nkx],_gauss_k[nky], _mu, _hi);
ces.compute(_bdg);
_bdg_E.row(i) = ces.eigenvalues();
_bdg_u.row(i) = ces.eigenvectors().row(0);
_bdg_a.row(i) = ces.eigenvectors().row(1);
_bdg_b.row(i) = ces.eigenvectors().row(2);
_bdg_v.row(i) = ces.eigenvectors().row(3);
}
}
}
}
int main(int, char**)
{
cout.precision(3);
SelfAdjointEigenSolver<Matrix4f> es;
Matrix4f X = Matrix4f::Random(4,4);
Matrix4f A = X + X.transpose();
es.compute(A);
cout << "The eigenvalues of A are: " << es.eigenvalues().transpose() << endl;
es.compute(A + Matrix4f::Identity(4,4)); // re-use es to compute eigenvalues of A+I
cout << "The eigenvalues of A+I are: " << es.eigenvalues().transpose() << endl;
return 0;
}
void MaterialFitting::solveByNNLS(){
const SXMatrix M1 = assembleObjMatrix();
vector<SX> smooth_funs;
computeSmoothObjFunctions(smooth_funs);
SXMatrix obj_fun_m(M1.size1()*M1.size2()+smooth_funs.size(),1);
for (int i = 0; i < M1.size1(); ++i){
for (int j = 0; j < M1.size2(); ++j)
obj_fun_m.elem(i*M1.size2()+j,0) = M1.elem(i,j);
}
for (int i = 0; i < smooth_funs.size(); ++i){
obj_fun_m.elem(M1.size2()*M1.size2()+i,0) = smooth_funs[i];
}
VSX variable_x;
initAllVariables(variable_x);
CasADi::SXFunction g_fun = CasADi::SXFunction(variable_x,obj_fun_m);
g_fun.init();
VectorXd x0(variable_x.size()), b;
x0.setZero();
CASADI::evaluate(g_fun, x0, b);
b = -b;
MatrixXd J = CASADI::convert<double>(g_fun.jac());
assert_eq(J.rows(), b.size());
assert_eq(J.cols(), x0.size());
VectorXd x(b.size()), w(J.cols()), zz(J.rows());
VectorXi index(J.cols()*2);
getInitValue(x);
int exit_code = 0;
double residual = 1e-18;
nnls(&J(0,0), J.rows(), J.rows(), J.cols(),
&b[0], &x[0], &residual,
&w[0], &zz[0], &index[0], &exit_code);
INFO_LOG("residual: " << residual);
print_NNLS_exit_code(exit_code);
rlst.resize(x.size());
for (int i = 0; i < x.size(); ++i){
rlst[i] = x[i];
}
const MatrixXd H = J.transpose()*J;
SelfAdjointEigenSolver<MatrixXd> es;
es.compute(H);
cout << "The eigenvalues of H are: " << es.eigenvalues().transpose() << endl;
}
bool IsotropicMaxStressDamage(const Matrix3d S, const double maxStress) {
/*
* compute Eigenvalues of strain matrix
*/
SelfAdjointEigenSolver < Matrix3d > es;
es.compute(S); // compute eigenvalue and eigenvectors of strain
double max_eigenvalue = es.eigenvalues().maxCoeff();
if (max_eigenvalue > maxStress) {
return true;
} else {
return false;
}
}
void MaterialFitting::solveByLinearSolver(){
MatrixXd H;
VectorXd g;
hessGrad(H,g);
SelfAdjointEigenSolver<MatrixXd> es;
es.compute(H);
cout << "H.norm() = " << H.norm() << endl;
cout << "The eigenvalues of H are: \n" << es.eigenvalues().transpose() << endl;
const VectorXd x = H.lu().solve(-g);
rlst.resize(x.size());
for (int i = 0; i < x.size(); ++i)
rlst[i] = x[i];
cout<< "\n(H*x+g).norm() = " << (H*x+g).norm() << "\n\n";
}
void drwnNNGraphMLearner::subGradientStep(const MatrixXd& G, double alpha)
{
DRWN_FCN_TIC;
// gradient step
_M -= alpha * G;
// project onto psd
SelfAdjointEigenSolver<MatrixXd> es;
es.compute(_M);
const VectorXd d = es.eigenvalues().real();
if ((d.array() < 0.0).any()) {
const MatrixXd V = es.eigenvectors();
_M = V * d.cwiseMax(VectorXd::Constant(d.rows(), DRWN_EPSILON)).asDiagonal() * V.inverse();
}
DRWN_FCN_TOC;
}
template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
/* this test covers the following files:
EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h)
*/
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
RealScalar largerEps = 10*test_precision<RealScalar>();
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
MatrixType symmC = symmA;
svd_fill_random(symmA,Symmetric);
symmA.template triangularView<StrictlyUpper>().setZero();
symmC.template triangularView<StrictlyUpper>().setZero();
MatrixType b = MatrixType::Random(rows,cols);
MatrixType b1 = MatrixType::Random(rows,cols);
MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
symmB.template triangularView<StrictlyUpper>().setZero();
CALL_SUBTEST( selfadjointeigensolver_essential_check(symmA) );
SelfAdjointEigenSolver<MatrixType> eiSymm(symmA);
// generalized eigen pb
GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmC, symmB);
SelfAdjointEigenSolver<MatrixType> eiSymmNoEivecs(symmA, false);
VERIFY_IS_EQUAL(eiSymmNoEivecs.info(), Success);
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues());
// generalized eigen problem Ax = lBx
eiSymmGen.compute(symmC, symmB,Ax_lBx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox(
symmB.template selfadjointView<Lower>() * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem BAx = lx
eiSymmGen.compute(symmC, symmB,BAx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmB.template selfadjointView<Lower>() * (symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem ABx = lx
eiSymmGen.compute(symmC, symmB,ABx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmC.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
eiSymm.compute(symmC);
MatrixType sqrtSymmA = eiSymm.operatorSqrt();
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA);
VERIFY_IS_APPROX(sqrtSymmA, symmC.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt());
MatrixType id = MatrixType::Identity(rows, cols);
VERIFY_IS_APPROX(id.template selfadjointView<Lower>().operatorNorm(), RealScalar(1));
SelfAdjointEigenSolver<MatrixType> eiSymmUninitialized;
VERIFY_RAISES_ASSERT(eiSymmUninitialized.info());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvalues());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
eiSymmUninitialized.compute(symmA, false);
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
// test Tridiagonalization's methods
Tridiagonalization<MatrixType> tridiag(symmC);
VERIFY_IS_APPROX(tridiag.diagonal(), tridiag.matrixT().diagonal());
VERIFY_IS_APPROX(tridiag.subDiagonal(), tridiag.matrixT().template diagonal<-1>());
Matrix<RealScalar,Dynamic,Dynamic> T = tridiag.matrixT();
if(rows>1 && cols>1) {
// FIXME check that upper and lower part are 0:
//VERIFY(T.topRightCorner(rows-2, cols-2).template triangularView<Upper>().isZero());
}
VERIFY_IS_APPROX(tridiag.diagonal(), T.diagonal());
VERIFY_IS_APPROX(tridiag.subDiagonal(), T.template diagonal<1>());
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint());
// Test computation of eigenvalues from tridiagonal matrix
if(rows > 1)
{
SelfAdjointEigenSolver<MatrixType> eiSymmTridiag;
eiSymmTridiag.computeFromTridiagonal(tridiag.matrixT().diagonal(), tridiag.matrixT().diagonal(-1), ComputeEigenvectors);
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmTridiag.eigenvalues());
VERIFY_IS_APPROX(tridiag.matrixT(), eiSymmTridiag.eigenvectors().real() * eiSymmTridiag.eigenvalues().asDiagonal() * eiSymmTridiag.eigenvectors().real().transpose());
}
if (rows > 1)
{
// Test matrix with NaN
symmC(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmC);
VERIFY_IS_EQUAL(eiSymmNaN.info(), NoConvergence);
}
}
template<typename MatrixType> void selfadjointeigensolver_essential_check(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
RealScalar eival_eps = (std::min)(test_precision<RealScalar>(), NumTraits<Scalar>::dummy_precision()*20000);
SelfAdjointEigenSolver<MatrixType> eiSymm(m);
VERIFY_IS_EQUAL(eiSymm.info(), Success);
VERIFY_IS_APPROX(m.template selfadjointView<Lower>() * eiSymm.eigenvectors(),
eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal());
VERIFY_IS_APPROX(m.template selfadjointView<Lower>().eigenvalues(), eiSymm.eigenvalues());
VERIFY_IS_UNITARY(eiSymm.eigenvectors());
if(m.cols()<=4)
{
SelfAdjointEigenSolver<MatrixType> eiDirect;
eiDirect.computeDirect(m);
VERIFY_IS_EQUAL(eiDirect.info(), Success);
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiDirect.eigenvalues());
if(! eiSymm.eigenvalues().isApprox(eiDirect.eigenvalues(), eival_eps) )
{
std::cerr << "reference eigenvalues: " << eiSymm.eigenvalues().transpose() << "\n"
<< "obtained eigenvalues: " << eiDirect.eigenvalues().transpose() << "\n"
<< "diff: " << (eiSymm.eigenvalues()-eiDirect.eigenvalues()).transpose() << "\n"
<< "error (eps): " << (eiSymm.eigenvalues()-eiDirect.eigenvalues()).norm() / eiSymm.eigenvalues().norm() << " (" << eival_eps << ")\n";
}
VERIFY(eiSymm.eigenvalues().isApprox(eiDirect.eigenvalues(), eival_eps));
VERIFY_IS_APPROX(m.template selfadjointView<Lower>() * eiDirect.eigenvectors(),
eiDirect.eigenvectors() * eiDirect.eigenvalues().asDiagonal());
VERIFY_IS_APPROX(m.template selfadjointView<Lower>().eigenvalues(), eiDirect.eigenvalues());
VERIFY_IS_UNITARY(eiDirect.eigenvectors());
}
}
template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
/* this test covers the following files:
EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h)
*/
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
RealScalar largerEps = 10*test_precision<RealScalar>();
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
symmA.template triangularView<StrictlyUpper>().setZero();
MatrixType b = MatrixType::Random(rows,cols);
MatrixType b1 = MatrixType::Random(rows,cols);
MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
symmB.template triangularView<StrictlyUpper>().setZero();
SelfAdjointEigenSolver<MatrixType> eiSymm(symmA);
// generalized eigen pb
GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmA, symmB);
#ifdef HAS_GSL
if (internal::is_same<RealScalar,double>::value)
{
// restore symmA and symmB.
symmA = MatrixType(symmA.template selfadjointView<Lower>());
symmB = MatrixType(symmB.template selfadjointView<Lower>());
typedef GslTraits<Scalar> Gsl;
typename Gsl::Matrix gEvec=0, gSymmA=0, gSymmB=0;
typename GslTraits<RealScalar>::Vector gEval=0;
RealVectorType _eval;
MatrixType _evec;
convert<MatrixType>(symmA, gSymmA);
convert<MatrixType>(symmB, gSymmB);
convert<MatrixType>(symmA, gEvec);
gEval = GslTraits<RealScalar>::createVector(rows);
Gsl::eigen_symm(gSymmA, gEval, gEvec);
convert(gEval, _eval);
convert(gEvec, _evec);
// test gsl itself !
VERIFY((symmA * _evec).isApprox(_evec * _eval.asDiagonal(), largerEps));
// compare with eigen
VERIFY_IS_APPROX(_eval, eiSymm.eigenvalues());
VERIFY_IS_APPROX(_evec.cwiseAbs(), eiSymm.eigenvectors().cwiseAbs());
// generalized pb
Gsl::eigen_symm_gen(gSymmA, gSymmB, gEval, gEvec);
convert(gEval, _eval);
convert(gEvec, _evec);
// test GSL itself:
VERIFY((symmA * _evec).isApprox(symmB * (_evec * _eval.asDiagonal()), largerEps));
// compare with eigen
MatrixType normalized_eivec = eiSymmGen.eigenvectors()*eiSymmGen.eigenvectors().colwise().norm().asDiagonal().inverse();
VERIFY_IS_APPROX(_eval, eiSymmGen.eigenvalues());
VERIFY_IS_APPROX(_evec.cwiseAbs(), normalized_eivec.cwiseAbs());
Gsl::free(gSymmA);
Gsl::free(gSymmB);
GslTraits<RealScalar>::free(gEval);
Gsl::free(gEvec);
}
#endif
VERIFY_IS_EQUAL(eiSymm.info(), Success);
VERIFY((symmA.template selfadjointView<Lower>() * eiSymm.eigenvectors()).isApprox(
eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal(), largerEps));
VERIFY_IS_APPROX(symmA.template selfadjointView<Lower>().eigenvalues(), eiSymm.eigenvalues());
SelfAdjointEigenSolver<MatrixType> eiSymmNoEivecs(symmA, false);
VERIFY_IS_EQUAL(eiSymmNoEivecs.info(), Success);
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues());
// generalized eigen problem Ax = lBx
eiSymmGen.compute(symmA, symmB,Ax_lBx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmA.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox(
symmB.template selfadjointView<Lower>() * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem BAx = lx
eiSymmGen.compute(symmA, symmB,BAx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmB.template selfadjointView<Lower>() * (symmA.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem ABx = lx
eiSymmGen.compute(symmA, symmB,ABx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmA.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
MatrixType sqrtSymmA = eiSymm.operatorSqrt();
VERIFY_IS_APPROX(MatrixType(symmA.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA);
VERIFY_IS_APPROX(sqrtSymmA, symmA.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt());
MatrixType id = MatrixType::Identity(rows, cols);
VERIFY_IS_APPROX(id.template selfadjointView<Lower>().operatorNorm(), RealScalar(1));
SelfAdjointEigenSolver<MatrixType> eiSymmUninitialized;
VERIFY_RAISES_ASSERT(eiSymmUninitialized.info());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvalues());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
eiSymmUninitialized.compute(symmA, false);
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
// test Tridiagonalization's methods
Tridiagonalization<MatrixType> tridiag(symmA);
// FIXME tridiag.matrixQ().adjoint() does not work
VERIFY_IS_APPROX(MatrixType(symmA.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
if (rows > 1)
{
// Test matrix with NaN
symmA(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmA);
VERIFY_IS_EQUAL(eiSymmNaN.info(), NoConvergence);
}
}
bool CPCA::init(CFeatures* features)
{
if (!m_initialized)
{
REQUIRE(features->get_feature_class()==C_DENSE, "PCA only works with dense features")
REQUIRE(features->get_feature_type()==F_DREAL, "PCA only works with real features")
SGMatrix<float64_t> feature_matrix = ((CDenseFeatures<float64_t>*)features)
->get_feature_matrix();
int32_t num_vectors = feature_matrix.num_cols;
int32_t num_features = feature_matrix.num_rows;
SG_INFO("num_examples: %ld num_features: %ld \n", num_vectors, num_features)
// max target dim allowed
int32_t max_dim_allowed = CMath::min(num_vectors, num_features);
num_dim=0;
REQUIRE(m_target_dim<=max_dim_allowed,
"target dimension should be less or equal to than minimum of N and D")
// center data
Map<MatrixXd> fmatrix(feature_matrix.matrix, num_features, num_vectors);
m_mean_vector = SGVector<float64_t>(num_features);
Map<VectorXd> data_mean(m_mean_vector.vector, num_features);
data_mean = fmatrix.rowwise().sum()/(float64_t) num_vectors;
fmatrix = fmatrix.colwise()-data_mean;
m_eigenvalues_vector = SGVector<float64_t>(max_dim_allowed);
Map<VectorXd> eigenValues(m_eigenvalues_vector.vector, max_dim_allowed);
if (m_method == AUTO)
m_method = (num_vectors>num_features) ? EVD : SVD;
if (m_method == EVD)
{
// covariance matrix
MatrixXd cov_mat(num_features, num_features);
cov_mat = fmatrix*fmatrix.transpose();
cov_mat /= (num_vectors-1);
SG_INFO("Computing Eigenvalues ... ")
// eigen value computed
SelfAdjointEigenSolver<MatrixXd> eigenSolve =
SelfAdjointEigenSolver<MatrixXd>(cov_mat);
eigenValues = eigenSolve.eigenvalues().tail(max_dim_allowed);
// target dimension
switch (m_mode)
{
case FIXED_NUMBER :
num_dim = m_target_dim;
break;
case VARIANCE_EXPLAINED :
{
float64_t eig_sum = eigenValues.sum();
float64_t com_sum = 0;
for (int32_t i=num_features-1; i<-1; i++)
{
num_dim++;
com_sum += m_eigenvalues_vector.vector[i];
if (com_sum/eig_sum>=m_thresh)
break;
}
}
break;
case THRESHOLD :
for (int32_t i=num_features-1; i<-1; i++)
{
if (m_eigenvalues_vector.vector[i]>m_thresh)
num_dim++;
else
break;
}
break;
};
SG_INFO("Done\nReducing from %i to %i features..", num_features, num_dim)
m_transformation_matrix = SGMatrix<float64_t>(num_features,num_dim);
Map<MatrixXd> transformMatrix(m_transformation_matrix.matrix,
num_features, num_dim);
num_old_dim = num_features;
// eigenvector matrix
transformMatrix = eigenSolve.eigenvectors().block(0,
num_features-num_dim, num_features,num_dim);
if (m_whitening)
{
for (int32_t i=0; i<num_dim; i++)
{
if (CMath::fequals_abs<float64_t>(0.0, eigenValues[i+max_dim_allowed-num_dim],
m_eigenvalue_zero_tolerance))
{
SG_WARNING("Covariance matrix has almost zero Eigenvalue (ie "
"Eigenvalue within a tolerance of %E around 0) at "
"dimension %d. Consider reducing its dimension.",
m_eigenvalue_zero_tolerance, i+max_dim_allowed-num_dim+1)
transformMatrix.col(i) = MatrixXd::Zero(num_features,1);
continue;
}
transformMatrix.col(i) /=
CMath::sqrt(eigenValues[i+max_dim_allowed-num_dim]*(num_vectors-1));
}
}
}
else
{
// compute SVD of data matrix
JacobiSVD<MatrixXd> svd(fmatrix.transpose(), ComputeThinU | ComputeThinV);
// compute non-negative eigen values from singular values
eigenValues = svd.singularValues();
eigenValues = eigenValues.cwiseProduct(eigenValues)/(num_vectors-1);
// target dimension
switch (m_mode)
{
case FIXED_NUMBER :
num_dim = m_target_dim;
break;
case VARIANCE_EXPLAINED :
{
float64_t eig_sum = eigenValues.sum();
float64_t com_sum = 0;
for (int32_t i=0; i<num_features; i++)
{
num_dim++;
com_sum += m_eigenvalues_vector.vector[i];
if (com_sum/eig_sum>=m_thresh)
break;
}
}
break;
case THRESHOLD :
for (int32_t i=0; i<num_features; i++)
{
if (m_eigenvalues_vector.vector[i]>m_thresh)
num_dim++;
else
break;
}
break;
};
SG_INFO("Done\nReducing from %i to %i features..", num_features, num_dim)
// right singular vectors form eigenvectors
m_transformation_matrix = SGMatrix<float64_t>(num_features,num_dim);
Map<MatrixXd> transformMatrix(m_transformation_matrix.matrix, num_features, num_dim);
num_old_dim = num_features;
transformMatrix = svd.matrixV().block(0, 0, num_features, num_dim);
if (m_whitening)
{
for (int32_t i=0; i<num_dim; i++)
{
if (CMath::fequals_abs<float64_t>(0.0, eigenValues[i],
m_eigenvalue_zero_tolerance))
{
SG_WARNING("Covariance matrix has almost zero Eigenvalue (ie "
"Eigenvalue within a tolerance of %E around 0) at "
"dimension %d. Consider reducing its dimension.",
m_eigenvalue_zero_tolerance, i+1)
transformMatrix.col(i) = MatrixXd::Zero(num_features,1);
continue;
}
transformMatrix.col(i) /= CMath::sqrt(eigenValues[i]*(num_vectors-1));
}
}
}
// restore feature matrix
fmatrix = fmatrix.colwise()+data_mean;
m_initialized = true;
return true;
}
return false;
}
CFeatures* CJade::apply(CFeatures* features)
{
ASSERT(features);
SG_REF(features);
SGMatrix<float64_t> X = ((CDenseFeatures<float64_t>*)features)->get_feature_matrix();
int n = X.num_rows;
int T = X.num_cols;
int m = n;
Map<MatrixXd> EX(X.matrix,n,T);
// Mean center X
VectorXd mean = (EX.rowwise().sum() / (float64_t)T);
MatrixXd SPX = EX.colwise() - mean;
MatrixXd cov = (SPX * SPX.transpose()) / (float64_t)T;
#ifdef DEBUG_JADE
std::cout << "cov" << std::endl;
std::cout << cov << std::endl;
#endif
// Whitening & Projection onto signal subspace
SelfAdjointEigenSolver<MatrixXd> eig;
eig.compute(cov);
#ifdef DEBUG_JADE
std::cout << "eigenvectors" << std::endl;
std::cout << eig.eigenvectors() << std::endl;
std::cout << "eigenvalues" << std::endl;
std::cout << eig.eigenvalues().asDiagonal() << std::endl;
#endif
// Scaling
VectorXd scales = eig.eigenvalues().cwiseSqrt();
MatrixXd B = scales.cwiseInverse().asDiagonal() * eig.eigenvectors().transpose();
#ifdef DEBUG_JADE
std::cout << "whitener" << std::endl;
std::cout << B << std::endl;
#endif
// Sphering
SPX = B * SPX;
// Estimation of the cumulant matrices
int dimsymm = (m * ( m + 1)) / 2; // Dim. of the space of real symm matrices
int nbcm = dimsymm; // number of cumulant matrices
m_cumulant_matrix = SGMatrix<float64_t>(m,m*nbcm); // Storage for cumulant matrices
Map<MatrixXd> CM(m_cumulant_matrix.matrix,m,m*nbcm);
MatrixXd R(m,m); R.setIdentity();
MatrixXd Qij = MatrixXd::Zero(m,m); // Temp for a cum. matrix
VectorXd Xim = VectorXd::Zero(m); // Temp
VectorXd Xjm = VectorXd::Zero(m); // Temp
VectorXd Xijm = VectorXd::Zero(m); // Temp
int Range = 0;
for (int im = 0; im < m; im++)
{
Xim = SPX.row(im);
Xijm = Xim.cwiseProduct(Xim);
Qij = SPX.cwiseProduct(Xijm.replicate(1,m).transpose()) * SPX.transpose() / (float)T - R - 2*R.col(im)*R.col(im).transpose();
CM.block(0,Range,m,m) = Qij;
Range = Range + m;
for (int jm = 0; jm < im; jm++)
{
Xjm = SPX.row(jm);
Xijm = Xim.cwiseProduct(Xjm);
Qij = SPX.cwiseProduct(Xijm.replicate(1,m).transpose()) * SPX.transpose() / (float)T - R.col(im)*R.col(jm).transpose() - R.col(jm)*R.col(im).transpose();
CM.block(0,Range,m,m) = sqrt(2)*Qij;
Range = Range + m;
}
}
#ifdef DEBUG_JADE
std::cout << "cumulatant matrices" << std::endl;
std::cout << CM << std::endl;
#endif
// Stack cumulant matrix into ND Array
index_t * M_dims = SG_MALLOC(index_t, 3);
M_dims[0] = m;
M_dims[1] = m;
M_dims[2] = nbcm;
SGNDArray< float64_t > M(M_dims, 3);
for (int i = 0; i < nbcm; i++)
{
Map<MatrixXd> EM(M.get_matrix(i),m,m);
EM = CM.block(0,i*m,m,m);
}
// Diagonalize
SGMatrix<float64_t> Q = CJADiagOrth::diagonalize(M, m_mixing_matrix, tol, max_iter);
Map<MatrixXd> EQ(Q.matrix,m,m);
EQ = -1 * EQ.inverse();
#ifdef DEBUG_JADE
std::cout << "diagonalizer" << std::endl;
std::cout << EQ << std::endl;
#endif
// Separating matrix
SGMatrix<float64_t> sep_matrix = SGMatrix<float64_t>(m,m);
Map<MatrixXd> C(sep_matrix.matrix,m,m);
C = EQ.transpose() * B;
// Sort
VectorXd A = (B.inverse()*EQ).cwiseAbs2().colwise().sum();
bool swap = false;
do
{
swap = false;
for (int j = 1; j < n; j++)
{
if ( A(j) < A(j-1) )
{
std::swap(A(j),A(j-1));
C.col(j).swap(C.col(j-1));
swap = true;
}
}
} while(swap);
for (int j = 0; j < m/2; j++)
C.row(j).swap( C.row(m-1-j) );
// Fix Signs
VectorXd signs = VectorXd::Zero(m);
for (int i = 0; i < m; i++)
{
if( C(i,0) < 0 )
signs(i) = -1;
else
signs(i) = 1;
}
C = signs.asDiagonal() * C;
#ifdef DEBUG_JADE
std::cout << "un mixing matrix" << std::endl;
std::cout << C << std::endl;
#endif
// Unmix
EX = C * EX;
m_mixing_matrix = SGMatrix<float64_t>(m,m);
Map<MatrixXd> Emixing_matrix(m_mixing_matrix.matrix,m,m);
Emixing_matrix = C.inverse();
return features;
}
bool HSpatialDivision2::divide(int target_count)
{
/* - variables - */
HSDVertexCluster2 vc;
int i, lastClusterCount = 1, continuousUnchangeCount = 0;
float diff;
// maximum/minimum curvature and the direction
float maxCurvature; // maximum curvature
float minCurvature; // minimum curvature
HNormal maxDir; // maximum curvature direction
HNormal minDir; // maximum curvature direction
SelfAdjointEigenSolver<Matrix3f> eigensolver;
solver = &eigensolver;
Matrix3f M;
htime.setCheckPoint();
/* - routines - */
// init the first cluster
for (i = 0; i < vertexCount; i ++)
if (vertices[i].clusterIndex != INVALID_CLUSTER_INDEX)
vc.addVertex(i, vertices[i]);
cout << "\t-----------------------------------------------" << endl
<< "\tnon-referenced vertices count:\t" << vertexCount - vc.vIndices->size() << endl
<< "\tminimum normal-vari factor:\t" << HSDVertexCluster2::MINIMUM_NORMAL_VARI << endl
<< "\tvalid vertices count:\t" << vc.vIndices->size() << endl;
flog << "\t-----------------------------------------------" << endl
<< "\tnon-referenced vertices count:\t" << vertexCount - vc.vIndices->size() << endl
<< "\tminimum normal-vari factor:\t" << HSDVertexCluster2::MINIMUM_NORMAL_VARI << endl
<< "\tvalid vertices count:\t" << vc.vIndices->size() << endl;
for (i = 0; i < faceCount; i ++) {
vc.addFace(i);
}
vc.importance = vc.getImportance();
clusters.addElement(vc);
vector<int> index; // index of eigenvalues in eigensolver.eigenvalues()
index.push_back(0);
index.push_back(1);
index.push_back(2);
// subdivide until the divided clusters reach the target count
while(clusters.count() < target_count)
{
//diff = ((float)target_count - (float)clusters.count()) / target_count;
//if (diff < 0.01) {
// break;
//}
if (clusters.count() == lastClusterCount) {
continuousUnchangeCount ++;
}
else {
continuousUnchangeCount = 0;
}
if (continuousUnchangeCount >= 50) {
cout << "\tstop without reaching the target count because of unchanged cluster count" << endl;
flog << "\tstop without reaching the target count because of unchanged cluster count" << endl;
break;
}
if (clusters.empty()) {
cerr << "#error: don't know why but the clusters heap have came to empty" << endl;
flog << "#error: don't know why but the clusters heap have came to empty" << endl;
return false;
}
// get the value of the top in the heap of clusters and delete it
lastClusterCount = clusters.count();
vc = clusters.getTop();
clusters.deleteTop();
//PrintHeap(fout, clusters);
// get the eigenvalue
M << vc.awQ.a11, vc.awQ.a12, vc.awQ.a13,
vc.awQ.a12, vc.awQ.a22, vc.awQ.a23,
vc.awQ.a13, vc.awQ.a23, vc.awQ.a33;
eigensolver.compute(M);
if (eigensolver.info() != Success) {
cerr << "#error: eigenvalues computing error" << endl;
flog << "#error: eigenvalues computing error" << endl;
return false;
}
// sort the eigenvalues in descending order by the index
std::sort(index.begin(), index.end(), cmp);
// get the maximum/minimum curvature and the direction
maxCurvature = eigensolver.eigenvalues()(index[1]); // maximum curvature
minCurvature = eigensolver.eigenvalues()(index[2]); // minimum curvature
maxDir.Set(eigensolver.eigenvectors()(0, index[1]),
eigensolver.eigenvectors()(1, index[1]),
eigensolver.eigenvectors()(2, index[1])); // maximum curvature direction
minDir.Set(eigensolver.eigenvectors()(0, index[2]),
eigensolver.eigenvectors()(1, index[2]),
eigensolver.eigenvectors()(2, index[2])); // maximum curvature direction
// partition to 8
if (vc.awN.Length() / vc.area < SPHERE_MEAN_NORMAL_THRESH)
{
HNormal p_nm = maxDir ^ minDir;
partition8(vc,
p_nm, HFaceFormula::calcD(p_nm, vc.meanVertex),
maxDir, HFaceFormula::calcD(maxDir, vc.meanVertex),
minDir, HFaceFormula::calcD(minDir, vc.meanVertex));
}
// partition to 4
else if (maxCurvature / minCurvature < MAX_MIN_CURVATURE_RATIO_TREATED_AS_HEMISPHERE)
{
partition4(vc,
maxDir, HFaceFormula::calcD(maxDir, vc.meanVertex),
minDir, HFaceFormula::calcD(minDir, vc.meanVertex));
}
// partition to 2
else {
partition2(vc, maxDir, HFaceFormula::calcD(maxDir, vc.meanVertex));
}
#ifdef PRINT_DEBUG_INFO
PrintHeap(fdebug, clusters);
#endif
}
//PrintHeap(cout, clusters);
cout << "\tsimplification time:\t" << htime.printElapseSec() << endl << endl;
flog << "\tsimplification time:\t" << htime.printElapseSec() << endl << endl;
return true;
}
template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
/* this test covers the following files:
EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h)
*/
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
RealScalar largerEps = 10*test_precision<RealScalar>();
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
MatrixType symmC = symmA;
// randomly nullify some rows/columns
{
Index count = 1;//internal::random<Index>(-cols,cols);
for(Index k=0; k<count; ++k)
{
Index i = internal::random<Index>(0,cols-1);
symmA.row(i).setZero();
symmA.col(i).setZero();
}
}
symmA.template triangularView<StrictlyUpper>().setZero();
symmC.template triangularView<StrictlyUpper>().setZero();
MatrixType b = MatrixType::Random(rows,cols);
MatrixType b1 = MatrixType::Random(rows,cols);
MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
symmB.template triangularView<StrictlyUpper>().setZero();
SelfAdjointEigenSolver<MatrixType> eiSymm(symmA);
SelfAdjointEigenSolver<MatrixType> eiDirect;
eiDirect.computeDirect(symmA);
// generalized eigen pb
GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmC, symmB);
VERIFY_IS_EQUAL(eiSymm.info(), Success);
VERIFY((symmA.template selfadjointView<Lower>() * eiSymm.eigenvectors()).isApprox(
eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal(), largerEps));
VERIFY_IS_APPROX(symmA.template selfadjointView<Lower>().eigenvalues(), eiSymm.eigenvalues());
VERIFY_IS_EQUAL(eiDirect.info(), Success);
VERIFY((symmA.template selfadjointView<Lower>() * eiDirect.eigenvectors()).isApprox(
eiDirect.eigenvectors() * eiDirect.eigenvalues().asDiagonal(), largerEps));
VERIFY_IS_APPROX(symmA.template selfadjointView<Lower>().eigenvalues(), eiDirect.eigenvalues());
SelfAdjointEigenSolver<MatrixType> eiSymmNoEivecs(symmA, false);
VERIFY_IS_EQUAL(eiSymmNoEivecs.info(), Success);
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues());
// generalized eigen problem Ax = lBx
eiSymmGen.compute(symmC, symmB,Ax_lBx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox(
symmB.template selfadjointView<Lower>() * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem BAx = lx
eiSymmGen.compute(symmC, symmB,BAx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmB.template selfadjointView<Lower>() * (symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
// generalized eigen problem ABx = lx
eiSymmGen.compute(symmC, symmB,ABx_lx);
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
VERIFY((symmC.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
eiSymm.compute(symmC);
MatrixType sqrtSymmA = eiSymm.operatorSqrt();
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA);
VERIFY_IS_APPROX(sqrtSymmA, symmC.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt());
MatrixType id = MatrixType::Identity(rows, cols);
VERIFY_IS_APPROX(id.template selfadjointView<Lower>().operatorNorm(), RealScalar(1));
SelfAdjointEigenSolver<MatrixType> eiSymmUninitialized;
VERIFY_RAISES_ASSERT(eiSymmUninitialized.info());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvalues());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
eiSymmUninitialized.compute(symmA, false);
VERIFY_RAISES_ASSERT(eiSymmUninitialized.eigenvectors());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorSqrt());
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
// test Tridiagonalization's methods
Tridiagonalization<MatrixType> tridiag(symmC);
// FIXME tridiag.matrixQ().adjoint() does not work
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
if (rows > 1)
{
// Test matrix with NaN
symmC(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmC);
VERIFY_IS_EQUAL(eiSymmNaN.info(), NoConvergence);
}
}
