void assemble_cortical(const Geometry& geo, Matrix& mat, const Head2EEGMat& M, const std::string& domain_name, const unsigned gauss_order, double alpha, double beta, const std::string &filename)
{
// Following the article: M. Clerc, J. Kybic "Cortical mapping by Laplace–Cauchy transmission using a boundary element method".
// Assumptions:
// - domain_name: the domain containing the sources is an innermost domain (defined as the interior of only one interface (called Cortex))
// - Cortex interface is composed of one mesh only (no shared vertices)
const Domain& SourceDomain = geo.domain(domain_name);
const Interface& Cortex = SourceDomain.begin()->interface();
const Mesh& cortex = Cortex.begin()->mesh();
om_error(SourceDomain.size()==1);
om_error(Cortex.size()==1);
// shape of the new matrix:
unsigned Nl = geo.size()-geo.nb_current_barrier_triangles()-Cortex.nb_vertices()-Cortex.nb_triangles();
unsigned Nc = geo.size()-geo.nb_current_barrier_triangles();
std::fstream f(filename.c_str());
Matrix P;
if ( !f ) {
// build the HeadMat:
// The following is the same as assemble_HM except N_11, D_11 and S_11 are not computed.
SymMatrix mat_temp(Nc);
mat_temp.set(0.0);
double K = 1.0 / (4.0 * M_PI);
// We iterate over the meshes (or pair of domains) to fill the lower half of the HeadMat (since its symmetry)
for ( Geometry::const_iterator mit1 = geo.begin(); mit1 != geo.end(); ++mit1) {
for ( Geometry::const_iterator mit2 = geo.begin(); (mit2 != (mit1+1)); ++mit2) {
// if mit1 and mit2 communicate, i.e they are used for the definition of a common domain
const int orientation = geo.oriented(*mit1, *mit2); // equals 0, if they don't have any domains in common
// equals 1, if they are both oriented toward the same domain
// equals -1, if they are not
if ( orientation != 0) {
double Scoeff = orientation * geo.sigma_inv(*mit1, *mit2) * K;
double Dcoeff = - orientation * geo.indicator(*mit1, *mit2) * K;
double Ncoeff;
if ( !(mit1->current_barrier() || mit2->current_barrier()) && ( (*mit1 != *mit2)||( *mit1 != cortex) ) ) {
// Computing S block first because it's needed for the corresponding N block
operatorS(*mit1, *mit2, mat_temp, Scoeff, gauss_order);
Ncoeff = geo.sigma(*mit1, *mit2)/geo.sigma_inv(*mit1, *mit2);
} else {
Ncoeff = orientation * geo.sigma(*mit1, *mit2) * K;
}
if ( !mit1->current_barrier() && (( (*mit1 != *mit2)||( *mit1 != cortex) )) ) {
// Computing D block
operatorD(*mit1, *mit2, mat_temp, Dcoeff, gauss_order,false);
}
if ( ( *mit1 != *mit2 ) && ( !mit2->current_barrier() ) ) {
// Computing D* block
operatorD(*mit1, *mit2, mat_temp, Dcoeff, gauss_order, true);
}
// Computing N block
if ( (*mit1 != *mit2)||( *mit1 != cortex) ) {
operatorN(*mit1, *mit2, mat_temp, Ncoeff, gauss_order);
}
}
}
}
// Deflate all current barriers as one
deflat(mat_temp,geo);
mat = Matrix(Nl, Nc);
mat.set(0.0);
// copy mat_temp into mat except the lines for cortex vertices [i_vb_c, i_ve_c] and cortex triangles [i_tb_c, i_te_c].
unsigned iNl = 0;
for ( Geometry::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
if ( *mit != cortex ) {
for ( Mesh::const_vertex_iterator vit = mit->vertex_begin(); vit != mit->vertex_end(); ++vit) {
mat.setlin(iNl, mat_temp.getlin((*vit)->index()));
++iNl;
}
if ( !mit->current_barrier() ) {
for ( Mesh::const_iterator tit = mit->begin(); tit != mit->end(); ++tit) {
mat.setlin(iNl, mat_temp.getlin(tit->index()));
++iNl;
}
}
}
}
// ** Construct P: the null-space projector **
Matrix W;
{
Matrix U, s;
mat.svd(U, s, W);
}
SparseMatrix S(Nc,Nc);
// we set S to 0 everywhere, except in the last part of the diag:
for ( unsigned i = Nl; i < Nc; ++i) {
S(i, i) = 1.0;
}
P = (W * S) * W.transpose(); // P is a projector: P^2 = P and mat*P*X = 0
if ( filename.length() != 0 ) {
std::cout << "Saving projector P (" << filename << ")." << std::endl;
P.save(filename);
}
} else {
std::cout << "Loading projector P (" << filename << ")." << std::endl;
P.load(filename);
}
// ** Get the gradient of P1&P0 elements on the meshes **
Matrix MM(M.transpose() * M);
SymMatrix RR(Nc, Nc); RR.set(0.);
for ( Geometry::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
mit->gradient_norm2(RR);
}
// ** Choose Regularization parameter **
SparseMatrix alphas(Nc,Nc); // diagonal matrix
Matrix Z;
if ( alpha < 0 ) { // try an automatic method... TODO find better estimation
double nRR_v = RR.submat(0, geo.nb_vertices(), 0, geo.nb_vertices()).frobenius_norm();
alphas.set(0.);
alpha = MM.frobenius_norm() / (1.e3*nRR_v);
beta = alpha * 50000.;
for ( Vertices::const_iterator vit = geo.vertex_begin(); vit != geo.vertex_end(); ++vit) {
alphas(vit->index(), vit->index()) = alpha;
}
for ( Meshes::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
if ( !mit->current_barrier() ) {
for ( Mesh::const_iterator tit = mit->begin(); tit != mit->end(); ++tit) {
alphas(tit->index(), tit->index()) = beta;
}
}
}
std::cout << "AUTOMATIC alphas = " << alpha << "\tbeta = " << beta << std::endl;
} else {
for ( Vertices::const_iterator vit = geo.vertex_begin(); vit != geo.vertex_end(); ++vit) {
alphas(vit->index(), vit->index()) = alpha;
}
for ( Meshes::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
if ( !mit->current_barrier() ) {
for ( Mesh::const_iterator tit = mit->begin(); tit != mit->end(); ++tit) {
alphas(tit->index(), tit->index()) = beta;
}
}
}
std::cout << "alphas = " << alpha << "\tbeta = " << beta << std::endl;
}
Z = P.transpose() * (MM + alphas*RR) * P;
// ** PseudoInverse and return **
// X = P * { (M*P)' * (M*P) + (R*P)' * (R*P) }¡(-1) * (M*P)'m
// X = P * { P'*M'*M*P + P'*R'*R*P }¡(-1) * P'*M'm
// X = P * { P'*(MM + a*RR)*P }¡(-1) * P'*M'm
// X = P * Z¡(-1) * P' * M'm
Matrix rhs = P.transpose() * M.transpose();
mat = P * Z.pinverse() * rhs;
}
MeshData * JSONDataParser::_parseMesh(const rapidjson::Value & rawData)
{
const auto mesh = BaseObject::borrowObject<MeshData>();
const auto rawVertices = rawData[VERTICES].GetArray();
const auto rawUVs = rawData[UVS].GetArray();
const auto rawTriangles = rawData[TRIANGLES].GetArray();
const auto numVertices = (unsigned)(rawVertices.Size() / 2);
const auto numTriangles = (unsigned)(rawTriangles.Size() / 3);
std::vector<Matrix> inverseBindPose(this->_armature->getSortedBones().size(), Matrix());
mesh->skinned = rawData.HasMember(WEIGHTS) && !rawData[WEIGHTS].GetArray().Empty();
mesh->uvs.resize(numVertices * 2);
mesh->vertices.resize(numVertices * 2);
mesh->vertexIndices.resize(numTriangles * 3);
if (mesh->skinned)
{
mesh->boneIndices.resize(numVertices);
mesh->weights.resize(numVertices);
mesh->boneVertices.resize(numVertices);
if (rawData.HasMember(SLOT_POSE))
{
const auto& rawSlotPose = rawData[SLOT_POSE].GetArray();
mesh->slotPose.a = rawSlotPose[0].GetFloat();
mesh->slotPose.b = rawSlotPose[1].GetFloat();
mesh->slotPose.c = rawSlotPose[2].GetFloat();
mesh->slotPose.d = rawSlotPose[3].GetFloat();
mesh->slotPose.tx = rawSlotPose[4].GetFloat();
mesh->slotPose.ty = rawSlotPose[5].GetFloat();
}
if (rawData.HasMember(BONE_POSE))
{
const auto& rawBonePose = rawData[BONE_POSE].GetArray();
for (std::size_t i = 0, l = rawBonePose.Size(); i < l; i += 7)
{
const auto rawBoneIndex = rawBonePose[i].GetUint();
auto& boneMatrix = inverseBindPose[rawBoneIndex];
boneMatrix.a = rawBonePose[i + 1].GetFloat();
boneMatrix.b = rawBonePose[i + 2].GetFloat();
boneMatrix.c = rawBonePose[i + 3].GetFloat();
boneMatrix.d = rawBonePose[i + 4].GetFloat();
boneMatrix.tx = rawBonePose[i + 5].GetFloat();
boneMatrix.ty = rawBonePose[i + 6].GetFloat();
boneMatrix.invert();
}
}
}
for (std::size_t i = 0, iW = 0, l = rawVertices.Size(); i < l; i += 2)
{
const auto iN = i + 1;
const auto vertexIndex = i / 2;
auto x = mesh->vertices[i] = rawVertices[i].GetFloat() * this->_armatureScale;
auto y = mesh->vertices[iN] = rawVertices[iN].GetFloat() * this->_armatureScale;
mesh->uvs[i] = rawUVs[i].GetFloat();
mesh->uvs[iN] = rawUVs[iN].GetFloat();
if (mesh->skinned)
{
const auto rawWeights = rawData[WEIGHTS].GetArray();
const auto numBones = rawWeights[iW].GetUint();
auto& indices = mesh->boneIndices[vertexIndex];
auto& weights = mesh->weights[vertexIndex];
auto& boneVertices = mesh->boneVertices[vertexIndex];
mesh->slotPose.transformPoint(x, y, _helpPoint);
x = mesh->vertices[i] = _helpPoint.x;
y = mesh->vertices[iN] = _helpPoint.y;
for (std::size_t iB = 0; iB < numBones; ++iB)
{
const auto iI = iW + 1 + iB * 2;
const auto rawBoneIndex = rawWeights[iI].GetUint();
const auto boneData = this->_rawBones[rawBoneIndex];
const auto iderator = std::find(mesh->bones.cbegin(), mesh->bones.cend(), boneData);
std::size_t boneIndex = 0;
if (iderator == mesh->bones.cend())
{
boneIndex = mesh->bones.size();
mesh->bones.push_back(boneData);
mesh->inverseBindPose.push_back(inverseBindPose[rawBoneIndex]); // copy
}
else
{
boneIndex = std::distance(mesh->bones.cbegin(), iderator);
}
mesh->inverseBindPose[boneIndex].transformPoint(x, y, _helpPoint);
indices.push_back(boneIndex);
weights.push_back(rawWeights[iI + 1].GetFloat());
boneVertices.push_back(_helpPoint.x);
boneVertices.push_back(_helpPoint.y);
}
iW += numBones * 2 + 1;
}
}
for (std::size_t i = 0, l = rawTriangles.Size(); i < l; ++i)
{
mesh->vertexIndices[i] = rawTriangles[i].GetUint();
}
return mesh;
}
MeshComponent::MeshComponent(Mesh* mesh, float scale) {
this->mesh = mesh;
this->scale = Matrix();
this->scale *= scale;
}
MeshComponent::MeshComponent() {
this->type = EActorComponent::MESH;
this->scale = Matrix();
}
void assemble_cortical2(const Geometry& geo, Matrix& mat, const Head2EEGMat& M, const std::string& domain_name, const unsigned gauss_order, double gamma, const std::string &filename)
{
// Re-writting of the optimization problem in M. Clerc, J. Kybic "Cortical mapping by Laplace–Cauchy transmission using a boundary element method".
// with a Lagrangian formulation as in see http://www.math.uh.edu/~rohop/fall_06/Chapter3.pdf eq3.3
// find argmin(norm(gradient(X)) under constraints:
// H * X = 0 and M * X = m
// let G be the gradient norm matrix, l1, l2 the lagrange parameters
//
// [ G H' M'] [ X ] [ 0 ]
// | H 0 | | l1 | = | 0 |
// [ M 0 ] [ l2 ] [ m ]
//
// {----,----}
// K
// we want a submat of the inverse of K (using blockwise inversion, (TODO maybe iterative solution better ?)).
// Assumptions:
// - domain_name: the domain containing the sources is an innermost domain (defined as the interior of only one interface (called Cortex)
// - Cortex interface is composed of one mesh only (no shared vertices)
const Domain& SourceDomain = geo.domain(domain_name);
const Interface& Cortex = SourceDomain.begin()->interface();
const Mesh& cortex = Cortex.begin()->mesh();
om_error(SourceDomain.size()==1);
om_error(Cortex.size()==1);
// shape of the new matrix:
unsigned Nl = geo.size()-geo.nb_current_barrier_triangles()-Cortex.nb_vertices()-Cortex.nb_triangles();
unsigned Nc = geo.size()-geo.nb_current_barrier_triangles();
std::fstream f(filename.c_str());
Matrix H;
if ( !f ) {
// build the HeadMat:
// The following is the same as assemble_HM except N_11, D_11 and S_11 are not computed.
SymMatrix mat_temp(Nc);
mat_temp.set(0.0);
double K = 1.0 / (4.0 * M_PI);
// We iterate over the meshes (or pair of domains) to fill the lower half of the HeadMat (since its symmetry)
for ( Geometry::const_iterator mit1 = geo.begin(); mit1 != geo.end(); ++mit1) {
for ( Geometry::const_iterator mit2 = geo.begin(); (mit2 != (mit1+1)); ++mit2) {
// if mit1 and mit2 communicate, i.e they are used for the definition of a common domain
const int orientation = geo.oriented(*mit1, *mit2); // equals 0, if they don't have any domains in common
// equals 1, if they are both oriented toward the same domain
// equals -1, if they are not
if ( orientation != 0) {
double Scoeff = orientation * geo.sigma_inv(*mit1, *mit2) * K;
double Dcoeff = - orientation * geo.indicator(*mit1, *mit2) * K;
double Ncoeff;
if ( !(mit1->current_barrier() || mit2->current_barrier()) && ( (*mit1 != *mit2)||( *mit1 != cortex) ) ) {
// Computing S block first because it's needed for the corresponding N block
operatorS(*mit1, *mit2, mat_temp, Scoeff, gauss_order);
Ncoeff = geo.sigma(*mit1, *mit2)/geo.sigma_inv(*mit1, *mit2);
} else {
Ncoeff = orientation * geo.sigma(*mit1, *mit2) * K;
}
if ( !mit1->current_barrier() && (( (*mit1 != *mit2)||( *mit1 != cortex) )) ) {
// Computing D block
operatorD(*mit1, *mit2, mat_temp, Dcoeff, gauss_order);
}
if ( ( *mit1 != *mit2 ) && ( !mit2->current_barrier() ) ) {
// Computing D* block
operatorD(*mit1, *mit2, mat_temp, Dcoeff, gauss_order, true);
}
// Computing N block
if ( (*mit1 != *mit2)||( *mit1 != cortex) ) {
operatorN(*mit1, *mit2, mat_temp, Ncoeff, gauss_order);
}
}
}
}
// Deflate all current barriers as one
deflat(mat_temp,geo);
H = Matrix(Nl + M.nlin(), Nc);
H.set(0.0);
// copy mat_temp into H except the lines for cortex vertices [i_vb_c, i_ve_c] and cortex triangles [i_tb_c, i_te_c].
unsigned iNl = 0;
for ( Geometry::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
if ( *mit != cortex ) {
for ( Mesh::const_vertex_iterator vit = mit->vertex_begin(); vit != mit->vertex_end(); ++vit) {
H.setlin(iNl, mat_temp.getlin((*vit)->index()));
++iNl;
}
if ( !mit->current_barrier() ) {
for ( Mesh::const_iterator tit = mit->begin(); tit != mit->end(); ++tit) {
H.setlin(iNl, mat_temp.getlin(tit->index()));
++iNl;
}
}
}
}
if ( filename.length() != 0 ) {
std::cout << "Saving matrix H (" << filename << ")." << std::endl;
H.save(filename);
}
} else {
std::cout << "Loading matrix H (" << filename << ")." << std::endl;
H.load(filename);
}
// concat M to H
for ( unsigned i = Nl; i < Nl + M.nlin(); ++i) {
for ( unsigned j = 0; j < Nc; ++j) {
H(i, j) = M(i-Nl, j);
}
}
// ** Get the gradient of P1&P0 elements on the meshes **
SymMatrix G(Nc);
G.set(0.);
for ( Geometry::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
mit->gradient_norm2(G);
}
// multiply by gamma the submat of current gradient norm2
for ( Meshes::const_iterator mit = geo.begin(); mit != geo.end(); ++mit) {
if ( !mit->current_barrier() ) {
for ( Mesh::const_iterator tit1 = mit->begin(); tit1 != mit->end(); ++tit1) {
for ( Mesh::const_iterator tit2 = mit->begin(); tit2 != mit->end(); ++tit2) {
G(tit1->index(), tit2->index()) *= gamma;
}
}
}
}
std::cout << "gamma = " << gamma << std::endl;
G.invert();
mat = (G * H.transpose() * (H * G * H.transpose()).inverse()).submat(0, Nc, Nl, M.nlin());
}
Response*
ElasticForceBeamColumn2d::setResponse(const char **argv, int argc, OPS_Stream &output)
{
Response *theResponse = 0;
output.tag("ElementOutput");
output.attr("eleType","ElasticForceBeamColumn2d");
output.attr("eleTag",this->getTag());
output.attr("node1",connectedExternalNodes[0]);
output.attr("node2",connectedExternalNodes[1]);
// global force -
if (strcmp(argv[0],"forces") == 0 || strcmp(argv[0],"force") == 0
|| strcmp(argv[0],"globalForce") == 0 || strcmp(argv[0],"globalForces") == 0) {
output.tag("ResponseType","Px_1");
output.tag("ResponseType","Py_1");
output.tag("ResponseType","Mz_1");
output.tag("ResponseType","Px_2");
output.tag("ResponseType","Py_2");
output.tag("ResponseType","Mz_2");
theResponse = new ElementResponse(this, 1, theVector);
// local force -
} else if (strcmp(argv[0],"localForce") == 0 || strcmp(argv[0],"localForces") == 0) {
output.tag("ResponseType","N_1");
output.tag("ResponseType","V_1");
output.tag("ResponseType","M_1");
output.tag("ResponseType","N_2");
output.tag("ResponseType","V_2");
output.tag("ResponseType","M_2");
theResponse = new ElementResponse(this, 2, theVector);
// basic force -
} else if (strcmp(argv[0],"basicForce") == 0 || strcmp(argv[0],"basicForces") == 0) {
output.tag("ResponseType","N");
output.tag("ResponseType","M_1");
output.tag("ResponseType","M_2");
theResponse = new ElementResponse(this, 7, Vector(3));
// chord rotation -
} else if (strcmp(argv[0],"chordRotation") == 0 || strcmp(argv[0],"chordDeformation") == 0
|| strcmp(argv[0],"basicDeformation") == 0) {
output.tag("ResponseType","eps");
output.tag("ResponseType","theta_1");
output.tag("ResponseType","theta_2");
theResponse = new ElementResponse(this, 3, Vector(3));
// plastic rotation -
} else if (strcmp(argv[0],"plasticRotation") == 0 || strcmp(argv[0],"plasticDeformation") == 0) {
output.tag("ResponseType","epsP");
output.tag("ResponseType","thetaP_1");
output.tag("ResponseType","thetaP_2");
theResponse = new ElementResponse(this, 4, Vector(3));
// point of inflection
} else if (strcmp(argv[0],"inflectionPoint") == 0) {
output.tag("ResponseType","inflectionPoint");
theResponse = new ElementResponse(this, 5, 0.0);
// tangent drift
} else if (strcmp(argv[0],"tangentDrift") == 0) {
theResponse = new ElementResponse(this, 6, Vector(2));
// basic forces
} else if (strcmp(argv[0],"basicForce") == 0)
theResponse = new ElementResponse(this, 7, Vector(3));
else if (strcmp(argv[0],"integrationPoints") == 0)
theResponse = new ElementResponse(this, 10, Vector(numSections));
else if (strcmp(argv[0],"integrationWeights") == 0)
theResponse = new ElementResponse(this, 11, Vector(numSections));
else if (strcmp(argv[0],"basicStiffness") == 0)
theResponse = new ElementResponse(this, 12, Matrix(3,3));
// section response -
else if (strstr(argv[0],"sectionX") != 0) {
if (argc > 2) {
double sectionLoc = atof(argv[1]);
double xi[maxNumSections];
double L = crdTransf->getInitialLength();
beamIntegr->getSectionLocations(numSections, L, xi);
sectionLoc /= L;
double minDistance = fabs(xi[0]-sectionLoc);
int sectionNum = 0;
for (int i = 1; i < numSections; i++) {
if (fabs(xi[i]-sectionLoc) < minDistance) {
minDistance = fabs(xi[i]-sectionLoc);
sectionNum = i;
}
}
output.tag("GaussPointOutput");
output.attr("number",sectionNum+1);
output.attr("eta",xi[sectionNum]*L);
theResponse = sections[sectionNum]->setResponse(&argv[2], argc-2, output);
}
}
// section response -
else if (strstr(argv[0],"section") != 0) {
if (argc > 2) {
int sectionNum = atoi(argv[1]);
if (sectionNum > 0 && sectionNum <= numSections) {
double xi[maxNumSections];
double L = crdTransf->getInitialLength();
beamIntegr->getSectionLocations(numSections, L, xi);
output.tag("GaussPointOutput");
output.attr("number",sectionNum);
output.attr("eta",xi[sectionNum-1]*L);
theResponse = sections[sectionNum-1]->setResponse(&argv[2], argc-2, output);
}
}
}
output.endTag(); // ElementOutput
return theResponse;
}
Matrix Matrix::operator/(float f) const
{
return Matrix(m_Matrix / f);
}
int main (int argc, char **argv)
{
#ifdef HAVE_MPI
MPI_Init(&argc, &argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
// define some Epetra objects
int n = Comm.NumProc() * 4;
Epetra_Map Map(n, 0, Comm);
Epetra_MultiVector x(Map, 2); x.Random();
Epetra_MultiVector b(Map, 2); x.Random();
Epetra_CrsMatrix Matrix(Copy, Map, 0);
// diagonal matrix
for (int i = 0; i < Map.NumMyElements(); ++i)
{
int ii = Map.GID(i);
double one = 1.0;
Matrix.InsertGlobalValues(ii, 1, &one, &ii);
}
Matrix.FillComplete();
Teuchos::ParameterList List;
List.set("int parameter", 10);
List.set("double parameter", 10.0);
List.set("std::string parameter", "std::string");
// ========================= //
// Part I: generate XML file //
// ========================= //
EpetraExt::XMLWriter XMLWriter(Comm, "data.xml");
std::vector<std::string> Content;
Content.push_back("This is an example of description");
Content.push_back("The description is as long as desired,");
Content.push_back("just put it in a std::vector of strings.");
XMLWriter.Create("MyProblem");
XMLWriter.Write("Author", "myself and others");
XMLWriter.Write("Date", "May 2006");
XMLWriter.Write("MyMap", Map);
XMLWriter.Write("MyMatrix", Matrix);
XMLWriter.Write("MyLHS", x);
XMLWriter.Write("MyRHS", b);
XMLWriter.Write("MyContent", Content);
XMLWriter.Write("MyParameters", List);
XMLWriter.Close();
// ================== //
// Part II: read data //
// ================== //
EpetraExt::XMLReader XMLReader(Comm, "data.xml");
Epetra_Map* MyMap;
Epetra_CrsMatrix* MyMatrix;
Epetra_MultiVector* MyLHS;
Epetra_MultiVector* MyRHS;
Teuchos::ParameterList MyParameters;
std::vector<std::string> Author;
std::vector<std::string> Date;
std::vector<std::string> MyContent;
XMLReader.Read("Author", Author);
XMLReader.Read("Date", Date);
XMLReader.Read("MyMap", MyMap);
XMLReader.Read("MyMatrix", MyMatrix);
XMLReader.Read("MyLHS", MyLHS);
XMLReader.Read("MyRHS", MyRHS);
XMLReader.Read("MyContent", MyContent);
XMLReader.Read("MyParameters", MyParameters);
std::cout << *MyMap;
std::cout << *MyMatrix;
std::cout << *MyLHS;
std::cout << *MyRHS;
if (Comm.MyPID() == 0)
{
int Msize = (int) MyContent.size();
for (int i = 0; i < Msize; ++i)
std::cout << MyContent[i] << std::endl;
std::cout << MyParameters;
std::cout << "Author = " << Author[0] << std::endl;
std::cout << "Date = " << Date[0] << std::endl;
}
delete MyMap;
delete MyMatrix;
delete MyLHS;
delete MyRHS;
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(EXIT_SUCCESS);
}
void Filter::Kalman(Joint joint, double &dx, double &dy)
{
Kalmans[Kalman_count++] = joint;
Kalman_count = Kalman_count % Kalman_limit;
Kalman_num++;
if (Kalman_num > Kalman_limit)
{
Kalman_num = Kalman_limit;
}
if (Kalman_num < Kalman_limit)
{
dx = joint.Position.X;
dy = joint.Position.Y;
return;
}
else
{
//X, Y
int haha;
haha = 1;
double x[5], y[5];
int pos = Kalman_count;
for (int i = 0; i < 5; i++)
{
x[i] = Kalmans[pos].Position.X;
y[i] = Kalmans[pos].Position.Y;
pos++;
pos = pos%Kalman_limit;
}
//求系数Ax, Ay
double Ax[5] = {
/*a0*/ x[0],
/*a1*/ 4 * (x[1] - x[0]) - 3 * x[2] + 4 * x[3] / 3 - x[4] / 4,
/*a2*/ 11 * x[4] / 24 - 7 * x[3] / 3 + 19 * x[2] / 4 - 13 * (x[1] - x[0]) / 3,
/*a3*/ x[4] / 3 - 7 * x[3] / 6 + x[2] - (x[1] - x[0]) / 2,
/*a4*/ (x[4] - 4 * x[3] + 6 * x[2] + 4 * (x[1] - x[0])) / 24
};
double Ay[5] = {
/*a0*/ y[0],
/*a1*/ 4 * (y[1] - y[0]) - 3 * y[2] + 4 * y[3] / 3 - y[4] / 4,
/*a2*/ 11 * y[4] / 24 - 7 * y[3] / 3 + 19 * y[2] / 4 - 13 * (y[1] - y[0]) / 3,
/*a3*/ y[4] / 3 - 7 * y[3] / 6 + y[2] - (y[1] - y[0]) / 2,
/*a4*/ (y[4] - 4 * y[3] + 6 * y[2] + 4 * (y[1] - y[0])) / 24
};
//求转换矩阵Fx, Fy
Matrix Fx(4, 4,
new double[16]{
1, 1, -0.5, (Ax[1] + 6 * Ax[3] - 4 * Ax[4]) / (24 * Ax[4]),
0, 1, 1, 0.5,
0, 0, 1, 1,
0, 0, 0, 1});
Matrix Fy(4, 4,
new double[16]{
1, 1, -0.5, (Ay[1] + 6 * Ay[3] - 4 * Ay[4]) / (24 * Ay[4]),
0, 1, 1, 0.5,
0, 0, 1, 1,
0, 0, 0, 1});
//求ε(t|t-1)
Matrix ex(4, 4), ey(4, 4);
ex = Fx*Kalman_ex*(!Fx);
ey = Fy*Kalman_ey*(!Fy);
//cout << "ex" << endl; ex.print();
//cout << "ey" << endl; ey.print();
Matrix Bx(4, 1), By(4, 1);
//cout << "!Kalman_C" << endl; (!Kalman_C).print();
//cout << "Kalman_vx" << endl; Kalman_vx.print();
//cout << "Kalman_C" << endl; Kalman_C.print();
//cout << "!Kalman_C" << endl; (!Kalman_C).print();
//cout << "Kalman_C*ex" << endl; (Kalman_C*ex).print();
//cout << "Kalman_C*ex*(!Kalman_C)" << endl; (Kalman_C*ex*(!Kalman_C)).print();
//cout << "(~(Kalman_vx + Kalman_C*ex*(!Kalman_C)))" << endl;
//(~(Kalman_vx + Kalman_C*ex*(!Kalman_C))).print();
Bx = ex*(!Kalman_C)*(~(Kalman_vx + Kalman_C*ex*(!Kalman_C)));
//cout << "Bx" << endl; Bx.print();
By = ey*(!Kalman_C)*(~(Kalman_vy + Kalman_C*ey*(!Kalman_C)));
Matrix I4(4, 4);
I4.SetIdentity();
Kalman_Sx = (I4 - Bx*Kalman_C)*(Fx*Kalman_Sx + Kalman_Gx) +
Bx*Matrix(1, 1, new double[1] {joint.Position.X});
//cout << "Kalman_Sx" << endl; Kalman_Sx.print();
Kalman_Sy = (I4 - By*Kalman_C)*(Fy*Kalman_Sy + Kalman_Gy) +
By*Matrix(1, 1, new double[1] {joint.Position.Y});
Kalman_ex = ex - Bx*Kalman_C*ex;
Kalman_ey = ey - By*Kalman_C*ey;
dx = Kalman_Sx.at(0, 0);
dy = Kalman_Sy.at(0, 0);
}
}
inline void GraphicsSystem::MatrixPush()
{
MatrixStack << Matrix(MatrixStack[curMatrix]);
++curMatrix;
}
//==========================================================================
int Ifpack_ICT::Compute()
{
if (!IsInitialized())
IFPACK_CHK_ERR(Initialize());
Time_.ResetStartTime();
IsComputed_ = false;
NumMyRows_ = A_.NumMyRows();
int Length = A_.MaxNumEntries();
vector<int> RowIndices(Length);
vector<double> RowValues(Length);
bool distributed = (Comm().NumProc() > 1)?true:false;
if (distributed)
{
SerialComm_ = Teuchos::rcp(new Epetra_SerialComm);
SerialMap_ = Teuchos::rcp(new Epetra_Map(NumMyRows_, 0, *SerialComm_));
assert (SerialComm_.get() != 0);
assert (SerialMap_.get() != 0);
}
else
SerialMap_ = Teuchos::rcp(const_cast<Epetra_Map*>(&A_.RowMatrixRowMap()), false);
int RowNnz;
#ifdef IFPACK_FLOPCOUNTERS
double flops = 0.0;
#endif
H_ = Teuchos::rcp(new Epetra_CrsMatrix(Copy,*SerialMap_,0));
if (H_.get() == 0)
IFPACK_CHK_ERR(-5); // memory allocation error
// get A(0,0) element and insert it (after sqrt)
IFPACK_CHK_ERR(A_.ExtractMyRowCopy(0,Length,RowNnz,
&RowValues[0],&RowIndices[0]));
// skip off-processor elements
if (distributed)
{
int count = 0;
for (int i = 0 ;i < RowNnz ; ++i)
{
if (RowIndices[i] < NumMyRows_){
RowIndices[count] = RowIndices[i];
RowValues[count] = RowValues[i];
++count;
}
else
continue;
}
RowNnz = count;
}
// modify diagonal
double diag_val = 0.0;
for (int i = 0 ;i < RowNnz ; ++i) {
if (RowIndices[i] == 0) {
double& v = RowValues[i];
diag_val = AbsoluteThreshold() * EPETRA_SGN(v) +
RelativeThreshold() * v;
break;
}
}
diag_val = sqrt(diag_val);
int diag_idx = 0;
EPETRA_CHK_ERR(H_->InsertGlobalValues(0,1,&diag_val, &diag_idx));
int oldSize = RowNnz;
// The 10 is just a small constant to limit collisons as the actual keys
// we store are the indices and not integers
// [0..A_.MaxNumEntries()*LevelofFill()].
Ifpack_HashTable Hash( 10 * A_.MaxNumEntries() * LevelOfFill(), 1);
// start factorization for line 1
for (int row_i = 1 ; row_i < NumMyRows_ ; ++row_i) {
// get row `row_i' of the matrix
IFPACK_CHK_ERR(A_.ExtractMyRowCopy(row_i,Length,RowNnz,
&RowValues[0],&RowIndices[0]));
// skip off-processor elements
if (distributed)
{
int count = 0;
for (int i = 0 ;i < RowNnz ; ++i)
{
if (RowIndices[i] < NumMyRows_){
RowIndices[count] = RowIndices[i];
RowValues[count] = RowValues[i];
++count;
}
else
continue;
}
RowNnz = count;
}
// number of nonzeros in this row are defined as the nonzeros
// of the matrix, plus the level of fill
int LOF = (int)(LevelOfFill() * RowNnz);
if (LOF == 0) LOF = 1;
// convert line `row_i' into hash for fast access
Hash.reset();
double h_ii = 0.0;
for (int i = 0 ; i < RowNnz ; ++i) {
if (RowIndices[i] == row_i) {
double& v = RowValues[i];
h_ii = AbsoluteThreshold() * EPETRA_SGN(v) + RelativeThreshold() * v;
}
else if (RowIndices[i] < row_i)
{
Hash.set(RowIndices[i], RowValues[i], true);
}
}
// form element (row_i, col_j)
// I start from the first row that has a nonzero column
// index in row_i.
for (int col_j = RowIndices[0] ; col_j < row_i ; ++col_j) {
double h_ij = 0.0, h_jj = 0.0;
// note: get() returns 0.0 if col_j is not found
h_ij = Hash.get(col_j);
// get pointers to row `col_j'
int* ColIndices;
double* ColValues;
int ColNnz;
H_->ExtractGlobalRowView(col_j, ColNnz, ColValues, ColIndices);
for (int k = 0 ; k < ColNnz ; ++k) {
int col_k = ColIndices[k];
if (col_k == col_j)
h_jj = ColValues[k];
else {
double xxx = Hash.get(col_k);
if (xxx != 0.0)
{
h_ij -= ColValues[k] * xxx;
#ifdef IFPACK_FLOPCOUNTERS
flops += 2.0;
#endif
}
}
}
h_ij /= h_jj;
if (IFPACK_ABS(h_ij) > DropTolerance_)
{
Hash.set(col_j, h_ij);
}
#ifdef IFPACK_FLOPCOUNTERS
// only approx
ComputeFlops_ += 2.0 * flops + 1.0;
#endif
}
int size = Hash.getNumEntries();
vector<double> AbsRow(size);
int count = 0;
// +1 because I use the extra position for diagonal in insert
vector<int> keys(size + 1);
vector<double> values(size + 1);
Hash.arrayify(&keys[0], &values[0]);
for (int i = 0 ; i < size ; ++i)
{
AbsRow[i] = IFPACK_ABS(values[i]);
}
count = size;
double cutoff = 0.0;
if (count > LOF) {
nth_element(AbsRow.begin(), AbsRow.begin() + LOF, AbsRow.begin() + count,
greater<double>());
cutoff = AbsRow[LOF];
}
for (int i = 0 ; i < size ; ++i)
{
h_ii -= values[i] * values[i];
}
if (h_ii < 0.0) h_ii = 1e-12;;
h_ii = sqrt(h_ii);
#ifdef IFPACK_FLOPCOUNTERS
// only approx, + 1 == sqrt
ComputeFlops_ += 2 * size + 1;
#endif
double DiscardedElements = 0.0;
count = 0;
for (int i = 0 ; i < size ; ++i)
{
if (IFPACK_ABS(values[i]) > cutoff)
{
values[count] = values[i];
keys[count] = keys[i];
++count;
}
else
DiscardedElements += values[i];
}
if (RelaxValue() != 0.0) {
DiscardedElements *= RelaxValue();
h_ii += DiscardedElements;
}
values[count] = h_ii;
keys[count] = row_i;
++count;
H_->InsertGlobalValues(row_i, count, &(values[0]), (int*)&(keys[0]));
oldSize = size;
}
IFPACK_CHK_ERR(H_->FillComplete());
#if 0
// to check the complete factorization
Epetra_Vector LHS(Matrix().RowMatrixRowMap());
Epetra_Vector RHS1(Matrix().RowMatrixRowMap());
Epetra_Vector RHS2(Matrix().RowMatrixRowMap());
Epetra_Vector RHS3(Matrix().RowMatrixRowMap());
LHS.Random();
Matrix().Multiply(false,LHS,RHS1);
H_->Multiply(true,LHS,RHS2);
H_->Multiply(false,RHS2,RHS3);
RHS1.Update(-1.0, RHS3, 1.0);
cout << endl;
cout << RHS1;
#endif
int MyNonzeros = H_->NumGlobalNonzeros();
Comm().SumAll(&MyNonzeros, &GlobalNonzeros_, 1);
IsComputed_ = true;
#ifdef IFPACK_FLOPCOUNTERS
double TotalFlops; // sum across all the processors
A_.Comm().SumAll(&flops, &TotalFlops, 1);
ComputeFlops_ += TotalFlops;
#endif
++NumCompute_;
ComputeTime_ += Time_.ElapsedTime();
return(0);
}
return *this;
}
Matrix& Matrix::operator=(const NxMat34& mat)
{
mat.getColumnMajor44(m_Matrix);
return *this;
}
Matrix& Matrix::operator=(const D3DXMATRIX& mat)
{
m_Matrix = mat;
return *this;
}
//Static
const Matrix Matrix::Identity = Matrix(1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
0, 0, 0, 1);
Matrix::~Matrix(void)
{
}
// casting operators
Matrix::operator float*()
{
return (float*)m_Matrix;
}
Matrix::operator const float*() const
{
return (const float*)m_Matrix;
Matrix Matrix::operator*(float f) const
{
return Matrix(m_Matrix * f);
}
//==============================================================================
int Ifpack_PointRelaxation::
ApplyInverseSGS_RowMatrix(const Epetra_MultiVector& X, Epetra_MultiVector& Y) const
{
int NumVectors = X.NumVectors();
int Length = Matrix().MaxNumEntries();
vector<int> Indices(Length);
vector<double> Values(Length);
Teuchos::RefCountPtr< Epetra_MultiVector > Y2;
if (IsParallel_) {
Y2 = Teuchos::rcp( new Epetra_MultiVector(Importer_->TargetMap(), NumVectors) );
}
else
Y2 = Teuchos::rcp( &Y, false );
double** y_ptr, ** y2_ptr, ** x_ptr, *d_ptr;
X.ExtractView(&x_ptr);
Y.ExtractView(&y_ptr);
Y2->ExtractView(&y2_ptr);
Diagonal_->ExtractView(&d_ptr);
for (int iter = 0 ; iter < NumSweeps_ ; ++iter) {
// only one data exchange per sweep
if (IsParallel_)
IFPACK_CHK_ERR(Y2->Import(Y,*Importer_,Insert));
for (int i = 0 ; i < NumMyRows_ ; ++i) {
int NumEntries;
int col;
double diag = d_ptr[i];
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
for (int m = 0 ; m < NumVectors ; ++m) {
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y2_ptr[m][col];
}
y2_ptr[m][i] += DampingFactor_ * (x_ptr[m][i] - dtemp) * diag;
}
}
for (int i = NumMyRows_ - 1 ; i > -1 ; --i) {
int NumEntries;
int col;
double diag = d_ptr[i];
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
for (int m = 0 ; m < NumVectors ; ++m) {
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y2_ptr[m][col];
}
y2_ptr[m][i] += DampingFactor_ * (x_ptr[m][i] - dtemp) * diag;
}
}
if (IsParallel_)
for (int m = 0 ; m < NumVectors ; ++m)
for (int i = 0 ; i < NumMyRows_ ; ++i)
y_ptr[m][i] = y2_ptr[m][i];
}
ApplyInverseFlops_ += NumVectors * (8 * NumGlobalRows_ + 4 * NumGlobalNonzeros_);
return(0);
}
returnValue CondensingExport::setupEvaluation( )
{
x.setup( "x", (getN()+1), getNX(), REAL,ACADO_VARIABLES );
u.setup( "u", getN(), getNU(), REAL,ACADO_VARIABLES );
p.setup( "p", 1, getNP(), REAL,ACADO_VARIABLES );
state.setup ( "state", 1,getNX()*(getNX()+getNU()+1) + getNU() +getNP(), REAL,ACADO_WORKSPACE );
if ( isInitialStateFixed( ) == BT_FALSE )
{
x0Ref.setup ( "x0Ref", 1, getNX(), REAL,ACADO_VARIABLES );
x0Ref2.setup( "x0Ref2", 1, getNX(), REAL,ACADO_VARIABLES );
}
E.setup ( "E", getN()*getNX(), getN()*getNU(), REAL,ACADO_WORKSPACE );
lbA.setup( "lbA", getNumStateBounds(), 1, REAL,ACADO_WORKSPACE );
ubA.setup( "ubA", getNumStateBounds(), 1, REAL,ACADO_WORKSPACE );
Matrix zeroXU = zeros( getNX(),getNU() );
Matrix idX = eye( getNX() );
//
// setupQP
//
setupQP.setup( "setupQP" );
if ( performsSingleShooting() == BT_TRUE )
setupQP.addStatement( state.getCols( 0,getNX() ) == x.getRow(0) );
// TODO: this part should be preinitialized. More specifically, E & QE matrices should be preinitializes to 0.
uint run1, run2;
for( run1 = 0; run1 < getN()-1; run1++ )
for( run2 = 1+run1; run2 < getN(); run2++ )
setupQP.addStatement( E.getSubMatrix( run1*getNX(),(run1+1)*getNX(), run2*getNU(),(run2+1)*getNU() ) == zeroXU );
// Write state bounds to the file
// TODO: Since the bounds are fixed in this case, they should be preinitialized
if( getNumStateBounds( ) > 0 )
{
Vector xLowerBounds(nxBounds), xUpperBounds(nxBounds);
for( run1 = 0; run1 < nxBounds; run1++ )
{
xLowerBounds(run1) = xBounds.getLowerBound( xBoundsIdx[run1]/getNX(),xBoundsIdx[run1]%getNX() );
xUpperBounds(run1) = xBounds.getUpperBound( xBoundsIdx[run1]/getNX(),xBoundsIdx[run1]%getNX() );
}
setupQP.addStatement( lbA == xLowerBounds );
setupQP.addStatement( ubA == xUpperBounds );
}
setupQP.addLinebreak( );
if ( isInitialStateFixed( ) == BT_FALSE )
{
setupQP.addStatement( Dx0 == x.getRow(0) - x0Ref );
setupQP.addStatement( Dx0b == x.getRow(0) - x0Ref2 );
setupQP.addLinebreak( );
}
// compute QQF if necessary
if ( QQF.isGiven( ) == BT_FALSE )
setupQP.addStatement( QQF == Q + QF );
ExportIndex reset( String("reset") );
setupQP.addIndex( reset );
setupQP.addStatement( reset == 1 );
ExportIndex run( String("run1") );
ExportForLoop loop( run, 0,getN() );
setupQP.addIndex( run );
if ( performsSingleShooting() == BT_FALSE ) {
loop.addStatement( reset == 1 );
loop.addStatement( state.getCols( 0,getNX() ) == x.getRow( run ) );
}
// no free parameters implemented yet!
uint uIdx = getNX() * ( 1+getNX() );
uint pIdx = getNX() * ( 1+getNX()+getNU() );
uIdx = pIdx;
pIdx = pIdx + getNU();
loop.addStatement( state.getCols( uIdx,pIdx ) == u.getRow( run ) );
loop.addStatement( state.getCols( pIdx,pIdx+getNP() ) == p );
loop.addLinebreak( );
if ( integrator->equidistantControlGrid() )
{
loop.addFunctionCall( "integrate", state, reset.makeArgument() );
}
else
{
loop.addFunctionCall( "integrate", state, reset.makeArgument(), run.makeArgument() );
}
if( performsSingleShooting() ) loop.addStatement( reset == 0 );
if ( performsSingleShooting() == BT_TRUE )
{
loop.addStatement( x.getRow( run+1 ) == state.getCols( 0,getNX() ) );
}
else
{
//
// Multiple shooting
// TODO: Check the sign here
//
loop.addStatement( residuum.getRow( run ) == state.getCols( 0,getNX() ) - x.getRow( run+1 ) );
}
loop.addLinebreak( );
loop.addFunctionCall( condense1, run.makeArgument(),state );
setupQP.addStatement( loop );
setupQP.addLinebreak( );
setupQP.addFunctionCall( condense2 );
////////////////////////////////////////////////////////////////////////////
//
// Get objective value
//
////////////////////////////////////////////////////////////////////////////
ExportVariable tmp("tmp", 1, 1, REAL, ACADO_LOCAL, BT_TRUE);
ExportVariable tmpDx("tmpDx", 1, getNX(), REAL, ACADO_LOCAL );
ExportVariable tmpDu("tmpDu", 1, getNU(), REAL, ACADO_LOCAL );
getObjectiveValue.setup( "getObjectiveValue" );
getObjectiveValue.setReturnValue( tmp );
getObjectiveValue.addVariable( tmpDx );
getObjectiveValue.addVariable( tmpDu );
getObjectiveValue.addStatement( tmp == 0.0 );
getObjectiveValue.addLinebreak( );
for (unsigned i = 0; i < getN(); ++i)
{
getObjectiveValue.addStatement( tmpDx == Dx.getRow( i ) * Q );
getObjectiveValue.addStatement( tmp += Dx.getRow( i ) * tmpDx.getTranspose() );
}
getObjectiveValue.addLinebreak( );
for (unsigned i = 0; i < getN(); ++i)
{
getObjectiveValue.addStatement( tmpDu == Du.getRow( i ) * R );
getObjectiveValue.addStatement( tmp += Du.getRow( i ) * tmpDu.getTranspose() );
}
getObjectiveValue.addLinebreak( );
getObjectiveValue.addStatement( tmp == tmp * Matrix( 0.5 ) );
return SUCCESSFUL_RETURN;
}
//==============================================================================
int Ifpack_PointRelaxation::Compute()
{
int ierr = 0;
if (!IsInitialized())
IFPACK_CHK_ERR(Initialize());
Time_->ResetStartTime();
// reset values
IsComputed_ = false;
Condest_ = -1.0;
if (NumSweeps_ == 0) ierr = 1; // Warning: no sweeps performed.
if (NumSweeps_ < 0)
IFPACK_CHK_ERR(-2); // at least one application
Diagonal_ = Teuchos::rcp( new Epetra_Vector(Matrix().RowMatrixRowMap()) );
if (Diagonal_ == Teuchos::null)
IFPACK_CHK_ERR(-5);
IFPACK_CHK_ERR(Matrix().ExtractDiagonalCopy(*Diagonal_));
// check diagonal elements, store the inverses, and verify that
// no zeros are around. If an element is zero, then by default
// its inverse is zero as well (that is, the row is ignored).
for (int i = 0 ; i < NumMyRows_ ; ++i) {
double& diag = (*Diagonal_)[i];
if (IFPACK_ABS(diag) < MinDiagonalValue_)
diag = MinDiagonalValue_;
if (diag != 0.0)
diag = 1.0 / diag;
}
ComputeFlops_ += NumMyRows_;
#if 0
// some methods require the inverse of the diagonal, compute it
// now and store it in `Diagonal_'
if ((PrecType_ == IFPACK_JACOBI) || (PrecType_ == IFPACK_GS)) {
Diagonal_->Reciprocal(*Diagonal_);
// update flops
ComputeFlops_ += NumMyRows_;
}
#endif
// We need to import data from external processors. Here I create an
// Epetra_Import object because I cannot assume that Matrix_ has one.
// This is a bit of waste of resources (but the code is more robust).
// Note that I am doing some strange stuff to set the components of Y
// from Y2 (to save some time).
//
if (IsParallel_ && ((PrecType_ == IFPACK_GS) || (PrecType_ == IFPACK_SGS))) {
Importer_ = Teuchos::rcp( new Epetra_Import(Matrix().RowMatrixColMap(),
Matrix().RowMatrixRowMap()) );
if (Importer_ == Teuchos::null) IFPACK_CHK_ERR(-5);
}
++NumCompute_;
ComputeTime_ += Time_->ElapsedTime();
IsComputed_ = true;
IFPACK_CHK_ERR(ierr);
return(0);
}
//==============================================================================
int Ifpack_PointRelaxation::
ApplyInverseGS_RowMatrix(const Epetra_MultiVector& X, Epetra_MultiVector& Y) const
{
int NumVectors = X.NumVectors();
int Length = Matrix().MaxNumEntries();
vector<int> Indices(Length);
vector<double> Values(Length);
Teuchos::RefCountPtr< Epetra_MultiVector > Y2;
if (IsParallel_)
Y2 = Teuchos::rcp( new Epetra_MultiVector(Importer_->TargetMap(), NumVectors) );
else
Y2 = Teuchos::rcp( &Y, false );
// extract views (for nicer and faster code)
double** y_ptr, ** y2_ptr, ** x_ptr, *d_ptr;
X.ExtractView(&x_ptr);
Y.ExtractView(&y_ptr);
Y2->ExtractView(&y2_ptr);
Diagonal_->ExtractView(&d_ptr);
for (int j = 0; j < NumSweeps_ ; j++) {
// data exchange is here, once per sweep
if (IsParallel_)
IFPACK_CHK_ERR(Y2->Import(Y,*Importer_,Insert));
// FIXME: do I really need this code below?
if (NumVectors == 1) {
double* y0_ptr = y_ptr[0];
double* y20_ptr = y2_ptr[0];
double* x0_ptr = x_ptr[0];
if(!DoBackwardGS_){
/* Forward Mode */
for (int i = 0 ; i < NumMyRows_ ; ++i) {
int NumEntries;
int col;
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y20_ptr[col];
}
y20_ptr[i] += DampingFactor_ * d_ptr[i] * (x0_ptr[i] - dtemp);
}
}
else {
/* Backward Mode */
for (int i = NumMyRows_ - 1 ; i > -1 ; --i) {
int NumEntries;
int col;
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y20_ptr[i];
}
y20_ptr[i] += DampingFactor_ * d_ptr[i] * (x0_ptr[i] - dtemp);
}
}
// using Export() sounded quite expensive
if (IsParallel_)
for (int i = 0 ; i < NumMyRows_ ; ++i)
y0_ptr[i] = y20_ptr[i];
}
else {
if(!DoBackwardGS_){
/* Forward Mode */
for (int i = 0 ; i < NumMyRows_ ; ++i) {
int NumEntries;
int col;
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
for (int m = 0 ; m < NumVectors ; ++m) {
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y2_ptr[m][col];
}
y2_ptr[m][i] += DampingFactor_ * d_ptr[i] * (x_ptr[m][i] - dtemp);
}
}
}
else {
/* Backward Mode */
for (int i = NumMyRows_ - 1 ; i > -1 ; --i) {
int NumEntries;
int col;
IFPACK_CHK_ERR(Matrix_->ExtractMyRowCopy(i, Length,NumEntries,
&Values[0], &Indices[0]));
for (int m = 0 ; m < NumVectors ; ++m) {
double dtemp = 0.0;
for (int k = 0 ; k < NumEntries ; ++k) {
col = Indices[k];
dtemp += Values[k] * y2_ptr[m][col];
}
y2_ptr[m][i] += DampingFactor_ * d_ptr[i] * (x_ptr[m][i] - dtemp);
}
}
}
// using Export() sounded quite expensive
if (IsParallel_)
for (int m = 0 ; m < NumVectors ; ++m)
for (int i = 0 ; i < NumMyRows_ ; ++i)
y_ptr[m][i] = y2_ptr[m][i];
}
}
ApplyInverseFlops_ += NumVectors * (4 * NumGlobalRows_ + 2 * NumGlobalNonzeros_);
return(0);
} //ApplyInverseGS_RowMatrix()
//==========================================================================
int Ifpack_SPARSKIT::Compute()
{
if (!IsInitialized())
IFPACK_CHK_ERR(Initialize());
IsComputed_ = false;
// convert the matrix into SPARSKIT format. The matrix is then
// free'd after method Compute() returns.
// convert the matrix into CSR format. Note that nnz is an over-estimate,
// since it contains the nonzeros corresponding to external nodes as well.
int n = Matrix().NumMyRows();
int nnz = Matrix().NumMyNonzeros();
vector<double> a(nnz);
vector<int> ja(nnz);
vector<int> ia(n + 1);
const int MaxNumEntries = Matrix().MaxNumEntries();
vector<double> Values(MaxNumEntries);
vector<int> Indices(MaxNumEntries);
int count = 0;
ia[0] = 1;
for (int i = 0 ; i < n ; ++i)
{
int NumEntries;
int NumMyEntries = 0;
Matrix().ExtractMyRowCopy(i, MaxNumEntries, NumEntries, &Values[0],
&Indices[0]);
// NOTE: There might be some issues here with the ILU(0) if the column indices aren't sorted.
// The other factorizations are probably OK.
for (int j = 0 ; j < NumEntries ; ++j)
{
if (Indices[j] < n) // skip non-local columns
{
a[count] = Values[j];
ja[count] = Indices[j] + 1; // SPARSKIT is FORTRAN
++count;
++NumMyEntries;
}
}
ia[i + 1] = ia[i] + NumMyEntries;
}
if (mbloc_ == -1) mbloc_ = n;
int iwk;
if (Type_ == "ILUT" || Type_ == "ILUTP" || Type_ == "ILUD" ||
Type_ == "ILUDP")
iwk = nnz + 2 * lfil_ * n;
else if (Type_ == "ILUK")
iwk = (2 * lfil_ + 1) * nnz + 1;
else if (Type_ == "ILU0")
iwk = nnz+2;
int ierr = 0;
alu_.resize(iwk);
jlu_.resize(iwk);
ju_.resize(n + 1);
vector<int> jw(n + 1);
vector<double> w(n + 1);
if (Type_ == "ILUT")
{
jw.resize(2 * n);
F77_ILUT(&n, &a[0], &ja[0], &ia[0], &lfil_, &droptol_,
&alu_[0], &jlu_[0], &ju_[0], &iwk, &w[0], &jw[0], &ierr);
}
else if (Type_ == "ILUD")
{
jw.resize(2 * n);
F77_ILUD(&n, &a[0], &ja[0], &ia[0], &alph_, &tol_,
&alu_[0], &jlu_[0], &ju_[0], &iwk, &w[0], &jw[0], &ierr);
}
else if (Type_ == "ILUTP")
{
jw.resize(2 * n);
iperm_.resize(2 * n);
F77_ILUTP(&n, &a[0], &ja[0], &ia[0], &lfil_, &droptol_, &permtol_,
&mbloc_, &alu_[0], &jlu_[0], &ju_[0], &iwk, &w[0], &jw[0],
&iperm_[0], &ierr);
for (int i = 0 ; i < n ; ++i)
iperm_[i]--;
}
else if (Type_ == "ILUDP")
{
jw.resize(2 * n);
iperm_.resize(2 * n);
F77_ILUDP(&n, &a[0], &ja[0], &ia[0], &alph_, &droptol_, &permtol_,
&mbloc_, &alu_[0], &jlu_[0], &ju_[0], &n, &w[0], &jw[0],
&iperm_[0], &ierr);
for (int i = 0 ; i < n ; ++i)
iperm_[i]--;
}
else if (Type_ == "ILUK")
{
vector<int> levs(iwk);
jw.resize(3 * n);
F77_ILUK(&n, &a[0], &ja[0], &ia[0], &lfil_,
&alu_[0], &jlu_[0], &ju_[0], &levs[0], &iwk, &w[0], &jw[0], &ierr);
}
else if (Type_ == "ILU0")
{
// here w is only of size n
jw.resize(2 * n);
F77_ILU0(&n, &a[0], &ja[0], &ia[0],
&alu_[0], &jlu_[0], &ju_[0], &jw[0], &ierr);
}
IFPACK_CHK_ERR(ierr);
IsComputed_ = true;
return(0);
}
FwdPeriodAdapter::FwdPeriodAdapter(
const ext::shared_ptr<MarketModel>& largeModel,
Size period,
Size offset,
const std::vector<Spread>& newDisplacements)
:
numberOfFactors_(largeModel->numberOfFactors()),
numberOfRates_((largeModel->numberOfRates()-offset) / (period > 0 ? period : 1) ),
numberOfSteps_(largeModel->numberOfSteps()),
pseudoRoots_(numberOfSteps_, Matrix(numberOfRates_,
numberOfFactors_)),
displacements_(newDisplacements)
{
QL_REQUIRE( period >0, "period must be greater than zero in fwdperiodadapter");
QL_REQUIRE(period > offset, "period must be greater than offset in fwdperiodadapter");
const std::vector<Spread>& largeDisplacements_ =
largeModel->displacements();
if (displacements_.size() == 1)
{
Real dis = displacements_[0];
displacements_.resize(numberOfRates_);
std::fill(displacements_.begin(), displacements_.end(), dis);
}
if (displacements_.size() ==0) // if not specified use average across rate
{
displacements_.reserve(numberOfRates_);
Size m=0;
Real sum=0.0;
for (Size k=0; k < numberOfRates_; ++k)
{
for (Size l=0; l < period; ++l, ++m)
sum+= largeDisplacements_[m];
displacements_.push_back(sum/period);
}
}
QL_REQUIRE( displacements_.size() == numberOfRates_,"newDisplacements should be empty,1, or number of new rates in fwdperiodadapter");
LMMCurveState largeCS(largeModel->evolution().rateTimes());
largeCS.setOnForwardRates(largeModel->initialRates());
LMMCurveState smallCS(
ForwardForwardMappings::RestrictCurveState(largeCS,
period, offset
));
initialRates_ =smallCS.forwardRates();
Real finalReset = smallCS.rateTimes()[smallCS.numberOfRates()-1];
std::vector<Time> oldEvolutionTimes(largeModel->evolution().evolutionTimes());
std::vector<Time> newEvolutionTimes;
for (Size i =0; i < oldEvolutionTimes.size() && oldEvolutionTimes[i]<= finalReset; ++i)
newEvolutionTimes.push_back(oldEvolutionTimes[i]);
evolution_=EvolutionDescription(smallCS.rateTimes(),
newEvolutionTimes);
numberOfSteps_ = newEvolutionTimes.size();
const std::vector<Time>& rateTimes =
smallCS.rateTimes();
// we must ensure we step through all rateTimes
const std::vector<Time>& evolutionTimes =
evolution_.evolutionTimes();
std::set<Time> setTimes(evolutionTimes.begin(),evolutionTimes.end());
for (Size i=0; i < rateTimes.size()-1; ++i)
QL_REQUIRE(setTimes.find(rateTimes[i]) != setTimes.end(),
"every new rate time except last must be an evolution time in fwdperiod adapter");
Matrix YMatrix =
ForwardForwardMappings::YMatrix( largeCS,
largeDisplacements_,
displacements_,
period,
offset
);
const std::vector<Size>& alive =
evolution_.firstAliveRate();
for (Size k = 0; k<numberOfSteps_; ++k) {
pseudoRoots_[k]=YMatrix*largeModel->pseudoRoot(k);
for (Size i=0; i<alive[k]; ++i)
std::fill(pseudoRoots_[k].row_begin(i),
pseudoRoots_[k].row_end(i),
0.0);
}
}
std::vector<double> NNTrainer::gradient( const double lambda)
{
int numExamples = trainingSet->size();
//-- Create n matrices with the dimensions of the weight matrices:
std::vector<Matrix> Delta;
for (int i = 0; i < (int) nn->getWeights().size(); i++)
Delta.push_back( Matrix( nn->getWeights().at(i)->getNumRows(), nn->getWeights().at(i)->getNumCols() ));
//-- Iterate over all training examples
for (int i = 0; i < numExamples; i++)
{
//-- Forward-propagate the network:
nn->setInput( trainingSet->at(i).x );
//-- Create vector to store the increments:
//-- Increments will be stored in reverse order (i.e. the last increment first)
std::vector<Matrix> inc;
//-- Increment for output layer
Matrix output = Matrix(nn->getOutput(), nn->getOutput().size(), 1);
Matrix y = Matrix(trainingSet->at(i).y , trainingSet->at(i).y.size(), 1);
inc.push_back( output - y);
//-- Increment for hidden layers
for (int l = nn->getL() - 2; l > 0; l--)
{
Matrix aux1 = nn->getWeights().at(l)->transpose() * inc.back();
Matrix aux2( aux1.getNumRows()-1, aux1.getNumCols());
for (int j = 0; j < aux2.getNumCols(); j++)
for (int k = 0; k < aux2.getNumRows(); k++)
aux2.set( k, j, aux1.get(k+1, j) * sigmoidGradient( nn->getActivation(l).at(k)) );
inc.push_back( aux2 );
}
//-- Input layer has no error associated (has no weights associated)
//-- Accumulate error:
for (int l = 0; l < (int) Delta.size(); l++)
{
Matrix aux1( Delta.at(l).getNumRows(), Delta.at(l).getNumCols() );
for (int j = 0; j < aux1.getNumRows(); j++)
aux1.set( j, 0, inc.at( inc.size()- l -1).get( j, 0) );
for (int j = 0; j < aux1.getNumRows(); j++)
for (int k = 1; k < aux1.getNumCols(); k++)
aux1.set( j, k, inc.at( inc.size()- l -1).get( j, 0) * nn->getActivation(l).at(k-1));
Delta.at(l) += aux1;
}
}
//-- Divide by number of training examples:
for (int l = 0; l < (int) Delta.size(); l++)
Delta.at(l) /= numExamples;
//-- Regularization
//------------------------------------------------------------------------
if (lambda != 0)
{
for (int l = 0; l < (int) Delta.size(); l++)
{
Matrix aux(nn->getWeights().at(l)->getNumRows(), nn->getWeights().at(l)->getNumCols() );
for (int j = 0; j < aux.getNumRows(); j++)
for (int k = 1; k < aux.getNumCols(); k++)
aux.set( j, k, nn->getWeights().at(l)->get(j, k) * lambda / numExamples);
Delta.at(l) += aux;
}
}
//-- Unroll gradient:
//---------------------------------------------------------------------------
std::vector<double> unrolled = Delta.front().unroll();
for (int l = 1; l < (int) Delta.size(); l++)
for (int j = 0; j < Delta.at(l).getNumRows(); j++)
for (int k = 0; k < Delta.at(l).getNumCols(); k++)
unrolled.push_back( Delta.at(l).get(j, k));
return unrolled;
}
/*
====================
build
====================
*/
VOID Sphere::build()
{
GUARD(Sphere::build);
// clear the old keys
mKeys.clear();
#define SPHERE_SEGMENTS 20
#define SPHERE_ROWS 10
std::vector<DVTN>vertexes;
std::vector<U32>indexes;
F32 l_delta = PI/(F32)SPHERE_ROWS;
F32 v_delta = 1.0f/(F32)SPHERE_ROWS;
F32 angle_delta = PI*2.0f/(F32)SPHERE_SEGMENTS;
F32 texcoord_horz_delta = 1.0f/(F32)SPHERE_SEGMENTS;
F32 l_base=-PI*0.5f;
F32 r_base=0.0f;
F32 z_base=-mRadius;
F32 v_base=0.0f;
F32 n_z_base=-1.0f;
F32 n_ratio_base=0.0f;
for(U32 rowi=0; rowi<SPHERE_ROWS; ++rowi)
{
F32 l_top = l_base+l_delta;
F32 r_top = GMath::cos(l_top)*mRadius;
F32 z_top = GMath::sin(l_top)*mRadius;
F32 v_top = v_base+v_delta;
F32 n_z_top= GMath::sin(l_top);
F32 n_ratio_top= GMath::cos(l_top);
F32 angle = 0.0f;
F32 texcoord = 0.0f;
for(U32 topi=0; topi<SPHERE_SEGMENTS;++topi,angle+=angle_delta,texcoord+=texcoord_horz_delta)
{
F32 c = GMath::cos(angle);
F32 s = GMath::sin(angle);
DVTN v;
v.normal[0] = c*n_ratio_top;
v.normal[1] = s*n_ratio_top;
v.normal[2] = n_z_top;
v.texcoord[0] = texcoord;
v.texcoord[1] = v_top;
v.point[0] = c*r_top;
v.point[1] = s*r_top;
v.point[2] = z_top;
vertexes.push_back(v);
v.normal[0] = c*n_ratio_base;
v.normal[1] = s*n_ratio_base;
v.normal[2] = n_z_base;
v.texcoord[0] = texcoord;
v.texcoord[1] = v_base;
v.point[0] = c*r_base;
v.point[1] = s*r_base;
v.point[2] = z_base;
vertexes.push_back(v);
}
// do last point by hand to ensure no round off errors.
DVTN v;
v.normal[0] = n_ratio_top;
v.normal[1] = 0.0f;
v.normal[2] = n_z_top;
v.texcoord[0] = 1.0f;
v.texcoord[1] = v_top;
v.point[0] = r_top;
v.point[1] = 0.0f;
v.point[2] = z_top;
vertexes.push_back(v);
v.normal[0] = n_ratio_base;
v.normal[1] = 0.0f;
v.normal[2] = n_z_base;
v.texcoord[0] = 1.0f;
v.texcoord[1] = v_base;
v.point[0] = r_base;
v.point[1] = 0.0f;
v.point[2] = z_base;
vertexes.push_back(v);
l_base=l_top;
r_base=r_top;
z_base=z_top;
v_base=v_top;
n_z_base=n_z_top;
n_ratio_base=n_ratio_top;
}
// QUAD_STRIP
U32 first = 0;
U32 count = vertexes.size();
for(U32 i=3, pos=first;i<count;i+=2,pos+=2)
{
indexes.push_back(pos);
indexes.push_back(pos+1);
indexes.push_back(pos+2);
indexes.push_back(pos+1);
indexes.push_back(pos+3);
indexes.push_back(pos+2);
}
// build the primitive
mPrimitivePtr = GNEW(Primitive); CHECK(mPrimitivePtr);
mPrimitivePtr->SetType(mType);
// set the wvp
Constant* wvp_constant_ptr = GNEW(Constant); CHECK(wvp_constant_ptr);
wvp_constant_ptr->SetMatrix(Matrix());
mPrimitivePtr->SetConstant("gWVP",wvp_constant_ptr);
// set the color
Constant* color_constant_ptr = GNEW(Constant); CHECK(color_constant_ptr);
color_constant_ptr->SetVector(Vector4(0,0,0,0));
mPrimitivePtr->SetConstant("gColor",color_constant_ptr);
// set the shader
Str key_name = "shader/color.xml";
KeyPtr key_ptr = Key::Find(key_name.c_str());
if(key_ptr == NULL)
{
Shader*shader = GNEW(Shader); CHECK(shader);
shader->Load(GLoad(key_name.c_str()));
key_ptr = GNEW(Key(key_name.c_str(), shader)); CHECK(key_ptr);
}
mKeys.push_back(key_ptr);
mPrimitivePtr->SetShader(dynamic_cast<Shader*>(key_ptr->Ptr()), "p0");
// build the vertex buffer
VertexBufferPtr vb_ptr = GNEW(VertexBuffer); CHECK(vb_ptr);
{
GDataPtr vd_ptr = GNEW(GData); CHECK(vd_ptr);
vd_ptr->Size(3*sizeof(U32) + 4*sizeof(U8) + vertexes.size()*sizeof(DVTN));
U8*data_ptr = (U8*)vd_ptr->Ptr();
*(U32*)data_ptr = MAKEFOURCC('G','V','B','O');
data_ptr += sizeof(U32);
*(U32*)data_ptr = vertexes.size();
data_ptr += sizeof(U32);
*(U32*)data_ptr = sizeof(DVTN);
data_ptr += sizeof(U32);
*(U8*)data_ptr = 3;
data_ptr += sizeof(U8);
*(U8*)data_ptr = VertexBuffer::VT_3F;
data_ptr += sizeof(U8);
*(U8*)data_ptr = VertexBuffer::VT_2F;
data_ptr += sizeof(U8);
*(U8*)data_ptr = VertexBuffer::VT_3F;
data_ptr += sizeof(U8);
::memcpy(data_ptr, &vertexes[0], vertexes.size()*sizeof(DVTN));
data_ptr += vertexes.size()*sizeof(DVTN);
vb_ptr->Load(vd_ptr.Ptr());
}
mPrimitivePtr->SetVertexBuffer(vb_ptr.Ptr());
// build the index
IndexBufferPtr ib_ptr = GNEW(IndexBuffer); CHECK(ib_ptr);
{
GDataPtr id_ptr = GNEW(GData); CHECK(id_ptr);
id_ptr->Size(3*sizeof(U32) + indexes.size()*sizeof(U32));
U8*data_ptr = (U8*)id_ptr->Ptr();
*(U32*)data_ptr = MAKEFOURCC('G','I','B','O');
data_ptr += sizeof(U32);
*(U32*)data_ptr = indexes.size();
data_ptr += sizeof(U32);
*(U32*)data_ptr = sizeof(U32);
data_ptr += sizeof(U32);
::memcpy(data_ptr, &indexes[0], indexes.size()*sizeof(U32));
data_ptr += indexes.size()*sizeof(U32);
ib_ptr->Load(id_ptr.Ptr());
}
mPrimitivePtr->SetIndexBuffer(ib_ptr.Ptr());
// build the bounding box
mBox.set(MAX_F32,MAX_F32,MAX_F32,MIN_F32,MIN_F32,MIN_F32);
for(U32 i = 0; i < vertexes.size(); i++)mBox.expand(vertexes[i].point);
mPrimitivePtr->SetBox(mBox);
UNGUARD;
}
IO::BinaryStream& operator << (IO::BinaryStream& Stream, CONST MatrixN<N,T>& Matrix)
{
Stream.Write( &Matrix(0,0), sizeof(T)*N*N );
return Stream;
}
ImagemapRenderRegion::ImagemapRenderRegion(ImagemapFilterOptions ifoOptions) :
RenderRegion(ImagemapFilterOptions::GetSizeOfExportArea(ifoOptions.m_stExportArea), Matrix(), 1)
{
m_Options=ifoOptions;
}
int XC::Concrete01::commitSensitivity(const double &strainGradient, int gradNumber, int numGrads)
{
// Initialize unconditaional stress sensitivity
UniaxialStateVars trialStateSensitivity;
trialStateSensitivity.Strain()= strainGradient;
double dktdh= 0.0;
// Assign values to parameter derivatives (depending on what's random)
double fpcSensitivity= 0.0;
double epsc0Sensitivity= 0.0;
double fpcuSensitivity= 0.0;
double epscuSensitivity= 0.0;
if(parameterID == 1)
{ fpcSensitivity= 1.0; }
else if(parameterID == 2)
{ epsc0Sensitivity= 1.0; }
else if(parameterID == 3)
{ fpcuSensitivity= 1.0; }
else if(parameterID == 4)
{ epscuSensitivity= 1.0; }
// Pick up sensitivity history variables
UniaxialHistoryVars convergedHistorySensitivity;
UniaxialStateVars convergedStateSensitivity;
if(SHVs.isEmpty())
{
SHVs= Matrix(5,numGrads);
convergedHistorySensitivity.UnloadSlope()= (2.0*fpcSensitivity*epsc0-2.0*fpc*epsc0Sensitivity) / (epsc0*epsc0);
}
else
{
convergedHistorySensitivity.MinStrain()= SHVs(0,(gradNumber-1));
convergedHistorySensitivity.UnloadSlope()= SHVs(1,(gradNumber-1));
convergedHistorySensitivity.EndStrain()= SHVs(2,(gradNumber-1));
convergedStateSensitivity.Stress()= SHVs(3,(gradNumber-1));
convergedStateSensitivity.Strain()= SHVs(4,(gradNumber-1));
}
// Strain increment
const double dStrain= trialState.getStrain() - convergedState.getStrain();
// Evaluate stress sensitivity
if(dStrain < 0.0)
{ // applying more compression to the material
if(trialState.getStrain() < convergedHistory.MinStrain())
{ // loading along the backbone curve
if(trialState.getStrain() > epsc0)
{ //on the parabola
trialStateSensitivity.Stress()= fpcSensitivity*(2.0*trialState.getStrain() /epsc0-(trialState.getStrain() /epsc0)*(trialState.getStrain() /epsc0))
+ fpc*( (2.0*trialStateSensitivity.Strain()*epsc0-2.0*trialState.getStrain() *epsc0Sensitivity)/(epsc0*epsc0)
- 2.0*(trialState.getStrain() /epsc0)*(trialStateSensitivity.Strain()*epsc0-trialState.getStrain() *epsc0Sensitivity)/(epsc0*epsc0));
dktdh= 2.0*((fpcSensitivity*epsc0-fpc*epsc0Sensitivity)/(epsc0*epsc0))
* (1.0-trialState.getStrain() /epsc0)
- 2.0*(fpc/epsc0)*(trialStateSensitivity.Strain()*epsc0-trialState.getStrain() *epsc0Sensitivity)
/ (epsc0*epsc0);
}
else if(trialState.getStrain() > epscu)
{ // on the straight inclined line
dktdh= ( (fpcSensitivity-fpcuSensitivity) * (epsc0-epscu)
- (fpc-fpcu)
* (epsc0Sensitivity-epscuSensitivity) )
/ ((epsc0-epscu)*(epsc0-epscu));
const double kt= (fpc-fpcu)/(epsc0-epscu);
trialStateSensitivity.Stress()= fpcSensitivity
+ dktdh*(trialState.getStrain() -epsc0)
+ kt*(trialStateSensitivity.Strain()-epsc0Sensitivity);
}
else
{ // on the horizontal line
trialStateSensitivity.Stress()= fpcuSensitivity;
dktdh= 0.0;
}
}
else if(trialState.getStrain() < convergedHistory.getEndStrain())
{ // reloading after an unloading that didn't go all the way to zero stress
trialStateSensitivity.Stress()= convergedHistorySensitivity.getUnloadSlope() * (trialState.getStrain() -convergedHistory.getEndStrain())
+ convergedHistory.getUnloadSlope() * (trialStateSensitivity.Strain()-convergedHistorySensitivity.EndStrain());
dktdh= convergedHistorySensitivity.getUnloadSlope();
}
else
{
trialStateSensitivity.Stress()= 0.0;
dktdh= 0.0;
}
}
else if(convergedState.getStress()+convergedHistory.getUnloadSlope()*dStrain<0.0)
{// unloading, but not all the way down to zero stress
trialStateSensitivity.Stress()= convergedStateSensitivity.getStress()
+ convergedHistorySensitivity.UnloadSlope()*dStrain
+ convergedHistory.getUnloadSlope()*(trialStateSensitivity.getStrain()-convergedState.getStrain());
dktdh= convergedHistorySensitivity.UnloadSlope();
}
else
{// unloading all the way down to zero stress
trialStateSensitivity.Stress()= 0.0;
dktdh= 0.0;
}
// Commit some history variables
SHVs(3,(gradNumber-1))= trialStateSensitivity.getStress();
SHVs(4,(gradNumber-1))= trialStateSensitivity.getStrain();
// Possibly update history variables for the three ordinary history variable derivatives
double epsTemp, epsTempSensitivity;
double eta, etaSensitivity;
double ratio, ratioSensitivity;
double temp1, temp1Sensitivity;
double temp2, temp2Sensitivity;
UniaxialHistoryVars trialHistorySensitivity;
if(dStrain<0.0 && trialState.getStrain() <convergedHistory.MinStrain())
{
trialHistorySensitivity.MinStrain()= trialStateSensitivity.Strain();
if(trialState.getStrain() < epscu)
{
epsTemp= epscu;
epsTempSensitivity= epscuSensitivity;
}
else
{
epsTemp= trialState.getStrain() ;
epsTempSensitivity= trialStateSensitivity.Strain();
}
eta= epsTemp/epsc0;
etaSensitivity= (epsTempSensitivity*epsc0-epsTemp*epsc0Sensitivity) / (epsc0*epsc0);
if(eta < 2.0)
{
ratio= 0.145 * eta*eta + 0.13*eta;
ratioSensitivity= 0.29 * eta * etaSensitivity + 0.13 * etaSensitivity;
}
else
{
ratio= 0.707*(eta-2.0) + 0.834;
ratioSensitivity= 0.707 * etaSensitivity;
}
temp1= trialState.getStrain() - ratio * epsc0;
temp1Sensitivity= trialStateSensitivity.Strain() - ratioSensitivity * epsc0 - ratio * epsc0Sensitivity;
temp2= trialState.getStress() * epsc0 / (2.0*fpc);
temp2Sensitivity= (2.0*fpc*(trialStateSensitivity.Stress()*epsc0+trialState.getStress() *epsc0Sensitivity)
-2.0*trialState.getStress() *epsc0*fpcSensitivity) / (4.0*fpc*fpc);
if(temp1 == 0.0)
{
trialHistorySensitivity.UnloadSlope()= (2.0*fpcSensitivity*epsc0-2.0*fpc*epsc0Sensitivity) / (epsc0*epsc0);
}
else if(temp1 < temp2)
{
trialHistorySensitivity.EndStrain()= trialStateSensitivity.Strain() - temp1Sensitivity;
trialHistorySensitivity.UnloadSlope()= (trialStateSensitivity.Stress()*temp1-trialState.getStress()*temp1Sensitivity) / (temp1*temp1);
}
else
{
trialHistorySensitivity.EndStrain()= trialStateSensitivity.Strain() - temp2Sensitivity;
trialHistorySensitivity.UnloadSlope()= (2.0*fpcSensitivity*epsc0-2.0*fpc*epsc0Sensitivity) / (epsc0*epsc0);
}
}
else
{ trialHistorySensitivity= convergedHistorySensitivity; }
SHVs(0,(gradNumber-1))= trialHistorySensitivity.getMinStrain();
SHVs(1,(gradNumber-1))= trialHistorySensitivity.getUnloadSlope();
SHVs(2,(gradNumber-1))= trialHistorySensitivity.getEndStrain();
return 0;
}
Property::Value::Value( Type type )
{
switch (type)
{
case Property::BOOLEAN:
{
mImpl = new Impl( false );
break;
}
case Property::FLOAT:
{
mImpl = new Impl( 0.f );
break;
}
case Property::INTEGER:
{
mImpl = new Impl( 0 );
break;
}
case Property::VECTOR2:
{
mImpl = new Impl( Vector2::ZERO );
break;
}
case Property::VECTOR3:
{
mImpl = new Impl( Vector3::ZERO );
break;
}
case Property::VECTOR4:
{
mImpl = new Impl( Vector4::ZERO );
break;
}
case Property::RECTANGLE:
{
mImpl = new Impl( Rect<int>(0,0,0,0) );
break;
}
case Property::ROTATION:
{
mImpl = new Impl( Quaternion() );
break;
}
case Property::STRING:
{
mImpl = new Impl( std::string() );
break;
}
case Property::MATRIX:
{
mImpl = new Impl( Matrix() );
break;
}
case Property::MATRIX3:
{
mImpl = new Impl( Matrix3() );
break;
}
case Property::ARRAY:
{
mImpl = new Impl( Property::Array() );
break;
}
case Property::MAP:
{
mImpl = new Impl( Property::Map() );
break;
}
case Property::NONE:
{
mImpl = new Impl();
break;
}
}
}
returnValue VariablesGrid::addVector( const Vector& newVector,
double newTime
)
{
return MatrixVariablesGrid::addMatrix( Matrix(newVector),newTime );
}
Matrix Matrix::operator-(const Matrix& mat) const
{
return Matrix(m_Matrix - mat.m_Matrix);
}
VariablesGrid::VariablesGrid( const Vector& arg,
const Grid& _grid,
VariableType _type
) : MatrixVariablesGrid( Matrix(arg),_grid,_type )
{
}
for (SIZE_T i=0; i<N; i++)
R.SetColumn(i,EigenVectors[i]);
}
//-----------------------------------------------------------------------------------------------------------------------------
template<SIZE_T N, class T>
IO::BinaryStream& operator << (IO::BinaryStream& Stream, CONST MatrixN<N,T>& Matrix)
{
Stream.Write( &Matrix(0,0), sizeof(T)*N*N );
return Stream;
}
//------------------------------------------------------------------------------------------------------------------------------
template<SIZE_T N, class T>
IO::BinaryStream& operator >> (IO::BinaryStream& Stream, MatrixN<N,T>& Matrix)
{
Stream.Read( &Matrix(0,0), sizeof(T)*N*N );
return Stream;
}
//------------------------------------------------------------------------------------------------------------------------------
template<SIZE_T N, class T>
MatrixN<N,T> MatrixN<N,T>::Read( LPCTSTR Filename )
{
MatrixN<N,T> M;
Ptr<IO::File> F = IO::File::Open(Filename, IO::FileMode::OPEN, IO::FileAccess::READ);
F->Read( &M(0,0), sizeof(T)*N*N );
return M;
}
//------------------------------------------------------------------------------------------------------------------------------
template<SIZE_T N, class T>
VOID MatrixN<N,T>::Write( LPCTSTR Filename )
{
GraphicsSystem::GraphicsSystem()
: curMatrix(0)
{
MatrixStack << Matrix().SetIdentity();
}
