本文整理汇总了C++中SparseMatrix::MultiplyVector方法的典型用法代码示例。如果您正苦于以下问题：C++ SparseMatrix::MultiplyVector方法的具体用法？C++ SparseMatrix::MultiplyVector怎么用？C++ SparseMatrix::MultiplyVector使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类SparseMatrix的用法示例。
在下文中一共展示了SparseMatrix::MultiplyVector方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: DefaultMultiplicator
void CGSolver::DefaultMultiplicator(const void * data, const double * x, double * Ax)
{
SparseMatrix * A = (SparseMatrix*)data;
A->MultiplyVector(x, Ax);
}示例2: malloc
void * MyFrame::NonLinearModesWorker(int * code, int dataOrigin, int numNonLinearModes, double ** modes_ )
{
int n3 = 3 * precomputationState.simulationMesh->getNumVertices();
int numDataVectors = 0;
// compute the lumped mass matrix
SparseMatrix * massMatrix; // will be sparse n3 x n3
GenerateMassMatrix::computeMassMatrix( precomputationState.simulationMesh, &massMatrix, true); // exitCode will always be 0
// mass-normalize modal derivatives
double * modalDerivatives = precomputationState.modalDerivativesMatrix->GetMatrix();
double * normalizedModalDerivatives = (double*) malloc (sizeof(double) * n3 * precomputationState.numDeriv);
memcpy(normalizedModalDerivatives, modalDerivatives, sizeof(double) * n3 * precomputationState.numDeriv);
for(int i=0; i < precomputationState.numDeriv; i++)
massMatrix->NormalizeVector(&(normalizedModalDerivatives[n3 * i]));
double * dataMatrix;
if (dataOrigin == 0)
{
// use linear modes and derivatives
// construct PCA data matrix:
int numUsedLinearModes = precomputationState.rLin - precomputationState.numRigidModes;
numDataVectors = numUsedLinearModes + numUsedLinearModes * (numUsedLinearModes + 1) / 2;
printf("Number of PCA datamatrix columns: %d.\n", numDataVectors);
dataMatrix = (double*) malloc (sizeof(double) * n3 * numDataVectors);
printf("Generating datamatrix for SVD...\n");
double lambda0 = precomputationState.frequencies[precomputationState.numRigidModes] * precomputationState.frequencies[precomputationState.numRigidModes];
// scale linear modes
double * Ulin = precomputationState.linearModalMatrix->GetMatrix();
for(int i=0; i<numUsedLinearModes; i++)
{
double lambda = precomputationState.frequencies[precomputationState.numRigidModes + i] * precomputationState.frequencies[precomputationState.numRigidModes + i];
double factor = lambda0 / lambda;
for(int vertex=0; vertex< n3; vertex++)
dataMatrix[ELT(n3,vertex,i)] = factor * Ulin[ELT(n3,vertex,precomputationState.numRigidModes + i)];
}
// scale modal derivatives
int pos = 0;
for(int i=0; i<numUsedLinearModes; i++)
{
for(int j=i; j<numUsedLinearModes; j++)
{
double lambdai = precomputationState.frequencies[precomputationState.numRigidModes + i] * precomputationState.frequencies[precomputationState.numRigidModes + i];
double lambdaj = precomputationState.frequencies[precomputationState.numRigidModes + j] * precomputationState.frequencies[precomputationState.numRigidModes + j];
double factor = lambda0 * lambda0 / (lambdai * lambdaj);
for(int vertex=0; vertex < n3; vertex++)
dataMatrix[ELT(n3, vertex, numUsedLinearModes + pos)] =
factor * normalizedModalDerivatives[ELT(n3, vertex, pos)];
pos++;
}
}
}
else
{
// data from external simulation
numDataVectors = precomputationState.sketchDataMatrix->Getr();
dataMatrix = (double*) malloc (sizeof(double) * n3 * numDataVectors);
memcpy(dataMatrix, precomputationState.sketchDataMatrix->GetMatrix(), sizeof(double) * n3 * numDataVectors);
}
free(normalizedModalDerivatives);
// do lumped-mass-PCA on dataMatrix ( n3 x numDataVectors )
double * ones = (double*) malloc (sizeof(double) * n3);
for(int i=0; i<n3; i++)
ones[i] = 1.0;
double * LTDiagonal = (double*) malloc (sizeof(double) * n3);
massMatrix->MultiplyVector(ones, LTDiagonal);
free(ones);
delete(massMatrix);
// sqrt
for(int i=0; i<n3; i++)
LTDiagonal[i] = sqrt(LTDiagonal[i]);
// number of retained dimensions can't be more than num linear modes + num derivatives
if (uiState.numComputedNonLinearModes > numDataVectors)
uiState.numComputedNonLinearModes = numDataVectors;
// premultiply by LT
for(int i=0; i<n3; i++)
for(int j=0; j < numDataVectors; j++)
dataMatrix[ELT(n3, i, j)] *= LTDiagonal[i];
// do SVD on dataMatrix ( n3 x numDataVectors ), retain uiState.numComputedNonLinearModes modes
ThresholdingSpecification thresholdingSpecification;
thresholdingSpecification.tresholdingType = ThresholdingSpecification::numberOfModesBased;
thresholdingSpecification.rDesired = uiState.numComputedNonLinearModes;
int outputr;
int matrixPCACode = 0;
//.........这里部分代码省略.........