Basis Basis::diagonalize() {
//NOTE: only implemented for symmetric matrices
//with the Jacobi iterative method method
ERR_FAIL_COND_V(!is_symmetric(), Basis());
const int ite_max = 1024;
real_t off_matrix_norm_2 = elements[0][1] * elements[0][1] + elements[0][2] * elements[0][2] + elements[1][2] * elements[1][2];
int ite = 0;
Basis acc_rot;
while (off_matrix_norm_2 > CMP_EPSILON2 && ite++ < ite_max ) {
real_t el01_2 = elements[0][1] * elements[0][1];
real_t el02_2 = elements[0][2] * elements[0][2];
real_t el12_2 = elements[1][2] * elements[1][2];
// Find the pivot element
int i, j;
if (el01_2 > el02_2) {
if (el12_2 > el01_2) {
i = 1;
j = 2;
} else {
i = 0;
j = 1;
}
} else {
if (el12_2 > el02_2) {
i = 1;
j = 2;
} else {
i = 0;
j = 2;
}
}
// Compute the rotation angle
real_t angle;
if (Math::abs(elements[j][j] - elements[i][i]) < CMP_EPSILON) {
angle = Math_PI / 4;
} else {
angle = 0.5 * Math::atan(2 * elements[i][j] / (elements[j][j] - elements[i][i]));
}
// Compute the rotation matrix
Basis rot;
rot.elements[i][i] = rot.elements[j][j] = Math::cos(angle);
rot.elements[i][j] = - (rot.elements[j][i] = Math::sin(angle));
// Update the off matrix norm
off_matrix_norm_2 -= elements[i][j] * elements[i][j];
// Apply the rotation
*this = rot * *this * rot.transposed();
acc_rot = rot * acc_rot;
}
return acc_rot;
}
static void validate_matrices(const SparseMatrix *L, const SparseMatrix *P)
{
EXPENSIVE_ASSERT(is_lower(L));
EXPENSIVE_ASSERT(check_symbolic_zeros(L));
EXPENSIVE_ASSERT(is_symmetric(P));
assert(L->N == P->N);
assert(P->nz == NZ_SYM(L->nz, L->N));
}
int cholesky_basic_checked(ublas::matrix<double,F,A> &m, const bool upper)
// Perform Cholesky decomposition, but do not zero out other-triangular part.
// Returns 0 if matrix was actual positive-definite, otherwise it returns a
// LAPACK info value.
{
if (m.size1() != m.size2())
throw LogicalError(ERROR_INFO("Matrix is not square"));
assert(is_symmetric(m));
// Call LAPACK routine
int info;
char uplo = detail::uplo_flag(m, upper);
int size = static_cast<int>(m.size1());
detail::dpotrf_( &uplo, &size, &m.data()[0], &size, &info );
// Check validity of result
if (info < 0)
throw LogicalError(ERROR_INFO("Invalid argument"), info);
return info;
}
void run() {
// Do some validation of the arguments
if (graph.rows() != graph.cols()) {
throw std::invalid_argument("Matrix must be square");
}
if (graph.any_element_is_inf_or_nan()) {
throw std::invalid_argument("Matrix includes Inf or NaN values");
}
if (graph.any_element_is_negative()) {
throw std::invalid_argument("Matrix includes negative values");
}
if (!is_symmetric(graph)) {
throw std::invalid_argument("Matrix must be symmetric");
}
if (!clustering.empty() && (int)clustering.size() != graph.rows()) {
throw std::invalid_argument("Initial value must have same size as the matrix");
}
// initialize Clustering object
params.lossfun = lossfun.get();
srand(seed);
Clustering clus(graph,params);
if (!clustering.empty()) {
clus.set_clustering(clustering);
}
// perform clustering
if (optimize) {
clus.optimize();
}
// outputs
clustering = clus.get_clustering();
loss = clus.get_loss();
num_clusters = clus.num_clusters();
}
/*
* Is this matrix hermitian?
*
* Definition: http://en.wikipedia.org/wiki/Hermitian_matrix
*
* For non-complex matrices, this function should return the same result as symmetric?.
*/
static VALUE nm_hermitian(VALUE self) {
return is_symmetric(self, true);
}
int main()
{
is_symmetric("abcba");
return 0;
}
