int ObjectiveFunctionTemplate::min_patch_layers() const
{
if (!get_quality_metric())
return 0;
else if (get_quality_metric()->get_metric_type() == QualityMetric::VERTEX_BASED)
return 2;
else
return 1;
}
bool LInfTemplate::evaluate( EvalType type,
PatchData& pd,
double& value_out,
bool free,
MsqError& err )
{
if (type != ObjectiveFunction::CALCULATE) {
MSQ_SETERR(err)(
"LInfTemplate does not support block coodinate descent algoritms",
MsqError::INVALID_STATE );
return false;
}
QualityMetric* qm = get_quality_metric();
qm->get_evaluations( pd, qmHandles, free, err ); MSQ_ERRFALSE(err);
const double negate = qm->get_negate_flag();
// calculate OF value for just the patch
std::vector<size_t>::const_iterator i;
double value;
value_out = -HUGE_VAL;
for (i = qmHandles.begin(); i != qmHandles.end(); ++i)
{
bool result = qm->evaluate( pd, *i, value, err );
if (MSQ_CHKERR(err) || !result)
return false;
value = negate * fabs(value);
if (value > value_out)
value_out = value;
}
return true;
}
bool ElementMaxQM::evaluate( PatchData& pd,
size_t handle,
double& value,
MsqError& err )
{
ElemSampleQM* qm = get_quality_metric();
mHandles.clear();
qm->get_element_evaluations( pd, handle, mHandles, err ); MSQ_ERRFALSE(err);
bool valid = true;
double tmpval;
bool tmpvalid;
value = -1.e+100; // initialize max computation
for (std::vector<size_t>::iterator h = mHandles.begin(); h != mHandles.end(); ++h) {
tmpvalid = qm->evaluate( pd, *h, tmpval, err ); // MSQ_ERRZERO(err);
if (!tmpvalid)
{
value = +1.e+100;
return false; // if any handle within the element makes tmpvalid false, then valid is false, no matter what the other handles say
}
else if (tmpval > value)
value = tmpval;
}
return valid;
}
bool PMeanPTemplate::evaluate( EvalType type,
PatchData& pd,
double& value_out,
bool free,
MsqError& err )
{
QualityMetric* qm = get_quality_metric();
if (type == ObjectiveFunction::ACCUMULATE)
qm->get_single_pass( pd, qmHandles, free, err );
else
qm->get_evaluations( pd, qmHandles, free, err );
MSQ_ERRFALSE(err);
// calculate OF value for just the patch
std::vector<size_t>::const_iterator i;
double value, working_sum = 0.0;
for (i = qmHandles.begin(); i != qmHandles.end(); ++i)
{
bool result = qm->evaluate( pd, *i, value, err );
if (MSQ_CHKERR(err) || !result)
return false;
working_sum += mPower.raise( value );
}
// get overall OF value, update member data, etc.
size_t global_count;
value_out = qm->get_negate_flag()
* get_value( working_sum, qmHandles.size(), type, global_count );
return true;
}
bool ElementAvgQM::evaluate( PatchData& pd,
size_t handle,
double& value,
MsqError& err )
{
ElemSampleQM* qm = get_quality_metric();
mHandles.clear();
qm->get_element_evaluations( pd, handle, mHandles, err ); MSQ_ERRFALSE(err);
bool valid = true;
double tmpval;
double accumulate = 0.0;
int num_values = 0;
bool tmpvalid;
value = -std::numeric_limits<double>::infinity();
for (std::vector<size_t>::iterator h = mHandles.begin(); h != mHandles.end(); ++h)
{
tmpvalid = qm->evaluate( pd, *h, tmpval, err ); MSQ_ERRZERO(err);
if (!tmpvalid)
{
valid = false;
break;
}
else
{
accumulate += tmpval;
num_values++;
}
}
if (valid)
value = accumulate/num_values;
return valid;
}
bool ElementPMeanP::evaluate( PatchData& pd,
size_t handle,
double& value,
MsqError& err )
{
ElemSampleQM* qm = get_quality_metric();
mHandles.clear();
qm->get_element_evaluations( pd, handle, mHandles, err ); MSQ_ERRFALSE(err);
bool result = average( pd, qm, mHandles, value, err );
return !MSQ_CHKERR(err) && result;
}
bool ElementPMeanP::evaluate_with_gradient( PatchData& pd,
size_t handle,
double& value,
std::vector<size_t>& indices,
std::vector<Vector3D>& gradient,
MsqError& err )
{
ElemSampleQM* qm = get_quality_metric();
mHandles.clear();
qm->get_element_evaluations( pd, handle, mHandles, err ); MSQ_ERRFALSE(err);
bool result = average_with_gradient( pd, qm, mHandles, value, indices, gradient, err );
return !MSQ_CHKERR(err) && result;
}
bool PMeanPTemplate::evaluate_with_gradient( EvalType type,
PatchData& pd,
double& value_out,
std::vector<Vector3D>& grad_out,
MsqError& err )
{
QualityMetric* qm = get_quality_metric();
qm->get_evaluations( pd, qmHandles, OF_FREE_EVALS_ONLY, err ); MSQ_ERRFALSE(err);
// zero gradient
grad_out.clear();
grad_out.resize( pd.num_free_vertices(), Vector3D(0.0,0.0,0.0) );
// calculate OF value and gradient for just the patch
std::vector<size_t>::const_iterator i;
double value, working_sum = 0.0;
const double f = qm->get_negate_flag() * mPower.value();
for (i = qmHandles.begin(); i != qmHandles.end(); ++i)
{
bool result = qm->evaluate_with_gradient( pd, *i, value, mIndices, mGradient, err );
if (MSQ_CHKERR(err) || !result)
return false;
if (fabs(value) < DBL_EPSILON)
continue;
const double r1 = mPowerMinus1.raise( value );
const double qmp = r1 * value;
working_sum += qmp;
value = f * r1;
for (size_t j = 0; j < mIndices.size(); ++j) {
mGradient[j] *= value;
grad_out[mIndices[j]] += mGradient[j];
}
}
// get overall OF value, update member data, etc.
size_t global_count;
value_out = qm->get_negate_flag()
* get_value( working_sum, qmHandles.size(), type, global_count );
const double inv_n = 1.0 / global_count;
std::vector<Vector3D>::iterator g;
for (g = grad_out.begin(); g != grad_out.end(); ++g)
*g *= inv_n;
return true;
}
bool ObjectiveFunctionTemplate::initialize_block_coordinate_descent( Mesh* mesh,
MeshDomain* domain,
const Settings* settings,
PatchSet* ,
MsqError& err )
{
std::auto_ptr<PatchSet> patch_set;
switch (get_quality_metric()->get_metric_type())
{
case QualityMetric::VERTEX_BASED:
patch_set = std::auto_ptr<PatchSet>(new VertexPatches( 1, false ));
break;
case QualityMetric::ELEMENT_BASED:
patch_set = std::auto_ptr<PatchSet>(new ElementPatches);
break;
default:
MSQ_SETERR(err)("Cannot initialize for BCD for unknown metric type",
MsqError::INVALID_STATE);
return false;
}
clear();
patch_set->set_mesh( mesh );
PatchIterator patches( patch_set.get() );
PatchData pd;
pd.set_mesh( mesh );
pd.set_domain( domain );
if (settings)
pd.attach_settings( settings );
bool result = true;
while (patches.get_next_patch( pd, err ) && !MSQ_CHKERR(err))
{
double value;
bool b = evaluate( ObjectiveFunction::ACCUMULATE, pd, value, false, err );
MSQ_ERRZERO(err);
result = result && b;
}
return result;
}
ObjectiveFunction* LInfTemplate::clone() const
{ return new LInfTemplate(get_quality_metric()); }
int ElementMaxQM::get_negate_flag() const
{
return get_quality_metric()->get_negate_flag();
}
bool PMeanPTemplate::evaluate_with_Hessian( EvalType type,
PatchData& pd,
double& value_out,
std::vector<Vector3D>& grad_out,
MsqHessian& Hessian_out,
MsqError& err )
{
QualityMetric* qm = get_quality_metric();
qm->get_evaluations( pd, qmHandles, OF_FREE_EVALS_ONLY, err ); MSQ_ERRFALSE(err);
// zero gradient and hessian
grad_out.clear();
grad_out.resize( pd.num_free_vertices(), 0.0 );
Hessian_out.zero_out();
// calculate OF value and gradient for just the patch
std::vector<size_t>::const_iterator i;
size_t j, k, n;
double value, working_sum = 0.0;
const double f1 = qm->get_negate_flag() * mPower.value();
const double f2 = f1 * (mPower.value() - 1);
Matrix3D m;
for (i = qmHandles.begin(); i != qmHandles.end(); ++i)
{
bool result = qm->evaluate_with_Hessian( pd, *i, value, mIndices, mGradient, mHessian, err );
if (MSQ_CHKERR(err) || !result)
return false;
if (fabs(value) < DBL_EPSILON)
continue;
const size_t nfree = mIndices.size();
n = 0;
if (mPower.value() == 1.0) {
working_sum += mPower.raise( value );
for (j = 0; j < nfree; ++j) {
mGradient[j] *= f1;
grad_out[mIndices[j]] += mGradient[j];
for (k = j; k < nfree; ++k) {
mHessian[n] *= f1;
Hessian_out.add( mIndices[j], mIndices[k], mHessian[n], err ); MSQ_ERRFALSE(err);
++n;
}
}
}
else {
const double r2 = mPowerMinus2.raise( value );
const double r1 = r2 * value;
working_sum += r1 * value;
const double hf = f2 * r2;
const double gf = f1 * r1;
for (j = 0; j < nfree; ++j) {
for (k = j; k < nfree; ++k) {
m.outer_product( mGradient[j], mGradient[k] );
m *= hf;
mHessian[n] *= gf;
m += mHessian[n];
Hessian_out.add( mIndices[j], mIndices[k], m, err ); MSQ_ERRFALSE(err);
++n;
}
}
for (j = 0; j < nfree; ++j) {
mGradient[j] *= gf;
grad_out[mIndices[j]] += mGradient[j];
}
}
}
// get overall OF value, update member data, etc.
size_t global_count;
value_out = qm->get_negate_flag()
* get_value( working_sum, qmHandles.size(), type, global_count );
const double inv_n = 1.0 / global_count;
std::vector<Vector3D>::iterator g;
for (g = grad_out.begin(); g != grad_out.end(); ++g)
*g *= inv_n;
Hessian_out.scale( inv_n );
return true;
}
bool PMeanPTemplate::evaluate_with_Hessian_diagonal( EvalType type,
PatchData& pd,
double& value_out,
std::vector<Vector3D>& grad_out,
std::vector<SymMatrix3D>& hess_diag_out,
MsqError& err )
{
QualityMetric* qm = get_quality_metric();
qm->get_evaluations( pd, qmHandles, OF_FREE_EVALS_ONLY, err ); MSQ_ERRFALSE(err);
// zero gradient and hessian
const size_t s = pd.num_free_vertices();
grad_out.clear();
grad_out.resize( s, 0.0 );
hess_diag_out.clear();
hess_diag_out.resize( s, 0.0 );
// calculate OF value and gradient for just the patch
std::vector<size_t>::const_iterator i;
size_t j;
double value, working_sum = 0.0;
const double f1 = qm->get_negate_flag() * mPower.value();
const double f2 = f1 * (mPower.value() - 1);
for (i = qmHandles.begin(); i != qmHandles.end(); ++i)
{
bool result = qm->evaluate_with_Hessian_diagonal( pd, *i, value, mIndices, mGradient, mDiag, err );
if (MSQ_CHKERR(err) || !result)
return false;
if (fabs(value) < DBL_EPSILON)
continue;
const size_t nfree = mIndices.size();
if (mPower.value() == 1.0) {
working_sum += mPower.raise( value );
for (j = 0; j < nfree; ++j) {
const size_t idx = mIndices[j];
hess_diag_out[idx] += f1 * mDiag[j];
mGradient[j] *= f1;
grad_out[idx] += mGradient[j];
}
}
else {
const double r2 = mPowerMinus2.raise( value );
const double r1 = r2 * value;
working_sum += r1 * value;
const double hf = f2 * r2;
const double gf = f1 * r1;
for (j = 0; j < nfree; ++j) {
const size_t idx = mIndices[j];
hess_diag_out[idx] += hf * outer( mGradient[j] );
hess_diag_out[idx] += gf * mDiag[j];
mGradient[j] *= gf;
grad_out[idx] += mGradient[j];
}
}
}
// get overall OF value, update member data, etc.
size_t global_count;
value_out = qm->get_negate_flag()
* get_value( working_sum, qmHandles.size(), type, global_count );
const double inv_n = 1.0 / global_count;
for (j = 0; j < s; ++j) {
grad_out[j] *= inv_n;
hess_diag_out[j] *= inv_n;
}
return true;
}
