EvaluationMX::EvaluationMX(const FX& fcn, const std::vector<MX> &arg) : fcn_(fcn) {
// Number inputs and outputs
int num_in = fcn.getNumInputs();
int num_out = fcn.getNumOutputs();
// All dependencies of the function
vector<MX> d = arg;
d.resize(num_in);
setDependencies(d);
setSparsity(CRSSparsity(1, 1, true));
}
void EvaluationMX::create(const FX& fcn, const std::vector<MX> &arg,
std::vector<MX> &res, const std::vector<std::vector<MX> > &fseed,
std::vector<std::vector<MX> > &fsens,
const std::vector<std::vector<MX> > &aseed,
std::vector<std::vector<MX> > &asens, bool output_given) {
// Number inputs and outputs
int num_in = fcn.getNumInputs();
int num_out = fcn.getNumOutputs();
// Number of directional derivatives
int nfdir = fseed.size();
int nadir = aseed.size();
// Create the evaluation node
MX ev;
if(nfdir>0 || nadir>0){
// Create derivative function
Derivative dfcn(fcn,nfdir,nadir);
stringstream ss;
ss << "der_" << fcn.getOption("name") << "_" << nfdir << "_" << nadir;
dfcn.setOption("verbose",fcn.getOption("verbose"));
dfcn.setOption("name",ss.str());
dfcn.init();
// All inputs
vector<MX> darg;
darg.reserve(num_in*(1+nfdir) + num_out*nadir);
darg.insert(darg.end(),arg.begin(),arg.end());
// Forward seeds
for(int dir=0; dir<nfdir; ++dir){
darg.insert(darg.end(),fseed[dir].begin(),fseed[dir].end());
}
// Adjoint seeds
for(int dir=0; dir<nadir; ++dir){
darg.insert(darg.end(),aseed[dir].begin(),aseed[dir].end());
}
ev.assignNode(new EvaluationMX(dfcn, darg));
} else {
ev.assignNode(new EvaluationMX(fcn, arg));
}
// Output index
int ind = 0;
// Create the output nodes corresponding to the nondifferented function
res.resize(num_out);
for (int i = 0; i < num_out; ++i, ++ind) {
if(!output_given){
if(!fcn.output(i).empty()){
res[i].assignNode(new OutputNode(ev, ind));
} else {
res[i] = MX();
}
}
}
// Forward sensitivities
fsens.resize(nfdir);
for(int dir = 0; dir < nfdir; ++dir){
fsens[dir].resize(num_out);
for (int i = 0; i < num_out; ++i, ++ind) {
if (!fcn.output(i).empty()){
fsens[dir][i].assignNode(new OutputNode(ev, ind));
} else {
fsens[dir][i] = MX();
}
}
}
// Adjoint sensitivities
asens.resize(nadir);
for (int dir = 0; dir < nadir; ++dir) {
asens[dir].resize(num_in);
for (int i = 0; i < num_in; ++i, ++ind) {
if (!fcn.input(i).empty()) {
asens[dir][i].assignNode(new OutputNode(ev, ind));
} else {
asens[dir][i] = MX();
}
}
}
}