Esempio n. 1
0
void EvaluationMX::evaluateMX(const MXPtrV& arg, MXPtrV& res, const MXPtrVV& fseed, MXPtrVV& fsens, const MXPtrVV& aseed, MXPtrVV& asens, bool output_given) {
  
  // Number of sensitivity directions
  int nfdir = fsens.size();
  casadi_assert(nfdir==0 || fcn_.spCanEvaluate(true));
  int nadir = aseed.size();
  casadi_assert(nadir==0 || fcn_.spCanEvaluate(false));

  // Get/generate the derivative function
  FX d = fcn_.derivative(nfdir, nadir);

  // Temporary
  vector<MX> tmp;

  // Assemble inputs
  vector<MX> d_arg;
  d_arg.reserve(d.getNumInputs());

  // Nondifferentiated inputs
  tmp = getVector(arg);
  d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  for (MXPtrVV::const_iterator i = fseed.begin(); i != fseed.end(); ++i) {
    tmp = getVector(*i);
    d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  }
  for (MXPtrVV::const_iterator i = aseed.begin(); i != aseed.end(); ++i) {
    tmp = getVector(*i);
    d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  }

  // Evaluate symbolically
  vector<MX> d_res = d.call(d_arg);
  vector<MX>::const_iterator d_res_it = d_res.begin();

  // Collect the nondifferentiated results
  for (MXPtrV::iterator i = res.begin(); i != res.end(); ++i, ++d_res_it) {
    if (!output_given && *i) **i = *d_res_it;
  }

  // Collect the forward sensitivities
  for (MXPtrVV::iterator j = fsens.begin(); j != fsens.end(); ++j) {
    for (MXPtrV::iterator i = j->begin(); i != j->end(); ++i, ++d_res_it) {
      if (*i) **i = *d_res_it;
    }
  }

  // Collect the adjoint sensitivities
  for (MXPtrVV::iterator j = asens.begin(); j != asens.end(); ++j) {
    for (MXPtrV::iterator i = j->begin(); i != j->end(); ++i, ++d_res_it) {
      if(*i && !d_res_it->isNull()){
        **i += *d_res_it;
      }
    }
  }

  // Make sure that we've got to the end of the outputs
  casadi_assert(d_res_it==d_res.end());
}
Esempio n. 2
0
EvaluationMX::EvaluationMX(const FX& fcn, const std::vector<MX> &arg) : fcn_(fcn) {
      
  // Number inputs and outputs
  int num_in = fcn.getNumInputs();

  // All dependencies of the function
  vector<MX> d = arg;
  d.resize(num_in);

  setDependencies(d);
  setSparsity(CRSSparsity(1, 1, true));
}
Esempio n. 3
0
MXFunction vec (const FX &a_) {
  FX a = a_;

  // Pass null if input is null
  if (a.isNull()) return MXFunction();
  
  // Get the MX inputs, only used for shape
  const std::vector<MX> &symbolicInputMX = a.symbolicInput();
  // Have a vector with MX that have the shape of vec(symbolicInputMX )
  std::vector<MX> symbolicInputMX_vec(a.getNumInputs());
  // Make vector valued MX's out of them
  std::vector<MX> symbolicInputMX_vec_reshape(a.getNumInputs());

  // Apply the vec-transformation to the inputs
  for (int i=0;i<symbolicInputMX.size();++i) {
    std::stringstream s;
    s << "X_flat_" << i;
    symbolicInputMX_vec[i] = MX(s.str(),vec(symbolicInputMX[i].sparsity()));
    symbolicInputMX_vec_reshape[i] = trans(reshape(symbolicInputMX_vec[i],trans(symbolicInputMX[i].sparsity())));
  }
  
  // Call the original function with the vecced inputs
  std::vector<MX> symbolicOutputMX = a.call(symbolicInputMX_vec_reshape);
  
  // Apply the vec-transformation to the outputs
  for (int i=0;i<symbolicOutputMX.size();++i)
    symbolicOutputMX[i] = vec(symbolicOutputMX[i]);
    
  // Make a new function with the vecced input/outputs
  MXFunction ret(symbolicInputMX_vec,symbolicOutputMX);
  
  // Initialize it if a was
  if (a.isInit()) ret.init();
  return ret;

}  
Esempio n. 4
0
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();
      }
    }
  }
}