source: src/FunctionApproximation/FunctionApproximation.cpp@ b8f2ea

/*
 * Project: MoleCuilder
 * Description: creates and alters molecular systems
 * Copyright (C)  2012 University of Bonn. All rights reserved.
 * Please see the COPYING file or "Copyright notice" in builder.cpp for details.
 *
 *
 *   This file is part of MoleCuilder.
 *
 *    MoleCuilder is free software: you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation, either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    MoleCuilder is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with MoleCuilder.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 * FunctionApproximation.cpp
 *
 *  Created on: 02.10.2012
 *      Author: heber
 */

// include config.h
#ifdef HAVE_CONFIG_H
#include <config.h>
#endif

#include "CodePatterns/MemDebug.hpp"

#include "FunctionApproximation.hpp"

#include <algorithm>
#include <boost/bind.hpp>
#include <boost/function.hpp>
#include <iostream>
#include <iterator>
#include <numeric>
#include <sstream>

#include <levmar.h>

#include "CodePatterns/Assert.hpp"
#include "CodePatterns/Log.hpp"

#include "FunctionApproximation/FunctionModel.hpp"
#include "FunctionApproximation/TrainingData.hpp"

FunctionApproximation::FunctionApproximation(
    const TrainingData &_data,
    FunctionModel &_model,
    const double _precision) :
    input_dimension(_data.getTrainingInputs().size()),
    output_dimension(_data.getTrainingOutputs().size()),
    precision(_precision),
    input_data(_data.getTrainingInputs()),
    output_data(_data.getTrainingOutputs()),
    model(_model)
{}

void FunctionApproximation::setTrainingData(const filtered_inputs_t &input, const outputs_t &output)
{
  ASSERT( input.size() == output.size(),
      "FunctionApproximation::setTrainingData() - the number of input and output tuples differ: "+toString(input.size())+"!="
      +toString(output.size())+".");
  if (input.size() != 0) {
    ASSERT( input[0].size() == input_dimension,
        "FunctionApproximation::setTrainingData() - the dimension of the input tuples and input dimension differ: "+toString(input[0].size())+"!="
        +toString(input_dimension)+".");
    input_data = input;
    ASSERT( output[0].size() == output_dimension,
        "FunctionApproximation::setTrainingData() - the dimension of the output tuples and output dimension differ: "+toString(output[0].size())+"!="
        +toString(output_dimension)+".");
    output_data = output;
  } else {
    ELOG(2, "Given vectors of training data are empty, clearing internal vectors accordingly.");
    input_data.clear();
    output_data.clear();
  }
}

void FunctionApproximation::setModelFunction(FunctionModel &_model)
{
  model= _model;
}

/** Callback to circumvent boost::bind, boost::function and function pointer problem.
 *
 * See here (second answer!) to the nature of the problem:
 * http://stackoverflow.com/questions/282372/demote-boostfunction-to-a-plain-function-pointer
 *
 * We cannot use a boost::bind bounded boost::function as a function pointer.
 * boost::function::target() will just return NULL because the signature does not
 * match. We have to use a C-style callback function and our luck is that
 * the levmar signature provides for a void* additional data pointer which we
 * can cast back to our FunctionApproximation class, as we need access to the
 * data contained, e.g. the FunctionModel reference FunctionApproximation::model.
 *
 */
void FunctionApproximation::LevMarCallback(double *p, double *x, int m, int n, void *data)
{
  FunctionApproximation *approximator = static_cast<FunctionApproximation *>(data);
  ASSERT( approximator != NULL,
      "LevMarCallback() - received data does not represent a FunctionApproximation object.");
  boost::function<void(double*,double*,int,int,void*)> function =
      boost::bind(&FunctionApproximation::evaluate, approximator, _1, _2, _3, _4, _5);
  function(p,x,m,n,data);
}

void FunctionApproximation::LevMarDerivativeCallback(double *p, double *x, int m, int n, void *data)
{
  FunctionApproximation *approximator = static_cast<FunctionApproximation *>(data);
  ASSERT( approximator != NULL,
      "LevMarDerivativeCallback() - received data does not represent a FunctionApproximation object.");
  boost::function<void(double*,double*,int,int,void*)> function =
      boost::bind(&FunctionApproximation::evaluateDerivative, approximator, _1, _2, _3, _4, _5);
  function(p,x,m,n,data);
}

void FunctionApproximation::prepareParameters(double *&p, int &m) const
{
  m = model.getParameterDimension();
  const FunctionModel::parameters_t params = model.getParameters();
  {
    p = new double[m];
    size_t index = 0;
    for(FunctionModel::parameters_t::const_iterator paramiter = params.begin();
        paramiter != params.end();
        ++paramiter, ++index) {
      p[index] = *paramiter;
    }
  }
}

void FunctionApproximation::prepareOutput(double *&x, int &n) const
{
  n = output_data.size();
  {
    x = new double[n];
    size_t index = 0;
    for(outputs_t::const_iterator outiter = output_data.begin();
        outiter != output_data.end();
        ++outiter, ++index) {
      x[index] = (*outiter)[0];
    }
  }
}

void FunctionApproximation::operator()(const enum JacobianMode mode)
{
  // let levmar optimize
  register int i, j;
  int ret;
  double *p;
  double *x;
  int m, n;
  double opts[LM_OPTS_SZ], info[LM_INFO_SZ];

  // minim. options [\tau, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
  // * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2.
  opts[0]=LM_INIT_MU; opts[1]=1e-15; opts[2]=1e-15; opts[3]=precision;
  opts[4]= LM_DIFF_DELTA; // relevant only if the Jacobian is approximated using finite differences; specifies forward differencing
  //opts[4]=-LM_DIFF_DELTA; // specifies central differencing to approximate Jacobian; more accurate but more expensive to compute!

  prepareParameters(p,m);
  prepareOutput(x,n);

  {
    double *work, *covar;
    work=(double *)malloc((LM_DIF_WORKSZ(m, n)+m*m)*sizeof(double));
    if(!work){
      ELOG(0, "FunctionApproximation::operator() - memory allocation request failed.");
      return;
    }
    covar=work+LM_DIF_WORKSZ(m, n);

    // give this pointer as additional data to construct function pointer in
    // LevMarCallback and call
    if (model.isBoxConstraint()) {
      FunctionModel::parameters_t lowerbound = model.getLowerBoxConstraints();
      FunctionModel::parameters_t upperbound = model.getUpperBoxConstraints();
      double *lb = new double[m];
      double *ub = new double[m];
      for (size_t i=0;i<(size_t)m;++i) {
        lb[i] = lowerbound[i];
        ub[i] = upperbound[i];
      }
      if (mode == FiniteDifferences) {
        ret=dlevmar_bc_dif(
            &FunctionApproximation::LevMarCallback,
            p, x, m, n, lb, ub, NULL, 1000, opts, info, work, covar, this); // no Jacobian, caller allocates work memory, covariance estimated
      } else if (mode == ParameterDerivative) {
        ret=dlevmar_bc_der(
            &FunctionApproximation::LevMarCallback,
            &FunctionApproximation::LevMarDerivativeCallback,
            p, x, m, n, lb, ub, NULL, 1000, opts, info, work, covar, this); // no Jacobian, caller allocates work memory, covariance estimated
      } else {
        ASSERT(0, "FunctionApproximation::operator() - Unknown jacobian method chosen.");
      }
      delete[] lb;
      delete[] ub;
    } else {
      ASSERT(0, "FunctionApproximation::operator() - Unknown jacobian method chosen.");
      if (mode == FiniteDifferences) {
        ret=dlevmar_dif(
            &FunctionApproximation::LevMarCallback,
            p, x, m, n, 1000, opts, info, work, covar, this); // no Jacobian, caller allocates work memory, covariance estimated
      } else if (mode == ParameterDerivative) {
        ret=dlevmar_der(
            &FunctionApproximation::LevMarCallback,
            &FunctionApproximation::LevMarDerivativeCallback,
            p, x, m, n, 1000, opts, info, work, covar, this); // no Jacobian, caller allocates work memory, covariance estimated
      } else {
        ASSERT(0, "FunctionApproximation::operator() - Unknown jacobian method chosen.");
      }
    }

    {
      std::stringstream covar_msg;
      covar_msg << "Covariance of the fit:\n";
      for(i=0; i<m; ++i){
        for(j=0; j<m; ++j)
          covar_msg << covar[i*m+j] << " ";
        covar_msg << std::endl;
      }
      covar_msg << std::endl;
      LOG(1, "INFO: " << covar_msg.str());
    }

    free(work);
  }

  {
   std::stringstream result_msg;
   result_msg << "Levenberg-Marquardt returned " << ret << " in " << info[5] << " iter, reason " << info[6] << "\nSolution: ";
   for(i=0; i<m; ++i)
     result_msg << p[i] << " ";
   result_msg << "\n\nMinimization info:\n";
   std::vector<std::string> infonames(LM_INFO_SZ);
   infonames[0] = std::string("||e||_2 at initial p");
   infonames[1] = std::string("||e||_2");
   infonames[2] = std::string("||J^T e||_inf");
   infonames[3] = std::string("||Dp||_2");
   infonames[4] = std::string("mu/max[J^T J]_ii");
   infonames[5] = std::string("# iterations");
   infonames[6] = std::string("reason for termination");
   infonames[7] = std::string(" # function evaluations");
   infonames[8] = std::string(" # Jacobian evaluations");
   infonames[9] = std::string(" # linear systems solved");
   for(i=0; i<LM_INFO_SZ; ++i)
      result_msg << infonames[i] << ": " << info[i] << " ";
    result_msg << std::endl;
    LOG(1, "INFO: " << result_msg.str());
  }

  delete[] p;
  delete[] x;
}

bool FunctionApproximation::checkParameterDerivatives()
{
  double *p;
  int m;
  const FunctionModel::parameters_t backupparams = model.getParameters();
  prepareParameters(p,m);
  int n = output_data.size();
  double *err = new double[n];
  dlevmar_chkjac(
    &FunctionApproximation::LevMarCallback,
    &FunctionApproximation::LevMarDerivativeCallback,
    p, m, n, this, err);
  int i;
  for(i=0; i<n; ++i)
   LOG(1, "INFO: gradient " << i << ", err " << err[i] << ".");
  bool status = true;
  for(i=0; i<n; ++i)
    status &= err[i] > 0.5;

  if (!status) {
    int faulty;
    ELOG(0, "At least one of the parameter derivatives are incorrect.");
    for (faulty=1; faulty<=m; ++faulty) {
      LOG(1, "INFO: Trying with only the first " << faulty << " parameters...");
      model.setParameters(backupparams);
      dlevmar_chkjac(
        &FunctionApproximation::LevMarCallback,
        &FunctionApproximation::LevMarDerivativeCallback,
        p, faulty, n, this, err);
      bool status = true;
      for(i=0; i<n; ++i)
        status &= err[i] > 0.5;
      for(i=0; i<n; ++i)
        LOG(1, "INFO: gradient(" << faulty << ") " << i << ", err " << err[i] << ".");
      if (!status)
        break;
    }
    ELOG(0, "The faulty parameter derivative is with respect to the " << faulty << " parameter.");
  } else
    LOG(1, "INFO: parameter derivatives are ok.");

  delete[] err;
  delete[] p;
  model.setParameters(backupparams);

  return status;
}

double SquaredDifference(const double res1, const double res2)
{
  return (res1-res2)*(res1-res2);
}

void FunctionApproximation::prepareModel(double *p, int m)
{
//  ASSERT( (size_t)m == model.getParameterDimension(),
//      "FunctionApproximation::prepareModel() - LevMar expects "+toString(m)
//      +" parameters but the model function expects "+toString(model.getParameterDimension())+".");
  FunctionModel::parameters_t params(m, 0.);
  std::copy(p, p+m, params.begin());
  model.setParameters(params);
}

void FunctionApproximation::evaluate(double *p, double *x, int m, int n, void *data)
{
  // first set parameters
  prepareModel(p,m);

  // then evaluate
  ASSERT( (size_t)n == output_data.size(),
      "FunctionApproximation::evaluate() - LevMar expects "+toString(n)
      +" outputs but we provide "+toString(output_data.size())+".");
  if (!output_data.empty()) {
    filtered_inputs_t::const_iterator initer = input_data.begin();
    outputs_t::const_iterator outiter = output_data.begin();
    size_t index = 0;
    for (; initer != input_data.end(); ++initer, ++outiter) {
      // result may be a vector, calculate L2 norm
      const FunctionModel::results_t functionvalue =
          model(*initer);
      x[index++] = functionvalue[0];
//      std::vector<double> differences(functionvalue.size(), 0.);
//      std::transform(
//          functionvalue.begin(), functionvalue.end(), outiter->begin(),
//          differences.begin(),
//          &SquaredDifference);
//      x[index] = std::accumulate(differences.begin(), differences.end(), 0.);
    }
  }
}

void FunctionApproximation::evaluateDerivative(double *p, double *jac, int m, int n, void *data)
{
  // first set parameters
  prepareModel(p,m);

  // then evaluate
  ASSERT( (size_t)n == output_data.size(),
      "FunctionApproximation::evaluateDerivative() - LevMar expects "+toString(n)
      +" outputs but we provide "+toString(output_data.size())+".");
  if (!output_data.empty()) {
    filtered_inputs_t::const_iterator initer = input_data.begin();
    outputs_t::const_iterator outiter = output_data.begin();
    size_t index = 0;
    for (; initer != input_data.end(); ++initer, ++outiter) {
      // result may be a vector, calculate L2 norm
      for (int paramindex = 0; paramindex < m; ++paramindex) {
        const FunctionModel::results_t functionvalue =
            model.parameter_derivative(*initer, paramindex);
        jac[index++] = functionvalue[0];
      }
//      std::vector<double> differences(functionvalue.size(), 0.);
//      std::transform(
//          functionvalue.begin(), functionvalue.end(), outiter->begin(),
//          differences.begin(),
//          &SquaredDifference);
//      x[index] = std::accumulate(differences.begin(), differences.end(), 0.);
    }
  }
}