/*************************************************************************** *   Copyright (C) 2008 by Yann LeCun and Pierre Sermanet  * *   yann@cs.nyu.edu, pierre.sermanet@gmail.com   * * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: *     * Redistributions of source code must retain the above copyright *       notice, this list of conditions and the following disclaimer. *     * Redistributions in binary form must reproduce the above copyright *       notice, this list of conditions and the following disclaimer in the *       documentation and/or other materials provided with the distribution. *     * Redistribution under a license not approved by the Open Source *       Initiative (http://www.opensource.org) must display the *       following acknowledgement in all advertising material: *        This product includes software developed at the Courant *        Institute of Mathematical Sciences (http://cims.nyu.edu). *     * The names of the authors may not be used to endorse or promote products *       derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL ThE AUTHORS BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ***************************************************************************/#ifndef TRAINER_HPP_#define TRAINER_HPP_namespace ebl {////////////////////////////////////////////////////////////////////template <class T, class L> void supervised::train(LabeledDataSource<T, L> *ds, classifier_meter *mtr) {  ds->seek_begin();  mtr->clear();}template <class T, class L> void supervised::test(LabeledDataSource<T, L> *ds, classifier_meter *mtr) {  ds->seek_begin();  mtr->clear();  for (int i = 0; i < ds->size(); ++i) {    ds->fprop(input, desired);    machine->fprop(input, output, desired, energy);    mtr->update(age, output, desired->get(), energy);    ds->next();  }}template <class T, class L> void supervised::test_sample(LabeledDataSource<T, L> *ds, classifier_meter *mtr, intg i) {/*  ds->seek(i);  ds->fprop(input, desired);  machine->fprop(input, output, desired, energy);  mtr->test(output, desired, energy);*/	err_not_implemented();}/////////////////////////////////////////////////////////////////////////////////template <class T, class L>  void supervised_gradient::train_online(LabeledDataSource<T, L> *ds, classifier_meter *mtr,		intg n, gd_param *gdp, double kappa = 0.0) {	for (int i = 0; i < n; ++i) {    ds->fprop(input, desired);    machine->fprop(input, output, desired, energy);    mtr->update(age, output, desired->get(), energy);    param->clear_dx();    machine->bprop(input, output, desired, energy);    param->update_gd(*gdp);    if (kappa != 0.0) {    	err_not_implemented();    	/*    if (kappa > 0.0)    	param->update_xaverage(kappa);    else if (kappa < 0.0)    	param->update_xaverage((-kappa) / (1 + age));    	*/    }    age++;    // info/*    cout << "age: " << age << " desired: " << (unsigned int) desired->get();    cout << " output_class: " << (unsigned int) output->output_class << " machine->mout->x ";    machine->mout->x.printElems();    cout << endl;*/    // (print (==> ds tell) (desired) :machine:mout:x)    ds->next();	}  // mtr->info(); // TODO}template <class T, class L>  void supervised_gradient::train(LabeledDataSource<T, L> *ds, classifier_meter *mtr,		gd_param *gdp, double kappa) {  ds->seek_begin();  mtr->clear();  this->train_online(ds, mtr, ds->size(), gdp, kappa);}template <class T, class L>  void supervised_gradient::compute_diaghessian(LabeledDataSource<T, L> *ds, intg n, double mu) {  param->clear_ddeltax();  for (int i = 0; i < n; ++i) {    ds->fprop(input, desired);    machine->fprop(input, output, desired, energy);    param->clear_dx();    machine->bprop(input, output, desired, energy);    param->clear_ddx();    machine->bbprop(input, output, desired, energy);    param->update_ddeltax((1 / (double) n), 1.0);    ds->next();  }  param->compute_epsilons(mu);  std::cout << "diaghessian inf: " << idx_min(param->epsilons);  std::cout << " sup: " << idx_max(param->epsilons) << std::endl;}template <class T, class L>  void supervised_gradient::saliencies(LabeledDataSource<T, L> *ds, intg n) {	err_not_implemented();	/*  param->clear_deltax();  param->clear_ddeltax();  for (int i = 0; i < n; ++i) {    ds->fprop(input, desired);    machine->fprop(input, output, desired, energy);    param->clear_dx();    param->update_deltax((1 / n), 1);    machine->bprop(input, output, desired, energy);    param->clear_ddx();    machine->bbprop(input, output, desired, energy);    param->update_ddeltax((1 / n), 1);    ds->next();  }  param->saliencies();  param->clear_deltax();  param->clear_ddeltax();  */}} /* namespace ebl */#endif /* TRAINER_HPP_ */