Removed sferes folder and add a script for fetching sferes

modules/dnns_easily_fooled/download_sferes.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+path="./sferes"
+
+if [ -d "${path}" ]; then
+  echo "Please remove the existing [${path}] folder and re-run this script."
+  exit 1
+fi
+
+# Download the version of Caffe that can be used for generating fooling images via EAs.
+echo "Downloading Caffe ..."
+wget https://github.com/Evolving-AI-Lab/fooling/archive/master.zip
+
+echo "Extracting into ${path}"
+unzip master.zip
+mv ./fooling-master/sferes ./
+
+# Clean up
+rm -rf fooling-master master.zip
+
+echo "Done."

modules/dnns_easily_fooled/sferes/.cproject

@@ -1,210 +0,0 @@
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modules/dnns_easily_fooled/sferes/.gitignore

@@ -1,6 +0,0 @@
-*.pyc
-build/
-.waf-1.5.14*/
-.Totti-*/
-Totti-*/
-.lock-wscript

modules/dnns_easily_fooled/sferes/.project

@@ -1,33 +0,0 @@
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modules/dnns_easily_fooled/sferes/COPYING

@@ -1,506 +0,0 @@
-
-CeCILL FREE SOFTWARE LICENSE AGREEMENT
-
-
-    Notice
-
-This Agreement is a Free Software license agreement that is the result
-of discussions between its authors in order to ensure compliance with
-the two main principles guiding its drafting:
-
-    * firstly, compliance with the principles governing the distribution
-      of Free Software: access to source code, broad rights granted to
-      users,
-    * secondly, the election of a governing law, French law, with which
-      it is conformant, both as regards the law of torts and
-      intellectual property law, and the protection that it offers to
-      both authors and holders of the economic rights over software.
-
-The authors of the CeCILL (for Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre])
-license are:
-
-Commissariat à l'Energie Atomique - CEA, a public scientific, technical
-and industrial research establishment, having its principal place of
-business at 25 rue Leblanc, immeuble Le Ponant D, 75015 Paris, France.
-
-Centre National de la Recherche Scientifique - CNRS, a public scientific
-and technological establishment, having its principal place of business
-at 3 rue Michel-Ange, 75794 Paris cedex 16, France.
-
-Institut National de Recherche en Informatique et en Automatique -
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-Version 2.0 dated 2006-09-05.

modules/dnns_easily_fooled/sferes/README.md

@@ -1,107 +0,0 @@
-You can access this repo with SSH or with HTTPS.
-
-sferes2
-=======
-
-Sferes2 is a high-performance, lightweight, generic C++ framework for evolutionary computation.
-
-**If you use this software in an academic article, please cite:**
-
-Mouret, J.-B. and Doncieux, S. (2010). SFERESv2: Evolvin' in the Multi-Core World. _Proc. of Congress on Evolutionary Computation (CEC)_ Pages 4079--4086.
-
-The article is available here: http://www.isir.upmc.fr/files/2010ACTI1524.pdf
-
-@INPROCEEDINGS{Mouret2010,
-	AUTHOR = {Mouret, J.-B. and Doncieux, S.},
-	TITLE = {{SFERES}v2: Evolvin' in the Multi-Core World},
-	YEAR = {2010},
-	BOOKTITLE = {Proc. of Congress on Evolutionary Computation (CEC)},
-	PAGES = {4079--4086}
-}
-
-Documentation (including instruction for compilation)
--------------
-
-We are in the process of porting the documentation to the github wiki: https://github.com/jbmouret/sferes2/wiki
-
-Optional modules
----------------
-- evolvable neural networks: https://github.com/jbmouret/nn2
-- khepera-like simulator: https://github.com/jbmouret/fastsim
-
-
-Design
------
-The following choices were made in the initial design:
-- use of modern c++ techniques (template-based programming) to employ object-oriented programming without the cost of virtual functions;
-- use of Intel TBB to take full advantages of multicore and SMP systems;
-- use of boost libraries when it's useful (shared_ptr, serialization, filesystem, test,...);
-- use of MPI to distribute the computational cost on clusters;
-- a full set of unit tests;
-- no configuration file: a fully optimized executable is built for each particular experiment.
-
-Sferes2 is extended via modules and experiments.
-
-Sferes2 work on most Unix systems (in particular, GNU/Linux and OSX). It successfully compiles with gcc, clang and icc.
-
-
-Authors
--------
-- Jean-Baptiste Mouret mouret@isir.upmc.frfr: main author and maintainer
-- Stephane Doncieux doncieux@isir.upmc.fr
-- Paul Tonellitonelli@isir.upmc.fr (documentation)
-- Many members of ISIR (http://isir.upmc.fr)
-
-Academic papers that used Sferes2:
------------------------------------
-*If you used Sferes2 in an academic paper, please send us an e-mail (mouret@isir.upmc.fr) so that we can add it here!*
-
-(you can find a pdf for most of these publications on http://scholar.google.com).
-
-### 2014
-- Lesaint, F., Sigaud, O., Clark, J. J., Flagel, S. B., & Khamassi, M. (2014). Experimental predictions drawn from a computational model of sign-trackers and goal-trackers. Journal of Physiology-Paris.
-- Lesaint, F., Sigaud, O., Flagel, S. B., Robinson, T. E., & Khamassi, M. (2014). Modelling Individual Differences in the Form of Pavlovian Conditioned Approach Responses: A Dual Learning Systems Approach with Factored Representations. PLoS computational biology, 10(2), e1003466.
-- Shrouf, F., Ordieres-Meré, J., García-Sánchez, A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197-207.
-- Huizinga, J., Mouret, J. B., & Clune, J. (2014). Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique. In Proceedings of GECCO (pp. 1-8).
-- Li, J., Storie, J., & Clune, J. (2014). Encouraging Creative Thinking in Robots Improves Their Ability to Solve Challenging Problems. Proceedings of GECCO (pp 1-8)
-- Tarapore, D. and Mouret, J.-B. (2014). Comparing the evolvability of generative encoding schemes.
-Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, MIT Press, publisher. Pages 1-8.
-
-
-### 2013
-- Koos, S. and Cully, A. and Mouret, J.-B. (2013). Fast Damage Recovery in Robotics with the T-Resilience Algorithm. International Journal of Robotics Research. Vol 32 No 14 Pages 1700-1723.
-- Tonelli, P. and Mouret, J.-B. (2013). On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks. PLoS One. Vol 8 No 11 Pages e79138
-- Clune*, J. and Mouret, J.-B. and Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B. Vol 280 (J. Clune and J.-B. Mouret contributed equally to this work) Pages 20122863
-- Koos, S. and Mouret, J.-B. and Doncieux, S. (2013). The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics. IEEE Transactions on Evolutionary Computation. Vol 17 No 1 Pages 122 - 145 
-- Doncieux, S. and Mouret, J.B. (2013). Behavioral Diversity with Multiple Behavioral Distances. Proc. of IEEE Congress on Evolutionary Computation, 2013 (CEC 2013). Pages 1-8
-- Cully, A. and Mouret, J.-B. (2013). Behavioral Repertoire Learning in Robotics. Genetic and Evolutionary Computation Conference (GECCO). Pages 175-182.
-- Doncieux, S. (2013). Transfer Learning for Direct Policy Search: A Reward Shaping Approach. Proceedings of ICDL-EpiRob conference. Pages 1-6.
-
-### 2012
-- Mouret, J.-B. and Doncieux, S. (2012). Encouraging Behavioral Diversity in Evolutionary Robotics: an Empirical Study. Evolutionary Computation. Vol 20 No 1 Pages 91-133.
-- Ollion, Charles and Doncieux, Stéphane (2012). Towards Behavioral Consistency in Neuroevolution. From Animals to Animats: Proceedings of the 12th International Conference on Adaptive Behaviour (SAB 2012), Springer, publisher. Pages 1-10.
-- Ollion, C. and Pinville, T. and Doncieux, S. (2012). With a little help from selection pressures: evolution of memory in robot controllers. Proc. Alife XIII. Pages 1-8.
-
-### 2011
-- Rubrecht, S. and Singla, E. and Padois, V. and Bidaud, P. and de Broissia, M. (2011). Evolutionary design of a robotic manipulator for a highly constrained environment. Studies in Computational Intelligence, New Horizons in Evolutionary Robotics, Springer, publisher. Vol 341 Pages 109-121.
--  Doncieux, S. and Hamdaoui, M. (2011). Evolutionary Algorithms to Analyse and Design a Controller for a Flapping Wings Aircraft. New Horizons in Evolutionary Robotics Extended Contributions from the 2009 EvoDeRob Workshop, Springer, publisher. Pages 67--83.
--  Mouret, J.-B. (2011). Novelty-based Multiobjectivization. New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRob Workshop, Springer, publisher. Pages 139--154.
--  Pinville, T. and Koos, S. and Mouret, J-B. and Doncieux, S. (2011). How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach. GECCO'11: Proceedings of the 13th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 259--266.
--  Koos, S. and Mouret, J-B. (2011). Online Discovery of Locomotion Modes for Wheel-Legged Hybrid Robots: a Transferability-based Approach. Proceedings of CLAWAR, World Scientific Publishing Co., publisher. Pages 70-77.
-- Tonelli, P. and Mouret, J.-B. (2011). On the Relationships between Synaptic Plasticity and Generative Systems. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. Pages 1531--1538. (Best paper of the Generative and Developmental Systems (GDS) track).
--  Terekhov, A.V. and Mouret, J.-B. and Grand, C. (2011). Stochastic optimization of a chain sliding mode controller for the mobile robot maneuvering. Proceedings of IEEE / IROS Int. Conf. on Robots and Intelligents Systems. Pages 4360 - 4365
-
-### 2010
-- Mouret, J.-B. and Doncieux, S. and Girard, B. (2010). Importing the Computational Neuroscience Toolbox into Neuro-Evolution---Application to Basal Ganglia. GECCO'10: Proceedings of the 12th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 587--594.
-- Koos, S. and Mouret, J.-B. and Doncieux, S. (2010). Crossing the Reality Gap in Evolutionary Robotics by Promoting Transferable Controllers. GECCO'10: Proceedings of the 12th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 119--126.
-- Doncieux, S. and Mouret, J.-B. (2010). Behavioral diversity measures for Evolutionary Robotics. WCCI 2010 IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation (CEC). Pages 1303--1310.
-- Terekhov, A.V. and Mouret, J.-B. and Grand, C. (2010). Stochastic optimization of a neural network-based controller for aggressive maneuvers on loose surfaces. Proceedings of IEEE / IROS Int. Conf. on Robots and Intelligents Systems. Pages 4782 - 4787
-- Terekhov, A.V and Mouret, J.-B. and Grand, C (2010). Stochastic multi-objective optimization for aggressive maneuver trajectory planning on loose surface.
-Proceedings of IFAC: the 7th Symposium on Intelligent Autonomous Vehicles. Pages 1-6 
--  Liénard, J. and Guillot, A. and Girard, B. (2010). Multi-Objective Evolutionary Algorithms to Investigate Neurocomputational Issues : The Case Study of Basal Ganglia Models. From animals to animats 11, Springer, publisher. Vol 6226 Pages 597--606
-
-### 2009
-- Koos, S. and Mouret, J.-B. and Doncieux, S. (2009). Automatic system identification based on coevolution of models and tests. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 560--567
--  Mouret, J.-B. and Doncieux, S. (2009). Evolving modular neural-networks through exaptation. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 1570--1577. (Best student paper award)
--  Mouret, J.-B. and Doncieux, S. (2009). Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 1161 - 1168
--  Mouret, J.-B. and Doncieux, S. (2009). Using Behavioral Exploration Objectives to Solve Deceptive Problems in Neuro-evolution. GECCO'09: Proceedings of the 11th annual conference on Genetic and evolutionary computation , ACM, publisher. Pages 627--634.

modules/dnns_easily_fooled/sferes/build.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-home=$(echo ~)
-
-quit=0
-
-# Remove the build folder
-rm -rf ./build
-echo "Build folder removed."
-
-# Check the building folder, either on local or Moran
-if [ "$home" == "/home/anh" ]
-then    
-    echo "Configuring sferes for local.."    
-    echo "..."
-    ./waf clean
-    ./waf distclean
-    #./waf configure --boost-include=/home/anh/src/sferes/include --boost-lib=/home/anh/src/sferes/lib --eigen3=/home/anh/src/sferes/include --mpi=/home/anh/openmpi
-    ./waf configure --boost-include=/home/anh/src/sferes/include --boost-lib=/home/anh/src/sferes/lib --eigen3=/home/anh/src/sferes/include
-    
-    quit=1
-    
-else
-  if [ "$home" == "/home/anguyen8" ]
-  then 
-      echo "Configuring sferes for Moran.."
-      echo "..."
-      ./waf clean
-      ./waf distclean
-
-      # TBB
-      # ./waf configure --boost-include=/project/RIISVis/anguyen8/sferes/include/ --boost-libs=/project/RIISVis/anguyen8/sferes/lib/ --eigen3=/home/anguyen8/local/include --mpi=/apps/OPENMPI/gnu/4.8.2/1.6.5 --tbb=/home/anguyen8/sferes --libs=/home/anguyen8/local/lib
-      
-      # MPI (No TBB)
-      ./waf configure --boost-include=/project/RIISVis/anguyen8/sferes/include/ --boost-libs=/project/RIISVis/anguyen8/sferes/lib/ --eigen3=/home/anguyen8/local/include --mpi=/apps/OPENMPI/gnu/4.8.2/1.6.5 --libs=/home/anguyen8/local/lib
-      
-      quit=1
-
-  else
-    echo "Unknown environment. Building stopped."    
-  fi
-fi
-
-if [ "$quit" -eq "1" ]
-then
-    echo "Building sferes.."    
-    echo "..."
-    echo "..."
-    ./waf build
-    
-    echo "Building exp/images.."
-    echo "..."
-    echo "..."
-    ./waf --exp images
-fi
-

modules/dnns_easily_fooled/sferes/contributors.txt

@@ -1,3 +0,0 @@
-Anh Nguyen
-
-Evolving AI Lab

modules/dnns_easily_fooled/sferes/eigen3.py

@@ -1,39 +0,0 @@
-#! /usr/bin/env python
-# encoding: utf-8
-# JB Mouret - 2009
-
-"""
-Quick n dirty eigen3 detection
-"""
-
-import os, glob, types
-import Options, Configure
-
-def detect_eigen3(conf):
-	env = conf.env
-	opt = Options.options
-
-	conf.env['LIB_EIGEN3'] = ''
-	conf.env['EIGEN3_FOUND'] = False
-	if Options.options.no_eigen3:
-		return 0
-	if Options.options.eigen3:
-		conf.env['CPPPATH_EIGEN3'] = [Options.options.eigen3]
-		conf.env['LIBPATH_EIGEN3'] = [Options.options.eigen3]
-	else:
-		conf.env['CPPPATH_EIGEN3'] = ['/usr/include/eigen3', '/usr/local/include/eigen3', '/usr/include', '/usr/local/include']
-		conf.env['LIBPATH_EIGEN3'] = ['/usr/lib', '/usr/local/lib']
-
-	res = Configure.find_file('Eigen/Core', conf.env['CPPPATH_EIGEN3'])
-	conf.check_message('header','Eigen/Core', (res != '') , res)
-	if (res == '') :
-		return 0
-	conf.env['EIGEN3_FOUND'] = True
-	return 1
-
-def detect(conf):
-	return detect_eigen3(conf)
-
-def set_options(opt):
-	opt.add_option('--eigen3', type='string', help='path to eigen3', dest='eigen3')
-	opt.add_option('--no-eigen3', type='string', help='disable eigen3', dest='no_eigen3')

modules/dnns_easily_fooled/sferes/examples/ex_ea.cpp

@@ -1,121 +0,0 @@
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/ea/rank_simple.hpp>
-#include <sferes/eval/eval.hpp>
-#include <sferes/stat/best_fit.hpp>
-#include <sferes/stat/mean_fit.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-using namespace sferes;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-  struct evo_float
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    SFERES_CONST float mutation_rate = 0.1f;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-  struct pop
-  {
-    // size of the population
-    SFERES_CONST unsigned size = 200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 2000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    SFERES_CONST int dump_period = 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-};
-
-SFERES_FITNESS(FitTest, sferes::fit::Fitness)
-{
- public:
-  // indiv will have the type defined in the main (phen_t)
-  template<typename Indiv>
-    void eval(const Indiv& ind)
-  {
-    float v = 0;
-    for (unsigned i = 0; i < ind.size(); ++i)
-      {
-	float p = ind.data(i);
-	v += p * p * p * p;
-      }
-    this->_value = -v;
-  }
-};
-
-
-
-int main(int argc, char **argv)
-{
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-  typedef FitTest<Params> fit_t;
-  // We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-  typedef gen::EvoFloat<10, Params> gen_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::Parameters<gen_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-  typedef eval::Eval<Params> eval_t;
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the comannd line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-  run_ea(argc, argv, ea);
-  //
-  return 0;
-}

modules/dnns_easily_fooled/sferes/examples/ex_ea.json

@@ -1,11 +0,0 @@
-{
-  "machines" :
-    {
-      "localhost" : 3,
-      "fortaleza" : 2
-    },
-  "nb_runs": 3,
-  "exp" : "examples/ex_ea",
-  "dir" : "res/ex1",
-  "debug" : 0
-}

modules/dnns_easily_fooled/sferes/examples/ex_ea_mpi.cpp

@@ -1,93 +0,0 @@
-#include <iostream>
-
-#ifdef MPI_ENABLED
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/ea/rank_simple.hpp>
-#include <sferes/eval/eval.hpp>
-#include <sferes/stat/best_fit.hpp>
-#include <sferes/stat/mean_fit.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-#include <boost/program_options.hpp>
-#include <sferes/eval/mpi.hpp>
-
-using namespace sferes;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-  struct evo_float
-  {
-    SFERES_CONST float cross_rate = 0.5f;
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 10.0f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-  };
-  struct pop
-  {
-    SFERES_CONST unsigned size = 200;
-    SFERES_CONST unsigned nb_gen = 40;
-    SFERES_CONST int dump_period = 5;
-    SFERES_CONST int initial_aleat = 1;
-    SFERES_CONST float coeff = 1.1f;
-    SFERES_CONST float keep_rate = 0.6f;    
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -10.0f;
-    SFERES_CONST float max = 10.0f;
-  };
-};
-
-SFERES_FITNESS(FitTest, sferes::fit::Fitness)
-{
- public:
-  FitTest()
-    {}
-  template<typename Indiv>
-    void eval(const Indiv& ind) 
-  {
-    float v = 0;
-    for (unsigned i = 0; i < ind.size(); ++i)
-      {
-	float p = ind.data(i);
-	v += p * p * p * p;
-      }
-    // slow down to simulate a slow fitness
-    usleep(1e4);
-    this->_value = -v;
-  }
-};
-
-
-
-int main(int argc, char **argv)
-{
-  dbg::out(dbg::info)<<"running ex_ea ... try --help for options (verbose)"<<std::endl;
-  std::cout << "To run this example, you need to use mpirun" << std::endl;
-  std::cout << "mpirun -x LD_LIBRARY_PATH=/home/creadapt/lib  -np 50 -machinefile machines.pinfo build/debug/examples/ex_ea_mpi" << std::endl;
-  typedef gen::EvoFloat<10, Params> gen_t;
-  typedef phen::Parameters<gen_t, FitTest<Params>, Params> phen_t;
-  typedef eval::Mpi<Params> eval_t;
-  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef modif::Dummy<> modifier_t;
-  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  ea_t ea;
-
-  run_ea(argc, argv, ea);
-
-  std::cout<<"==> best fitness ="<<ea.stat<0>().best()->fit().value()<<std::endl;
-//   std::cout<<"==> mean fitness ="<<ea.stat<1>().mean()<<std::endl;
-  return 0;
-}
-#else
-#warning MPI is disabled, ex_ea_mpi is not compiled
-int main()
-{
-  std::cerr<<"MPI is disabled"<<std::endl;
-  return 0;
-}
-#endif

modules/dnns_easily_fooled/sferes/examples/ex_eps_moea.cpp

@@ -1,91 +0,0 @@
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/ea/eps_moea.hpp>
-#include <sferes/eval/eval.hpp>
-#include <sferes/stat/pareto_front.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-#include <boost/program_options.hpp>
-
-using namespace sferes;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-  struct evo_float
-  {
-
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.5f;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 10.0f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-  };
-  struct pop
-  {
-    SFERES_CONST unsigned size = 200;
-    SFERES_CONST int dump_period = 50;
-    SFERES_ARRAY(float, eps, 0.0075f, 0.0075f);
-    SFERES_ARRAY(float, min_fit, 0.0f, 0.0f);
-    SFERES_CONST size_t grain = size / 4;
-    SFERES_CONST unsigned nb_gen = 2000;
-  };
-
-  struct parameters
-  {
-    SFERES_CONST float min = 0.0f;
-    SFERES_CONST float max = 1.0f;
-  };
-};
-
-
-template<typename Indiv>
-float _g(const Indiv &ind)
-{
-  float g = 0.0f;
-  assert(ind.size() == 30);
-  for (size_t i = 1; i < 30; ++i)
-    g += ind.data(i);
-  g = 9.0f * g / 29.0f;
-  g += 1.0f;
-  return g;
-}
-
-SFERES_FITNESS(FitZDT2, sferes::fit::Fitness)
-{
- public:
-  FitZDT2()  {}
-  template<typename Indiv>
-    void eval(Indiv& ind) 
-  {
-    this->_objs.resize(2);
-    float f1 = ind.data(0);
-    float g = _g(ind);
-    float h = 1.0f - pow((f1 / g), 2.0);
-    float f2 = g * h;
-    this->_objs[0] = -f1;
-    this->_objs[1] = -f2;
-  }
-};
-
-
-
-
-int main(int argc, char **argv)
-{
-  std::cout<<"running "<<argv[0]<<" ... try --help for options (verbose)"<<std::endl;
-
-  typedef gen::EvoFloat<30, Params> gen_t;
-  typedef phen::Parameters<gen_t, FitZDT2<Params>, Params> phen_t;
-  typedef eval::Eval<Params> eval_t;
-  typedef boost::fusion::vector<stat::ParetoFront<phen_t, Params> >  stat_t;
-  typedef modif::Dummy<> modifier_t;
-  typedef ea::EpsMOEA<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  ea_t ea;
-
-  run_ea(argc, argv, ea);
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/examples/ex_nsga2.cpp

@@ -1,87 +0,0 @@
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/ea/nsga2.hpp>
-#include <sferes/eval/eval.hpp>
-#include <sferes/stat/pareto_front.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-#include <boost/program_options.hpp>
-
-using namespace sferes;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-  struct evo_float
-  {
-    SFERES_CONST float cross_rate = 0.5f;
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 10.0f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-  };
-  struct pop
-  {
-    SFERES_CONST unsigned size = 300;
-    SFERES_CONST unsigned nb_gen = 500;
-    SFERES_CONST int dump_period = 50;
-    SFERES_CONST int initial_aleat = 1;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = 0.0f;
-    SFERES_CONST float max = 1.0f;
-  };
-};
-
-
-template<typename Indiv>
-float _g(const Indiv &ind)
-{
-  float g = 0.0f;
-  assert(ind.size() == 30);
-  for (size_t i = 1; i < 30; ++i)
-    g += ind.data(i);
-  g = 9.0f * g / 29.0f;
-  g += 1.0f;
-  return g;
-}
-
-SFERES_FITNESS(FitZDT2, sferes::fit::Fitness)
-{ 
- public:
-  FitZDT2()  {}
-  template<typename Indiv>
-    void eval(Indiv& ind) 
-  {
-    this->_objs.resize(2);
-    float f1 = ind.data(0);
-    float g = _g(ind);
-    float h = 1.0f - pow((f1 / g), 2.0);
-    float f2 = g * h;
-    this->_objs[0] = -f1;
-    this->_objs[1] = -f2;
-  }
-};
-
-
-
-
-int main(int argc, char **argv)
-{
-  std::cout<<"running "<<argv[0]<<" ... try --help for options (verbose)"<<std::endl;
-
-  typedef gen::EvoFloat<30, Params> gen_t;
-  typedef phen::Parameters<gen_t, FitZDT2<Params>, Params> phen_t;
-  typedef eval::Eval<Params> eval_t;
-  typedef boost::fusion::vector<stat::ParetoFront<phen_t, Params> >  stat_t;
-  typedef modif::Dummy<> modifier_t;
-  typedef ea::Nsga2<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  ea_t ea;
-
-  run_ea(argc, argv, ea);
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/examples/qsub.json

@@ -1,8 +0,0 @@
-{
-  "email" : "mouret@isir.upmc.fr",
-  "wall_time" : "24:00:00",
-  "nb_runs": 3,
-  "bin_dir": "/home/mouret/svn/sferes2/trunk/build/default/examples/",
-  "res_dir": "/home/mouret/svn/sferes2/trunk/res/",
-  "exps" : ["ex_ea"]
-}

modules/dnns_easily_fooled/sferes/examples/wscript

@@ -1,34 +0,0 @@
-#! /usr/bin/env python    
-def build(bld):    
-    return None
-    # ex_ea
-    #obj = bld.new_task_gen('cxx', 'program')
-    #obj.source = 'ex_ea.cpp'
-    #obj.includes = '../'
-    #obj.target = 'ex_ea'
-    #obj.uselib_local = 'sferes2'
-    #obj.uselib = 'TBB BOOST BOOST_UNIT_TEST_FRAMEWORK EIGEN3'
-
-    ## ex_ea
-    #obj = bld.new_task_gen('cxx', 'program')
-    #obj.source = 'ex_ea_mpi.cpp'
-    #obj.includes = '../'
-    #obj.target = 'ex_ea_mpi'
-    #obj.uselib_local = 'sferes2'
-    #obj.uselib = 'TBB BOOST BOOST_UNIT_TEST_FRAMEWORK EIGEN3'
-
-    ## ex_nsga2
-    #obj = bld.new_task_gen('cxx', 'program')
-    #obj.source = 'ex_nsga2.cpp'
-    #obj.includes = '../'
-    #obj.target = 'ex_nsga2'
-    #obj.uselib_local = 'sferes2'
-    #obj.uselib = 'TBB BOOST BOOST_UNIT_TEST_FRAMEWORK EIGEN3'
-
-    ## ex_eps_moea
-    #obj = bld.new_task_gen('cxx', 'program')
-    #obj.source = 'ex_eps_moea.cpp'
-    #obj.includes = '../'
-    #obj.target = 'ex_eps_moea'
-    #obj.uselib_local = 'sferes2'
-    #obj.uselib = 'TBB BOOST BOOST_UNIT_TEST_FRAMEWORK EIGEN3'

modules/dnns_easily_fooled/sferes/exp/example/example.cpp

@@ -1,88 +0,0 @@
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/ea/nsga2.hpp>
-#include <sferes/eval/eval.hpp>
-#include <sferes/stat/pareto_front.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-#include <boost/program_options.hpp>
-
-using namespace sferes;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-  struct evo_float
-  {
-
-	SFERES_CONST float cross_rate = 0.1f;
-	SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 10.0f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-  };
-  struct pop
-  {
-    SFERES_CONST unsigned size = 300;
-    SFERES_CONST unsigned nb_gen = 500;
-    SFERES_CONST int dump_period = 50;
-    SFERES_CONST int initial_aleat = 1;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = 0.0f;
-    SFERES_CONST float max = 1.0f;
-  };
-};
-
-
-template<typename Indiv>
-float _g(const Indiv &ind)
-{
-  float g = 0.0f;
-  assert(ind.size() == 30);
-  for (size_t i = 1; i < 30; ++i)
-    g += ind.data(i);
-  g = 9.0f * g / 29.0f;
-  g += 1.0f;
-  return g;
-}
-
-SFERES_FITNESS(FitZDT2, sferes::fit::Fitness)
-{ 
- public:
-  FitZDT2()  {}
-  template<typename Indiv>
-    void eval(Indiv& ind) 
-  {
-    this->_objs.resize(2);
-    float f1 = ind.data(0);
-    float g = _g(ind);
-    float h = 1.0f - pow((f1 / g), 2.0);
-    float f2 = g * h;
-    this->_objs[0] = -f1;
-    this->_objs[1] = -f2;
-  }
-};
-
-
-
-
-int main(int argc, char **argv)
-{
-  std::cout<<"running "<<argv[0]<<" ... try --help for options (verbose)"<<std::endl;
-
-  typedef gen::EvoFloat<30, Params> gen_t;
-  typedef phen::Parameters<gen_t, FitZDT2<Params>, Params> phen_t;
-  typedef eval::Eval<Params> eval_t;
-  typedef boost::fusion::vector<stat::ParetoFront<phen_t, Params> >  stat_t;
-  typedef modif::Dummy<> modifier_t;
-  typedef ea::Nsga2<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  ea_t ea;
-
-  run_ea(argc, argv, ea);
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/example/wscript

@@ -1,10 +0,0 @@
-
-#! /usr/bin/env python
-def build(bld):
-    obj = bld.new_task_gen('cxx', 'program')
-    obj.source = 'example.cpp'
-    obj.includes = '. ../../'
-    obj.uselib_local = 'sferes2'
-    obj.uselib = ''
-    obj.target = 'example'
-    obj.uselib_local = 'sferes2'

modules/dnns_easily_fooled/sferes/exp/images/build_wscript.sh

@@ -1,22 +0,0 @@
-#!/bin/bash
-home=$(echo ~)
-echo "You are building from: $home"
-
-# Check the building folder, either on local or Moran
-if [ "$home" == "/home/anh" ]
-then
-    echo "Enabled local settings.."
-    cp ./wscript.local ./wscript
-  
-  
-else
-  if [ "$home" == "/home/anguyen8" ]
-  then 
-      echo "Enabled Moran settings.."
-      cp ./wscript.moran ./wscript
-  else
-    echo "Unknown environment. Building stopped."    
-  fi
-fi
-
-

modules/dnns_easily_fooled/sferes/exp/images/continue_run/continue_run.hpp

@@ -1,88 +0,0 @@
-/*
- * continue_run.hpp
- *
- *  Created on: Aug 14, 2014
- *      Author: joost
- */
-
-#ifndef CONTINUE_RUN_HPP_
-#define CONTINUE_RUN_HPP_
-
-#include <boost/fusion/container.hpp>
-#include <boost/fusion/include/vector.hpp>
-
-#include <sferes/dbg/dbg.hpp>
-
-#include "global_options.hpp"
-#include <exp/images/stat/stat_map_image.hpp>
-
-namespace sferes
-{
-	namespace cont
-	{
-
-		template<typename EAType, typename Params>
-		class Continuator
-		{
-
-		public:
-			typedef std::vector<typename EAType::indiv_t> pop_t;
-
-			bool enabled()
-			{
-				return options::vm.count("continue");
-			}
-
-			pop_t getPopulationFromFile(EAType& ea)
-			{
-				ea.load(options::vm["continue"].as<std::string>());
-
-				return boost::fusion::at_c<Params::cont::getPopIndex>(ea.stat()).getPopulation();
-			}
-
-			pop_t getPopulationFromFile(EAType& ea, const std::string& path_gen_file)
-			{
-				ea.load(path_gen_file);
-
-				return boost::fusion::at_c<Params::cont::getPopIndex>(ea.stat()).getPopulation();
-			}
-
-			void run_with_current_population(EAType& ea, const std::string filename)
-			{
-				// Read the number of generation from gen file. Ex: gen_450
-				int start = 0;
-				std::string gen_prefix("gen_");
-				std::size_t pos = filename.rfind(gen_prefix) + gen_prefix.size();
-				std::string gen_number = filename.substr(pos);
-				std::istringstream ss(gen_number);
-				ss >> start;
-				start++;
-				dbg::out(dbg::info, "continue") << "File name: " << filename << " number start: " << pos << " gen number: " << gen_number << " result: " << start << std::endl;
-
-				// Similar to the run() function in <sferes/ea/ea.hpp>
-				for (int _gen = start; _gen < Params::pop::nb_gen; ++_gen)
-				{
-					ea.setGen(_gen);
-					ea.epoch();
-					ea.update_stats();
-					if (_gen % Params::pop::dump_period == 0)
-					{
-						ea.write();
-					}
-				}
-
-				std::cout << "Finished all the runs.\n";
-				exit(0);
-			}
-
-			void run_with_current_population(EAType& ea)
-			{
-				const std::string filename = options::vm["continue"].as<std::string>();
-				run_with_current_population(ea, filename);
-			}
-
-		};
-
-	}
-}
-#endif /* CONTINUE_RUN_HPP_ */

modules/dnns_easily_fooled/sferes/exp/images/continue_run/global_options.hpp

@@ -1,120 +0,0 @@
-/*
- * global_options.hpp
- *
- *  Created on: Aug 14, 2014
- *      Author: Joost Huizinga
- *
- *  Header file created to allow for custom command-line options to be added to an experiment.
- *  Requires you to replace run_ea with options::run_ea to works.
- *  Options can be added by:
- *  options::add()("option_name","description");
- */
-
-#ifndef GLOBAL_OPTIONS_HPP_
-#define GLOBAL_OPTIONS_HPP_
-
-#include <boost/program_options.hpp>
-#include <boost/archive/xml_iarchive.hpp>
-#include <boost/foreach.hpp>
-
-#include <sferes/eval/parallel.hpp>
-//#include <sferes/dbg/dbg.hpp>
-
-namespace sferes
-{
-	namespace options
-	{
-
-		boost::program_options::variables_map vm;
-
-		template<typename Ea>
-		static void run_ea ( int argc, char **argv, Ea& ea,
-				const boost::program_options::options_description& add_opts =
-						boost::program_options::options_description(),
-				bool init_rand = true )
-		{
-			namespace po = boost::program_options;
-			std::cout << "sferes2 version: " << VERSION << std::endl;
-			if (init_rand)
-			{
-				time_t t = time(0) + ::getpid();
-				std::cout << "seed: " << t << std::endl;
-				srand(t);
-			}
-			po::options_description desc("Allowed sferes2 options");
-			desc.add(add_opts);
-			desc.add_options()("help,h", "produce help message")("stat,s",
-					po::value<int>(), "statistic number")("out,o",
-					po::value<std::string>(), "output file")("number,n", po::value<int>(),
-					"number in stat")("load,l", po::value<std::string>(),
-					"load a result file")("verbose,v",
-					po::value<std::vector<std::string> >()->multitoken(),
-					"verbose output, available default streams : all, ea, fit, phen, trace");
-
-		//		po::variables_map vm;
-			po::store(po::parse_command_line(argc, argv, desc), vm);
-			po::notify(vm);
-
-			if (vm.count("help"))
-			{
-				std::cout << desc << std::endl;
-				return;
-			}
-			if (vm.count("verbose"))
-			{
-				dbg::init();
-				std::vector < std::string > streams = vm["verbose"].as<
-						std::vector<std::string> >();
-				attach_ostream(dbg::warning, std::cout);
-				attach_ostream(dbg::error, std::cerr);
-				attach_ostream(dbg::info, std::cout);
-				bool all = std::find(streams.begin(), streams.end(), "all")
-						!= streams.end();
-				bool trace = std::find(streams.begin(), streams.end(), "trace")
-						!= streams.end();
-				if (all)
-				{
-					streams.push_back("ea");
-					streams.push_back("fit");
-					streams.push_back("phen");
-					streams.push_back("eval");
-				}
-				BOOST_FOREACH(const std::string& s, streams){
-				dbg::enable(dbg::all, s.c_str(), true);
-				dbg::attach_ostream(dbg::info, s.c_str(), std::cout);
-				if (trace)
-				dbg::attach_ostream(dbg::tracing, s.c_str(), std::cout);
-			}
-				if (trace)
-					attach_ostream(dbg::tracing, std::cout);
-			}
-
-			parallel::init();
-			if (vm.count("load"))
-			{
-				ea.load(vm["load"].as<std::string>());
-
-				if (!vm.count("out"))
-				{
-					std::cerr << "You must specifiy an out file" << std::endl;
-					return;
-				}
-				else
-				{
-					int stat = 0;
-					int n = 0;
-					if (vm.count("stat"))
-						stat = vm["stat"].as<int>();
-					if (vm.count("number"))
-						n = vm["number"].as<int>();
-					std::ofstream ofs(vm["out"].as<std::string>().c_str());
-					ea.show_stat(stat, ofs, n);
-				}
-			}
-			else
-				ea.run();
-		}
-
-	}
-}
-#endif /* GLOBAL_OPTIONS_HPP_ */

modules/dnns_easily_fooled/sferes/exp/images/dl_images.hpp

@@ -1,32 +0,0 @@
-#ifndef DEEP_LEARNING_IMAGES_HPP
-#define DEEP_LEARNING_IMAGES_HPP
-
-#include "settings.h"
-#include <sferes/phen/parameters.hpp>
-
-using namespace sferes;
-
-// Parameters required by Caffe separated from those introduced by Sferes
-struct ParamsCaffe
-{
-  struct image
-	{
-		// Size of the square image 256x256
-		SFERES_CONST int size = 256;
-		SFERES_CONST int crop_size = 227;
-		SFERES_CONST bool use_crops = true;
-
-		SFERES_CONST bool color = true;	// true: color, false: grayscale images
-
-		// GPU configurations
-		SFERES_CONST bool use_gpu = false;
-
-		// GPU on Moran can only handle max of 512 images in a batch at a time.
-		SFERES_CONST int batch = 1;
-		SFERES_CONST int num_categories = 1000;		// ILSVR2012 ImageNet has 1000 categories
-		static int category_id;
-		SFERES_CONST bool record_lineage = false;	// Flag to save the parent's assigned class
-	};
-};
-
-#endif /* DEEP_LEARNING_IMAGES_HPP */

modules/dnns_easily_fooled/sferes/exp/images/dl_map_elites_images.cpp

@@ -1,232 +0,0 @@
-#include "dl_images.hpp"
-
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/eval/eval.hpp>
-
-#include "stat/best_fit_map_image.hpp"
-#include "stat/stat_map_image.hpp"
-
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-
-// Evolutionary algorithms --------------------------------
-#include "fit/fit_map_deep_learning.hpp"
-#include <modules/nn2/gen_dnn.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-#include "phen/phen_color_image.hpp"
-
-#include <glog/logging.h>
-// Caffe -------------------------------------------------
-
-#include <modules/map_elite/map_elite.hpp>
-#include "eval/mpi_parallel.hpp" // MPI
-#include "continue_run/continue_run.hpp" // MPI
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-	struct cont
-	{
-	    static const int getPopIndex = 0;
-  };
-
-	struct log
-	{
-		SFERES_CONST bool best_image = false;
-	};
-
-	struct ea
-	{
-		SFERES_CONST size_t res_x = 1; // 256;
-		SFERES_CONST size_t res_y = 1000; // 256;
-	};
-
-	struct dnn
-	{
-		SFERES_CONST size_t nb_inputs = 4;
-		SFERES_CONST size_t nb_outputs = 3;		// Red, Green, Blue
-
-		SFERES_CONST float m_rate_add_conn = 0.5f;
-		SFERES_CONST float m_rate_del_conn = 0.3f;
-		SFERES_CONST float m_rate_change_conn = 0.5f;
-		SFERES_CONST float m_rate_add_neuron = 0.5f;
-		SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-		SFERES_CONST init_t init = ff;
-	};
-
-  struct evo_float
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    SFERES_CONST float mutation_rate = 0.1f;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-
-  struct pop
-  {
-  	//number of initial random points
-		static const size_t init_size = 400; // 1000
-    // size of the population
-    SFERES_CONST unsigned size = 400; //200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 5001; //10,000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    static int dump_period;// 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-
-  struct cppn
-	{
-		 // params of the CPPN
-		struct sampled
-		{
-			SFERES_ARRAY(float, values, nn::cppn::sine, nn::cppn::sigmoid, nn::cppn::gaussian, nn::cppn::linear);
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.25f;
-			SFERES_CONST bool ordered = false;
-		};
-		struct evo_float
-		{
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.1f;
-			SFERES_CONST mutation_t mutation_type = polynomial;
-			SFERES_CONST cross_over_t cross_over_type = sbx;
-			SFERES_CONST float eta_m = 15.0f;
-			SFERES_CONST float eta_c = 15.0f;
-		};
-	};
-
-  // Specific settings for MNIST database of grayscale
-	struct image : ParamsCaffe::image
-	{
-		static const std::string model_definition;
-		static const std::string pretrained_model;
-	};
-
-};
-
-// Initialize the parameter files for Caffe network.
-#ifdef LOCAL_RUN
-
-const std::string Params::image::model_definition = "/home/anh/src/model/imagenet_deploy_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/home/anh/src/model/caffe_reference_imagenet_model";
-
-#else
-
-const std::string Params::image::model_definition = "/project/EvolvingAI/anguyen8/model/imagenet_deploy_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/project/EvolvingAI/anguyen8/model/caffe_reference_imagenet_model";
-
-#endif
-
-int Params::pop::dump_period = 1000;
-
-
-int main(int argc, char **argv)
-{
-	// Disable GLOG output from experiment and also Caffe
-	// Comment out for debugging
-	google::InitGoogleLogging("");
-	google::SetStderrLogging(3);
-
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-	typedef sferes::fit::FitMapDeepLearning<Params> fit_t;
-  // We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-  //typedef gen::EvoFloat<10, Params> gen_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef gen::HyperNn<weight_t, Params> cppn_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::ColorImage<cppn_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-//  typedef eval::Eval<Params> eval_t;
-  typedef eval::MpiParallel<Params> eval_t;	// TBB
-
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-//  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef boost::fusion::vector<stat::MapImage<phen_t, Params>, stat::BestFitMapImage<phen_t, Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-//  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the command line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-
-  if (argc > 1) // if a number is provided on the command line
-	{
-		int randomSeed = atoi(argv[1]);
-		printf("randomSeed:%i\n", randomSeed);
-		srand(randomSeed);  //set it as the random seed
-
-		boost::program_options::options_description add_opts =
-								boost::program_options::options_description();
-
-		shared_ptr<boost::program_options::option_description> opt (new boost::program_options::option_description(
-				"continue,t", boost::program_options::value<std::string>(),
-																"continue from the loaded file starting from the generation provided"
-				));
-
-		add_opts.add(opt);
-
-		options::run_ea(argc, argv, ea, add_opts, false);
-	}
-	else
-	{
-		run_ea(argc, argv, ea);
-	}
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/images/dl_map_elites_images_imagenet_direct_encoding.cpp

@@ -1,197 +0,0 @@
-#include "dl_images.hpp"
-
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include "gen/evo_float_image.hpp"
-#include <sferes/eval/eval.hpp>
-
-#include "stat/best_fit_map_image.hpp"
-#include "stat/stat_map_image.hpp"
-
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-
-// Evolutionary algorithms --------------------------------
-#include "fit/fit_map_deep_learning.hpp"
-#include <modules/nn2/gen_dnn.hpp>
-#include <modules/nn2/phen_dnn.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-#include "phen/phen_image_direct.hpp"
-
-#include <glog/logging.h>
-// Caffe -------------------------------------------------
-
-#include <modules/map_elite/map_elite.hpp>
-#include "eval/mpi_parallel.hpp" // MPI
-#include "continue_run/continue_run.hpp" // MPI
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float_image;
-
-struct Params
-{
-	struct cont
-	{
-	    static const int getPopIndex = 0;
-  };
-
-	struct log
-	{
-		SFERES_CONST bool best_image = false;
-	};
-
-	struct ea
-	{
-		SFERES_CONST size_t res_x = 1; // 256;
-		SFERES_CONST size_t res_y = 1000; // 256;
-	};
-
-  struct evo_float_image
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    static float mutation_rate;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-
-  struct pop
-  {
-  	//number of initial random points
-  	SFERES_CONST size_t init_size = 200; // 1000
-    // size of the population
-    SFERES_CONST unsigned size = 200; //200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 5010; //10,000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    static int dump_period;// 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-
-  // Specific settings for MNIST database of grayscale
-	struct image : ParamsCaffe::image
-	{
-		static const std::string model_definition;
-		static const std::string pretrained_model;
-	};
-
-};
-
-// Initialize the parameter files for Caffe network.
-#ifdef LOCAL_RUN
-
-const std::string Params::image::model_definition = "/home/anh/src/model/imagenet_deploy_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/home/anh/src/model/caffe_reference_imagenet_model";
-
-#else
-
-const std::string Params::image::model_definition = "/project/EvolvingAI/anguyen8/model/imagenet_deploy_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/project/EvolvingAI/anguyen8/model/caffe_reference_imagenet_model";
-
-#endif
-
-int Params::pop::dump_period = 1000;
-float Params::evo_float_image::mutation_rate = 0.1f;
-
-
-int main(int argc, char **argv)
-{
-	// Disable GLOG output from experiment and also Caffe
-	// Comment out for debugging
-	google::InitGoogleLogging("");
-	google::SetStderrLogging(3);
-
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-	typedef sferes::fit::FitMapDeepLearning<Params> fit_t;
-	// We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-	typedef gen::EvoFloatImage<Params::image::size * Params::image::size * 3, Params> gen_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::ImageDirect<gen_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-//  typedef eval::Eval<Params> eval_t;
-  typedef eval::MpiParallel<Params> eval_t;	// TBB
-
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-//  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef boost::fusion::vector<stat::MapImage<phen_t, Params>, stat::BestFitMapImage<phen_t, Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-//  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the command line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-
-  if (argc > 1) // if a number is provided on the command line
-	{
-		int randomSeed = atoi(argv[1]);
-		printf("randomSeed:%i\n", randomSeed);
-		srand(randomSeed);  //set it as the random seed
-
-		boost::program_options::options_description add_opts =
-								boost::program_options::options_description();
-
-		shared_ptr<boost::program_options::option_description> opt (new boost::program_options::option_description(
-				"continue,t", boost::program_options::value<std::string>(),
-																"continue from the loaded file starting from the generation provided"
-				));
-
-		add_opts.add(opt);
-
-		options::run_ea(argc, argv, ea, add_opts, false);
-	}
-	else
-	{
-		run_ea(argc, argv, ea);
-	}
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/images/dl_map_elites_images_mnist.cpp

@@ -1,241 +0,0 @@
-#include "dl_images.hpp"
-
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/eval/eval.hpp>
-
-#include "stat/best_fit_map_image.hpp"
-#include "stat/stat_map_image.hpp"
-
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-
-// Evolutionary algorithms --------------------------------
-#include "fit/fit_map_deep_learning.hpp"
-#include <modules/nn2/gen_dnn.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-#include "phen/phen_grayscale_image.hpp"
-
-#include <glog/logging.h>
-// Caffe -------------------------------------------------
-
-#include <modules/map_elite/map_elite.hpp>
-#include "eval/mpi_parallel.hpp" // MPI
-#include "continue_run/continue_run.hpp" // MPI
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-	struct cont
-	{
-	    static const int getPopIndex = 0;
-  };
-
-	struct log
-	{
-		SFERES_CONST bool best_image = false;
-	};
-
-	struct ea
-	{
-		SFERES_CONST size_t res_x = 1; // 256;
-		SFERES_CONST size_t res_y = 10; // 256;
-	};
-
-	struct dnn
-	{
-		SFERES_CONST size_t nb_inputs = 4;
-		SFERES_CONST size_t nb_outputs = 1;		// Red, Green, Blue
-
-		SFERES_CONST float m_rate_add_conn = 0.5f;
-		SFERES_CONST float m_rate_del_conn = 0.3f;
-		SFERES_CONST float m_rate_change_conn = 0.5f;
-		SFERES_CONST float m_rate_add_neuron = 0.5f;
-		SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-		SFERES_CONST init_t init = ff;
-	};
-
-  struct evo_float
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    SFERES_CONST float mutation_rate = 0.1f;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-
-  struct pop
-  {
-  	//number of initial random points
-		static const size_t init_size = 200; // 1000
-    // size of the population
-    SFERES_CONST unsigned size = 200; //200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 1001; //10,000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    static int dump_period;// 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-
-  struct cppn
-	{
-		 // params of the CPPN
-		struct sampled
-		{
-			SFERES_ARRAY(float, values, nn::cppn::sine, nn::cppn::sigmoid, nn::cppn::gaussian, nn::cppn::linear);
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.25f;
-			SFERES_CONST bool ordered = false;
-		};
-		struct evo_float
-		{
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.1f;
-			SFERES_CONST mutation_t mutation_type = polynomial;
-			SFERES_CONST cross_over_t cross_over_type = sbx;
-			SFERES_CONST float eta_m = 15.0f;
-			SFERES_CONST float eta_c = 15.0f;
-		};
-	};
-
-  // Specific settings for MNIST database of grayscale
-  struct image : ParamsCaffe::image
-	{
-		// Size of the square image 256x256
-		SFERES_CONST int size = 28;
-		SFERES_CONST bool use_crops = false;
-		SFERES_CONST bool color = false;	// Grayscale
-
-		SFERES_CONST int num_categories = 10;	// MNIST has 10 categories
-
-		static const std::string model_definition;
-		static const std::string pretrained_model;
-
-		SFERES_CONST bool record_lineage = true;
-	};
-
-};
-
-// Initialize the parameter files for Caffe network.
-#ifdef LOCAL_RUN
-
-const std::string Params::image::model_definition = "/home/anh/src/model/lenet_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/home/anh/src/model/lenet_iter_10000";
-
-#else
-
-const std::string Params::image::model_definition = "/project/EvolvingAI/anguyen8/model/lenet_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/project/EvolvingAI/anguyen8/model/lenet_iter_10000";
-
-#endif
-
-int Params::pop::dump_period = 100;
-
-
-int main(int argc, char **argv)
-{
-	// Disable GLOG output from experiment and also Caffe
-	// Comment out for debugging
-	google::InitGoogleLogging("");
-	google::SetStderrLogging(3);
-
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-	typedef sferes::fit::FitMapDeepLearning<Params> fit_t;
-  // We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-  //typedef gen::EvoFloat<10, Params> gen_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef gen::HyperNn<weight_t, Params> cppn_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::GrayscaleImage<cppn_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-//  typedef eval::Eval<Params> eval_t;
-  typedef eval::MpiParallel<Params> eval_t;	// TBB
-
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-//  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef boost::fusion::vector<stat::MapImage<phen_t, Params>, stat::BestFitMapImage<phen_t, Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-//  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the command line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-
-  if (argc > 1) // if a number is provided on the command line
-	{
-		int randomSeed = atoi(argv[1]);
-		printf("randomSeed:%i\n", randomSeed);
-		srand(randomSeed);  //set it as the random seed
-
-		boost::program_options::options_description add_opts =
-								boost::program_options::options_description();
-
-		shared_ptr<boost::program_options::option_description> opt (new boost::program_options::option_description(
-				"continue,t", boost::program_options::value<std::string>(),
-																"continue from the loaded file starting from the generation provided"
-				));
-
-		add_opts.add(opt);
-
-		options::run_ea(argc, argv, ea, add_opts, false);
-	}
-	else
-	{
-		run_ea(argc, argv, ea);
-	}
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/images/dl_map_elites_images_mnist_direct_encoding.cpp

@@ -1,202 +0,0 @@
-#include "dl_images.hpp"
-
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include "gen/evo_float_image.hpp"
-#include <sferes/eval/eval.hpp>
-
-#include "stat/best_fit_map_image.hpp"
-#include "stat/stat_map_image.hpp"
-
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-
-// Evolutionary algorithms --------------------------------
-#include "fit/fit_map_deep_learning.hpp"
-#include <modules/nn2/gen_dnn.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-#include "phen/phen_grayscale_image_direct.hpp"
-
-#include <glog/logging.h>
-// Caffe -------------------------------------------------
-
-#include <modules/map_elite/map_elite.hpp>
-#include "eval/mpi_parallel.hpp" // MPI
-#include "continue_run/continue_run.hpp" // MPI
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float_image;
-
-struct Params
-{
-	struct cont
-	{
-	    static const int getPopIndex = 0;
-  };
-
-	struct log
-	{
-		SFERES_CONST bool best_image = false;
-	};
-
-	struct ea
-	{
-		SFERES_CONST size_t res_x = 1; // 256;
-		SFERES_CONST size_t res_y = 10; // 256;
-	};
-
-  struct evo_float_image
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    SFERES_CONST float mutation_rate = 0.1f;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-
-  struct pop
-  {
-  	//number of initial random points
-		static const size_t init_size = 200; // 1000
-    // size of the population
-    SFERES_CONST unsigned size = 200; //200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 1010; //10,000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    static int dump_period;// 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-
-  // Specific settings for MNIST database of grayscale
-  struct image : ParamsCaffe::image
-	{
-		// Size of the square image 256x256
-		SFERES_CONST int size = 28;
-		SFERES_CONST bool use_crops = false;
-		SFERES_CONST bool color = false;	// Grayscale
-
-		SFERES_CONST int num_categories = 10;	// MNIST has 10 categories
-
-		static const std::string model_definition;
-		static const std::string pretrained_model;
-	};
-
-};
-
-// Initialize the parameter files for Caffe network.
-#ifdef LOCAL_RUN
-
-const std::string Params::image::model_definition = "/home/anh/src/model/lenet_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/home/anh/src/model/lenet_iter_10000";
-
-#else
-
-const std::string Params::image::model_definition = "/project/EvolvingAI/anguyen8/model/lenet_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/project/EvolvingAI/anguyen8/model/lenet_iter_10000";
-
-#endif
-
-int Params::pop::dump_period = 10;
-
-
-int main(int argc, char **argv)
-{
-	// Disable GLOG output from experiment and also Caffe
-	// Comment out for debugging
-	google::InitGoogleLogging("");
-	google::SetStderrLogging(3);
-
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-	typedef sferes::fit::FitMapDeepLearning<Params> fit_t;
-  // We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-	typedef gen::EvoFloatImage<Params::image::size * Params::image::size, Params> gen_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::GrayscaleImageDirect<gen_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-//  typedef eval::Eval<Params> eval_t;
-  typedef eval::MpiParallel<Params> eval_t;	// TBB
-
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-//  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef boost::fusion::vector<stat::MapImage<phen_t, Params>, stat::BestFitMapImage<phen_t, Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-//  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the command line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-
-  if (argc > 1) // if a number is provided on the command line
-	{
-		int randomSeed = atoi(argv[1]);
-		printf("randomSeed:%i\n", randomSeed);
-		srand(randomSeed);  //set it as the random seed
-
-		boost::program_options::options_description add_opts =
-								boost::program_options::options_description();
-
-		shared_ptr<boost::program_options::option_description> opt (new boost::program_options::option_description(
-				"continue,t", boost::program_options::value<std::string>(),
-																"continue from the loaded file starting from the generation provided"
-				));
-
-		add_opts.add(opt);
-
-		options::run_ea(argc, argv, ea, add_opts, false);
-	}
-	else
-	{
-		run_ea(argc, argv, ea);
-	}
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/images/dl_map_elites_images_test.cpp

@@ -1,237 +0,0 @@
-#include "dl_images.hpp"
-
-#include <iostream>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/eval/eval.hpp>
-
-#include "stat/best_fit_map_image.hpp"
-#include "stat/stat_map_image.hpp"
-
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-
-
-// Evolutionary algorithms --------------------------------
-#include "fit/fit_map_deep_learning.hpp"
-#include <modules/nn2/gen_dnn.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-#include "phen/phen_color_image.hpp"
-
-#include <glog/logging.h>
-// Caffe -------------------------------------------------
-
-#include <modules/map_elite/map_elite.hpp>
-#include "eval/mpi_parallel.hpp" // MPI
-#include "continue_run/continue_run.hpp" // MPI
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-struct Params
-{
-	struct cont
-	{
-	    static const int getPopIndex = 0;
-  };
-
-	struct log
-	{
-		SFERES_CONST bool best_image = false;
-	};
-
-	struct ea
-	{
-		SFERES_CONST size_t res_x = 1; // 256;
-		SFERES_CONST size_t res_y = 10; // 256;
-	};
-
-	struct dnn
-	{
-		SFERES_CONST size_t nb_inputs = 4;
-		SFERES_CONST size_t nb_outputs = 3;		// Red, Green, Blue
-
-		SFERES_CONST float m_rate_add_conn = 0.5f;
-		SFERES_CONST float m_rate_del_conn = 0.3f;
-		SFERES_CONST float m_rate_change_conn = 0.5f;
-		SFERES_CONST float m_rate_add_neuron = 0.5f;
-		SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-		SFERES_CONST init_t init = ff;
-	};
-
-  struct evo_float
-  {
-    // we choose the polynomial mutation type
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    // we choose the polynomial cross-over type
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    // the mutation rate of the real-valued vector
-    SFERES_CONST float mutation_rate = 0.1f;
-    // the cross rate of the real-valued vector
-    SFERES_CONST float cross_rate = 0.5f;
-    // a parameter of the polynomial mutation
-    SFERES_CONST float eta_m = 15.0f;
-    // a parameter of the polynomial cross-over
-    SFERES_CONST float eta_c = 10.0f;
-  };
-
-  struct pop
-  {
-  	//number of initial random points
-		static const size_t init_size = 10; // 1000
-    // size of the population
-    SFERES_CONST unsigned size = 10; //200;
-    // number of generations
-    SFERES_CONST unsigned nb_gen = 10; //10,000;
-    // how often should the result file be written (here, each 5
-    // generation)
-    static int dump_period;// 5;
-    // how many individuals should be created during the random
-    // generation process?
-    SFERES_CONST int initial_aleat = 1;
-    // used by RankSimple to select the pressure
-    SFERES_CONST float coeff = 1.1f;
-    // the number of individuals that are kept from on generation to
-    // another (elitism)
-    SFERES_CONST float keep_rate = 0.6f;
-  };
-
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -10.0f;
-    // minimum value
-    SFERES_CONST float max = 10.0f;
-  };
-
-  struct cppn
-	{
-		 // params of the CPPN
-		struct sampled
-		{
-			SFERES_ARRAY(float, values, nn::cppn::sine, nn::cppn::sigmoid, nn::cppn::gaussian, nn::cppn::linear);
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.25f;
-			SFERES_CONST bool ordered = false;
-		};
-		struct evo_float
-		{
-			SFERES_CONST float mutation_rate = 0.1f;
-			SFERES_CONST float cross_rate = 0.1f;
-			SFERES_CONST mutation_t mutation_type = polynomial;
-			SFERES_CONST cross_over_t cross_over_type = sbx;
-			SFERES_CONST float eta_m = 15.0f;
-			SFERES_CONST float eta_c = 15.0f;
-		};
-	};
-
-  // Specific settings for MNIST database of grayscale
-	struct image : ParamsCaffe::image
-	{
-		static const std::string model_definition;
-		static const std::string pretrained_model;
-
-		SFERES_CONST bool record_lineage = false;
-		SFERES_CONST int size = 28;
-		SFERES_CONST bool use_crops = false;
-		SFERES_CONST int num_categories = 10;	// ILSVR2012 ImageNet has 1000 categories
-	};
-
-};
-
-// Initialize the parameter files for Caffe network.
-#ifdef LOCAL_RUN
-
-const std::string Params::image::model_definition = "/home/anh/src/model/imagenet_deploy_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/home/anh/src/model/caffe_reference_imagenet_model";
-
-#else
-
-const std::string Params::image::model_definition = "/project/EvolvingAI/anguyen8/model/imagenet_deploy_image_memory_data.prototxt";
-const std::string Params::image::pretrained_model = "/project/EvolvingAI/anguyen8/model/caffe_reference_imagenet_model";
-
-#endif
-
-int Params::pop::dump_period = 1;
-
-
-int main(int argc, char **argv)
-{
-	// Disable GLOG output from experiment and also Caffe
-	// Comment out for debugging
-	google::InitGoogleLogging("");
-	google::SetStderrLogging(3);
-
-  // Our fitness is the class FitTest (see above), that we will call
-  // fit_t. Params is the set of parameters (struct Params) defined in
-  // this file.
-	typedef sferes::fit::FitMapDeepLearning<Params> fit_t;
-  // We define the genotype. Here we choose EvoFloat (real
-  // numbers). We evolve 10 real numbers, with the params defined in
-  // Params (cf the beginning of this file)
-  //typedef gen::EvoFloat<10, Params> gen_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef gen::HyperNn<weight_t, Params> cppn_t;
-  // This genotype should be simply transformed into a vector of
-  // parameters (phen::Parameters). The genotype could also have been
-  // transformed into a shape, a neural network... The phenotype need
-  // to know which fitness to use; we pass fit_t.
-  typedef phen::ColorImage<cppn_t, fit_t, Params> phen_t;
-  // The evaluator is in charge of distributing the evaluation of the
-  // population. It can be simple eval::Eval (nothing special),
-  // parallel (for multicore machines, eval::Parallel) or distributed
-  // (for clusters, eval::Mpi).
-//  typedef eval::Eval<Params> eval_t;
-  typedef eval::MpiParallel<Params> eval_t;	// TBB
-
-  // Statistics gather data about the evolutionary process (mean
-  // fitness, Pareto front, ...). Since they can also stores the best
-  // individuals, they are the container of our results. We can add as
-  // many statistics as required thanks to the boost::fusion::vector.
-//  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
-  typedef boost::fusion::vector<stat::MapImage<phen_t, Params>, stat::BestFitMapImage<phen_t, Params> >  stat_t;
-  // Modifiers are functors that are run once all individuals have
-  // been evalutated. Their typical use is to add some evolutionary
-  // pressures towards diversity (e.g. fitness sharing). Here we don't
-  // use this feature. As a consequence we use a "dummy" modifier that
-  // does nothing.
-  typedef modif::Dummy<> modifier_t;
-  // We can finally put everything together. RankSimple is the
-  // evolutianary algorithm. It is parametrized by the phenotype, the
-  // evaluator, the statistics list, the modifier and the general params.
-//  typedef ea::RankSimple<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-  // We now have a special class for our experiment: ea_t. The next
-  // line instantiate an object of this class
-  ea_t ea;
-  // we can now process the command line options an run the
-  // evolutionary algorithm (if a --load argument is passed, the file
-  // is loaded; otherwise, the algorithm is launched).
-
-  if (argc > 1) // if a number is provided on the command line
-	{
-		int randomSeed = atoi(argv[1]);
-		printf("randomSeed:%i\n", randomSeed);
-		srand(randomSeed);  //set it as the random seed
-
-		boost::program_options::options_description add_opts =
-								boost::program_options::options_description();
-
-		shared_ptr<boost::program_options::option_description> opt (new boost::program_options::option_description(
-				"continue,t", boost::program_options::value<std::string>(),
-																"continue from the loaded file starting from the generation provided"
-				));
-
-		add_opts.add(opt);
-
-		options::run_ea(argc, argv, ea, add_opts, false);
-	}
-	else
-	{
-		run_ea(argc, argv, ea);
-	}
-
-  return 0;
-}

modules/dnns_easily_fooled/sferes/exp/images/ea/ea_custom.hpp

@@ -1,127 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef EA_CUSTOM_HPP_
-#define EA_CUSTOM_HPP_
-
-#include <iostream>
-#include <vector>
-#include <fstream>
-#include <boost/algorithm/string.hpp>
-#include <sferes/ea/ea.hpp>
-
-namespace sferes {
-  namespace ea {
-
-  	SFERES_EA(EaCustom, Ea) {
-  	protected:
-  		std::string _gen_file_path;
-
-  	public:
-  		EaCustom () : _gen_file_path("")
-			{
-				this->_make_res_dir();
-			}
-
-			void _make_res_dir()
-			{
-				if (Params::pop::dump_period == -1)
-				{
-					return;
-				}
-
-				// Delete the unused folder by Ea
-				std::string to_delete = misc::hostname() + "_" + misc::date() + "_" + misc::getpid();
-
-				if (boost::filesystem::is_directory(to_delete) && boost::filesystem::is_empty(to_delete))
-				{
-					boost::filesystem::remove(to_delete);
-				}
-
-				// Check if such a folder already exists
-				this->_res_dir = "mmm";	// Only one folder regardless which platform the program is running on
-
-				boost::filesystem::path my_path(this->_res_dir);
-
-				// Create a new folder if it doesn't exist
-				if (!boost::filesystem::exists(boost::filesystem::status(my_path)))
-				{
-					// Create a new folder if it does not exist
-					boost::filesystem::create_directory(my_path);
-				}
-				// Run experiment from that folder
-				else
-				{
-					std::vector<std::string> gens;
-
-					// The file to find
-					int max = 0;
-
-					// Find a gen file
-					for(boost::filesystem::directory_entry& entry : boost::make_iterator_range(boost::filesystem::directory_iterator(my_path), {}))
-					{
-						// Find out if '/gen_' exists in the filename
-						std::string e = entry.path().string();
-
-						std::string prefix = this->_res_dir + "/gen_";
-						size_t found = e.find(prefix);
-
-						if (found != std::string::npos)
-						{
-							// Extract out the generation number
-							std::string number = std::string(e).replace(found, prefix.length(), "");
-
-							// Remove double quotes
-//							number = boost::replace_all_copy(number, "\"", "");.string()
-
-							int gen = boost::lexical_cast<int>(number);
-							if (gen > max)
-							{
-								max = gen;
-								_gen_file_path = e;
-							}
-
-						} // end if
-					} // end for-loop
-
-					// Start run from that gen file
-//					_continue_run = boost::filesystem::current_path().string() + "/" + _continue_run;
-					std::cout << "[A]: " <<  _gen_file_path << "\n";
-				}
-			}
-  	};
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/ea/rank_simple.hpp

@@ -1,167 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef RANK_SIMPLE_HPP_
-#define RANK_SIMPLE_HPP_
-
-#include <algorithm>
-#include <boost/foreach.hpp>
-#include <sferes/stc.hpp>
-#include "ea_custom.hpp"
-#include <sferes/fit/fitness.hpp>
-
-#include <exp/images/continue_run/continue_run.hpp>
-
-namespace sferes {
-  namespace ea {
-    SFERES_EA(RankSimple, EaCustom) {
-    public:
-
-    	typedef boost::shared_ptr<Phen> indiv_t;
-    	typedef std::vector<indiv_t> raw_pop_t;
-    	typedef typename std::vector<indiv_t> pop_t;
-    	typedef RankSimple<Phen, Eval, Stat, FitModifier, Params, Exact> this_t;
-
-      SFERES_CONST unsigned nb_keep = (unsigned)(Params::pop::keep_rate * Params::pop::size);
-
-      void random_pop()
-      {
-    		sferes::cont::Continuator<this_t, Params> continuator;
-
-    		// Continuing a run manually from command line or continuing a run automatically if the job was pre-empted
-    		bool continue_run = continuator.enabled() || this->_gen_file_path != "";
-    		if(continue_run)
-    		{
-    			// Load the population file
-    			raw_pop_t raw_pop;
-
-    			if (this->_gen_file_path == "")
-    			{
-    				raw_pop = continuator.getPopulationFromFile(*this);
-    			}
-    			else
-    			{
-    				raw_pop = continuator.getPopulationFromFile(*this, this->_gen_file_path);
-    			}
-
-					// Get the number of population to continue with
-					const size_t init_size = raw_pop.size();
-
-					// Resize the current population archive
-					this->_pop.resize(init_size);
-
-					// Add loaded individuals to the new population
-					int i = 0;
-					BOOST_FOREACH(boost::shared_ptr<Phen>&indiv, this->_pop)
-					{
-						indiv = boost::shared_ptr<Phen>(new Phen(*raw_pop[i]));
-						++i;
-					}
-    		}
-    		else
-    		{
-    			// Original Map-Elites code
-					// Intialize a random population
-					this->_pop.resize(Params::pop::size * Params::pop::initial_aleat);
-					BOOST_FOREACH(boost::shared_ptr<Phen>& indiv, this->_pop)
-					{
-						indiv = boost::shared_ptr<Phen>(new Phen());
-						indiv->random();
-					}
-    		}
-
-        // Evaluate the initialized population
-        this->_eval.eval(this->_pop, 0, this->_pop.size());
-        this->apply_modifier();
-        std::partial_sort(this->_pop.begin(), this->_pop.begin() + Params::pop::size,
-                          this->_pop.end(), fit::compare());
-        this->_pop.resize(Params::pop::size);
-
-        // Continue a run from a specific generation
-				if(continue_run)
-				{
-					if (this->_gen_file_path == "")
-					{
-						continuator.run_with_current_population(*this);
-					}
-					else
-					{
-						continuator.run_with_current_population(*this, this->_gen_file_path);
-					}
-				}
-      }
-
-			//ADDED
-			void setGen(size_t gen)
-			{
-				this->_gen = gen;
-			}
-			//ADDED END
-
-      void epoch()
-      {
-        assert(this->_pop.size());
-        for (unsigned i = nb_keep; i < 	this->_pop.size(); i += 2) {
-          unsigned r1 = _random_rank();
-          unsigned r2 = _random_rank();
-          boost::shared_ptr<Phen> i1, i2;
-          this->_pop[r1]->cross(this->_pop[r2], i1, i2);
-          i1->mutate();
-          i2->mutate();
-          this->_pop[i] = i1;
-          this->_pop[i + 1] = i2;
-        }
-#ifndef EA_EVAL_ALL
-        this->_eval.eval(this->_pop, nb_keep, Params::pop::size);
-#else
-        this->_eval.eval(this->_pop, 0, Params::pop::size);
-#endif
-        this->apply_modifier();
-        std::partial_sort(this->_pop.begin(), this->_pop.begin() + nb_keep,
-                          this->_pop.end(), fit::compare());
-        dbg::out(dbg::info, "ea")<<"best fitness: " << this->_pop[0]->fit().value() << std::endl;
-      }
-    protected:
-      unsigned _random_rank() {
-        static float kappa = pow(Params::pop::coeff, nb_keep + 1.0f) - 1.0f;
-        static float facteur = nb_keep / ::log(kappa + 1);
-        return (unsigned) (this->_pop.size() - facteur * log(misc::rand<float>(1) * kappa + 1));
-      }
-    };
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/batch_mpi_parallel.hpp

@@ -1,238 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef BATCH_EVAL_MPI_PARALLEL_HPP_
-#define BATCH_EVAL_MPI_PARALLEL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <boost/mpi.hpp>
-#include "tbb_parallel_eval.hpp"
-#include <cmath>
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-
-//#ifndef BOOST_MPI_HAS_NOARG_INITIALIZATION
-//#error MPI need arguments (we require a full MPI2 implementation)
-//#endif
-
-#define MPI_INFO dbg::out(dbg::info, "mpi")<<"["<<_world->rank()<<"] "
-namespace sferes {
-
-  namespace eval {
-    SFERES_CLASS(BatchMpiTBBParallel) {
-    public:
-    	BatchMpiTBBParallel()
-    	{
-        static char* argv[] = {(char*)"sferes2", 0x0};
-        char** argv2 = (char**) malloc(sizeof(char*) * 2);
-        int argc = 1;
-        argv2[0] = argv[0];
-        argv2[1] = argv[1];
-        using namespace boost;
-        dbg::out(dbg::info, "mpi")<<"Initializing MPI..."<<std::endl;
-        _env = shared_ptr<mpi::environment>(new mpi::environment(argc, argv2, true));
-        dbg::out(dbg::info, "mpi")<<"MPI initialized"<<std::endl;
-        _world = shared_ptr<mpi::communicator>(new mpi::communicator());
-        MPI_INFO << "communicator initialized"<<std::endl;
-
-        // Disable dumping out results for slave processes.
-        if (_world->rank() > 0)
-        {
-        	Params::pop::dump_period = -1;
-        }
-      }
-
-      template<typename Phen>
-      void eval(std::vector<boost::shared_ptr<Phen> >& pop,
-                size_t begin, size_t end) {
-        dbg::trace("mpi", DBG_HERE);
-
-        // Develop phenotypes in parallel
-        // Each MPI process develops one phenotype
-        if (_world->rank() == 0)
-          _master_develop(pop, begin, end);
-        else
-          _slave_develop<Phen>();
-
-        // Make sure the processes have finished developing phenotypes
-
-        // Evaluate phenotypes in parallel but in batches of 256.
-        // Caffe GPU supports max of 512.
-        // There is no limit for CPU but we try to find out what batch size works best.
-        if (_world->rank() == 0)
-        {
-        	_master_eval(pop, begin, end);
-        }
-      }
-
-      ~BatchMpiTBBParallel()
-      {
-        MPI_INFO << "Finalizing MPI..."<<std::endl;
-        std::string s("bye");
-        if (_world->rank() == 0)
-          for (size_t i = 1; i < _world->size(); ++i)
-            _world->send(i, _env->max_tag(), s);
-        _finalize();
-      }
-
-    protected:
-      void _finalize()
-      {
-        _world = boost::shared_ptr<boost::mpi::communicator>();
-        dbg::out(dbg::info, "mpi")<<"MPI world destroyed"<<std::endl;
-        _env = boost::shared_ptr<boost::mpi::environment>();
-        dbg::out(dbg::info, "mpi")<<"environment destroyed"<<std::endl;
-      }
-
-      template<typename Phen>
-      void _master_develop(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        size_t current = begin;
-        std::vector<bool> developed(pop.size());
-        std::fill(developed.begin(), developed.end(), false);
-        // first round
-        for (size_t i = 1; i < _world->size() && current < end; ++i) {
-          MPI_INFO << "[master] [send-init...] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(i, current, pop[current]->gen());
-          MPI_INFO << "[master] [send-init ok] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        // send a new indiv each time we received a fitness
-        while (current < end) {
-          boost::mpi::status s = _recv(developed, pop);
-          MPI_INFO << "[master] [send...] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(s.source(), current, pop[current]->gen());
-          MPI_INFO << "[master] [send ok] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        //join
-        bool done = true;
-        do {
-          dbg::out(dbg::info, "mpi")<<"joining..."<<std::endl;
-          done = true;
-          for (size_t i = begin; i < end; ++i)
-            if (!developed[i]) {
-              _recv(developed, pop);
-              done = false;
-            }
-        } while (!done);
-      }
-
-      template<typename Phen>
-      boost::mpi::status _recv(std::vector<bool>& developed,
-                               std::vector<boost::shared_ptr<Phen> >& pop)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        using namespace boost::mpi;
-        status s = _world->probe();
-        MPI_INFO << "[rcv...]" << getpid() << " tag=" << s.tag() << std::endl;
-        //_world->recv(s.source(), s.tag(), pop[s.tag()]->fit());
-
-        // Receive the whole developed phenotype from slave processes
-        Phen p;
-        _world->recv(s.source(), s.tag(), p);
-
-        // Assign the developed phenotype back to the current population for further evaluation
-        pop[s.tag()]->image() = p.image();
-
-        MPI_INFO << "[rcv ok]" << " tag=" << s.tag() << std::endl;
-        developed[s.tag()] = true;
-        return s;
-      }
-
-      template<typename Phen>
-      void _slave_develop()
-      {
-        dbg::trace("mpi", DBG_HERE);
-        while(true) {
-          Phen p;
-          boost::mpi::status s = _world->probe();
-          if (s.tag() == _env->max_tag()) {
-            MPI_INFO << "[slave] Quit requested" << std::endl;
-            MPI_Finalize();
-            exit(0);
-          } else {
-            MPI_INFO <<"[slave] [rcv...] [" << getpid()<< "]" << std::endl;
-            _world->recv(0, s.tag(), p.gen());
-            MPI_INFO <<"[slave] [rcv ok] " << " tag="<<s.tag()<<std::endl;
-            p.develop();
-
-            MPI_INFO <<"[slave] [send...]"<<" tag=" << s.tag()<<std::endl;
-            //_world->send(0, s.tag(), p.fit());	// Send only the fitness back to master process
-
-            // Send the whole phenotype back to master process
-            _world->send(0, s.tag(), p);
-            MPI_INFO <<"[slave] [send ok]"<<" tag=" << s.tag()<<std::endl;
-
-          }
-        }
-      }
-
-      // ----------------------------------------------------------------------------------
-      template<typename Phen>
-			void _master_eval(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-			{
-      	dbg::trace trace("eval", DBG_HERE);
-
-				assert(pop.size());
-				assert(begin < pop.size());
-				assert(end <= pop.size());
-
-				// Number of eval iterations
-				const size_t count = end - begin;
-
-				LOG(INFO) << "Total: " << count << " | Batch: " << Params::image::batch << "\n";
-
-				// Evaluate phenotypes in parallel using TBB.
-				parallel::init();
-				parallel::p_for(
-						parallel::range_t(begin, end, Params::image::batch),
-						sferes::eval::parallel_tbb_eval<Phen>(pop, Params::image::model_definition, Params::image::pretrained_model));
-
-				// The barrier is implicitly set here after the for-loop in TBB.
-			}
-
-      boost::shared_ptr<boost::mpi::environment> _env;
-      boost::shared_ptr<boost::mpi::communicator> _world;
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/batch_mpi_tbb_parallel.hpp

@@ -1,238 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef BATCH_EVAL_MPI_TBB_PARALLEL_HPP_
-#define BATCH_EVAL_MPI_TBB_PARALLEL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <boost/mpi.hpp>
-#include "tbb_parallel_eval.hpp"
-#include <cmath>
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-
-//#ifndef BOOST_MPI_HAS_NOARG_INITIALIZATION
-//#error MPI need arguments (we require a full MPI2 implementation)
-//#endif
-
-#define MPI_INFO dbg::out(dbg::info, "mpi")<<"["<<_world->rank()<<"] "
-namespace sferes {
-
-  namespace eval {
-    SFERES_CLASS(BatchMpiParallel) {
-    public:
-    	BatchMpiParallel()
-    	{
-        static char* argv[] = {(char*)"sferes2", 0x0};
-        char** argv2 = (char**) malloc(sizeof(char*) * 2);
-        int argc = 1;
-        argv2[0] = argv[0];
-        argv2[1] = argv[1];
-        using namespace boost;
-        dbg::out(dbg::info, "mpi")<<"Initializing MPI..."<<std::endl;
-        _env = shared_ptr<mpi::environment>(new mpi::environment(argc, argv2, true));
-        dbg::out(dbg::info, "mpi")<<"MPI initialized"<<std::endl;
-        _world = shared_ptr<mpi::communicator>(new mpi::communicator());
-        MPI_INFO << "communicator initialized"<<std::endl;
-
-        // Disable dumping out results for slave processes.
-        if (_world->rank() > 0)
-        {
-        	Params::pop::dump_period = -1;
-        }
-      }
-
-      template<typename Phen>
-      void eval(std::vector<boost::shared_ptr<Phen> >& pop,
-                size_t begin, size_t end) {
-        dbg::trace("mpi", DBG_HERE);
-
-        // Develop phenotypes in parallel
-        // Each MPI process develops one phenotype
-        if (_world->rank() == 0)
-          _master_develop(pop, begin, end);
-        else
-          _slave_develop<Phen>();
-
-        // Make sure the processes have finished developing phenotypes
-
-        // Evaluate phenotypes in parallel but in batches of 256.
-        // Caffe GPU supports max of 512.
-        // There is no limit for CPU but we try to find out what batch size works best.
-        if (_world->rank() == 0)
-        {
-        	_master_eval(pop, begin, end);
-        }
-      }
-
-      ~BatchMpiParallel()
-      {
-        MPI_INFO << "Finalizing MPI..."<<std::endl;
-        std::string s("bye");
-        if (_world->rank() == 0)
-          for (size_t i = 1; i < _world->size(); ++i)
-            _world->send(i, _env->max_tag(), s);
-        _finalize();
-      }
-
-    protected:
-      void _finalize()
-      {
-        _world = boost::shared_ptr<boost::mpi::communicator>();
-        dbg::out(dbg::info, "mpi")<<"MPI world destroyed"<<std::endl;
-        _env = boost::shared_ptr<boost::mpi::environment>();
-        dbg::out(dbg::info, "mpi")<<"environment destroyed"<<std::endl;
-      }
-
-      template<typename Phen>
-      void _master_develop(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        size_t current = begin;
-        std::vector<bool> developed(pop.size());
-        std::fill(developed.begin(), developed.end(), false);
-        // first round
-        for (size_t i = 1; i < _world->size() && current < end; ++i) {
-          MPI_INFO << "[master] [send-init...] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(i, current, pop[current]->gen());
-          MPI_INFO << "[master] [send-init ok] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        // send a new indiv each time we received a fitness
-        while (current < end) {
-          boost::mpi::status s = _recv(developed, pop);
-          MPI_INFO << "[master] [send...] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(s.source(), current, pop[current]->gen());
-          MPI_INFO << "[master] [send ok] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        //join
-        bool done = true;
-        do {
-          dbg::out(dbg::info, "mpi")<<"joining..."<<std::endl;
-          done = true;
-          for (size_t i = begin; i < end; ++i)
-            if (!developed[i]) {
-              _recv(developed, pop);
-              done = false;
-            }
-        } while (!done);
-      }
-
-      template<typename Phen>
-      boost::mpi::status _recv(std::vector<bool>& developed,
-                               std::vector<boost::shared_ptr<Phen> >& pop)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        using namespace boost::mpi;
-        status s = _world->probe();
-        MPI_INFO << "[rcv...]" << getpid() << " tag=" << s.tag() << std::endl;
-        //_world->recv(s.source(), s.tag(), pop[s.tag()]->fit());
-
-        // Receive the whole developed phenotype from slave processes
-        Phen p;
-        _world->recv(s.source(), s.tag(), p);
-
-        // Assign the developed phenotype back to the current population for further evaluation
-        pop[s.tag()]->image() = p.image();
-
-        MPI_INFO << "[rcv ok]" << " tag=" << s.tag() << std::endl;
-        developed[s.tag()] = true;
-        return s;
-      }
-
-      template<typename Phen>
-      void _slave_develop()
-      {
-        dbg::trace("mpi", DBG_HERE);
-        while(true) {
-          Phen p;
-          boost::mpi::status s = _world->probe();
-          if (s.tag() == _env->max_tag()) {
-            MPI_INFO << "[slave] Quit requested" << std::endl;
-            MPI_Finalize();
-            exit(0);
-          } else {
-            MPI_INFO <<"[slave] [rcv...] [" << getpid()<< "]" << std::endl;
-            _world->recv(0, s.tag(), p.gen());
-            MPI_INFO <<"[slave] [rcv ok] " << " tag="<<s.tag()<<std::endl;
-            p.develop();
-
-            MPI_INFO <<"[slave] [send...]"<<" tag=" << s.tag()<<std::endl;
-            //_world->send(0, s.tag(), p.fit());	// Send only the fitness back to master process
-
-            // Send the whole phenotype back to master process
-            _world->send(0, s.tag(), p);
-            MPI_INFO <<"[slave] [send ok]"<<" tag=" << s.tag()<<std::endl;
-
-          }
-        }
-      }
-
-      // ----------------------------------------------------------------------------------
-      template<typename Phen>
-			void _master_eval(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-			{
-      	dbg::trace trace("eval", DBG_HERE);
-
-				assert(pop.size());
-				assert(begin < pop.size());
-				assert(end <= pop.size());
-
-				// Number of eval iterations
-				const size_t count = end - begin;
-
-				LOG(INFO) << "Total: " << count << " | Batch: " << Params::image::batch << "\n";
-
-				// Evaluate phenotypes in parallel using TBB.
-				parallel::init();
-				parallel::p_for(
-						parallel::range_t(begin, end, Params::image::batch),
-						sferes::eval::parallel_tbb_eval<Phen>(pop, Params::image::model_definition, Params::image::pretrained_model));
-
-				// The barrier is implicitly set here after the for-loop in TBB.
-			}
-
-      boost::shared_ptr<boost::mpi::environment> _env;
-      boost::shared_ptr<boost::mpi::communicator> _world;
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/cuda_parallel.hpp

@@ -1,272 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef EVAL_CUDA_PARALLEL_HPP_
-#define EVAL_CUDA_PARALLEL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <cmath>
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-
-#include <stdexcept>
-
-namespace sferes {
-
-	namespace caffe
-	{
-	 /**
-		 * Using a shared_ptr to hold a pointer to a statically allocated object.
-		 * http://www.boost.org/doc/libs/1_55_0/libs/smart_ptr/sp_techniques.html#static
-		 */
-		struct null_deleter
-		{
-				void operator()(void const *) const
-				{
-				}
-		};
-
-		class CaffeFactory
-		{
-		private:
-			static bool initialized;
-			static Net<float>* _net_1;
-			static Net<float>* _net_2;
-			static int _status;
-
-		public:
-			static shared_ptr<Net<float> > getCaffe(const std::string model_definition, const std::string pretrained_model)
-			{
-				if (!initialized)
-				{
-					// Initialize Caffe net 1
-					_net_1 = new Net<float>(model_definition);
-
-					// Get the trained model
-					_net_1->CopyTrainedLayersFrom(pretrained_model);
-
-					// Initialize Caffe net 2
-					_net_2 = new Net<float>(model_definition);
-
-					// Get the trained model
-					_net_2->CopyTrainedLayersFrom(pretrained_model);
-
-					initialized = true;
-				}
-
-				if (_status == 1)
-				{
-					_status = 2;
-					shared_ptr<Net<float> > c(_net_1, null_deleter());
-					return c;
-				}
-				else
-				{
-					_status = 1;
-					shared_ptr<Net<float> > c(_net_2, null_deleter());
-					return c;
-				}
-			}
-
-			CaffeFactory()
-			{
-				initialized = false;
-				_status = 1;
-			}
-
-		};
-	}
-
-  namespace eval {
-  	/**
-  	 * Develop phenotypes in parallel using TBB.
-  	 */
-    template<typename Phen>
-    struct _parallel_develop {
-      typedef std::vector<boost::shared_ptr<Phen> > pop_t;
-      pop_t _pop;
-
-      ~_parallel_develop() { }
-      _parallel_develop(pop_t& pop) : _pop(pop) {}
-      _parallel_develop(const _parallel_develop& ev) : _pop(ev._pop) {}
-
-      void operator() (const parallel::range_t& r) const {
-        for (size_t i = r.begin(); i != r.end(); ++i) {
-          assert(i < _pop.size());
-          _pop[i]->develop();
-        }
-      }
-    };
-
-
-    SFERES_CLASS(CudaParallel)
-    {
-    private:
-
-      /**
-  		 * Develop phenotypes in parallel using TBB.
-  		 */
-  		template<typename Phen>
-  		struct _parallel_cuda_eval {
-  			typedef std::vector<boost::shared_ptr<Phen> > pop_t;
-  			pop_t _pop;
-
-  			~_parallel_cuda_eval() { }
-  			_parallel_cuda_eval(pop_t& pop) : _pop(pop) {}
-  			_parallel_cuda_eval(const _parallel_cuda_eval& ev) : _pop(ev._pop) {}
-
-  			void operator() (const parallel::range_t& r) const
-				{
-					size_t begin = r.begin();
-					size_t end = r.end();
-
-					LOG(INFO) << "Begin: " << begin << " --> " << end << "\n";
-
-					dbg::trace trace("eval_cuda", DBG_HERE);
-					assert(_pop.size());
-					assert(begin < _pop.size());
-					assert(end <= _pop.size());
-
-					// Algorithm works as follow:
-					// Send the individuals to Caffe first
-					// Get back a list of results
-					// Assign the results to individuals
-
-					// Construct a list of images to be in the batch
-					std::vector<cv::Mat> images(0);
-
-					for (size_t i = begin; i < end; ++i)
-					{
-						cv::Mat output;
-						_pop[i]->imageBGR(output);
-
-						images.push_back( output );	// Add to a list of images
-					}
-
-					// Initialize Caffe net
-					shared_ptr<Net<float> > caffe_test_net = sferes::caffe::CaffeFactory::getCaffe(
-							Params::image::model_definition,
-							Params::image::pretrained_model
-							);
-
-//					shared_ptr<Net<float> > caffe_test_net =
-//					boost::shared_ptr<Net<float> >(new Net<float>(Params::image::model_definition));
-//
-//					// Get the trained model
-//					caffe_test_net->CopyTrainedLayersFrom(Params::image::pretrained_model);
-
-					// Run ForwardPrefilled
-					float loss;		//	const vector<Blob<float>*>& result = caffe_test_net.ForwardPrefilled(&loss);
-
-					// Number of eval iterations
-					const size_t num_images = end - begin;
-
-					// Add images and labels manually to the ImageDataLayer
-					//	vector<cv::Mat> images(num_images, image);
-					vector<int> labels(num_images, 0);
-					const shared_ptr<ImageDataLayer<float> > image_data_layer =
-						boost::static_pointer_cast<ImageDataLayer<float> >(
-								caffe_test_net->layer_by_name("data"));
-
-					image_data_layer->AddImagesAndLabels(images, labels);
-
-					// Classify this batch of 512 images
-					const vector<Blob<float>*>& result = caffe_test_net->ForwardPrefilled(&loss);
-
-					// Get the highest layer of Softmax
-					const float* argmaxs = result[1]->cpu_data();
-
-					// Get back a list of results
-					LOG(INFO) << "Number of results: " << result[1]->num() << "\n";
-
-					// Assign the results to individuals
-					for(int i = 0; i < num_images * 2; i += 2)
-					{
-						LOG(INFO)<< " Image: "<< i/2 + 1 << " class:" << argmaxs[i] << " : " << argmaxs[i+1] << "\n";
-
-						int pop_index = begin + i/2;	// Index of individual in the batch
-
-						// Set the fitness of this individual
-						_pop[pop_index]->fit().setFitness((float) argmaxs[i+1]);
-
-						// For Map-Elite, set the cell description
-						_pop[pop_index]->fit().set_desc(0, argmaxs[i]);
-					}
-				}
-  		};
-
-    public:
-      template<typename Phen>
-      void eval(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-      {
-        dbg::trace trace("eval", DBG_HERE);
-
-        assert(pop.size());
-				assert(begin < pop.size());
-				assert(end <= pop.size());
-
-				// Develop phenotypes in parallel using TBB.
-				// The barrier is implicitly set here after the for-loop in TBB.
-				//parallel::init();
-				// We have only 2 GPUs per node
-				//tbb::task_scheduler_init init1(4);
-				parallel::p_for(parallel::range_t(begin, end),
-						_parallel_develop<Phen>(pop));
-
-        // Number of eval iterations
-        const size_t count = end - begin;
-
-        LOG(INFO) << "Size: " << count << " vs " << Params::image::batch << "\n";
-
-        // Load balancing
-				// We have only 2 GPUs per node
-				//tbb::task_scheduler_init init2(2);
-
-				parallel::p_for(
-						parallel::range_t(begin, end, Params::image::batch),
-						_parallel_cuda_eval<Phen>(pop));
-      }
-
-    };
-
-  }
-}
-
-bool sferes::caffe::CaffeFactory::initialized;
-int sferes::caffe::CaffeFactory::_status;
-Net<float>* sferes::caffe::CaffeFactory::_net_1;
-Net<float>* sferes::caffe::CaffeFactory::_net_2;
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/mpi_parallel.hpp

@@ -1,196 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef EVAL_MPI_PARALLEL_HPP_
-#define EVAL_MPI_PARALLEL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <boost/mpi.hpp>
-
-//#ifndef BOOST_MPI_HAS_NOARG_INITIALIZATION
-//#error MPI need arguments (we require a full MPI2 implementation)
-//#endif
-
-#define MPI_INFO dbg::out(dbg::info, "mpi")<<"["<<_world->rank()<<"] "
-namespace sferes {
-
-  namespace eval {
-    SFERES_CLASS(MpiParallel) {
-    public:
-    	MpiParallel() {
-        static char* argv[] = {(char*)"sferes2", 0x0};
-        char** argv2 = (char**) malloc(sizeof(char*) * 2);
-        int argc = 1;
-        argv2[0] = argv[0];
-        argv2[1] = argv[1];
-        using namespace boost;
-        dbg::out(dbg::info, "mpi")<<"Initializing MPI..."<<std::endl;
-        _env = shared_ptr<mpi::environment>(new mpi::environment(argc, argv2, true));
-        dbg::out(dbg::info, "mpi")<<"MPI initialized"<<std::endl;
-        _world = shared_ptr<mpi::communicator>(new mpi::communicator());
-        MPI_INFO << "communicator initialized"<<std::endl;
-
-        // Disable dumping out results for slave processes.
-        if (_world->rank() > 0)
-        {
-        	Params::pop::dump_period = -1;
-        }
-
-      }
-
-      template<typename Phen>
-      void eval(std::vector<boost::shared_ptr<Phen> >& pop,
-                size_t begin, size_t end) {
-        dbg::trace("mpi", DBG_HERE);
-        if (_world->rank() == 0)
-          _master_loop(pop, begin, end);
-        else
-          _slave_loop<Phen>();
-      }
-
-      ~MpiParallel()
-      {
-        MPI_INFO << "Finalizing MPI..."<<std::endl;
-        std::string s("bye");
-        if (_world->rank() == 0)
-          for (size_t i = 1; i < _world->size(); ++i)
-            _world->send(i, _env->max_tag(), s);
-        _finalize();
-      }
-
-    protected:
-      void _finalize()
-      {
-        _world = boost::shared_ptr<boost::mpi::communicator>();
-        dbg::out(dbg::info, "mpi")<<"MPI world destroyed"<<std::endl;
-        _env = boost::shared_ptr<boost::mpi::environment>();
-        dbg::out(dbg::info, "mpi")<<"environment destroyed"<<std::endl;
-      }
-
-      template<typename Phen>
-      void _master_loop(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        size_t current = begin;
-        std::vector<bool> evaluated(pop.size());
-        std::fill(evaluated.begin(), evaluated.end(), false);
-        // first round
-        for (size_t i = 1; i < _world->size() && current < end; ++i) {
-          MPI_INFO << "[master] [send-init...] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(i, current, pop[current]->gen());
-          MPI_INFO << "[master] [send-init ok] ->" <<i<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        // send a new indiv each time we received a fitness
-        while (current < end) {
-          boost::mpi::status s = _recv(evaluated, pop);
-          MPI_INFO << "[master] [send...] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          _world->send(s.source(), current, pop[current]->gen());
-          MPI_INFO << "[master] [send ok] ->" <<s.source()<<" [indiv="<<current<<"]"<<std::endl;
-          ++current;
-        }
-        //join
-        bool done = true;
-        do {
-          dbg::out(dbg::info, "mpi")<<"joining..."<<std::endl;
-          done = true;
-          for (size_t i = begin; i < end; ++i)
-            if (!evaluated[i]) {
-              _recv(evaluated, pop);
-              done = false;
-            }
-        } while (!done);
-      }
-
-      template<typename Phen>
-      boost::mpi::status _recv(std::vector<bool>& evaluated,
-                               std::vector<boost::shared_ptr<Phen> >& pop)
-      {
-        dbg::trace("mpi", DBG_HERE);
-        using namespace boost::mpi;
-        status s = _world->probe();
-        MPI_INFO << "[rcv...]" << getpid() << " tag=" << s.tag() << std::endl;
-        //_world->recv(s.source(), s.tag(), pop[s.tag()]->fit());
-
-        // Receive the whole developed phenotype from slave processes
-        Phen p;
-        _world->recv(s.source(), s.tag(), p);
-
-        // Assign the developed data back to the current population for further evaluation
-        pop[s.tag()]->fit() = p.fit();
-        pop[s.tag()]->image() = p.image();
-
-        MPI_INFO << "[rcv ok]" << " tag=" << s.tag() << std::endl;
-        evaluated[s.tag()] = true;
-        return s;
-      }
-
-      template<typename Phen>
-      void _slave_loop()
-      {
-        dbg::trace("mpi", DBG_HERE);
-        while(true) {
-          Phen p;
-          boost::mpi::status s = _world->probe();
-          if (s.tag() == _env->max_tag()) {
-            MPI_INFO << "[slave] Quit requested" << std::endl;
-            MPI_Finalize();
-            exit(0);
-          } else {
-            MPI_INFO <<"[slave] [rcv...] [" << getpid()<< "]" << std::endl;
-            _world->recv(0, s.tag(), p.gen());
-            MPI_INFO <<"[slave] [rcv ok] " << " tag="<<s.tag()<<std::endl;
-            p.develop();
-            p.fit().eval(p);
-            MPI_INFO <<"[slave] [send...]"<<" tag=" << s.tag()<<std::endl;
-            //_world->send(0, s.tag(), p.fit());	// Send only the fitness back to master process
-
-            // Send the whole phenotype back to master process
-            _world->send(0, s.tag(), p);
-            MPI_INFO <<"[slave] [send ok]"<<" tag=" << s.tag()<<std::endl;
-
-          }
-        }
-      }
-      boost::shared_ptr<boost::mpi::environment> _env;
-      boost::shared_ptr<boost::mpi::communicator> _world;
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/tbb_parallel.hpp

@@ -1,87 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef EVAL_TBB_PARALLEL_HPP_
-#define EVAL_TBB_PARALLEL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <cmath>
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-#include "tbb_parallel_develop.hpp"
-#include "tbb_parallel_eval.hpp"
-
-#include <stdexcept>
-
-namespace sferes {
-
-  namespace eval {
-
-    SFERES_CLASS(TBBParallel)
-    {
-
-    public:
-      template<typename Phen>
-      void eval(std::vector<boost::shared_ptr<Phen> >& pop, size_t begin, size_t end)
-      {
-        dbg::trace trace("eval", DBG_HERE);
-
-        assert(pop.size());
-				assert(begin < pop.size());
-				assert(end <= pop.size());
-
-				// Develop phenotypes in parallel using TBB.
-				// The barrier is implicitly set here after the for-loop in TBB.
-				parallel::init();
-				parallel::p_for(parallel::range_t(begin, end),
-						sferes::eval::parallel_develop<Phen>(pop));
-
-        // Number of eval iterations
-        const size_t count = end - begin;
-
-        LOG(INFO) << "Size: " << count << " vs " << Params::image::batch << "\n";
-
-        // Evaluate phenotypes in parallel using TBB.
-				parallel::p_for(
-						parallel::range_t(begin, end, Params::image::batch),
-						sferes::eval::parallel_tbb_eval<Phen>(pop, Params::image::model_definition, Params::image::pretrained_model));
-      }
-
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/tbb_parallel_develop.hpp

@@ -1,66 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef TBB_PARALLEL_DEVELOP_HPP_
-#define TBB_PARALLEL_DEVELOP_HPP_
-
-#include <sferes/parallel.hpp>
-
-namespace sferes {
-
-  namespace eval {
-  	/**
-  	 * Develop phenotypes in parallel using TBB.
-  	 */
-    template<typename Phen>
-    struct parallel_develop {
-      typedef std::vector<boost::shared_ptr<Phen> > pop_t;
-      pop_t _pop;
-
-      ~parallel_develop() { }
-      parallel_develop(pop_t& pop) : _pop(pop) {}
-      parallel_develop(const parallel_develop& ev) : _pop(ev._pop) {}
-
-      void operator() (const parallel::range_t& r) const {
-        for (size_t i = r.begin(); i != r.end(); ++i) {
-          assert(i < _pop.size());
-          _pop[i]->develop();
-        }
-      }
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/eval/tbb_parallel_eval.hpp

@@ -1,154 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef EVAL_TBB_PARALLEL_EVAL_HPP_
-#define EVAL_TBB_PARALLEL_EVAL_HPP_
-
-#include <sferes/parallel.hpp>
-#include <cmath>
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-#include "tbb_parallel_develop.hpp"
-
-#include <stdexcept>
-
-namespace sferes {
-
-  namespace eval {
-
-  	/**
-		 * Develop phenotypes in parallel using TBB.
-		 */
-		template<typename Phen>
-		struct parallel_tbb_eval {
-			typedef std::vector<boost::shared_ptr<Phen> > pop_t;
-			pop_t _pop;
-			std::string _model_definition;
-			std::string _pretrained_model;
-
-			~parallel_tbb_eval() { }
-			parallel_tbb_eval(pop_t& pop, const std::string model_definition, const std::string pretrained_model) :
-				_pop(pop),
-				_model_definition(model_definition),
-				_pretrained_model(pretrained_model)
-			{
-			}
-
-			parallel_tbb_eval(const parallel_tbb_eval& ev) :
-				_pop(ev._pop),
-				_model_definition(_model_definition),
-				_pretrained_model(_pretrained_model)
-			{
-			}
-
-			void operator() (const parallel::range_t& r) const
-			{
-				size_t begin = r.begin();
-				size_t end = r.end();
-
-				LOG(INFO) << "Begin: " << begin << " --> " << end << "\n";
-
-				dbg::trace trace("eval_cuda", DBG_HERE);
-				assert(_pop.size());
-				assert(begin < _pop.size());
-				assert(end <= _pop.size());
-
-				// Algorithm works as follow:
-				// Send the individuals to Caffe first
-				// Get back a list of results
-				// Assign the results to individuals
-
-				// Construct a list of images to be in the batch
-				std::vector<cv::Mat> images(0);
-
-				for (size_t i = begin; i < end; ++i)
-				{
-					cv::Mat output;
-					_pop[i]->imageBGR(output);
-
-					images.push_back( output );	// Add to a list of images
-				}
-
-				// Initialize Caffe net
-				shared_ptr<Net<float> > caffe_test_net =
-				boost::shared_ptr<Net<float> >(new Net<float>(_model_definition));
-
-				// Get the trained model
-				caffe_test_net->CopyTrainedLayersFrom(_pretrained_model);
-
-				// Run ForwardPrefilled
-				float loss;		//	const vector<Blob<float>*>& result = caffe_test_net.ForwardPrefilled(&loss);
-
-				// Number of eval iterations
-				const size_t num_images = end - begin;
-
-				// Add images and labels manually to the ImageDataLayer
-				//	vector<cv::Mat> images(num_images, image);
-				vector<int> labels(num_images, 0);
-				const shared_ptr<ImageDataLayer<float> > image_data_layer =
-					boost::static_pointer_cast<ImageDataLayer<float> >(
-							caffe_test_net->layer_by_name("data"));
-
-				image_data_layer->AddImagesAndLabels(images, labels);
-
-				// Classify this batch of 512 images
-				const vector<Blob<float>*>& result = caffe_test_net->ForwardPrefilled(&loss);
-
-				// Get the highest layer of Softmax
-				const float* argmaxs = result[1]->cpu_data();
-
-				// Get back a list of results
-				LOG(INFO) << "Number of results: " << result[1]->num() << "\n";
-
-				// Assign the results to individuals
-				for(int i = 0; i < num_images * 2; i += 2)
-				{
-					LOG(INFO)<< " Image: "<< i/2 + 1 << " class:" << argmaxs[i] << " : " << argmaxs[i+1] << "\n";
-
-					int pop_index = begin + i/2;	// Index of individual in the batch
-
-					// Set the fitness of this individual
-					_pop[pop_index]->fit().setFitness((float) argmaxs[i+1]);
-
-					// For Map-Elite, set the cell description
-					_pop[pop_index]->fit().set_desc(0, argmaxs[i]);
-				}
-			}
-		};
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/fit/fit_deep_learning.hpp

@@ -1,296 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef FIT_DEEP_LEARNING_HPP
-#define FIT_DEEP_LEARNING_HPP
-
-#include <sferes/fit/fitness.hpp>
-
-// Caffe -------------------------------------------------
-#include <cuda_runtime.h>
-
-#include <cstring>
-#include <cstdlib>
-#include <vector>
-#include <stdio.h>
-
-#include <caffe/caffe.hpp>
-#include <caffe/vision_layers.hpp>
-
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-// Caffe -------------------------------------------------
-
-using namespace caffe;
-
-#define FIT_DEEP_LEARNING(Name) SFERES_FITNESS(Name, sferes::fit::Fitness)
-
-namespace sferes
-{
-  namespace fit
-  {
-    SFERES_FITNESS(FitDeepLearning, sferes::fit::Fitness)
-    {
-    	protected:
-
-    	/**
-    	 * Crop an image based on the coordinates and the size of the crop.
-    	 */
-    	static cv::Mat crop(const cv::Mat& image,
-    			const size_t x, const size_t y, const size_t width, const size_t height, const size_t offset, const bool flip = false)
-    	{
-    		// Setup a rectangle to define your region of interest
-    		// int x, int y, int width, int height
-    		cv::Rect myROI(x, y, width, height);	// top-left
-
-    		// Crop the full image to that image contained by the rectangle myROI
-    		// Note that this doesn't copy the data
-    		cv::Mat croppedImage = image(myROI);
-
-    		// Create a background image of size 256x256
-    		cv::Mat background (Params::image::size, Params::image::size, CV_8UC3, cv::Scalar(255, 255, 255));
-
-    		// Because we are using crop size of 227x227 which is odd, when the image size is even 256x256
-    		// This adjustment helps aligning the crop.
-    		int left = offset/2;
-    		if (flip)
-    		{
-    			left++;
-    		}
-
-    		// Because Caffe requires 256x256 images, we paste the crop back to a dummy background.
-    		croppedImage.copyTo(background(cv::Rect(left, offset/2, width, height)));
-
-    		return background;
-    	}
-
-    	/**
-    	 * Create ten crops (4 corners, 1 center, and x2 for mirrors).
-    	 * Following Alex 2012 paper.
-    	 * The 10 crops are added back to the list.
-    	 */
-    	static void _createTenCrops(const cv::Mat& image, vector<cv::Mat>& list)
-    	{
-    		// Offset
-    		const int crop_size = Params::image::crop_size;
-				const int offset = Params::image::size - crop_size;
-
-				// 1. Top-left
-				{
-					cv::Mat cropped = crop(image, 0, 0, crop_size, crop_size, offset);
-
-					// Add a crop to list
-					list.push_back(cropped);
-
-					cv::Mat flipped;
-					cv::flip(crop(image, 0, 0, crop_size, crop_size, offset, true), flipped, 1);
-
-					// Add a flipped crop to list
-					list.push_back(flipped);
-				}
-
-				// 2. Top-Right
-				{
-					cv::Mat cropped = crop(image, offset, 0, crop_size, crop_size, offset);
-
-					// Add a crop to list
-					list.push_back(cropped);
-
-					cv::Mat flipped;
-					cv::flip(crop(image, offset, 0, crop_size, crop_size, offset, true), flipped, 1);
-
-					// Add a flipped crop to list
-					list.push_back(flipped);
-				}
-
-				// 3. Bottom-left
-				{
-					cv::Mat cropped = crop(image, 0, offset, crop_size, crop_size, offset);
-
-					// Add a crop to list
-					list.push_back(cropped);
-
-					cv::Mat flipped;
-					cv::flip(crop(image, 0, offset, crop_size, crop_size, offset, true), flipped, 1);
-
-					// Add a flipped crop to list
-					list.push_back(flipped);
-				}
-
-				// 4. Bottom-right
-				{
-					cv::Mat cropped = crop(image, offset, offset, crop_size, crop_size, offset);
-
-					// Add a crop to list
-					list.push_back(cropped);
-
-					cv::Mat flipped;
-					cv::flip(crop(image, offset, offset, crop_size, crop_size, offset, true), flipped, 1);
-
-					// Add a flipped crop to list
-					list.push_back(flipped);
-				}
-
-				// 5. Center and its mirror
-				{
-					cv::Mat cropped = crop(image, offset/2, offset/2, crop_size, crop_size, offset);
-
-					// Add a crop to list
-					list.push_back(cropped);
-
-					cv::Mat flipped;
-					cv::flip(crop(image, offset/2, offset/2, crop_size, crop_size, offset, true), flipped, 1);
-
-					// Add a flipped crop to list
-					list.push_back(flipped);
-				}
-    	}
-
-    	private:
-    	/**
-    	 * Evaluate the given image to see its probability in the given category.
-    	 */
-    	float _getProbability(const cv::Mat& image, const int category)
-    	{
-    		this->initCaffeNet();	//Initialize caffe
-
-				// Initialize test network
-				shared_ptr<Net<float> > caffe_test_net = shared_ptr<Net<float> >( new Net<float>(Params::image::model_definition));
-
-				// Get the trained model
-				caffe_test_net->CopyTrainedLayersFrom(Params::image::pretrained_model);
-
-				// Run ForwardPrefilled
-				float loss;
-
-				// Add images and labels manually to the ImageDataLayer
-				vector<int> labels(10, 0);
-				vector<cv::Mat> images;
-
-				// Add images to the list
-				if (Params::image::use_crops)
-				{
-					// Ten crops have been stored in the vector
-					_createTenCrops(image, images);
-				}
-				else
-				{
-					images.push_back(image);
-				}
-
-				// Classify images
-				const shared_ptr<ImageDataLayer<float> > image_data_layer =
-						boost::static_pointer_cast<ImageDataLayer<float> >(
-								caffe_test_net->layer_by_name("data"));
-
-				image_data_layer->AddImagesAndLabels(images, labels);
-
-				const vector<Blob<float>*>& result = caffe_test_net->ForwardPrefilled(&loss);
-
-				// Get the highest layer of Softmax
-				const float* softmax = result[1]->cpu_data();
-
-				// If use 10 crops, we have to average the predictions of 10 crops
-				if (Params::image::use_crops)
-				{
-					vector<double> values;
-
-					// Average the predictions of evaluating 10 crops
-					for(int i = 0; i < Params::image::num_categories; ++i)
-					{
-						boost::accumulators::accumulator_set<double, boost::accumulators::stats<boost::accumulators::tag::mean> > avg;
-
-						for(int j = 0; j < 10 * Params::image::num_categories; j += Params::image::num_categories)
-						{
-							avg(softmax[i + j]);
-						}
-
-						double mean = boost::accumulators::mean(avg);
-
-						values.push_back(mean);
-					}
-
-	    		return values[category];
-				}
-				// If use only 1 crop
-				else
-				{
-					return softmax[category];
-				}
-
-    	}
-
-     public:
-
-      // Indiv will have the type defined in the main (phen_t)
-      template<typename Indiv>
-        void eval(const Indiv& ind)
-      {
-      	// Convert image to BGR before evaluating
-				cv::Mat output;
-
-				// Convert HLS into BGR because imwrite uses BGR color space
-				cv::cvtColor(ind.image(), output, CV_HLS2BGR);
-
-      	// Evolve images to be categorized as a soccer ball
-      	this->_value = _getProbability(output, Params::image::category_id);
-      }
-
-      // Indiv will have the type defined in the main (phen_t)
-			void setFitness(float value)
-			{
-				this->_value = value;
-			}
-
-      void initCaffeNet()
-			{
-				// Set test phase
-				Caffe::set_phase(Caffe::TEST);
-
-				if (Params::image::use_gpu)
-				{
-					// Set GPU mode
-					Caffe::set_mode(Caffe::GPU);
-				}
-				else
-				{
-					// Set CPU mode
-					Caffe::set_mode(Caffe::CPU);
-				}
-			}
-    };
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/fit/fit_map_deep_learning.hpp

@@ -1,238 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef FIT_MAP_DEEP_LEARNING_HPP
-#define FIT_MAP_DEEP_LEARNING_HPP
-
-#include "fit_deep_learning.hpp"
-#include <modules/map_elite/fit_map.hpp>
-
-#include <boost/accumulators/accumulators.hpp>
-#include <boost/accumulators/statistics/stats.hpp>
-
-// Headers specifics to the computations we need
-#include <boost/accumulators/statistics/mean.hpp>
-#include <boost/accumulators/statistics/max.hpp>
-
-
-#define FIT_MAP_DEEP_LEARNING(Name) SFERES_FITNESS(Name, sferes::fit::FitDeepLearning)
-
-namespace sferes
-{
-  namespace fit
-  {
-    SFERES_FITNESS(FitMapDeepLearning, sferes::fit::FitDeepLearning)
-    {
-    	/*
-    	private:
-    	struct ArgMax
-    	{
-    		unsigned int category;
-    		float probability;
-    	};
-
-    	ArgMax getMaxProbability(const cv::Mat& image)
-			{
-    		this->initCaffeNet();	//Initialize caffe
-
-				// Initialize test network
-				shared_ptr<Net<float> > caffe_test_net = shared_ptr<Net<float> >( new Net<float>(Params::image::model_definition));
-
-				// Get the trained model
-				caffe_test_net->CopyTrainedLayersFrom(Params::image::pretrained_model);
-
-				// Run ForwardPrefilled
-				float loss;		//	const vector<Blob<float>*>& result = caffe_test_net.ForwardPrefilled(&loss);
-
-				// Add images and labels manually to the ImageDataLayer
-				vector<cv::Mat> images(1, image);
-				vector<int> labels(1, 0);
-				const shared_ptr<ImageDataLayer<float> > image_data_layer =
-						boost::static_pointer_cast<ImageDataLayer<float> >(
-								caffe_test_net->layer_by_name("data"));
-
-				image_data_layer->AddImagesAndLabels(images, labels);
-
-				vector<Blob<float>* > dummy_bottom_vec;
-				const vector<Blob<float>*>& result = caffe_test_net->Forward(dummy_bottom_vec, &loss);
-
-				// Get the highest layer of Softmax
-				const float* argmax = result[1]->cpu_data();
-
-				ArgMax m;
-				m.category = (int) argmax[0];		// Category
-				m.probability = (float) argmax[1];	// Probability
-
-				return m;
-			}
-    	*/
-
-    private:
-    	void _setProbabilityList(const cv::Mat& image)
-			{
-				this->initCaffeNet();	//Initialize caffe
-
-				// Initialize test network
-				shared_ptr<Net<float> > caffe_test_net = shared_ptr<Net<float> >( new Net<float>(Params::image::model_definition));
-
-				// Get the trained model
-				caffe_test_net->CopyTrainedLayersFrom(Params::image::pretrained_model);
-
-				// Run ForwardPrefilled
-				float loss;
-
-				// Add images and labels manually to the ImageDataLayer
-				vector<int> labels(10, 0);
-				vector<cv::Mat> images;
-
-				// Add images to the list
-				if (Params::image::use_crops)
-				{
-					// Ten crops have been stored in the vector
-					this->_createTenCrops(image, images);
-				}
-				else
-				{
-					images.push_back(image);
-				}
-
-				// Classify images
-				const shared_ptr<ImageDataLayer<float> > image_data_layer =
-						boost::static_pointer_cast<ImageDataLayer<float> >(
-								caffe_test_net->layer_by_name("data"));
-
-				image_data_layer->AddImagesAndLabels(images, labels);
-
-				const vector<Blob<float>*>& result = caffe_test_net->ForwardPrefilled(&loss);
-
-				// Get the highest layer of Softmax
-				const float* softmax = result[1]->cpu_data();
-
-				vector<double> values;
-
-				boost::accumulators::accumulator_set<double, boost::accumulators::stats<boost::accumulators::tag::max> > max;
-
-				// Clear the probability in case it is called twice
-				_prob.clear();
-
-				// If use 10 crops, we have to average the predictions of 10 crops
-				if (Params::image::use_crops)
-				{
-					// Average the predictions of evaluating 10 crops
-					for(int i = 0; i < Params::image::num_categories; ++i)
-					{
-						boost::accumulators::accumulator_set<double, boost::accumulators::stats<boost::accumulators::tag::mean> > avg;
-
-						for(int j = 0; j < 10 * Params::image::num_categories; j += Params::image::num_categories)
-						{
-							avg(softmax[i + j]);
-						}
-
-						double mean = boost::accumulators::mean(avg);
-
-						// Push 1000 probabilities in the list
-						_prob.push_back(mean);
-
-						max(mean);	// Add this mean to a list for computing the max later
-					}
-				}
-				else
-				{
-					for(int i = 0; i < Params::image::num_categories; ++i)
-					{
-						float v = softmax[i];
-
-						// Push 1000 probabilities in the list
-						_prob.push_back(v);
-
-						max(v);	// Add this mean to a list for computing the max later
-					}
-				}
-
-				float max_prob = boost::accumulators::max(max);
-
-				// Set the fitness
-				this->_value = max_prob;
-			}
-
-      public:
-    	FitMapDeepLearning() : _prob(Params::image::num_categories) { }
-			const std::vector<float>& desc() const { return _prob; }
-
-			// Indiv will have the type defined in the main (phen_t)
-			template<typename Indiv>
-			void eval(const Indiv& ind)
-			{
-				if (Params::image::color)
-				{
-					// Convert image to BGR before evaluating
-					cv::Mat output;
-
-					// Convert HLS into BGR because imwrite uses BGR color space
-					cv::cvtColor(ind.image(), output, CV_HLS2BGR);
-
-					// Create an empty list to store get 1000 probabilities
-					_setProbabilityList(output);
-				}
-				else	// Grayscale
-				{
-					// Create an empty list to store get 1000 probabilities
-					_setProbabilityList(ind.image());
-				}
-			}
-
-			float value(int category) const
-			{
-				assert(category < _prob.size());
-				return _prob[category];
-			}
-
-			float value() const
-			{
-				return this->_value;
-			}
-
-			template<class Archive>
-			void serialize(Archive & ar, const unsigned int version) {
-				sferes::fit::Fitness<Params,  typename stc::FindExact<FitMapDeepLearning<Params, Exact>, Exact>::ret>::serialize(ar, version);
-				ar & BOOST_SERIALIZATION_NVP(_prob);
-			}
-
-      protected:
-			  std::vector<float> _prob; // List of probabilities
-    };
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/gen/evo_float_image.hpp

@@ -1,245 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef EVO_FLOAT_IMAGE_HPP_
-#define EVO_FLOAT_IMAGE_HPP_
-
-#include <vector>
-#include <limits>
-#include <boost/foreach.hpp>
-#include <boost/serialization/vector.hpp>
-#include <boost/serialization/nvp.hpp>
-#include <sferes/stc.hpp>
-#include <sferes/misc.hpp>
-#include <sferes/dbg/dbg.hpp>
-#include <sferes/gen/float.hpp>
-#include <iostream>
-#include <cmath>
-namespace sferes {
-  namespace gen {
-    namespace evo_float_image {
-      enum mutation_t { polynomial = 0, gaussian, uniform };
-      enum cross_over_t { recombination = 0, sbx, no_cross_over };
-
-      template<typename Ev, unsigned long int T>
-      struct Mutation_f {
-        void operator()(Ev& ev, size_t i) {
-          assert(0);
-        }
-      };
-      template<typename Ev, unsigned long int T>
-      struct CrossOver_f {
-        void operator()(const Ev& f1, const Ev& f2, Ev &c1, Ev &c2) {
-          assert(0);
-        }
-      };
-    }
-
-    /// in range [0;1]
-    template<unsigned long int Size, typename Params, typename Exact = stc::Itself>
-    class EvoFloatImage :
-      public Float<Size, Params,
-      typename stc::FindExact<Float<Size, Params, Exact>, Exact>::ret> {
-     public:
-      typedef Params params_t;
-      typedef EvoFloatImage<Size, Params, Exact> this_t;
-
-      EvoFloatImage() {}
-
-      //@{
-      void mutate() {
-        for (size_t i = 0; i < Size; i++)
-          if (misc::rand<float>() < Params::evo_float_image::mutation_rate)
-            _mutation_op(*this, i);
-        _check_invariant();
-      }
-      void cross(const EvoFloatImage& o, EvoFloatImage& c1, EvoFloatImage& c2) {
-        if (Params::evo_float_image::cross_over_type != evo_float_image::no_cross_over &&
-            misc::rand<float>() < Params::evo_float_image::cross_rate)
-          _cross_over_op(*this, o, c1, c2);
-        else if (misc::flip_coin()) {
-          c1 = *this;
-          c2 = o;
-        } else {
-          c1 = o;
-          c2 = *this;
-        }
-        _check_invariant();
-      }
-      void random() {
-        BOOST_FOREACH(float &v, this->_data) v = misc::rand<float>();
-        _check_invariant();
-      }
-      //@}
-
-     protected:
-      evo_float_image::Mutation_f<this_t, Params::evo_float_image::mutation_type> _mutation_op;
-      evo_float_image::CrossOver_f<this_t, Params::evo_float_image::cross_over_type> _cross_over_op;
-      void _check_invariant() const {
-#ifdef DBG_ENABLED
-        BOOST_FOREACH(float p, this->_data) {
-          assert(!std::isnan(p));
-          assert(!std::isinf(p));
-          assert(p >= 0 && p <= 1);
-        }
-#endif
-      }
-    };
-
-    // partial specialization for operators
-    namespace evo_float_image {
-      // polynomial mutation. Cf Deb 2001, p 124 ; param: eta_m
-      // perturbation of the order O(1/eta_m)
-      template<typename Ev>
-      struct Mutation_f<Ev, polynomial> {
-        void operator()(Ev& ev, size_t i) {
-          SFERES_CONST float eta_m = Ev::params_t::evo_float_image::eta_m;
-          assert(eta_m != -1.0f);
-          float ri = misc::rand<float>();
-          float delta_i = ri < 0.5 ?
-                          pow(2.0 * ri, 1.0 / (eta_m + 1.0)) - 1.0 :
-                          1 - pow(2.0 * (1.0 - ri), 1.0 / (eta_m + 1.0));
-          assert(!std::isnan(delta_i));
-          assert(!std::isinf(delta_i));
-          float f = ev.data(i) + delta_i;
-          ev.data(i, misc::put_in_range(f, 0.0f, 1.0f));
-        }
-      };
-
-      // gaussian mutation
-      template<typename Ev>
-      struct Mutation_f<Ev, gaussian> {
-        void operator()(Ev& ev, size_t i) {
-          SFERES_CONST float sigma = Ev::params_t::evo_float_image::sigma;
-          float f = ev.data(i)
-                    + misc::gaussian_rand<float>(0, sigma * sigma);
-          ev.data(i, misc::put_in_range(f, 0.0f, 1.0f));
-        }
-      };
-      // uniform mutation
-      template<typename Ev>
-      struct Mutation_f<Ev, uniform> {
-        void operator()(Ev& ev, size_t i) {
-          SFERES_CONST float max = Ev::params_t::evo_float_image::max;
-          float f = ev.data(i)
-                    + misc::rand<float>(max) - max / 2.0f;
-          ev.data(i, misc::put_in_range(f, 0.0f, 1.0f));
-        }
-      };
-
-      // recombination
-      template<typename Ev>
-      struct CrossOver_f<Ev, recombination> {
-        void operator()(const Ev& f1, const Ev& f2, Ev &c1, Ev &c2) {
-          size_t k = misc::rand<unsigned long int>(f1.size());
-          for (size_t i = 0; i < k; ++i) {
-            c1.data(i, f1.data(i));
-            c2.data(i, f2.data(i));
-          }
-          for (size_t i = k; i < f1.size(); ++i) {
-            c1.data(i, f2.data(i));
-            c2.data(i, f1.data(i));
-          }
-        }
-      };
-
-      // no cross-over
-      template<typename Ev>
-      struct CrossOver_f<Ev, no_cross_over> {
-        void operator()(const Ev& f1, const Ev& f2, Ev &c1, Ev &c2) {
-        }
-      };
-
-      // SBX (cf Deb 2001, p 113) Simulated Binary Crossover
-      // suggested eta : 15
-      /// WARNING : this code is from deb's code (different from the
-      // article ...)
-      // A large value ef eta gives a higher probablitity for
-      // creating a `near-parent' solutions and a small value allows
-      // distant solutions to be selected as offspring.
-      template<typename Ev>
-      struct CrossOver_f<Ev, sbx> {
-        void operator()(const Ev& f1, const Ev& f2, Ev &child1, Ev &child2) {
-          SFERES_CONST float eta_c = Ev::params_t::evo_float_image::eta_c;
-          assert(eta_c != -1);
-          for (unsigned long int i = 0; i < f1.size(); i++) {
-            float y1 = std::min(f1.data(i), f2.data(i));
-            float y2 = std::max(f1.data(i), f2.data(i));
-            SFERES_CONST float yl = 0.0;
-            SFERES_CONST float yu = 1.0;
-            if (fabs(y1 - y2) > std::numeric_limits<float>::epsilon()) {
-              float rand = misc::rand<float>();
-              float beta = 1.0 + (2.0 * (y1 - yl) / (y2 - y1));
-              float alpha = 2.0 - pow(beta, -(eta_c + 1.0));
-              float betaq = 0;
-              if (rand <= (1.0 / alpha))
-                betaq = pow((rand * alpha), (1.0 / (eta_c + 1.0)));
-              else
-                betaq = pow ((1.0 / (2.0 - rand * alpha)) , (1.0 / (eta_c + 1.0)));
-              float c1 = 0.5 * ((y1 + y2) - betaq * (y2 - y1));
-              beta = 1.0 + (2.0 * (yu - y2) / (y2 - y1));
-              alpha = 2.0 - pow(beta, -(eta_c + 1.0));
-              if (rand <= (1.0 / alpha))
-                betaq = pow ((rand * alpha), (1.0 / (eta_c + 1.0)));
-              else
-                betaq = pow ((1.0/(2.0 - rand * alpha)), (1.0 / (eta_c + 1.0)));
-              float c2 = 0.5 * ((y1 + y2) + betaq * (y2 - y1));
-
-              c1 = misc::put_in_range(c1, yl, yu);
-              c2 = misc::put_in_range(c2, yl, yu);
-
-              assert(!std::isnan(c1));
-              assert(!std::isnan(c2));
-
-              if (misc::flip_coin()) {
-                child1.data(i, c1);
-                child2.data(i, c2);
-              } else {
-                child1.data(i, c2);
-                child2.data(i, c1);
-              }
-            }
-          }
-        }
-      };
-
-    } //evo_float_image
-  } // gen
-} // sferes
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/imagenet/image_list.txt

@@ -1 +0,0 @@
-/home/anh/workspace/sferes/exp/images/imagenet/hen_256.png 1

modules/dnns_easily_fooled/sferes/exp/images/imagenet/imagenet_deploy_image_memory_data.prototxt

@@ -1,254 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: IMAGE_DATA
-  top: "data"
-  top: "label"
-  image_data_param {
-    source: "/home/anh/workspace/sferes/exp/images/imagenet/image_list.txt"
-    mean_file: "/home/anh/src/caffe/data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 1
-    crop_size: 227
-    mirror: false
-    new_height: 256
-    new_width: 256
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"       
-  top: "conv1"         
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"      
-  top: "conv1"         
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}

modules/dnns_easily_fooled/sferes/exp/images/phen/cvmat_serialization.h

@@ -1,64 +0,0 @@
-/*
- * cvmat_serialization.h
- *
- *  Created on: Jul 11, 2014
- *      Author: anh
- */
-
-#ifndef CVMAT_SERIALIZATION_H_
-#define CVMAT_SERIALIZATION_H_
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
-#include "opencv2/imgproc/imgproc.hpp"
-
-#include <boost/serialization/split_free.hpp>
-#include <boost/serialization/vector.hpp>
-
-BOOST_SERIALIZATION_SPLIT_FREE(cv::Mat)
-namespace boost {
-  namespace serialization {
-
-    /** Serialization support for cv::Mat */
-  	template<class Archive>
-    void save(Archive & ar, const cv::Mat& m, const unsigned int version)
-    {
-      size_t elem_size = m.elemSize();
-      size_t elem_type = m.type();
-
-      int cols = m.cols;
-      int rows = m.rows;
-
-      ar & BOOST_SERIALIZATION_NVP(cols);
-      ar & BOOST_SERIALIZATION_NVP(rows);
-      ar & BOOST_SERIALIZATION_NVP(elem_size);
-      ar & BOOST_SERIALIZATION_NVP(elem_type);
-
-      const size_t data_size = cols * rows * elem_size;
-      ar & boost::serialization::make_array(m.ptr(), data_size);
-    }
-
-    /** Serialization support for cv::Mat */
-  	template<class Archive>
-    void load(Archive & ar, cv::Mat& m, const unsigned int version)
-    {
-      int cols, rows;
-      size_t elem_size, elem_type;
-
-      ar & BOOST_SERIALIZATION_NVP(cols);
-      ar & BOOST_SERIALIZATION_NVP(rows);
-      ar & BOOST_SERIALIZATION_NVP(elem_size);
-      ar & BOOST_SERIALIZATION_NVP(elem_type);
-
-      m.create(rows, cols, elem_type);
-
-      size_t data_size = m.cols * m.rows * elem_size;
-      ar & boost::serialization::make_array(m.ptr(), data_size);
-    }
-
-  }
-}
-
-
-
-#endif /* CVMAT_SERIALIZATION_H_ */

modules/dnns_easily_fooled/sferes/exp/images/phen/phen_color_image.hpp

@@ -1,265 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef PHEN_COLOR_IMAGE_HPP
-#define PHEN_COLOR_IMAGE_HPP
-
-#include <map>
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-
-
-// New stuff added ------------------------------------------
-
-#include <cmath>
-#include <boost/lexical_cast.hpp>
-#include <boost/algorithm/string/join.hpp>
-#include <string>
-
-#include "cvmat_serialization.h" // Serialize cv::Mat
-#include <glog/logging.h>	// Google Logging
-
-#include "phen_image.hpp" // Base Image class
-
-// New stuff added ------------------------------------------
-
-namespace sferes
-{
-  namespace phen
-  {
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(ColorImage, Image)
-    {
-      public:
-        typedef Gen gen_t;
-        typedef typename gen_t::nn_t gen_nn_t;
-        SFERES_CONST size_t nb_cppn_inputs = 2 + 2;
-        SFERES_CONST size_t nb_cppn_outputs = 3;	// Red, Green, Blue
-
-        ColorImage():_developed(false)
-				{
-				}
-
-        void develop()
-        {
-        	// Check if phenotype has not been developed
-        	if (!_developed)
-        	{
-						// Initialize the image to be a white background image
-						reset_image();
-
-						this->gen().init();
-						 // develop the parameters
-						BGL_FORALL_VERTICES_T(v, this->gen().get_graph(),
-																	typename gen_t::nn_t::graph_t)
-						{
-							this->gen().get_graph()[v].get_afparams().develop();
-							this->gen().get_graph()[v].get_pfparams().develop();
-						}
-						BGL_FORALL_EDGES_T(e, this->gen().get_graph(),
-															 typename gen_t::nn_t::graph_t)
-						this->gen().get_graph()[e].get_weight().develop();
-
-						assert(nb_cppn_inputs == this->gen().get_nb_inputs());
-						assert(nb_cppn_outputs == this->gen().get_nb_outputs());
-
-						// Change specific color of every pixel in the image
-						for (int x = 0; x < _image.cols; ++x)
-						{
-							for (int y = 0; y < _image.rows; ++y)
-							{
-								std::vector<float> output =	cppn_value(x, y);	// HLS array
-
-								cv::Vec3b color = _image.at<cv::Vec3b>(cv::Point(x,y));
-
-								color[0] = this->convert_to_color_scale(255, output[0]);	// H
-								color[1] = this->convert_to_color_scale(255, output[1]);	// L
-								color[2] = this->convert_to_color_scale(255, output[2]);	// S
-
-								_image.at<cv::Vec3b>(cv::Point(x,y)) = color;
-							}
-						}
-
-						_developed = true;	// Raise the flag that this phenotype has been developed.
-        	}
-        }
-
-        /**
-				 * Programmatically put the patterns in here.
-				 */
-				void reset_image()
-				{
-					// Paint background : white
-					_image = cv::Mat(Params::image::size, Params::image::size, CV_8UC3, cv::Scalar(255,255,255));
-				}
-
-				double normalize_map_xy_to_grid(const int & r_xyVal, const int & r_numVoxelsXorY)
-				{
-					// turn the xth or yth node into its coordinates on a grid from -1 to 1, e.g. x values (1,2,3,4,5) become (-1, -.5 , 0, .5, 1)
-					// this works with even numbers, and for x or y grids only 1 wide/tall, which was not the case for the original
-					// e.g. see findCluster for the orignal versions where it was not a funciton and did not work with odd or 1 tall/wide #s
-
-					double coord;
-
-					if (r_numVoxelsXorY==1) coord = 0;
-					else coord = -1 + ( r_xyVal * 2.0/(r_numVoxelsXorY-1) );
-
-					return(coord);
-				}
-
-				std::vector<float> cppn_value(size_t i, size_t j)
-        {
-          // Euclidean distance from center
-          const float xNormalized = normalize_map_xy_to_grid(i, Params::image::size);
-					const float yNormalized = normalize_map_xy_to_grid(j, Params::image::size);
-					const float distanceFromCenter = sqrt(pow(double(xNormalized),2.0)+pow(double(yNormalized),2.0));
-
-					// CPPN inputs
-          std::vector<float> in(nb_cppn_inputs);
-          this->gen().init();
-          in[0] = i;										// x
-          in[1] = j;										// y
-          in[2] = distanceFromCenter;		// distance from center
-          in[3] = 1.0;									// bias
-
-          for (size_t k = 0; k < this->gen().get_depth(); ++k)
-            this->gen().step(in);
-
-          // Get the CPPN output
-          std::vector<float> out(nb_cppn_outputs);
-          out[0] = this->gen().get_outf(0);		// Hue
-          out[1] = this->gen().get_outf(1);		// Lightness
-          out[2] = this->gen().get_outf(2);		// Saturation
-
-          return out;
-        }
-
-				/**
-				 * Convert [-1, 1] range to a color scale
-				 * [0, 255] for Saturation / Brightness or
-				 * [0, 180] for Hue
-				 */
-				static int convert_to_color_scale(const int scale, const float value)
-				{
-					int color = value * scale;
-
-					if (value < 0)
-					{
-						color *= -1;
-					}
-
-					return color;
-				}
-
-        void write_png_image(const std::string fileName, const cv::Mat& map)
-				{
-					// Read the target bitmap
-					try
-					{
-						cv::Mat output;
-
-						// Convert HLS into BGR because imwrite uses BGR color space
-						cv::cvtColor(map, output, CV_HLS2BGR);
-
-//						// DEBUGGING
-//						// Convert to 256x256
-//						cv::Size size(256, 256);
-//						resize(output, output, size);
-
-						// Parameters for cv::imwrite
-						std::vector<int> write_params;
-						write_params.push_back(CV_IMWRITE_PNG_COMPRESSION);
-						write_params.push_back(0);	// Fastest writing without compression
-
-						// Write to a file
-						imwrite(fileName, output, write_params);
-					}
-					catch (std::runtime_error& ex)
-					{
-						std::cout << "Failed to write image: " << fileName << std::endl;
-						fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what());
-					}
-				}
-
-        void log_best_image_fitness(const std::string title)
-				{
-					std::vector < std::string > list;
-					list.push_back (title);
-					list.push_back (".png");
-					const std::string fileName = boost::algorithm::join (list, "");
-
-					write_png_image(fileName, _image);
-
-					std::cout << "Written to " << title << std::endl;
-				}
-
-				cv::Mat& image() {
-					return _image;
-				}
-				const cv::Mat& image() const {
-					return _image;
-				}
-
-        /**
-				 * Returns image in BGR color space.
-				 */
-				void imageBGR(cv::Mat& output) {
-					// Convert image to BGR before evaluating
-//					cv::Mat output;
-
-					// Convert HLS into BGR because imwrite uses BGR color space
-					cv::cvtColor(_image, output, CV_HLS2BGR);
-				}
-
-				template<class Archive>
-				void serialize(Archive & ar, const unsigned int version) {
-					dbg::trace trace("phen", DBG_HERE);
-					sferes::phen::Indiv<Gen, Fit, Params,  typename stc::FindExact<ColorImage<Gen, Fit, Params, Exact>, Exact>::ret>::serialize(ar, version);
-					ar & BOOST_SERIALIZATION_NVP(_image);
-					ar & BOOST_SERIALIZATION_NVP(_developed);
-				}
-
-			protected:
-				cv::Mat _image;
-				bool _developed;
-
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/phen/phen_grayscale_image.hpp

@@ -1,233 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef PHEN_GRAYSCALE_IMAGE_HPP
-#define PHEN_GRAYSCALE_IMAGE_HPP
-
-#include <map>
-#include "phen_image.hpp"
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-
-
-// New stuff added ------------------------------------------
-
-#include <cmath>
-#include <boost/lexical_cast.hpp>
-#include <boost/algorithm/string/join.hpp>
-#include <string>
-
-#include "cvmat_serialization.h" // Serialize cv::Mat
-#include <glog/logging.h>	// Google Logging
-
-// New stuff added ------------------------------------------
-
-namespace sferes
-{
-  namespace phen
-  {
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(GrayscaleImage, Image)
-    {
-      public:
-        typedef Gen gen_t;
-        typedef typename gen_t::nn_t gen_nn_t;
-        SFERES_CONST size_t nb_cppn_inputs = Params::dnn::nb_inputs;
-        SFERES_CONST size_t nb_cppn_outputs = Params::dnn::nb_outputs;	// Red, Green, Blue
-
-        GrayscaleImage():_developed(false)
-        {
-        }
-
-        void develop()
-        {
-        	// Check if phenotype has not been developed
-        	if (!_developed)
-        	{
-						// Initialize the image to be a white background image
-						reset_image();
-
-						this->gen().init();
-						 // develop the parameters
-						BGL_FORALL_VERTICES_T(v, this->gen().get_graph(),
-																	typename gen_t::nn_t::graph_t)
-						{
-							this->gen().get_graph()[v].get_afparams().develop();
-							this->gen().get_graph()[v].get_pfparams().develop();
-						}
-						BGL_FORALL_EDGES_T(e, this->gen().get_graph(),
-															 typename gen_t::nn_t::graph_t)
-						this->gen().get_graph()[e].get_weight().develop();
-
-						assert(nb_cppn_inputs == this->gen().get_nb_inputs());
-						assert(nb_cppn_outputs == this->gen().get_nb_outputs());
-
-						// Change specific color of every pixel in the image
-						for (int x = 0; x < _image.cols; ++x)
-						{
-							for (int y = 0; y < _image.rows; ++y)
-							{
-								float output =	cppn_value(x, y);	// Single grayscale value (intensity)
-
-								// Change pixel intensity of grayscale images
-								// Ref: http://docs.opencv.org/doc/user_guide/ug_mat.html
-								_image.at<uchar>(cv::Point(x,y)) = convert_to_color_scale(255, output);
-							}
-						}
-
-						_developed = true;	// Raise the flag that this phenotype has been developed.
-        	}
-        }
-
-        /**
-				 * Programmatically put the patterns in here.
-				 */
-				void reset_image()
-				{
-					// Paint background : black
-					_image = cv::Mat(Params::image::size, Params::image::size, CV_8UC1, cv::Scalar(0, 0, 0));
-				}
-
-				double normalize_map_xy_to_grid(const int & r_xyVal, const int & r_numVoxelsXorY)
-				{
-					// turn the xth or yth node into its coordinates on a grid from -1 to 1, e.g. x values (1,2,3,4,5) become (-1, -.5 , 0, .5, 1)
-					// this works with even numbers, and for x or y grids only 1 wide/tall, which was not the case for the original
-					// e.g. see findCluster for the orignal versions where it was not a funciton and did not work with odd or 1 tall/wide #s
-
-					double coord;
-
-					if (r_numVoxelsXorY==1) coord = 0;
-					else coord = -1 + ( r_xyVal * 2.0/(r_numVoxelsXorY-1) );
-
-					return(coord);
-				}
-
-				float cppn_value(size_t i, size_t j)
-        {
-          // Euclidean distance from center
-          const float xNormalized = normalize_map_xy_to_grid(i, Params::image::size);
-					const float yNormalized = normalize_map_xy_to_grid(j, Params::image::size);
-					const float distanceFromCenter = sqrt(pow(double(xNormalized),2.0)+pow(double(yNormalized),2.0));
-
-					// CPPN inputs
-          std::vector<float> in(nb_cppn_inputs);
-          this->gen().init();
-          in[0] = i;										// x
-          in[1] = j;										// y
-          in[2] = distanceFromCenter;		// distance from center
-          in[3] = 1.0;									// bias
-
-          for (size_t k = 0; k < this->gen().get_depth(); ++k)
-            this->gen().step(in);
-
-          // Get the CPPN output
-          return this->gen().get_outf(0);	// Grayscale value
-        }
-
-				/**
-				 * Convert [-1, 1] range to a color scale
-				 * [0, 255] for Saturation / Brightness or
-				 * [0, 180] for Hue
-				 */
-				static int convert_to_color_scale(const int scale, const float value)
-				{
-					int color = value * scale;
-
-					if (value < 0)
-					{
-						color *= -1;
-					}
-
-					return color;
-				}
-
-        void write_png_image(const std::string fileName, const cv::Mat& map)
-				{
-					// Read the target bitmap
-					try
-					{
-						// Parameters for cv::imwrite
-						std::vector<int> write_params;
-						write_params.push_back(CV_IMWRITE_PNG_COMPRESSION);
-						write_params.push_back(0);	// Fastest writing without compression
-
-						// Write to a file
-						imwrite(fileName, map, write_params);
-					}
-					catch (std::runtime_error& ex)
-					{
-						std::cout << "Failed to write image: " << fileName << std::endl;
-						fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what());
-					}
-				}
-
-        void log_best_image_fitness(const std::string title)
-				{
-          std::vector < std::string > list;
-					list.push_back (title);
-					list.push_back (".png");
-					const std::string fileName = boost::algorithm::join (list, "");
-
-					write_png_image(fileName, _image);
-
-					std::cout << "Written to " << title << std::endl;
-				}
-
-        cv::Mat& image() {
-          return _image;
-        }
-        const cv::Mat& image() const {
-          return _image;
-        }
-
-        template<class Archive>
-				void serialize(Archive & ar, const unsigned int version) {
-        	dbg::trace trace("phen", DBG_HERE);
-        	sferes::phen::Image<Gen, Fit, Params,  typename stc::FindExact<GrayscaleImage<Gen, Fit, Params, Exact>, Exact>::ret>::serialize(ar, version);
-					ar & BOOST_SERIALIZATION_NVP(_image);
-					ar & BOOST_SERIALIZATION_NVP(_developed);
-				}
-
-      protected:
-        cv::Mat _image;
-        bool _developed;
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/phen/phen_grayscale_image_direct.hpp

@@ -1,183 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef PHEN_GRAYSCALE_IMAGE_DIRECT_HPP
-#define PHEN_GRAYSCALE_IMAGE_DIRECT_HPP
-
-#include <map>
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-
-
-// New stuff added ------------------------------------------
-
-#include <cmath>
-#include <boost/lexical_cast.hpp>
-#include <boost/algorithm/string/join.hpp>
-#include <string>
-
-#include "cvmat_serialization.h" // Serialize cv::Mat
-#include <glog/logging.h>	// Google Logging
-
-#include "phen_image.hpp" // Base Image class
-
-// New stuff added ------------------------------------------
-
-namespace sferes
-{
-  namespace phen
-  {
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(GrayscaleImageDirect, Image)
-    {
-      public:
-
-        GrayscaleImageDirect():_developed(false)
-        {
-        }
-
-        void develop()
-        {
-        	// Check if phenotype has not been developed
-        	if (!_developed)
-        	{
-						// Initialize the image to be a white background image
-						reset_image();
-
-						unsigned i = 0;	// Index to access genome
-
-						// Change specific color of every pixel in the image
-						for (int x = 0; x < _image.cols; ++x)
-						{
-							for (int y = 0; y < _image.rows; ++y)
-							{
-								float output = this->_gen.data(i);
-
-								// Change pixel intensity of grayscale images
-								// Ref: http://docs.opencv.org/doc/user_guide/ug_mat.html
-								_image.at<uchar>(cv::Point(x,y)) = convert_to_color_scale(255, output);
-
-								++i;	// Move to the next pixel location in genome
-							}
-						}
-
-						_developed = true;	// Raise the flag that this phenotype has been developed.
-        	}
-        }
-
-        /**
-				 * Programmatically put the patterns in here.
-				 */
-				void reset_image()
-				{
-					// Paint background : black
-					_image = cv::Mat(Params::image::size, Params::image::size, CV_8UC1, cv::Scalar(0, 0, 0));
-				}
-
-				/**
-				 * Convert [-1, 1] range to a color scale
-				 * [0, 255] for Saturation / Brightness or
-				 * [0, 180] for Hue
-				 */
-				static int convert_to_color_scale(const int scale, const float value)
-				{
-					int color = value * scale;
-
-					if (value < 0)
-					{
-						color *= -1;
-					}
-
-					return color;
-				}
-
-        void write_png_image(const std::string fileName, const cv::Mat& map)
-				{
-					// Read the target bitmap
-					try
-					{
-						// Parameters for cv::imwrite
-						std::vector<int> write_params;
-						write_params.push_back(CV_IMWRITE_PNG_COMPRESSION);
-						write_params.push_back(0);	// Fastest writing without compression
-
-						// Write to a file
-						imwrite(fileName, map, write_params);
-					}
-					catch (std::runtime_error& ex)
-					{
-						std::cout << "Failed to write image: " << fileName << std::endl;
-						fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what());
-					}
-				}
-
-        void log_best_image_fitness(const std::string title)
-				{
-          std::vector < std::string > list;
-					list.push_back (title);
-					list.push_back (".png");
-					const std::string fileName = boost::algorithm::join (list, "");
-
-					write_png_image(fileName, _image);
-
-					std::cout << "Written to " << title << std::endl;
-				}
-
-        cv::Mat& image() {
-          return _image;
-        }
-        const cv::Mat& image() const {
-          return _image;
-        }
-
-        template<class Archive>
-				void serialize(Archive & ar, const unsigned int version) {
-        	dbg::trace trace("phen", DBG_HERE);
-        	sferes::phen::Indiv<Gen, Fit, Params,  typename stc::FindExact<GrayscaleImageDirect<Gen, Fit, Params, Exact>, Exact>::ret>::serialize(ar, version);
-					ar & BOOST_SERIALIZATION_NVP(_image);
-					ar & BOOST_SERIALIZATION_NVP(_developed);
-				}
-
-      protected:
-        cv::Mat _image;
-        bool _developed;
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/phen/phen_image.hpp

@@ -1,119 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef PHEN_IMAGE_HPP
-#define PHEN_IMAGE_HPP
-
-#include <map>
-#include <sferes/phen/indiv.hpp>
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-
-
-// New stuff added ------------------------------------------
-
-#include <cmath>
-#include <boost/lexical_cast.hpp>
-#include <boost/algorithm/string/join.hpp>
-#include <string>
-
-#include "cvmat_serialization.h" // Serialize cv::Mat
-#include <glog/logging.h>	// Google Logging
-
-//#include <boost/uuid/uuid.hpp>            // uuid class
-#include <boost/uuid/uuid_generators.hpp> // generators
-#include <boost/uuid/uuid_io.hpp>         // streaming operators etc.
-#include <boost/uuid/uuid_serialize.hpp>	// serialization
-
-// New stuff added ------------------------------------------
-
-namespace sferes
-{
-  namespace phen
-  {
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(Image, Indiv)
-    {
-    public:
-    	  Image(): _created_gen(0)
-        {
-        	boost::uuids::uuid uuid = boost::uuids::random_generator()();
-        	_id = uuid;
-        }
-
-        /*
-         * Get the ID of this organism.
-         */
-        boost::uuids::uuid id()
-        {
-        	return _id;
-        }
-
-        /*
-         * Set the generation when this organism is created.
-         */
-        void set_created_gen(const size_t generation)
-        {
-        	_created_gen = generation;
-        }
-
-        /*
-         * Get the generation when this organism is created.
-         */
-        size_t created_gen() const
-        {
-        	return _created_gen;
-        }
-
-
-        template<class Archive>
-				void serialize(Archive & ar, const unsigned int version)
-        {
-					sferes::phen::Indiv<Gen, Fit, Params,  typename stc::FindExact<Image<Gen, Fit, Params, Exact>, Exact>::ret>::serialize(ar, version);
-					ar & boost::serialization::make_nvp("uuid", _id.data);
-					ar & BOOST_SERIALIZATION_NVP(_created_gen);
-        }
-
-      protected:
-        boost::uuids::uuid _id;		// The unique id of this organism
-        size_t _created_gen;							// The generation when this image is created
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/phen/phen_image_direct.hpp

@@ -1,210 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef PHEN_IMAGE_DIRECT_HPP
-#define PHEN_IMAGE_DIRECT_HPP
-
-#include <map>
-#include "phen_image.hpp"
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include <modules/nn2/gen_hyper_nn.hpp>
-
-
-// New stuff added ------------------------------------------
-
-#include <cmath>
-#include <boost/lexical_cast.hpp>
-#include <boost/algorithm/string/join.hpp>
-#include <string>
-
-#include "cvmat_serialization.h" // Serialize cv::Mat
-#include <glog/logging.h>	// Google Logging
-
-// New stuff added ------------------------------------------
-
-namespace sferes
-{
-  namespace phen
-  {
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(ImageDirect, Image)
-    {
-      public:
-
-        ImageDirect():_developed(false)
-        {
-        }
-
-        void develop()
-        {
-        	// Check if phenotype has not been developed
-        	if (!_developed)
-        	{
-						// Initialize the image to be a white background image
-						reset_image();
-
-						unsigned long int i = 0;	// Index to access genome
-
-						// Change specific color of every pixel in the image
-						for (int x = 0; x < _image.cols; ++x)
-						{
-							for (int y = 0; y < _image.rows; ++y)
-							{
-								std::vector<float> output;
-
-								// Extract 3 values for H, L, S
-								for (int v = 0; v < 3; ++v)
-								{
-									output.push_back(this->_gen.data(i));
-									++i;	// Move to the next color value for the current pixel location in genome
-								}
-
-								cv::Vec3b color = _image.at<cv::Vec3b>(cv::Point(x,y));
-
-								color[0] = convert_to_color_scale(255, output[0]);	// H
-								color[1] = convert_to_color_scale(255, output[1]);	// L
-								color[2] = convert_to_color_scale(255, output[2]);	// S
-
-								_image.at<cv::Vec3b>(cv::Point(x,y)) = color;
-							}
-						}
-
-						_developed = true;	// Raise the flag that this phenotype has been developed.
-        	}
-        }
-
-        /**
-				 * Programmatically put the patterns in here.
-				 */
-				void reset_image()
-				{
-					// Paint background : white
-					_image = cv::Mat(Params::image::size, Params::image::size, CV_8UC3, cv::Scalar(255,255,255));
-				}
-
-				/**
-				 * Convert [-1, 1] range to a color scale
-				 * [0, 255] for Saturation / Brightness or
-				 * [0, 180] for Hue
-				 */
-				static int convert_to_color_scale(const int scale, const float value)
-				{
-					int color = value * scale;
-
-					if (value < 0)
-					{
-						color *= -1;
-					}
-
-					return color;
-				}
-
-        void write_png_image(const std::string fileName, const cv::Mat& map)
-				{
-					// Read the target bitmap
-					try
-					{
-						cv::Mat output;
-
-						// Convert HLS into BGR because imwrite uses BGR color space
-						cv::cvtColor(map, output, CV_HLS2BGR);
-
-						// Parameters for cv::imwrite
-						std::vector<int> write_params;
-						write_params.push_back(CV_IMWRITE_PNG_COMPRESSION);
-						write_params.push_back(0);	// Fastest writing without compression
-
-						// Write to a file
-						imwrite(fileName, output, write_params);
-					}
-					catch (std::runtime_error& ex)
-					{
-						std::cout << "Failed to write image: " << fileName << std::endl;
-						fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what());
-					}
-				}
-
-        void log_best_image_fitness(const std::string title)
-				{
-          std::vector < std::string > list;
-					list.push_back (title);
-					list.push_back (".png");
-					const std::string fileName = boost::algorithm::join (list, "");
-
-					write_png_image(fileName, _image);
-
-					std::cout << "Written to " << title << std::endl;
-				}
-
-        cv::Mat& image() {
-          return _image;
-        }
-        const cv::Mat& image() const {
-          return _image;
-        }
-
-        /**
-				 * Returns image in BGR color space.
-				 */
-				void imageBGR(cv::Mat& output)
-				{
-					// Convert image to BGR before evaluating
-//					cv::Mat output;
-
-					// Convert HLS into BGR because imwrite uses BGR color space
-					cv::cvtColor(_image, output, CV_HLS2BGR);
-
-//					return output;
-				}
-
-        template<class Archive>
-				void serialize(Archive & ar, const unsigned int version) {
-        	dbg::trace trace("phen", DBG_HERE);
-        	sferes::phen::Indiv<Gen, Fit, Params,  typename stc::FindExact<ImageDirect<Gen, Fit, Params, Exact>, Exact>::ret>::serialize(ar, version);
-					ar & BOOST_SERIALIZATION_NVP(_image);
-					ar & BOOST_SERIALIZATION_NVP(_developed);
-				}
-
-      protected:
-        cv::Mat _image;
-        bool _developed;
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/settings.h

@@ -1,17 +0,0 @@
-/*
- * settings.h
- *
- *  Created on: Jul 16, 2014
- *      Author: anh
- */
-
-#ifndef SETTINGS_H_
-#define SETTINGS_H_
-
-
-//#define LOCAL_RUN
-
-//#define NB_THREADS 16
-
-
-#endif /* SETTINGS_H_ */

modules/dnns_easily_fooled/sferes/exp/images/stat/best_fit_image.hpp

@@ -1,171 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef BEST_FIT_IMAGE_
-#define BEST_FIT_IMAGE_
-
-#include <boost/serialization/shared_ptr.hpp>
-#include <boost/serialization/nvp.hpp>
-#include "image_stat.hpp"
-#include <sferes/fit/fitness.hpp>
-#include <boost/lexical_cast.hpp>
-
-#include <boost/accumulators/accumulators.hpp>
-#include <boost/accumulators/statistics/stats.hpp>
-
-// Headers specifics to the computations we need
-#include <boost/accumulators/statistics/median.hpp>
-#include <boost/accumulators/statistics/mean.hpp>
-#include <boost/accumulators/statistics/max.hpp>
-
-#include <exp/images/util/median.hpp>
-#include <boost/filesystem.hpp>
-
-namespace sferes {
-  namespace stat {
-    // assume that the population is sorted !
-    SFERES_STAT(BestFitImage, ImageStat){
-    public:
-    	typedef boost::shared_ptr<Phen> indiv_t;
-    	typedef std::vector<indiv_t> pop_t;
-
-      template<typename E>
-      void refresh(const E& ea) {
-        assert(!ea.pop().empty());
-        //_best = *ea.pop().begin();
-
-        // Create the log file
-        this->_create_log_file(ea, "bestfit.dat");
-
-        if (ea.dump_enabled() && ( ea.gen() % Params::pop::dump_period == 0 ))
-        {
-        	// The data for which we wish to calculate median as the boost accumulator does not.
-					std::vector< double > data;
-
-					// Calculate all stats: mean, max, min
-					boost::accumulators::accumulator_set<double, boost::accumulators::stats<
-					boost::accumulators::tag::mean,
-					boost::accumulators::tag::max,
-					boost::accumulators::tag::min
-					> > stats;
-
-        	int best_id = 0;
-        	float best_fitness = 0.0f;
-        	int id = 0;
-
-        	BOOST_FOREACH(indiv_t i, ea.pop())
-        	{
-        		// With the stats object in hand, all we need is to push in the data.
-        		float fitness = i->fit().value();
-        		stats(fitness);
-        		data.push_back(fitness);							// Add it to the list for calculating median later
-
-        		// Get the best individual by fitness
-        		if (fitness > best_fitness)
-        		{
-        			best_id = id;
-        			best_fitness = fitness;
-        		}
-
-        		++id;
-        	}
-
-        	// Best individual in the current pop
-        	_best = ea.pop()[best_id];
-
-        	assert(data.size() == Params::pop::size);	// Make sure the list of values is of correct size
-					double median = sferes::util::Median::calculate_median(data); // Get the mdian
-
-        	// Dump best_fit.dat file
-          (*this->_log_file) << ea.gen()
-          		<< " " << median
-          		<< " " << boost::accumulators::mean(stats)
-          		<< " " << boost::accumulators::max(stats)
-          		<< " " << boost::accumulators::min(stats)
-          		<< std::endl;
-
-          // Dump best image
-					if (Params::log::best_image)
-					{
-						std::string image_fitness = boost::lexical_cast<std::string>(_best->fit().value());
-						std::string image_gen = boost::lexical_cast<std::string>(ea.gen());
-						std::string image_file = ea.res_dir() + "/" + image_gen + "_" + image_fitness;
-
-						_best->log_best_image_fitness(image_file);
-					}
-
-					// Save this generation population to a file
-					// Clear all individuals
-					_pop.clear();
-
-					// The mixed population (before selection)
-					for (size_t i = 0; i < ea.pop().size(); ++i)
-					{
-						_pop.push_back(ea.pop()[i]);
-					}
-        }
-      }
-      void show(std::ostream& os, size_t k) {
-        _best->develop();
-        _best->show(os);
-        _best->fit().set_mode(fit::mode::view);
-        _best->fit().eval(*_best);
-
-      }
-      const boost::shared_ptr<Phen> best() const {
-        return _best;
-      }
-
-      template<class Archive>
-			void serialize(Archive& ar, const unsigned int version)
-			{
-				ar & BOOST_SERIALIZATION_NVP(_pop);
-				ar & BOOST_SERIALIZATION_NVP(_best);
-			}
-
-      const pop_t& getPopulation() const
-			{
-				return _pop;
-			}
-
-    protected:
-      indiv_t _best;
-      pop_t _pop;
-    };
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/stat/best_fit_map_image.hpp

@@ -1,162 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef BEST_FIT_MAP_IMAGE_
-#define BEST_FIT_MAP_IMAGE_
-
-#include <boost/serialization/shared_ptr.hpp>
-#include <boost/serialization/nvp.hpp>
-#include "image_stat.hpp"
-#include <sferes/fit/fitness.hpp>
-#include <boost/lexical_cast.hpp>
-
-#include <boost/accumulators/accumulators.hpp>
-#include <boost/accumulators/statistics/stats.hpp>
-
-// Headers specifics to the computations we need
-#include <boost/accumulators/statistics/median.hpp>
-#include <boost/accumulators/statistics/mean.hpp>
-#include <boost/accumulators/statistics/max.hpp>
-
-#include <exp/images/util/median.hpp>
-#include <boost/filesystem.hpp>
-
-namespace sferes {
-  namespace stat {
-    // assume that the population is sorted !
-    SFERES_STAT(BestFitMapImage, ImageStat){
-
-    public:
-    	typedef boost::shared_ptr<Phen> indiv_t;
-    	typedef std::vector<indiv_t> pop_t;
-
-      template<typename E>
-      void refresh(const E& ea) {
-        assert(!ea.pop().empty());
-
-        // Create the log file
-        this->_create_log_file(ea, "bestfit.dat");
-
-        if (ea.dump_enabled() && ( ea.gen() % Params::pop::dump_period == 0 ))
-        {
-        	// The data for which we wish to calculate median as the boost accumulator does not.
-        	std::vector< double > data;
-
-        	// Calculate all stats: mean, max, min
-        	boost::accumulators::accumulator_set<double, boost::accumulators::stats<
-        	boost::accumulators::tag::mean,
-        	boost::accumulators::tag::max,
-        	boost::accumulators::tag::min
-        	> > stats;
-
-        	int best_x = 0;
-        	int best_y = 0;
-        	float best_fitness = 0.0f;
-
-        	// Iterate through the map of phenotypes in MAP-Elites
-        	for (int x = 0; x < Params::ea::res_x; ++x)
-					{
-						for (int y = 0; y < Params::ea::res_y; ++y)
-						{
-							indiv_t i = ea.archive()[x][y];
-
-							float fitness = i->fit().value(y);		// Get the fitness of individual
-							stats(fitness);												// Add it to the stats accumulator
-							data.push_back(fitness);							// Add it to the list for calculating median later
-
-							// Get the best individual by fitness
-							if (fitness > best_fitness)
-							{
-								best_x = x;		// Record the best x and y
-								best_y = y;
-								best_fitness = fitness;
-							}
-						}
-					}
-
-        	// Best individual in the current pop
-        	_best = ea.archive()[best_x][best_y];
-
-        	assert(data.size() == Params::ea::res_y);	// Make sure the list of values is of correct size
-        	double median = sferes::util::Median::calculate_median(data); // Get the mdian
-
-        	// Dump best_fit.dat file
-          (*this->_log_file) << ea.gen()
-          		<< " " << median
-          		<< " " << boost::accumulators::mean(stats)
-          		<< " " << boost::accumulators::max(stats)
-          		<< " " << ea.jumps()
-          		<< " " << boost::accumulators::min(stats)
-          		<< std::endl;
-
-          // Dump best image
-					if (Params::log::best_image)
-					{
-						std::string image_fitness = boost::lexical_cast<std::string>(best_fitness);
-						std::string image_gen = boost::lexical_cast<std::string>(ea.gen());
-						std::string image_file = ea.res_dir() + "/" + image_gen + "_" + image_fitness;
-
-						_best->log_best_image_fitness(image_file);
-					}
-
-        }
-      }
-      void show(std::ostream& os, size_t k) {
-        _best->develop();
-        _best->show(os);
-        _best->fit().set_mode(fit::mode::view);
-        _best->fit().eval(*_best);
-      }
-
-      const boost::shared_ptr<Phen> best() const {
-        return _best;
-      }
-
-      template<class Archive>
-			void serialize(Archive& ar, const unsigned int version)
-			{
-				ar & BOOST_SERIALIZATION_NVP(_best);
-			}
-
-
-    protected:
-      indiv_t _best;
-
-    };
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/stat/image_stat.hpp

@@ -1,70 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef IMAGE_STAT_
-#define IMAGE_STAT_
-
-#include <boost/serialization/shared_ptr.hpp>
-#include <boost/serialization/nvp.hpp>
-#include <sferes/stat/stat.hpp>
-#include <sferes/fit/fitness.hpp>
-#include <boost/lexical_cast.hpp>
-
-#include <boost/filesystem.hpp>
-
-namespace sferes {
-  namespace stat {
-    // assume that the population is sorted !
-    SFERES_STAT(ImageStat, Stat){
-
-    protected:
-      template<typename E>
-      void _create_log_file(const E& ea, const std::string& name) {
-        if (!this->_log_file && ea.dump_enabled()) {
-
-        	// Create a new file if not exists, append if exists
-          std::string log = ea.res_dir() + "/" + name;
-
-          // Append if file exists: std::fstream::app
-          this->_log_file = boost::shared_ptr<std::ofstream>(new std::ofstream(log.c_str(), std::fstream::app));
-        }
-      }
-
-    };
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/stat/stat_map_image.hpp

@@ -1,208 +0,0 @@
-#ifndef STAT_MAP_IMAGE_HPP
-#define STAT_MAP_IMAGE_HPP
-
-#include <numeric>
-#include <boost/multi_array.hpp>
-#include <sferes/stat/stat.hpp>
-#include <glog/logging.h>
-#include <boost/filesystem.hpp>
-
-namespace sferes
-{
-  namespace stat
-  {
-    SFERES_STAT(MapImage, Stat)
-    {
-    public:
-      typedef boost::shared_ptr<Phen> phen_t;
-      typedef boost::multi_array<phen_t, 2> array_t;
-      typedef boost::array<float, 2> point_t;
-
-      MapImage() : _xs(0), _ys(0) {}
-      template<typename E>
-			void refresh(const E& ea)
-			{
-				_archive.clear();
-				_xs = ea.archive().shape()[0];
-				_ys = ea.archive().shape()[1];
-				assert(_xs == Params::ea::res_x);
-				assert(_ys == Params::ea::res_y);
-
-				for (size_t i = 0; i < _xs; ++i)
-				for (size_t j = 0; j < _ys; ++j)
-				{
-					phen_t p = ea.archive()[i][j];
-					_archive.push_back(p);
-				}
-
-				// Report current generation every 10 generations
-				if (ea.gen() % 10 == 0)
-				{
-					std::cout << "gen.. " << ea.gen() << std::endl;
-				}
-
-				if (ea.gen() % Params::pop::dump_period == 0)
-				{
-					_write_archive(ea.archive(), ea.parents(), std::string("archive_"), ea, ea.gen());
-
-			#ifdef MAP_WRITE_PARENTS
-					_write_parents(ea.archive(), ea.parents(), std::string("parents_"), ea);
-			#endif
-				}
-			}
-
-      const std::vector<phen_t>& getPopulation() const
-			{
-				return _archive;
-			}
-
-    	const std::vector<phen_t>& archive() const
-    	{
-    		return _archive;
-    	}
-
-      void show(std::ostream& os, size_t k)
-			{
-				std::cerr << "loading "<< k / _ys << "," << k % _ys << std::endl;
-				if (_archive[k])
-				{
-					_archive[k]->develop();
-					_archive[k]->show(os);
-					_archive[k]->fit().set_mode(fit::mode::view);
-					_archive[k]->fit().eval(*_archive[k]);
-				}
-				else
-				std::cerr << "Warning, no point here" << std::endl;
-			}
-
-      template<class Archive>
-        void serialize(Archive& ar, const unsigned int version)
-			{
-				ar & BOOST_SERIALIZATION_NVP(_archive);
-				ar & BOOST_SERIALIZATION_NVP(_xs);
-				ar & BOOST_SERIALIZATION_NVP(_ys);
-			}
-
-    protected:
-      std::vector<phen_t> _archive;
-      int _xs, _ys;
-
-      template<typename EA>
-      void _write_parents(const array_t& array,
-			  const array_t& p_array,
-			  const std::string& prefix,
-			  const EA& ea) const
-			{
-				std::cout << "writing..." << prefix << ea.gen() << std::endl;
-				std::string fname = ea.res_dir() + "/"
-				+ prefix
-				+ boost::lexical_cast<
-				std::string>(ea.gen())
-				+ std::string(".dat");
-				std::ofstream ofs(fname.c_str());
-				for (size_t i = 0; i < _xs; ++i)
-				for (size_t j = 0; j < _ys; ++j)
-				if (array[i][j] && p_array[i][j])
-				{
-					point_t p = _get_point(p_array[i][j]);
-					size_t x = round(p[0] * _xs);
-					size_t y = round(p[1] * _ys);
-					ofs << i / (float) _xs
-					<< " " << j / (float) _ys
-					<< " " << p_array[i][j]->fit().value()
-					<< " " << x / (float) _xs
-					<< " " << y / (float) _ys
-					<< " " << array[i][j]->fit().value()
-					<< std::endl;
-				}
-			}
-
-      std::string _make_gen_dir(const std::string& res_dir, const int gen) const
-      {
-      	std::string gen_dir = res_dir + std::string("/map_gen_") + boost::lexical_cast<std::string>(gen);
-				boost::filesystem::path my_path(gen_dir);
-				boost::filesystem::create_directory(my_path);
-
-				return gen_dir;
-      }
-
-      template<typename EA>
-      void _write_archive(const array_t& array,
-      	const array_t& p_array,
-			  const std::string& prefix,
-			  const EA& ea,
-			  const int gen) const
-			{
-				std::cout << "writing..." << prefix << ea.gen() << std::endl;
-				std::string fname = ea.res_dir() + "/"
-				+ prefix
-				+ boost::lexical_cast<
-				std::string>(ea.gen())
-				+ std::string(".dat");
-
-				std::ofstream ofs(fname.c_str());
-				for (size_t i = 0; i < _xs; ++i)
-				{
-					for (size_t j = 0; j < _ys; ++j)
-					{
-						if (array[i][j])
-						{
-							float fitness = array[i][j]->fit().value(j);
-
-							ofs
-							<< " " << j		// This dimension is categorical (1-1000). No need to normalize to be [0, 1].
-							<< " " << fitness;
-
-							// CPPN genome info
-//							<< " " << array[i][j]->gen().get_nb_neurons()
-//							<< " " << array[i][j]->gen().get_nb_connections();
-
-							if (Params::image::record_lineage)
-							{
-								ofs << " " << array[i][j]->id();			// Record the id of this organism
-
-								// Only print out the parent if this is a newly created organism
-								if (array[i][j]->created_gen() == ea.gen())
-								{
-									ofs << " " << p_array[i][j]->id();		// Record the id of this organism's parent
-								}
-							}
-
-							ofs << std::endl;	// End of line
-
-							bool dump_map = false;
-
-							// Always print the map in the first generation and last generation
-//							if (
-//									(gen == 0 || gen == Params::pop::nb_gen - Params::pop::dump_period)
-									// Print out only when there is an improvement of 0.1
-//									|| (fitness - p_array[i][j]->fit().value(j) >= 0.3)
-//									)
-							{
-								dump_map = true;
-							}
-
-							// Check if we should print out
-							if (dump_map)
-							{
-								// Create the directory
-								const std::string gen_dir = _make_gen_dir(ea.res_dir(), gen);
-
-								// Print out images at the current generation
-								std::string image_gen = boost::lexical_cast<std::string>(ea.gen());
-								std::string category = boost::lexical_cast<std::string>(j);
-								std::string image_fitness = boost::lexical_cast<std::string>(fitness);
-								std::string image_file = gen_dir + "/map_" + image_gen + "_" + category + "_" + image_fitness;
-
-								array[i][j]->log_best_image_fitness(image_file);
-							}
-						}
-					}
-				}
-			}
-
-    };
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/exp/images/util/median.hpp

@@ -1,80 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef UTIL_MEDIAN
-#define UTIL_MEDIAN
-
-#include <boost/range/algorithm.hpp>
-#include <vector>       // std::vector
-
-namespace sferes {
-  namespace util {
-  	class Median
-		{
-		public:
-  		/**
-  		 * Calculate the median of the doubles in the given list.
-  		 */
-  		static double calculate_median(std::vector<double>& list)
-  		{
-  			size_t size = list.size();	// Size of the list
-
-  			assert(size > 0);
-
-  			std::sort(list.begin(), list.end());
-
-  			// If there are an odd number of doubles
-  			if (size % 2 == 1)
-  			{
-  				// Take the middle number
-  				size_t index_middle = (size - 1)/2;
-  				return list[index_middle];
-  			}
-  			// If there are an even number of doubles
-  			else
-  			{
-  				size_t index_above = size / 2;
-  				size_t index_below = index_above - 1;
-
-  				// Average of two middle numbers
-  				return (list[index_above] + list[index_below]) / 2;
-  			}
-  		}
-		};
-  }
-}
-#endif

modules/dnns_easily_fooled/sferes/exp/images/wscript

@@ -1,14 +0,0 @@
-
-#! /usr/bin/env python
-def build(bld):
-    obj = bld.new_task_gen('cxx', 'program')
-    obj.source = 'dl_map_elites_images_imagenet_direct_encoding.cpp'
-    #obj.source = 'dl_rank_simple_images_mnist_direct_encoding.cpp'
-    #obj.source = 'dl_rank_simple_images.cpp'    
-    obj.includes = '. ../../ /usr/local/cuda-6.0/include'
-    obj.uselib_local = 'sferes2'
-    obj.uselib = ''
-    obj.cxxflags = ['-std=c++11']
-    obj.target = 'images'
-    obj.uselib_local = 'sferes2'
-    obj.lib=['png', 'cudart', 'caffe', 'opencv_core', 'opencv_highgui', 'opencv_imgproc', 'lmdb', 'glog']

modules/dnns_easily_fooled/sferes/install_caffe.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-
-# Uninstall caffe from Sferes
-rm -rf ~/src/sferes/include/caffe/
-rm ~/src/sferes/lib/libcaffe.*
-echo "Removed old installation in ~/src/sferes/"
-
-# Reinstall caffe to Sferes
-
-# Include files
-cp -R ~/src/caffe/include/caffe/ ~/src/sferes/include/
-echo "Installed header files from ~/src/caffe/include/caffe/"
-
-cp -R ~/src/caffe/build/src/caffe/ ~/src/sferes/include/
-echo "Installed header files from ~/src/caffe/build/src/caffe/"
-
-# Library files
-cp ~/src/caffe/build/lib/libcaffe.* ~/src/sferes/lib/
-echo "Installed library files from ~/src/caffe/build/lib/"
-
-echo "Done."

modules/dnns_easily_fooled/sferes/modules.conf

@@ -1,3 +0,0 @@
-nn2
-map_elite
-

modules/dnns_easily_fooled/sferes/modules/map_elite/fit_map.hpp

@@ -1,62 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#include <sferes/fit/fitness.hpp>
-
-#define FIT_MAP(Name) SFERES_FITNESS(Name, sferes::fit::FitMap)
-
-namespace sferes
-{
-  namespace fit
-  {
-    SFERES_FITNESS(FitMap, sferes::fit::Fitness)
-    {
-      public:
-        FitMap() : _desc(2) { }
-        const std::vector<float>& desc() const { return _desc; }
-        void set_desc(float x1, float x2)
-        {
-          assert(x1 >= 0);
-          assert(x2 >= 0);
-          assert(x1 <= 1);
-          assert(x2 <= 1);
-          assert(_desc.size() >= 2);
-          _desc[0] = x1;
-          _desc[1] = x2;
-        }
-      protected:
-        std::vector<float> _desc;
-    };
-  }
-}

modules/dnns_easily_fooled/sferes/modules/map_elite/map_elite.hpp

@@ -1,284 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef MAP_ELITE_HPP_
-#define MAP_ELITE_HPP_
-
-#include <algorithm>
-#include <limits>
-
-#include <boost/foreach.hpp>
-#include <boost/multi_array.hpp>
-#include <boost/array.hpp>
-#include <boost/fusion/algorithm/iteration/for_each.hpp>
-#include <boost/fusion/include/for_each.hpp>
-
-#include <sferes/stc.hpp>
-#include <exp/images/ea/ea_custom.hpp>
-#include <sferes/fit/fitness.hpp>
-
-#include <exp/images/continue_run/continue_run.hpp>
-
-namespace sferes
-{
-	namespace ea
-	{
-		// Main class
-		SFERES_EA(MapElite, EaCustom){
-public:
-
-	typedef boost::shared_ptr<Phen> indiv_t;
-
-	typedef std::vector<indiv_t> raw_pop_t;
-	typedef MapElite<Phen, Eval, Stat, FitModifier, Params, Exact> this_t;
-
-	typedef typename std::vector<indiv_t> pop_t;
-	typedef typename pop_t::iterator it_t;
-	typedef typename std::vector<std::vector<indiv_t> > front_t;
-	typedef boost::array<float, 2> point_t;
-	typedef boost::shared_ptr<Phen> phen_t;
-	typedef boost::multi_array<phen_t, 2> array_t;
-//	typedef boost::shared_ptr<Stat> stat_t;
-
-	static const size_t res_x = Params::ea::res_x;
-	static const size_t res_y = Params::ea::res_y;
-
-	typedef Stat stat_t;
-
-	MapElite() :
-	_array(boost::extents[res_x][res_y]),
-	_array_parents(boost::extents[res_x][res_y]),
-	_jumps(0)
-	{
-	}
-
-	void random_pop()
-	{
-		// parallel::init(); We are not using TBB
-
-		// Continuing a run
-		sferes::cont::Continuator<this_t, Params> continuator;
-
-		bool continue_run = continuator.enabled() || this->_gen_file_path != "";
-
-		// Continuing a run manually from command line or continuing a run automatically if the job was pre-empted
-		if(continue_run)
-		{
-			// Load the population file
-			raw_pop_t raw_pop;
-			if (this->_gen_file_path == "")
-			{
-				raw_pop = continuator.getPopulationFromFile(*this);
-			}
-			else
-			{
-				raw_pop = continuator.getPopulationFromFile(*this, this->_gen_file_path);
-			}
-
-			// Assign this pop also to the current map
-			for (size_t i = 0; i < raw_pop.size(); ++i)
-			{
-				_add_to_archive(raw_pop[i], raw_pop[i]);
-			}
-
-			// Get the number of population to continue with
-			const size_t init_size = raw_pop.size();
-
-			// Resize the current population archive
-			this->_pop.resize(init_size);
-
-			// Add loaded individuals to the new population
-			int i = 0;
-			BOOST_FOREACH(boost::shared_ptr<Phen>&indiv, this->_pop)
-			{
-				indiv = boost::shared_ptr<Phen>(new Phen(*raw_pop[i]));
-				++i;
-			}
-		}
-		else	// Run normally from gen = 0
-		{
-			// Original Map-Elites code
-			// Intialize a random population
-			this->_pop.resize(Params::pop::init_size);
-			BOOST_FOREACH(boost::shared_ptr<Phen>&indiv, this->_pop)
-			{
-				indiv = boost::shared_ptr<Phen>(new Phen());
-				indiv->random();
-			}
-		}
-
-		// Evaluate the initialized population
-		this->_eval.eval(this->_pop, 0, this->_pop.size());
-		BOOST_FOREACH(boost::shared_ptr<Phen>&indiv, this->_pop)
-		_add_to_archive(indiv, indiv);
-
-		// Continue a run from a specific generation
-		if(continue_run)
-		{
-			if (this->_gen_file_path == "")
-			{
-				continuator.run_with_current_population(*this);
-			}
-			else
-			{
-				continuator.run_with_current_population(*this, this->_gen_file_path);
-			}
-		}
-	}
-
-	//ADDED
-	void setGen(size_t gen)
-	{
-		this->_gen = gen;
-	}
-	//ADDED END
-
-	void epoch()
-	{
-#ifdef PHELOGENETIC_TREE
-		// We start with only 1 organism in order to construct the phylogenetic tree
-		// Thus, no evolution happening at generation 0
-		if (this->_gen == 0) return;
-#endif
-
-		this->_pop.clear();
-
-		for (size_t i = 0; i < res_x; ++i)
-		{
-			for (size_t j = 0; j < res_y; ++j)
-			{
-				if (_array[i][j])
-				{
-					this->_pop.push_back(_array[i][j]);
-				}
-			}
-		}
-
-		pop_t ptmp, p_parents;
-		for (size_t i = 0; i < Params::pop::size / 2; ++i)
-		{
-			indiv_t p1 = _selection(this->_pop);
-			indiv_t p2 = _selection(this->_pop);
-			boost::shared_ptr<Phen> i1, i2;
-			p1->cross(p2, i1, i2);
-			i1->mutate();
-			i2->mutate();
-
-			/*
-			 Phenotypes are to be developed in eval() called below
-			 this->_eval.eval(ptmp, 0, ptmp.size());
-			 So no need to develop them here.
-			 // i1->develop();
-			 // i2->develop();
-			 */
-
-			// Add the generation when these two new organisms are created (mutated)
-			i1->set_created_gen(this->_gen);
-			i2->set_created_gen(this->_gen);
-
-			ptmp.push_back(i1);
-			ptmp.push_back(i2);
-			p_parents.push_back(p1);
-			p_parents.push_back(p2);
-		}
-
-		this->_eval.eval(ptmp, 0, ptmp.size());
-
-		assert(ptmp.size() == p_parents.size());
-
-		for (size_t i = 0; i < ptmp.size(); ++i)
-		{
-			_add_to_archive(ptmp[i], p_parents[i]);
-		}
-	}
-
-	const array_t& archive() const
-	{	return _array;}
-	const array_t& parents() const
-	{	return _array_parents;}
-
-	const unsigned long jumps() const
-	{	return _jumps;}
-
-protected:
-	array_t _array;
-	array_t _prev_array;
-	array_t _array_parents;
-	unsigned long _jumps;
-
-	bool _add_to_archive(indiv_t i1, indiv_t parent)
-	{
-		bool added = false;	// Flag raised when the individual is added to the archive in any cell
-
-		// We have a map of 1x1000 for the total of 1000 categories
-		assert(1 == res_x);
-		assert(i1->fit().desc().size() == res_y);
-
-		// Compare this individual with every top individual in every cell.
-		// If this individual is better, replace the current cell occupant with it.
-		for (int x = 0; x < res_x; ++x)
-		{
-			for (int y = 0; y < res_y; ++y)
-			{
-				float i1_fitness = i1->fit().value(y);
-
-				if (!_array[x][y] || i1_fitness > _array[x][y]->fit().value(y))
-				{
-					// Replace the current cell occupant with new individual and its parent
-					_array[x][y] = i1;
-					_array_parents[x][y] = parent;
-
-					added = true;
-
-					// Record a jump of an indiv to a cell
-					// One indiv could jump to many cells
-					_jumps++;
-				}
-			}
-		}
-
-		return added;
-	}
-
-	indiv_t _selection(const pop_t& pop)
-	{
-		int x1 = misc::rand< int > (0, pop.size());
-		return pop[x1];
-	}
-
-};
-}
-}
-#endif
-

modules/dnns_easily_fooled/sferes/modules/map_elite/plot_map.py

@@ -1,85 +0,0 @@
-import sys
-import numpy as np
-
-import matplotlib.pyplot as plt
-import matplotlib
-from matplotlib.ticker import FuncFormatter
-
-cdict = {'red': [(0.0,  0.0, 0.0),
-                 (0.33, 0.0, 0.0),
-                 (0.66,  1.0, 1.0),
-                 (1.0,  1.0, 1.0)],
-         'blue': [(0.0,  0.0, 0.0),
-                  (0.33, 1.0, 1.0),
-                  (0.66,  0.0, 0.0),
-                  (1.0,  0.0, 0.0)],
-         'green': [(0.0,  0.0, 0.0),
-                   (0.33, 0.0, 0.0),
-                   (0.66,  0.0, 0.0),
-                   (1.0,  1.0, 1.0)]}
-my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)
-
-def scale(x, pos):
-    'The two args are the value and tick position'
-    return '%1.1f' % (x / 100.0)
-def scale2(x, pos):
-    'The two args are the value and tick position'
-    return '%1.1f' % (x / 100.0)
-
-
-
-
-
-
-size = int(sys.argv[2])
-
-x, y, z = np.loadtxt(sys.argv[1]).T
-
-data = np.zeros((size, size))
-m = 0
-x_m = 0
-y_m = 0
-for i in range(0, len(z)):
-    data[round(x[i] * size), round(y[i] * size)] = z[i]
-    if z[i] > m:
-        x_m = round(x[i] * size)
-        y_m = round(y[i] * size)
-        m = z[i]
-data = np.ma.masked_where(data == 0, data)
-
-print "best:"+str(max(z))
-
-def load_points(fname):
-    p_z, p_y, p_x = np.loadtxt(fname).T
-    p_x *= size
-    p_y *= size
-    p_p_x = []
-    p_p_y = []
-    np_p_x = []
-    np_p_y = []
-
-    for i in range(0, len(p_x)):
-        if p_z[i] == 1.0:
-            p_p_x += [p_x[i]]
-            p_p_y += [p_y[i]]
-        else:
-            np_p_x += [p_x[i]]
-            np_p_y += [p_y[i]]
-    return p_p_x, p_p_y, np_p_x, np_p_y
-
-
-
-fig = plt.figure()
-im = plt.imshow(data.T, origin='lower', cmap=my_cmap)
-im.set_interpolation('nearest')
-fig.subplots_adjust(top=0.98)
-cb = plt.colorbar()
-for t in cb.ax.get_xticklabels():
-    t.set_fontsize(130)
-
-
-ax = fig.add_subplot(111)
-ax.yaxis.set_major_formatter(FuncFormatter(scale))
-ax.xaxis.set_major_formatter(FuncFormatter(scale2))
-
-plt.savefig('heatmap.pdf')

modules/dnns_easily_fooled/sferes/modules/map_elite/stat_map.hpp

@@ -1,127 +0,0 @@
-#ifndef STAT_MAP_HPP_
-#define STAT_MAP_HPP_
-
-#include <numeric>
-#include <boost/multi_array.hpp>
-#include <sferes/stat/stat.hpp>
-
-namespace sferes
-{
-  namespace stat
-  {
-    SFERES_STAT(Map, Stat)
-    {
-    public:
-      typedef boost::shared_ptr<Phen> phen_t;
-      typedef boost::multi_array<phen_t, 2> array_t;
-      typedef boost::array<float, 2> point_t;
-
-      Map() : _xs(0), _ys(0) {}
-      template<typename E>
-        void refresh(const E& ea)
-      {
-	_archive.clear();	  
-	_xs = ea.archive().shape()[0];
-	_ys = ea.archive().shape()[1];
-	assert(_xs == Params::ea::res_x);
-	assert(_ys == Params::ea::res_y);
-
-	for (size_t i = 0; i < _xs; ++i)
-	  for (size_t j = 0; j < _ys; ++j)
-	    {
-	      phen_t p = ea.archive()[i][j];
-	      _archive.push_back(p);
-	    }
-
-	if (ea.gen() % Params::pop::dump_period == 0)
-	  {
-	    _write_archive(ea.archive(), std::string("archive_"), ea);
-#ifdef MAP_WRITE_PARENTS
-	    _write_parents(ea.archive(), ea.parents(), std::string("parents_"), ea);
-#endif
-	  }
-      }
-      void show(std::ostream& os, size_t k)
-      {
-	std::cerr << "loading "<< k / _ys << "," << k % _ys << std::endl;
-	if (_archive[k])
-	  {
-	    _archive[k]->develop();
-	    _archive[k]->show(os);
-	    _archive[k]->fit().set_mode(fit::mode::view);
-	    _archive[k]->fit().eval(*_archive[k]);
-	  }
-	else
-	  std::cerr << "Warning, no point here" << std::endl;
-      }
-      template<class Archive>
-        void serialize(Archive& ar, const unsigned int version)
-      {	 
-	ar & BOOST_SERIALIZATION_NVP(_archive);
-	ar & BOOST_SERIALIZATION_NVP(_xs);
-	ar & BOOST_SERIALIZATION_NVP(_ys);
-      }
-    protected:
-      std::vector<phen_t> _archive;
-      int _xs, _ys;
-
-      template<typename EA>
-      void _write_parents(const array_t& array,
-			  const array_t& p_array,
-			  const std::string& prefix,
-			  const EA& ea) const
-      {
-	std::cout << "writing..." << prefix << ea.gen() << std::endl;
-	std::string fname =  ea.res_dir() + "/"
-	  + prefix
-	  + boost::lexical_cast<
-	    std::string>(ea.gen())
-	  + std::string(".dat");
-	std::ofstream ofs(fname.c_str());
-	for (size_t i = 0; i < _xs; ++i)
-	  for (size_t j = 0; j < _ys; ++j)
-	    if (array[i][j] && p_array[i][j])
-	      {
-		point_t p = _get_point(p_array[i][j]);
-		size_t x = round(p[0] * _xs);
-		size_t y = round(p[1] * _ys);
-		ofs << i / (float) _xs
-		    << " " << j / (float) _ys
-		    << " " << p_array[i][j]->fit().value()
-		    << " " << x / (float) _xs
-		    << " " << y / (float) _ys
-		    << " " << array[i][j]->fit().value()
-		    << std::endl;
-	      }
-      }
-
-      template<typename EA>
-      void _write_archive(const array_t& array,
-			  const std::string& prefix,
-			  const EA& ea) const
-      {
-	std::cout << "writing..." << prefix << ea.gen() << std::endl;
-	std::string fname = ea.res_dir() + "/"
-	  + prefix
-	  + boost::lexical_cast<
-	    std::string>(ea.gen())
-	  + std::string(".dat");
-	
-	std::ofstream ofs(fname.c_str());
-	for (size_t i = 0; i < _xs; ++i)
-	  for (size_t j = 0; j < _ys; ++j)
-	    if (array[i][j])
-	      {
-		ofs << i / (float) _xs
-		    << " " << j / (float) _ys
-		    << " " << array[i][j]->fit().value()
-		    << std::endl;
-	      }
-      }
-
-
-    };
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/map_elite/test_map_elite.cpp

@@ -1,124 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#define BOOST_TEST_DYN_LINK 
-#define BOOST_TEST_MODULE map_elite
-
-#include <iostream>
-#include <cmath>
-
-#include <boost/test/unit_test.hpp>
-
-#include <sferes/eval/parallel.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/phen/parameters.hpp>
-#include <sferes/modif/dummy.hpp>
-#include <sferes/run.hpp>
-#include <sferes/stat/best_fit.hpp>
-
-#include "map_elite.hpp"
-#include "fit_map.hpp"
-#include "stat_map.hpp"
-
-using namespace sferes::gen::evo_float;
-
-
-struct Params
-{
-  struct ea
-  {
-    SFERES_CONST size_t res_x = 256;
-    SFERES_CONST size_t res_y = 256;
-  };
-  struct pop
-  {
-    // number of initial random points
-    SFERES_CONST size_t init_size = 1000;
-    // size of a batch
-    SFERES_CONST size_t size = 2000;    
-    SFERES_CONST size_t nb_gen = 5001;
-    SFERES_CONST size_t dump_period = 1000;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -5;
-    SFERES_CONST float max = 5;
-  };
-  struct evo_float
-  {
-    SFERES_CONST float cross_rate = 0.25f;
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float eta_m = 10.0f;
-    SFERES_CONST float eta_c = 10.0f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-  };
-
-};
-
-
-// Rastrigin
-FIT_MAP(Rastrigin)
-{
- public:
-  template<typename Indiv>
-    void eval(Indiv& ind)
-  {
-    float f = 10 * ind.size();
-    for (size_t i = 0; i < ind.size(); ++i)
-      f += ind.data(i) * ind.data(i) - 10 * cos(2 * M_PI * ind.data(i));
-    this->_value = -f;
-    this->set_desc(ind.gen().data(0), ind.gen().data(1));
-  }
-};
-
-//BOOST_AUTO_TEST_CASE(map_elite)
-//{
-//  using namespace sferes;
-//
-//  typedef Rastrigin<Params> fit_t;
-//  typedef gen::EvoFloat<10, Params> gen_t;
-//  typedef phen::Parameters<gen_t, fit_t, Params> phen_t;
-//  typedef eval::Parallel<Params> eval_t;
-//  typedef boost::fusion::vector<stat::Map<phen_t, Params>, stat::BestFit<phen_t, Params> > stat_t;
-//  typedef modif::Dummy<> modifier_t;
-//  typedef ea::MapElite<phen_t, eval_t, stat_t, modifier_t, Params> ea_t;
-//
-//  ea_t ea;
-//
-//  ea.run();
-//
-//}
-
- 

modules/dnns_easily_fooled/sferes/modules/map_elite/wscript

@@ -1,51 +0,0 @@
-#! /usr/bin/env python
-#| This file is a part of the sferes2 framework.
-#| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-#| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-#|
-#| This software is a computer program whose purpose is to facilitate
-#| experiments in evolutionary computation and evolutionary robotics.
-#| 
-#| This software is governed by the CeCILL license under French law
-#| and abiding by the rules of distribution of free software.  You
-#| can use, modify and/ or redistribute the software under the terms
-#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-#| following URL "http://www.cecill.info".
-#| 
-#| As a counterpart to the access to the source code and rights to
-#| copy, modify and redistribute granted by the license, users are
-#| provided only with a limited warranty and the software's author,
-#| the holder of the economic rights, and the successive licensors
-#| have only limited liability.
-#|
-#| In this respect, the user's attention is drawn to the risks
-#| associated with loading, using, modifying and/or developing or
-#| reproducing the software by the user in light of its specific
-#| status of free software, that may mean that it is complicated to
-#| manipulate, and that also therefore means that it is reserved for
-#| developers and experienced professionals having in-depth computer
-#| knowledge. Users are therefore encouraged to load and test the
-#| software's suitability as regards their requirements in conditions
-#| enabling the security of their systems and/or data to be ensured
-#| and, more generally, to use and operate it in the same conditions
-#| as regards security.
-#|
-#| The fact that you are presently reading this means that you have
-#| had knowledge of the CeCILL license and that you accept its terms.
-
-import os
-
-def set_options(blah) : pass
-
-def configure(blah): pass
-
-def build(bld):
-    print ("Entering directory `" + os.getcwd() + "/modules/'")
-    test_map_elite = bld.new_task_gen('cxx', 'program')
-    test_map_elite.source = 'test_map_elite.cpp'
-    test_map_elite.includes = '. ../../'
-    test_map_elite.uselib_local = 'sferes2'
-    test_map_elite.uselib = 'EIGEN3 BOOST BOOST_UNIT_TEST_FRAMEWORK'
-    test_map_elite.target = 'test_map_elite'
-    test_map_elite.unit_test = 1
-    test_map_elite.cxxflags = ['-std=c++11']

modules/dnns_easily_fooled/sferes/modules/nn2/README.md

@@ -1,34 +0,0 @@
-nn2
-===
-
-** This is a Sferes2 module ** 
-
-NN2 is a generic toolbox for evolving neural networks (it depends on sferes2).
-
-*If you use this software in an academic article, please cite:*
-
-Mouret, J.-B. and Doncieux, S. (2012). Encouraging Behavioral Diversity in Evolutionary Robotics: an Empirical Study. Evolutionary Computation. Vol 20 No 1 Pages 91-133.
-
-### Usage & installation
-- copy nn2 to the "modules" directory in the sferes2 root directory.
-- add nn2 in modules.conf in the sferes2 root directory
-- run ./waf configure and ./waf build
-
-
-### Academic paper that uses nn2
-* Please note that many of these papers extends nn2 *
-
-
-
-
--  Tonelli, P. and Mouret, J.-B. (2013). On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks. PLoS One. Vol 8 No 11 Pages e79138.
-- Clune*, J. and Mouret, J.-B. and Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B. Vol 280 (J. Clune and J.-B. Mouret contributed equally to this work) Pages 20122863.
-- Doncieux, S. and Mouret, J.B. (2013). Behavioral Diversity with Multiple Behavioral Distances. Proc. of IEEE Congress on Evolutionary Computation, 2013 (CEC 2013). Pages 1-8.
--  Mouret, J.-B. and Doncieux, S. (2012). Encouraging Behavioral Diversity in Evolutionary Robotics: an Empirical Study. Evolutionary Computation. Vol 20 No 1 Pages 91-133.
-- Ollion, Charles and Doncieux, Stéphane (2012). Towards Behavioral Consistency in Neuroevolution. From Animals to Animats: Proceedings of the 12th International Conference on Adaptive Behaviour (SAB 2012), Springer, publisher. Pages 1-10.
-- Ollion, C. and Pinville, T. and Doncieux, S. (2012). With a little help from selection pressures: evolution of memory in robot controllers. Proc. Alife XIII. Pages 1-8.
--  Mouret, J.-B. (2011). Novelty-based Multiobjectivization. New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRob Workshop, Springer, publisher. Pages 139--154.
--  Pinville, T. and Koos, S. and Mouret, J-B. and Doncieux, S. (2011). How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach.
-GECCO'11: Proceedings of the 13th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 259--266
--  Mouret, J.-B. and Doncieux, S. and Girard, B. (2010). Importing the Computational Neuroscience Toolbox into Neuro-Evolution---Application to Basal Ganglia. GECCO'10: Proceedings of the 12th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 587--594.
-- Doncieux, S. and Mouret, J.-B. (2010). Behavioral diversity measures for Evolutionary Robotics. WCCI 2010 IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation (CEC). Pages 1303--1310.

modules/dnns_easily_fooled/sferes/modules/nn2/af.hpp

@@ -1,127 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef _NN_AF_HPP_
-#define _NN_AF_HPP_
-
-#include "params.hpp"
-
-// classic activation functions
-namespace nn
-{
-  template <typename P = params::Dummy>
-  class Af
-  {
-  public:
-    typedef P params_t;
-    const params_t& get_params() const  { return _params; }
-    params_t& get_params() { return _params; }
-    void set_params(const params_t& params) { _params = params; }
-    void init() {}
-    Af() {}
-  protected:
-    params_t _params;
-  };
-
-  // -1 to +1 sigmoid
-  template <typename P>
-  struct AfTanh : public Af<P>
-  {
-    typedef P params_t;
-    BOOST_STATIC_CONSTEXPR float lambda = 5.0f;
-    AfTanh() { assert(trait<P>::size(this->_params) == 1); }
-    float operator()(float p) const
-    {
-      return tanh(p * lambda + trait<P>::single_value(this->_params));
-    }
-  protected:
-  };
-  // -1 to +1 sigmoid
-  template <typename P = float>
-  struct AfTanhNoBias : public Af<P>
-  {
-    typedef params::Dummy params_t;
-    BOOST_STATIC_CONSTEXPR float lambda = 5.0f;
-    AfTanhNoBias() { }
-    float operator()(float p) const
-    {
-      return tanh(p * lambda);
-    }
-  };
-
-
-
-  template <typename P = float>
-  struct AfSigmoidNoBias : public Af<>
-  {
-    typedef params::Dummy params_t;
-    BOOST_STATIC_CONSTEXPR float lambda = 5.0f;
-    AfSigmoidNoBias() { }
-    float operator()(float p) const { return 1.0 / (exp(-p * lambda) + 1); }
-  protected:
-  };
-
-  template <typename P = float>
-  struct AfSigmoidBias : public Af<P>
-  {
-    typedef P params_t;
-    BOOST_STATIC_CONSTEXPR float lambda = 5.0f;
-    AfSigmoidBias() { assert(this->_params.size() == 1); }
-    float operator()(float p) const
-    {
-      return 1.0 / (exp(-p + trait<P>::single_value(this->_params) * lambda) + 1);
-    }
-  protected:
-  };
-
-  // copy input to output
-  // store an arbitrary parameter
-  template<typename P = params::Dummy>
-  struct AfDirect : public Af<P>
-  {
-    typedef P params_t;
-    float operator()(float p) const { return p; }
-  };
-
-  // copy input to output
-  template<typename T>
-  struct AfDirectT : public Af<params::Dummy>
-  {
-    typedef params::Dummy params_t;
-    T operator()(T p) const { return p; }
-  };
-
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/af_cppn.hpp

@@ -1,121 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef AF_CPPN_HPP_
-#define AF_CPPN_HPP_
-
-#include <sferes/gen/sampled.hpp>
-#include <sferes/gen/evo_float.hpp>
-
-// classic activation functions
-namespace nn
-{
-  namespace cppn
-  {
-    enum func_e { sine = 0, sigmoid, gaussian, linear, tanh };
-    SFERES_CONST size_t nb_functions = 3;
-    SFERES_CLASS(AfParams)
-    {
-      public:
-        void set(float t, float p)
-        {
-          _type.set_data(0, t);
-          _param.data(0, p);
-        }
-        void mutate()
-        {
-          _type.mutate();
-          _param.mutate();
-        }
-        void random()
-        {
-          _type.random();
-          _param.random();
-        }
-        void develop() {
-        }
-        int type() const {
-          return _type.data(0);
-        }
-        float param() const {
-          return _param.data(0);
-        }
-        template<typename A>
-        void serialize(A& ar, unsigned int v)
-        {
-          ar& BOOST_SERIALIZATION_NVP(_type);
-          ar& BOOST_SERIALIZATION_NVP(_param);
-        }
-      protected:
-        sferes::gen::Sampled<1, Params> _type;
-        sferes::gen::EvoFloat<1, Params> _param;
-    };
-  }
-
-   // Activation function for Compositional Pattern Producing Networks
-  template<typename P>
-  struct AfCppn : public Af<P>
-  {
-    typedef P params_t;
-    float operator() (float p) const
-    {
-      float s = p > 0 ? 1 : -1;
-       //std::cout<<"type:"<<this->_params.type()<<" p:"<<p<<" this:"<<this
-       //<< " out:"<< p * exp(-powf(p * 10/ this->_params.param(), 2))<<std::endl;
-      switch (this->_params.type())
-      {
-      case cppn::sine:
-        return sin(p);
-      case cppn::sigmoid:
-        return ((1.0 / (1.0 + exp(-p))) - 0.5) * 2.0;
-      case cppn::gaussian:
-        return exp(-powf(p, 2));
-      case cppn::linear:
-        return std::min(std::max(p, -3.0f), 3.0f) / 3.0f;
-      case cppn::tanh:
-        return tanh(p * 5.0f);
-      default:
-        assert(0);
-      }
-      return 0;
-    }
-  };
-
-
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/bench_nn.cpp

@@ -1,70 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#include "nn.hpp"
-
-int main()
-{
-  using namespace nn;
-  typedef NN<Neuron<PfWSum<>, AfTanh<float> >, Connection<> > nn_t;
-
-  nn_t nn;
-
-  BOOST_STATIC_CONSTEXPR size_t nb_io = 5;
-  BOOST_STATIC_CONSTEXPR size_t nb_h = 100;
-
-  nn.set_nb_inputs(nb_io);
-  nn.set_nb_outputs(nb_io);
-
-  std::vector<nn_t::vertex_desc_t> neurons;
-  for (size_t i = 0; i < nb_h; ++i)
-    neurons.push_back(nn.add_neuron("n"));
-
-  for (size_t i = 0; i < nn.get_nb_inputs(); ++i)
-    for (size_t j = 0; j < neurons.size(); ++j)
-      nn.add_connection(nn.get_input(i), neurons[j], 1.0f);
-
-  for (size_t i = 0; i < nn.get_nb_outputs(); ++i)
-    for (size_t j = 0; j < neurons.size(); ++j)
-      nn.add_connection(neurons[j], nn.get_output(i), 0.20f);
-
-  std::vector<float> in(nn.get_nb_inputs()); 
-  nn.init();
-  std::fill(in.begin(), in.end(), 1.0f);
-  size_t nb_steps = 50000;
-  for (size_t i = 0; i < nb_steps; ++i)
-    nn.step(in);
-  return 0;
-}

modules/dnns_easily_fooled/sferes/modules/nn2/connection.hpp

@@ -1,55 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef _NN_CONNECTiON_HPP
-#define _NN_CONNECTiON_HPP
-
-#include "params.hpp"
-
-namespace nn
-{
-  template<typename W = float, typename IO = float>
-  struct Connection
-  {
-    typedef W weight_t;
-    typedef IO io_t;
-    const weight_t& get_weight() const { return _weight; }
-    weight_t& get_weight() { return _weight; }
-    void set_weight(const weight_t& w) { _weight = w; }
-  protected:
-    weight_t _weight;
-  };
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/elman.hpp

@@ -1,135 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef _NN_ELMAN_HPP_
-#define _NN_ELMAN_HPP_
-
-#include "nn.hpp"
-
-namespace nn
-{
-  // a "modified" Elman network with self-recurrent context units
-  // E.g. : Training Elman and Jordan networks for system
-  // identification using genetic algorithms
-  // Artificial Intelligence in Engineering
-  // Volume 13, Issue 2, April 1999, Pages 107-117
-  // first input is a BIAS input (it should be set to 1)
-  template<typename N, typename C>
-  class Elman : public NN<N, C>
-  {
-  public:
-    typedef nn::NN<N, C> nn_t;
-    typedef typename nn_t::io_t io_t;
-    typedef typename nn_t::vertex_desc_t vertex_desc_t;
-    typedef typename nn_t::edge_desc_t edge_desc_t;
-    typedef typename nn_t::adj_it_t adj_it_t;    
-    typedef typename nn_t::graph_t graph_t;
-    typedef N neuron_t;
-    typedef C conn_t;
-
-    Elman(size_t nb_inputs, 
-	  size_t nb_hidden, 
-	  size_t nb_outputs)
-    {
-      // neurons
-      this->set_nb_inputs(nb_inputs + 1);
-      this->set_nb_outputs(nb_outputs);
-      for (size_t i = 0; i < nb_hidden; ++i)
-	_hidden_neurons.
-	  push_back(this->add_neuron(std::string("h") 
-				     + boost::lexical_cast<std::string>(i)));
-      for (size_t i = 0; i < nb_hidden; ++i)
-	_context_neurons.
-	  push_back(this->add_neuron(std::string("c") 
-				     + boost::lexical_cast<std::string>(i)));
-      // connections
-      this->full_connect(this->_inputs, this->_hidden_neurons,
-			 trait<typename N::weight_t>::zero());
-      this->full_connect(this->_hidden_neurons, this->_outputs,
-			 trait<typename N::weight_t>::zero());
-      this->connect(this->_hidden_neurons, this->_context_neurons,
-		    trait<typename N::weight_t>::zero());
-      this->connect(this->_context_neurons, this->_context_neurons,
-		    trait<typename N::weight_t>::zero());
-      this->full_connect(this->_context_neurons, this->_hidden_neurons,
-			 trait<typename N::weight_t>::zero());
-      // bias
-      // (hidden layer is already connect to input(0))
-      size_t last = this->get_nb_inputs();
-      for (size_t i = 0; i < _context_neurons.size(); ++i)	
-	this->add_connection(this->get_input(last), _context_neurons[i], 
-			     trait<typename N::weight_t>::zero());
-      for (size_t i = 0; i < this->get_nb_outputs(); ++i)
-	this->add_connection(this->get_input(last), this->get_output(i), 
-			     trait<typename N::weight_t>::zero());
-    }
-    unsigned get_nb_inputs() const { return this->_inputs.size() - 1; }
-    void step(const std::vector<io_t>& in)
-    {
-      assert(in.size() == this->get_nb_inputs());
-      std::vector<io_t> inf = in;
-      inf.push_back(1.0f);
-      nn_t::_step(inf);
-    }
-  protected:
-
-    std::vector<vertex_desc_t> _hidden_neurons;
-    std::vector<vertex_desc_t> _context_neurons;
-
-  };
-  namespace elman
-  {
-    template<int NbInputs, int NbHidden, int NbOutputs>
-    struct Count
-    {
-      SFERES_CONST int nb_inputs = NbInputs + 1; // bias is an input
-      SFERES_CONST int nb_outputs = NbOutputs;
-      SFERES_CONST int nb_hidden = NbHidden;
-      SFERES_CONST int nb_params = 
-	nb_inputs * nb_hidden // input to hidden (full)
-	+ nb_hidden * nb_outputs // hidden to output (full)
-	+ nb_hidden // hidden to context (1-1)
-	+ nb_hidden // context to itself (1-1)
-	+ nb_hidden * nb_hidden // context to hidden (full)
-	+ nb_hidden // bias context
-	+ nb_outputs; // bias outputs
-    };
-      
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/gen_dnn.hpp

@@ -1,421 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef DNN_HPP_
-#define DNN_HPP_
-
-#include <bitset>
-#include <boost/serialization/serialization.hpp>
-#include <boost/archive/text_iarchive.hpp>
-#include <boost/archive/text_oarchive.hpp>
-#include <boost/serialization/base_object.hpp>
-#include <boost/serialization/vector.hpp>
-#include <boost/serialization/shared_ptr.hpp>
-#include <boost/serialization/map.hpp>
-#include <boost/serialization/utility.hpp>
-#include <sferes/dbg/dbg.hpp>
-#include <sferes/misc.hpp>
-
-#include "nn.hpp"
-#include "trait.hpp"
-
-namespace sferes
-{
-  namespace gen
-  {
-
-    template <class Graph>
-    typename boost::graph_traits<Graph>::vertex_descriptor
-    random_vertex(Graph& g)
-    {
-      assert(num_vertices(g));      
-      using namespace boost;
-      if (num_vertices(g) > 1)
-        {
-          std::size_t n = misc::rand(num_vertices(g));
-          typename graph_traits<Graph>::vertex_iterator i = vertices(g).first;
-          while (n-- > 0) ++i;
-          return *i;
-        } 
-      else
-	return *vertices(g).first;
-    }
-
-    template <class Graph>
-    typename boost::graph_traits<Graph>::edge_descriptor
-    random_edge(Graph& g)
-    {
-      assert(num_edges(g));
-      using namespace boost;
-      if (num_edges(g) > 1)
-        {
-          std::size_t n = misc::rand(num_edges(g));
-          typename graph_traits<Graph>::edge_iterator i = edges(g).first;
-          while (n-- > 0) ++i;
-          return *i;
-        } 
-      else
-	return *edges(g).first;
-    }
-
-  namespace dnn
-  {
-    enum init_t { ff = 0, random_topology };
-  }
-  template<typename N, typename C, typename Params>
-  class Dnn : public nn::NN<N, C>
-  {
-  public:
-    typedef nn::NN<N, C> nn_t;
-    typedef N neuron_t;
-    typedef C conn_t;
-    typedef typename nn_t::io_t io_t;
-    typedef typename nn_t::weight_t weight_t;
-    typedef typename nn_t::vertex_desc_t vertex_desc_t;
-    typedef typename nn_t::edge_desc_t edge_desc_t;
-    typedef typename nn_t::adj_it_t adj_it_t;
-    typedef typename nn_t::graph_t graph_t;
-    void random()
-    {
-      if (Params::dnn::init == dnn::ff)
-	_random_ff(Params::dnn::nb_inputs, Params::dnn::nb_outputs);
-      else
-	_random(Params::dnn::nb_inputs, Params::dnn::nb_outputs,
-		Params::dnn::min_nb_neurons, Params::dnn::max_nb_neurons,
-		Params::dnn::min_nb_conns, Params::dnn::max_nb_conns);
-    }
-
-    void mutate() 
-    {
-      _change_conns();
-
-      _change_neurons();
-
-      if (misc::rand<float>() < Params::dnn::m_rate_add_conn)
-	_add_conn_nodup();
-
-      if (misc::rand<float>() < Params::dnn::m_rate_del_conn)
-	_del_conn();
-
-      if (misc::rand<float>() < Params::dnn::m_rate_add_neuron)
-	_add_neuron_on_conn();
-
-      if (misc::rand<float>() < Params::dnn::m_rate_del_neuron)
-	_del_neuron();
-
-    }
-    void cross(const Dnn& o, Dnn& c1, Dnn& c2) 
-    {
-#ifdef PHELOGENETIC_TREE
-    	c1 = *this;
-    	c2 = o;
-#else
-      if (misc::flip_coin())
-			{
-				c1 = *this;
-				c2 = o;
-			}
-      else
-			{
-				c2 = *this;
-				c1 = o;
-			}
-#endif
-    }
-    // serialize the graph "by hand"...
-    template<typename Archive>
-    void save(Archive& a, const unsigned v) const
-    {
-      dbg::trace("nn", DBG_HERE);
-      std::vector<int> inputs;
-      std::vector<int> outputs;
-      std::vector<typename neuron_t::af_t::params_t> afparams;
-      std::vector<typename neuron_t::pf_t::params_t> pfparams;
-      std::map<vertex_desc_t, int> nmap;
-      std::vector<std::pair<int, int> > conns;
-      std::vector<weight_t> weights;
-
-      BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-	{
-	  if (this->is_input(v))
-	    inputs.push_back(afparams.size());
-	  if (this->is_output(v))
-	    outputs.push_back(afparams.size());
-	  nmap[v] = afparams.size();
-	  afparams.push_back(this->_g[v].get_afparams());
-	  pfparams.push_back(this->_g[v].get_pfparams());
-	}
-      BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-	{
-	  conns.push_back(std::make_pair(nmap[source(e, this->_g)],
-					 nmap[target(e, this->_g)]));
-	  weights.push_back(this->_g[e].get_weight());
-	}
-      assert(pfparams.size() == afparams.size());
-      assert(weights.size() == conns.size());
-
-      a & BOOST_SERIALIZATION_NVP(afparams);	
-      a & BOOST_SERIALIZATION_NVP(pfparams);	
-      a & BOOST_SERIALIZATION_NVP(weights);
-      a & BOOST_SERIALIZATION_NVP(conns);	
-      a & BOOST_SERIALIZATION_NVP(inputs);	
-      a & BOOST_SERIALIZATION_NVP(outputs);	
-    }
-    template<typename Archive>
-    void load(Archive& a, const unsigned v)
-    {
-      dbg::trace("nn", DBG_HERE);
-      std::vector<int> inputs;
-      std::vector<int> outputs;
-      std::vector<typename neuron_t::af_t::params_t> afparams;
-      std::vector<typename neuron_t::pf_t::params_t> pfparams;
-      std::map<size_t, vertex_desc_t> nmap;
-      std::vector<std::pair<int, int> > conns;
-      std::vector<weight_t> weights;
-
-      a & BOOST_SERIALIZATION_NVP(afparams);
-      a & BOOST_SERIALIZATION_NVP(pfparams);
-      a & BOOST_SERIALIZATION_NVP(weights);
-      a & BOOST_SERIALIZATION_NVP(conns);	
-      a & BOOST_SERIALIZATION_NVP(inputs);	
-      a & BOOST_SERIALIZATION_NVP(outputs);	
-
-      assert(pfparams.size() == afparams.size());
-
-      assert(weights.size() == conns.size());
-      this->set_nb_inputs(inputs.size());
-      this->set_nb_outputs(outputs.size());
-      for (size_t i = 0; i < this->get_nb_inputs(); ++i)
-	nmap[inputs[i]] = this->get_input(i);
-      for (size_t i = 0; i < this->get_nb_outputs(); ++i)
-	nmap[outputs[i]] = this->get_output(i);
-
-      for (size_t i = 0; i < afparams.size(); ++i)
-	if (std::find(inputs.begin(), inputs.end(), i) == inputs.end() 
-	    && std::find(outputs.begin(), outputs.end(), i) == outputs.end())
-	  nmap[i] = this->add_neuron("n", pfparams[i], afparams[i]);
-	else
-	  {
-	    this->_g[nmap[i]].set_pfparams(pfparams[i]);
-	    this->_g[nmap[i]].set_afparams(afparams[i]);
-	  }
-
-
-      //assert(nmap.size() == num_vertices(this->_g));
-      for (size_t i = 0; i < conns.size(); ++i)
-	this->add_connection(nmap[conns[i].first], nmap[conns[i].second], weights[i]);
-    }
-    BOOST_SERIALIZATION_SPLIT_MEMBER();
-
-  protected:
-    void _random_neuron_params()
-    {
-      BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-	{
-	  this->_g[v].get_pfparams().random();
-	  this->_g[v].get_afparams().random();
-	}
-    }
-    // we start with a fully connected 0-layer perceptron with
-    // random weights
-    void _random_ff(size_t nb_inputs, size_t nb_outputs) 
-    {
-      this->set_nb_inputs(nb_inputs);
-      this->set_nb_outputs(nb_outputs);
-    
-      BOOST_FOREACH(vertex_desc_t& i, this->_inputs)
-	BOOST_FOREACH(vertex_desc_t& o, this->_outputs)
-	this->add_connection(i, o, _random_weight());
-
-      _random_neuron_params();
-    }
-
-    void _random(size_t nb_inputs, size_t nb_outputs,
-		 size_t min_nb_neurons, size_t max_nb_neurons,
-		 size_t min_nb_conns, size_t max_nb_conns) 
-    {
-      // io
-      this->set_nb_inputs(nb_inputs);
-      this->set_nb_outputs(nb_outputs);
-      _random_neuron_params();
-
-      // neurons
-      size_t nb_neurons = misc::rand(min_nb_neurons, max_nb_neurons);
-      for (size_t i = 0; i < nb_neurons; ++i)
-	_add_neuron();//also call the random params 
-
-      // conns
-      size_t nb_conns = misc::rand(min_nb_conns, max_nb_conns);
-      for (size_t i = 0; i < nb_conns; ++i)
-	_add_conn_nodup();
-
-      this->simplify();
-    }
-
-    vertex_desc_t _random_tgt()
-    {
-      vertex_desc_t v;
-      do
-	v = random_vertex(this->_g);
-      while (this->is_input(v));
-      return v;
-    }
-    vertex_desc_t _random_src()
-    {
-      vertex_desc_t v;
-      do
-	v = random_vertex(this->_g);
-      while (this->is_output(v));
-      return v;
-    }
-
-    vertex_desc_t _add_neuron() 
-    { 
-      vertex_desc_t v = this->add_neuron("n");
-      this->_g[v].get_pfparams().random();
-      this->_g[v].get_afparams().random();
-      return v;
-    }
-
-    vertex_desc_t _add_neuron_on_conn()
-    {
-      if (!num_edges(this->_g))
-	return (vertex_desc_t)0x0;	
-      edge_desc_t e = random_edge(this->_g);
-      vertex_desc_t src = source(e, this->_g);
-      vertex_desc_t tgt = target(e, this->_g);
-      typename nn_t::weight_t w = this->_g[e].get_weight();
-      vertex_desc_t n = this->add_neuron("n");
-      this->_g[n].get_pfparams().random();
-      this->_g[n].get_afparams().random();
-      // 
-      remove_edge(e, this->_g);
-      this->add_connection(src, n, w);// todo : find a kind of 1 ??
-      this->add_connection(n, tgt, w);
-      return n;	
-    }
-
-    void _del_neuron()
-    {	
-      assert(num_vertices(this->_g));
-
-      if (this->get_nb_neurons() <= this->get_nb_inputs() + this->get_nb_outputs())
-	return;
-      vertex_desc_t v;
-      do
-	v = random_vertex(this->_g);
-      while (this->is_output(v) || this->is_input(v));
-
-      clear_vertex(v, this->_g);	
-      remove_vertex(v, this->_g);
-    }
-    typename nn_t::weight_t _random_weight()
-    {
-      typename nn_t::weight_t w;
-      w.random();
-      return w;
-    }
-    void _add_conn()
-    {
-      this->add_connection(_random_src(), _random_tgt(), _random_weight());
-    }
-    // add a random connection by avoiding to duplicate an existent connection
-    void _add_conn_nodup()
-    {
-      vertex_desc_t src, tgt;
-      // this is only an upper bound; a connection might of course
-      // be possible even after max_tries tries.
-      size_t max_tries = num_vertices(this->_g) * num_vertices(this->_g), 
-	nb_tries = 0;
-      do 
-	{
-	  src = _random_src();
-	  tgt = _random_tgt();
-	}
-      while (is_adjacent(this->_g, src, tgt) && ++nb_tries < max_tries);
-      if (nb_tries < max_tries)
-	{
-	  typename nn_t::weight_t w;
-	  w.random();
-	  this->add_connection(src, tgt, w);
-	}
-    }
-    void _del_conn()
-    {
-      if (!this->get_nb_connections())
-	return;
-      remove_edge(random_edge(this->_g), this->_g);
-    }
-    void _change_neurons()
-    {
-      BGL_FORALL_VERTICES_T(v, this->_g, graph_t) 
-	{
-	  this->_g[v].get_afparams().mutate();
-	  this->_g[v].get_pfparams().mutate();
-	}
-    }
-
-    // No dup version
-    void _change_conns()
-    {
-      BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-	this->_g[e].get_weight().mutate();
-
-      BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-	if (misc::rand<float>() < Params::dnn::m_rate_change_conn)
-	  {
-	    vertex_desc_t src = source(e, this->_g);
-	    vertex_desc_t tgt = target(e, this->_g);
-	    typename nn_t::weight_t w = this->_g[e].get_weight();
-	    remove_edge(e, this->_g);
-	    int max_tries = num_vertices(this->_g) * num_vertices(this->_g),
-	      nb_tries = 0;
-	    if (misc::flip_coin())
-	      do
-		src = _random_src();
-	      while(++nb_tries < max_tries && is_adjacent(this->_g, src, tgt));
-	    else
-	      do
-		tgt = _random_tgt();
-	      while(++nb_tries < max_tries && is_adjacent(this->_g, src, tgt));
-	    if (nb_tries < max_tries)
-	      this->add_connection(src, tgt, w);
-	    return;
-	  }
-    }
-  };
-}
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/gen_dnn_ff.hpp

@@ -1,211 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef DNN_FF_HPP_
-#define DNN_FF_HPP_
-
-#include <modules/nn2/gen_dnn.hpp>
-#include <boost/graph/dijkstra_shortest_paths.hpp>
-#include <boost/graph/dag_shortest_paths.hpp>
-#include <boost/graph/visitors.hpp>
-#include <boost/graph/reverse_graph.hpp>
-#include <boost/property_map/vector_property_map.hpp>
-
-namespace sferes
-{
-  namespace gen
-  {
-    template<typename N, typename C, typename Params>
-    class DnnFF : public Dnn<N, C, Params>
-    {
-    public:
-      typedef nn::NN<N, C> nn_t;
-      typedef N neuron_t;
-      typedef C conn_t;
-      typedef typename nn_t::io_t io_t;
-      typedef typename nn_t::vertex_desc_t vertex_desc_t;
-      typedef typename nn_t::edge_desc_t edge_desc_t;
-      typedef typename nn_t::graph_t graph_t;
-      DnnFF() {}
-      DnnFF& operator=(const DnnFF& o)
-      {
-	static_cast<nn::NN<N, C>& >(*this)
-	  = static_cast<const nn::NN<N, C>& >(o);
-	return *this;
-      }
-      DnnFF(const DnnFF& o)
-      { *this = o; }
-      void init()
-      {
-	Dnn<N, C, Params>::init();
-	_compute_depth();
-      }
-      void random()
-      {
-	assert(Params::dnn::init == dnn::ff);
-	this->_random_ff(Params::dnn::nb_inputs, Params::dnn::nb_outputs);
-	_make_all_vertices();
-      }
-      void mutate()
-      {
- 	_change_conns();
-	this->_change_neurons();
-
- 	if (misc::rand<float>() < Params::dnn::m_rate_add_conn)
- 	  _add_conn();
-
- 	if (misc::rand<float>() < Params::dnn::m_rate_del_conn)
-  	  this->_del_conn();
-
-  	if (misc::rand<float>() < Params::dnn::m_rate_add_neuron)
-  	  this->_add_neuron_on_conn();
-
- 	if (misc::rand<float>() < Params::dnn::m_rate_del_neuron)
-   	  this->_del_neuron();
-      }
-
-      void cross(const DnnFF& o, DnnFF& c1, DnnFF& c2)
-      {
-	if (misc::flip_coin())
-	  {
-	    c1 = *this;
-	    c2 = o;
-	  }
-	else
-	  {
-	    c2 = *this;
-	    c1 = o;
-	  }
-      }
-      size_t get_depth() const { return _depth; }
-    protected:
-      std::set<vertex_desc_t> _all_vertices;
-      size_t _depth;
-
-      void _make_all_vertices()
-      {
-	_all_vertices.clear();
-	BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-	  _all_vertices.insert(v);
-      }
-      void _change_conns()
-      {
-	BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-	  this->_g[e].get_weight().mutate();
-      }
-
-
-      // add only feed-forward connections
-      void _add_conn()
-      {
-	using namespace boost;
-	vertex_desc_t v = this->_random_src();
-	std::set<vertex_desc_t> preds;
-	nn::bfs_pred_visitor<vertex_desc_t> vis(preds);
-	breadth_first_search(make_reverse_graph(this->_g),
-			     v, color_map(get(&N::_color, this->_g)).visitor(vis));
-	_make_all_vertices();
-	std::set<vertex_desc_t> tmp, avail, in;
-	// avoid to connect to predecessors
-	std::set_difference(_all_vertices.begin(), _all_vertices.end(),
-			    preds.begin(), preds.end(),
-			    std::insert_iterator<std::set<vertex_desc_t> >(tmp, tmp.begin()));
-	// avoid to connect to inputs
-	BOOST_FOREACH(vertex_desc_t v, this->_inputs) // inputs need
-						      // to be sorted
-	    in.insert(v);
-	std::set_difference(tmp.begin(), tmp.end(),
-			    in.begin(), in.end(),
-			    std::insert_iterator<std::set<vertex_desc_t> >(avail, avail.begin()));
-
-	if (avail.empty())
-	  return;
-	vertex_desc_t tgt = *misc::rand_l(avail);
-	typename nn_t::weight_t w;
-	w.random();
-	this->add_connection(v, tgt, w);
-      }
-
-      // useful to make the right number of steps
-      void _compute_depth()
-      {
-	using namespace boost;
-	typedef std::map<vertex_desc_t, size_t> int_map_t;
-	typedef std::map<vertex_desc_t, vertex_desc_t> vertex_map_t;
-	typedef std::map<vertex_desc_t, default_color_type> color_map_t;
-	typedef std::map<edge_desc_t, int> edge_map_t;
-
-	typedef associative_property_map<int_map_t> a_map_t;
-	typedef associative_property_map<color_map_t> c_map_t;
-	typedef associative_property_map<vertex_map_t> v_map_t;
-	typedef associative_property_map<edge_map_t> e_map_t;
-
-	color_map_t cm; c_map_t cmap(cm);
-	vertex_map_t vm; v_map_t pmap(vm);
-	edge_map_t em;
-	BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-	  em[e] = 1;
-	e_map_t wmap(em);
-	_depth = 0;
-	// we compute the longest path between inputs and outputs
-	BOOST_FOREACH(vertex_desc_t s, this->_inputs)
-	  {
-	    int_map_t im; a_map_t dmap(im);
- 	    dag_shortest_paths
- 	      (this->_g, s, dmap, wmap, cmap, pmap,
-	       dijkstra_visitor<null_visitor>(),
- 	       std::greater<int>(),
-	       closed_plus<int>(),
-	       std::numeric_limits<int>::min(), 0);
-
- 	    BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-	      {
-		size_t d = get(dmap, v);
-		if (this->_g[v].get_out() != -1 && d <= num_vertices(this->_g))
-		  _depth = std::max(_depth, d);
-	      }
-	  }
-	// add one to be sure
-	_depth ++;
-      }
-
-    };
-
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/gen_hyper_nn.hpp

@@ -1,83 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef GEN_HYPER_NN_HPP_
-#define GEN_HYPER_NN_HPP_
-
-#include "neuron.hpp"
-#include "pf.hpp"
-
-#include "gen_dnn_ff.hpp"
-#include "af_cppn.hpp"
-
-namespace sferes
-{
-  namespace gen
-  {
-    template<typename W, typename Params>
-    class HyperNn : public DnnFF<nn::Neuron<nn::PfWSum<W>,
-                                            nn::AfCppn<nn::cppn::AfParams<typename Params::cppn> > >,
-                                 nn::Connection<W>,
-                                 Params>
-    {
-      public:
-        typedef DnnFF<nn::Neuron<nn::PfWSum<W>,
-                                 nn::AfCppn<nn::cppn::AfParams<typename Params::cppn> > >,
-                      nn::Connection<W>,
-                      Params> nn_t;
-        typedef typename nn_t::neuron_t neuron_t;
-        typedef typename nn_t::conn_t conn_t;
-        void init()
-        {
-          BGL_FORALL_EDGES_T(e, this->get_graph(),
-                             typename nn_t::graph_t)
-          this->get_graph()[e].get_weight().develop();
-
-           // develop the parameters
-          BGL_FORALL_VERTICES_T(v, this->get_graph(),
-                                typename nn_t::graph_t)
-          {
-            this->get_graph()[v].get_afparams().develop();
-            this->get_graph()[v].get_pfparams().develop();
-          }
-          nn_t::init();
-        }
-    };
-  }
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/io_trait.hpp

@@ -1,111 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef _IO_TRAIT_HPP_
-#define _IO_TRAIT_HPP_
-
-// for std::pair
-#include <map>
-#include <valarray>
-
-#ifdef EIGEN3_ENABLED
-#include <Eigen/Core>
-#endif
-
-namespace nn
-{
-  // io trait
-  template<typename T>
-  struct io_trait
-  {
-    static T zero() { return T(0.0f); }
-    static T zero(size_t k) { return zero(); }
-    typedef std::valarray<T> vector_t;
-  };
-
-#ifdef EIGEN3_ENABLED
-  // go with eigen with float (TODO : double)
-  template<>
-  struct io_trait<float>
-  {
-    typedef Eigen::VectorXf vector_t;
-    static float zero() { return 0.0f; }
-    static vector_t zero(size_t k) { return Eigen::VectorXf::Zero(k); }
-
-  };
-#endif
-
-  template<>
-  struct io_trait<std::pair<float, float> >
-  {
-    static std::pair<float, float> zero() { return std::make_pair(0.0f, 0.0f); }
-    typedef std::valarray<std::pair<float, float> > vector_t;
-  };
-
-  // useful but wrong place (?)
-  template<typename _CharT, typename _Traits>
-  std::basic_ostream<_CharT, _Traits>&
-  operator<<(std::basic_ostream<_CharT, _Traits>& ofs, const std::pair<float, float>& p)
-  {
-    return ofs<<p.first<<" "<<p.second;
-  }
-
- template<typename _CharT, typename _Traits>
-  std::basic_ostream<_CharT, _Traits>&
- operator<<(std::basic_ostream<_CharT, _Traits>& ofs, const std::vector<float>& p)
-  {
-    for (size_t i = 0; i < p.size(); ++i)
-      ofs<<p[i]<<" ";
-    return ofs;
-  }
-
-
-  template<typename T1, typename T2>
-  std::istream& operator>>(std::istream& ifs, std::pair<T1, T2>& p)
-  {
-    T1 t1;
-    T2 t2;
-    ifs >> t1;
-    ifs >> t2;
-    return std::make_pair(t1, t2);
-  }
-
-  
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/mlp.hpp

@@ -1,100 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef _NN_MLP_HPP_
-#define _NN_MLP_HPP_
-
-#include "nn.hpp"
-#include "connection.hpp"
-#include "neuron.hpp"
-
-namespace nn
-{
-  // a basic multi-layer perceptron (feed-forward neural network)
-  // only one hidden layer in this version
-  // there's one autmatically added input for the bias
-  template<typename N, typename C>
-  class Mlp : public NN<N, C>
-  {
-  public:
-    typedef nn::NN<N, C> nn_t;
-    typedef typename nn_t::io_t io_t;
-    typedef typename nn_t::vertex_desc_t vertex_desc_t;
-    typedef typename nn_t::edge_desc_t edge_desc_t;
-    typedef typename nn_t::adj_it_t adj_it_t;    
-    typedef typename nn_t::graph_t graph_t;
-    typedef N neuron_t;
-    typedef C conn_t;
-
-    Mlp(size_t nb_inputs, 
-	size_t nb_hidden, 
-	size_t nb_outputs)
-    {
-      // neurons
-      this->set_nb_inputs(nb_inputs + 1); 
-      this->set_nb_outputs(nb_outputs);
-      for (size_t i = 0; i < nb_hidden; ++i)
-	_hidden_neurons.
-	  push_back(this->add_neuron(std::string("h") + boost::lexical_cast<std::string>(i)));
-      // connections
-      this->full_connect(this->_inputs, this->_hidden_neurons, 
-			 trait<typename N::weight_t>::zero());
-      this->full_connect(this->_hidden_neurons, this->_outputs, 
-			 trait<typename N::weight_t>::zero());
-      // bias outputs too
-      for (size_t i = 0; i < nb_outputs; ++i)
-	this->add_connection(this->get_input(nb_inputs), this->get_output(i), 
-			     trait<typename N::weight_t>::zero());
-    }
-    unsigned get_nb_inputs() const { return this->_inputs.size() - 1; }
-    void step(const std::vector<io_t>& in)
-    {
-      assert(in.size() == this->get_nb_inputs());
-      std::vector<io_t> inf = in;
-      inf.push_back(1.0f);
-      nn_t::_step(inf);
-    }
-  protected:
-    std::vector<vertex_desc_t> _hidden_neurons;
-  };
-
-  // a basic MLP with float weights
-  typedef Mlp<Neuron<PfWSum<>, AfSigmoidNoBias<> >, Connection<> > mlp_t;
-
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/neuron.hpp

@@ -1,157 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef NN_NEURON_HPP
-#define NN_NEURON_HPP
-
-#include <boost/graph/properties.hpp>
-
-#include "trait.hpp"
-
-namespace nn
-{
-
-  // generic neuron
-  // Pot : potential functor (see pf.hpp)
-  // Act : activation functor (see af.hpp)
-  // IO : type of coupling between "neurons" (float or std::pair<float, float>)
-  template<typename Pot, typename Act, typename IO = float>
-  class Neuron
-  {
-  public:
-    typedef typename Pot::weight_t weight_t;
-    typedef IO io_t;
-    typedef Pot pf_t;
-    typedef Act af_t;
-    static io_t zero() { return trait<IO>::zero(); }
-    Neuron() : 
-      _current_output(zero()), 
-      _next_output(zero()), 
-      _fixed(false), 
-      _in(-1), 
-      _out(-1) 
-    {}
-    bool get_fixed() const { return _fixed; }
-    void set_fixed(bool b = true) { _fixed = b; }
-    io_t activate()
-    {
-      if (!_fixed)
-	_next_output = _af(_pf(_inputs));
-      return _next_output;
-    }
- 
-    void init() 
-    { 
-      _pf.init(); 
-      _af.init();
-      if (get_in_degree() != 0)
-	_inputs = trait<io_t>::zero(get_in_degree()); 
-      _current_output = zero(); 
-      _next_output = zero(); 
-    }
-
-    void set_input(unsigned i, const io_t& in) { assert(i < _inputs.size()); _inputs[i] = in; }
-
-    void set_weight(unsigned i, const weight_t& w) { _pf.set_weight(i, w); }
-   
-    typename af_t::params_t& get_afparams() { return _af.get_params(); }
-    typename pf_t::params_t& get_pfparams() { return _pf.get_params(); }
-    const typename af_t::params_t& get_afparams() const { return _af.get_params(); }
-    const typename pf_t::params_t& get_pfparams() const { return _pf.get_params(); }
-    void set_afparams(const typename af_t::params_t& p) { _af.set_params(p); }
-    void set_pfparams(const typename pf_t::params_t& p) { _pf.set_params(p); }
-
-    void step() { _current_output = _next_output; }
-    void set_in_degree(unsigned k)
-    { 
-      _pf.set_nb_weights(k); 
-      _inputs.resize(k);
-      if (k == 0)
-	return;
-      _inputs = trait<io_t>::zero(k); 
-    }
-    unsigned get_in_degree() const { return _pf.get_weights().size(); }
-
-    // for input neurons
-    void set_current_output(const io_t& v) { _current_output = v; }
-    void set_next_output(const io_t& v) { _next_output = v; }
-      
-    // standard output
-    const io_t& get_current_output() const { return _current_output; }
-
-    // next output
-    const io_t& get_next_output() const { return _next_output; }
-
-    // i/o
-    int get_in() const { return _in; }
-    void set_in(int i) { _in = i; }
-    int get_out() const { return _out; }
-    void set_out(int o) { _out = o; }
-    bool is_input() const { return _in != -1; }
-    bool is_output() const { return _out != -1; }
-    
-    const Pot& get_pf() const { return _pf; }
-    Pot& get_pf() { return _pf; }
-
-    const Act& get_af() const { return _af; }
-    Act& get_af() { return _af; }
-
-    void set_id(const std::string& s) { _id = s; }
-    const std::string& get_id() const { return _id; }
-    const std::string& get_label() const { return _label; }
-    
-    // for graph algorithms
-    std::string _id;
-    std::string _label;
-    boost::default_color_type _color;
-    int _index;
-  protected:
-    // activation functor
-    Act _af;
-    // potential functor
-    Pot _pf;
-    // outputs
-    io_t _current_output;
-    io_t _next_output;
-    // cache
-    typename trait<io_t>::vector_t _inputs;
-    // fixed = current_output is constant
-    bool _fixed;
-    // -1 if not an input of the nn, id of input otherwise
-    int _in;
-    // -1 if not an output of the nn, id of output otherwise
-    int _out;
-  };    
-} 
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/nn.hpp

@@ -1,627 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef _NN_HPP_
-#define _NN_HPP_
-
-#include <iostream>
-#include <fstream>
-#include <utility>
-#include <algorithm>
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/properties.hpp>
-
-#include <boost/graph/adjacency_list.hpp>
-#include <boost/graph/graphviz.hpp>
-#include <boost/graph/iteration_macros.hpp>
-#include <boost/foreach.hpp>
-#include <boost/graph/breadth_first_search.hpp>
-#include <boost/graph/reverse_graph.hpp>
-#include <boost/tuple/tuple.hpp>
-#include <boost/lexical_cast.hpp>
-#include <boost/graph/graph_utility.hpp>
-#include <boost/mpl/assert.hpp>
-
-#include <cmath>
-#include <valarray>
-
-#include "pf.hpp"
-#include "af.hpp"
-#include "neuron.hpp"
-#include "connection.hpp"
-
-namespace nn
-{
-  // a useful boost functor
-  template<typename V>
-   class bfs_pred_visitor : public boost::default_bfs_visitor
-   {
-    public:
-      bfs_pred_visitor(std::set<V>& pred) : _pred(pred) {}
-      template <typename Vertex, typename Graph >
-       void discover_vertex(Vertex u, const Graph & g)
-        {
-         _pred.insert(u);
-        }
-    protected:
-      std::set<V>& _pred;
-   };
-
-
-  // main class
-  // N : neuron type, C : connection type
-  template<typename N, typename C>
-   class NN
-    {
-     public:
-       // types
-       typedef boost::adjacency_list<boost::listS, boost::listS,
-               boost::bidirectionalS,
-               N, C> graph_t;
-       typedef typename boost::graph_traits<graph_t>::vertex_iterator vertex_it_t;
-       typedef typename boost::graph_traits<graph_t>::edge_iterator edge_it_t;
-       typedef typename boost::graph_traits<graph_t>::out_edge_iterator out_edge_it_t;
-       typedef typename boost::graph_traits<graph_t>::in_edge_iterator in_edge_it_t;
-       typedef typename boost::graph_traits<graph_t>::edge_descriptor edge_desc_t;
-       typedef typename boost::graph_traits<graph_t>::vertex_descriptor vertex_desc_t;
-       typedef typename boost::graph_traits<graph_t>::adjacency_iterator adj_it_t;
-       typedef typename std::vector<vertex_desc_t> vertex_list_t;
-       typedef N neuron_t;
-       typedef C conn_t;
-       typedef typename N::af_t af_t;
-       typedef typename N::pf_t pf_t;
-       typedef typename C::weight_t weight_t;
-       typedef typename C::io_t io_t;
-
-       // constructor
-       NN() : _neuron_counter(0), _init_done(false)
-     {}
-       NN(const NN& o) { *this = o; }
-       NN& operator=(const NN& o)
-        {
-         if (&o == this)
-         return *this;
-         _g = o._g;
-         _neuron_counter = o._neuron_counter;
-         _inputs.clear();
-         _outputs.clear();
-         _inputs.resize(o.get_nb_inputs());
-         _outputs.resize(o.get_nb_outputs());
-         _init_io();
-         _init_done = false;
-         return *this;
-        }
-       // init
-       void init() { _init(); }
-       // set id for inputs and outputs
-       void name_io() { _name_io(); }
-       // load/write
-       //void load(const std::string& fname) { _load_graph(fname); }
-       //    void write(const std::string& fname) { _write_graph(fname); }
-       void write(std::ostream& ofs) { _write_dot(ofs); }
-       void dump(std::ostream& ofs) const
-        {
-         std::pair<vertex_it_t, vertex_it_t> vp;
-         for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-         ofs<<_g[*vp.first]._id<<" "<<_g[*vp.first].get_next_output()<<" ";
-         ofs<<std::endl;
-        }
-
-       // get the graph for graph algorithms
-       const graph_t& get_graph() const { return _g; }
-       graph_t& get_graph() { return _g; }
-
-       // construction
-       vertex_desc_t add_neuron(const std::string& label)
-        {
-         vertex_desc_t v = add_vertex(_g);
-         _g[v]._id = boost::lexical_cast<std::string>(_neuron_counter++);
-         _g[v]._label = label;
-         return v;
-        }
-
-       vertex_desc_t add_neuron(const std::string& label,
-             const typename pf_t::params_t& pf_params,
-             const typename af_t::params_t& af_params)
-        {
-         vertex_desc_t v = add_neuron(label);
-         _g[v].set_pfparams(pf_params);
-         _g[v].set_afparams(af_params);
-         return v;
-        }
-
-       bool add_connection(const vertex_desc_t& u,
-             const vertex_desc_t& v,
-             weight_t weight)
-        {
-         std::pair<edge_desc_t, bool> e = add_edge(u, v, _g);
-         if (e.second)
-         _g[e.first].set_weight(weight);
-         return e.second;
-        }
-       // special version when you need to increase weight
-       bool add_connection_w(const vertex_desc_t& u,
-             const vertex_desc_t& v,
-             weight_t weight)
-        {
-         std::pair<edge_desc_t, bool> e = add_edge(u, v, _g);
-         if (e.second)
-         _g[e.first].set_weight(weight);
-         else
-         _g[e.first].set_weight(_g[e.first].get_weight() + weight);
-         return e.second;
-        }
-
-       void set_all_pfparams(const std::vector<typename pf_t::params_t>& pfs)
-        {
-         assert(num_vertices(_g) == pfs.size());
-         size_t k = 0;
-         BGL_FORALL_VERTICES_T(v, _g, graph_t)
-          _g[v].set_pfparamst(pfs[k++]);
-        }
-
-       void set_all_afparams(const std::vector<typename af_t::params_t>& afs)
-        {
-         assert(num_vertices(_g) == afs.size());
-         size_t k = 0;
-         BGL_FORALL_VERTICES_T(v, _g, graph_t)
-          _g[v].set_afparamst(afs[k++]);
-        }
-
-       void set_all_weights(const std::vector<weight_t>& ws)
-        {
-#ifndef NDEBUG
-         if (num_edges(_g) != ws.size())
-         std::cout << "param errors: "
-          << num_edges(_g)
-          << " whereas "
-          << ws.size()
-          << " provided" <<std::endl;
-#endif
-         assert(num_edges(_g) == ws.size());
-         size_t k = 0;
-         BGL_FORALL_EDGES_T(e, _g, graph_t)
-          _g[e].set_weight(ws[k++]);
-        }
-
-       void set_nb_inputs(unsigned i)
-        {
-         _inputs.resize(i);
-         size_t k = 0;
-         BOOST_FOREACH(vertex_desc_t& v, _inputs)
-          {
-           v = add_vertex(_g);
-           this->_g[v].set_in(k++);
-          }
-        }
-       void set_nb_outputs(unsigned i)
-        {
-         _outputs.resize(i);
-         size_t k = 0;
-         BOOST_FOREACH(vertex_desc_t& v, _outputs)
-          {
-           v = add_vertex(_g);
-           this->_g[v].set_out(k++);
-          }
-        }
-       vertex_desc_t get_input(int i) const
-        {
-         assert((size_t)i < _inputs.size());
-         assert(this->_g[_inputs[i]].get_in() != -1);
-         return _inputs[i];
-        }
-       const std::vector<vertex_desc_t>& get_inputs() const { return _inputs; }
-       const std::vector<vertex_desc_t>& get_outputs() const { return _outputs; }
-       // warning : O(n)
-       vertex_desc_t get_neuron(size_t i) const
-        {
-         i = std::min(num_vertices(_g) - 1, i);
-         size_t k = 0;
-         BGL_FORALL_VERTICES_T(v, _g, graph_t)
-          if (k++ == i)
-          return v;
-         assert(0);
-         return (vertex_desc_t)(0x0);;
-        }
-       vertex_list_t get_neuron_list()
-        {
-         vertex_list_t neuron_list;
-         std::pair<vertex_it_t, vertex_it_t> vp;
-         for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-          {
-           neuron_list.push_back(vertex_desc_t(*vp.first));
-          }
-         return neuron_list;
-        }
-       neuron_t& get_neuron_by_vertex(vertex_desc_t v)
-        {
-         return this->_g[v];
-        }
-       io_t get_neuron_output(size_t i) const
-        {
-         return _g[get_neuron(i)].get_current_output();
-        }
-
-       std::string get_neuron_id(size_t i) const
-        {
-         return _g[get_neuron(i)]._id;
-        }
-       vertex_desc_t get_output(int i) const
-        {
-         assert((size_t) i < _outputs.size());
-         assert(this->_g[_outputs[i]].get_out() != -1);
-         return _outputs[i];
-        }
-       const N& get_output_neuron(int i) const
-        {
-         return _g[_outputs[i]];
-        }
-       bool is_output(const vertex_desc_t& v) const
-        {
-         return std::find(_outputs.begin(), _outputs.end(), v) != _outputs.end();
-        }
-       bool is_input(const vertex_desc_t& v) const
-        {
-         return std::find(_inputs.begin(), _inputs.end(), v) != _inputs.end();
-        }
-
-       // step
-       void step(const std::vector<io_t>& inputs) { _step(inputs); }
-
-       // accessors
-       const std::vector<io_t>& get_outf() const { return _outf; }
-       io_t get_outf(unsigned i) const { return _outf[i]; }
-       const std::vector<io_t>& outf() const { return get_outf(); }
-       io_t outf(unsigned i) const { return get_outf(i); }
-       unsigned get_nb_inputs() const { return _inputs.size(); }
-       unsigned get_nb_outputs() const { return _outputs.size(); }
-       unsigned get_nb_connections() const { return num_edges(_g); }
-       unsigned get_nb_neurons() const { return num_vertices(_g); }
-
-       // subnns
-       void remove_subnn(const std::set<vertex_desc_t>& subnn)
-        {
-         BOOST_FOREACH(vertex_desc_t v, subnn)
-          if (!is_input(v) && !is_output(v))
-           {
-            clear_vertex(v, _g);
-            remove_vertex(v, _g);
-           }
-         _init_io();
-        }
-       template<typename NN>
-        void add_subnn(const NN& nn,
-              const std::vector<size_t>& inputs,
-              const std::vector<size_t>& outputs)
-         {
-          assert(inputs.size() == nn.get_nb_inputs());
-          assert(outputs.size() == nn.get_nb_outputs());
-          std::map<typename NN::vertex_desc_t, vertex_desc_t> rmap;
-          const typename NN::graph_t& g_src = nn.get_graph();
-          BGL_FORALL_VERTICES_T(v, g_src, typename NN::graph_t)
-           if (g_src[v].get_in() == -1 && g_src[v].get_out() == -1)
-            {
-             vertex_desc_t nv = add_vertex(_g);
-             _g[nv] = g_src[v];
-             _g[nv]._id = boost::lexical_cast<std::string>(_neuron_counter++);
-             rmap[v] = nv;
-            }
-
-          std::vector<vertex_desc_t> vnodes;
-          // hoping that the order did not change too much
-          BGL_FORALL_VERTICES_T(v, _g, graph_t)
-           vnodes.push_back(v);
-
-          BGL_FORALL_EDGES_T(e, g_src, typename NN::graph_t)
-           {
-            std::pair<edge_desc_t, bool> ne;
-            int in  = g_src[source(e, g_src)].get_in();
-            int out = g_src[target(e, g_src)].get_out();
-            assert(in == -1 || in < inputs.size());
-            assert(out == -1 || out < outputs.size());
-            if (in != -1 && out != -1)
-             {
-              int n_in = std::min(vnodes.size() - 1, inputs[in]);
-              int n_out = std::min(vnodes.size() - 1, outputs[out]);
-              ne = add_edge(vnodes[n_in], vnodes[n_out], _g);
-             }
-            else if (in != -1)
-             {
-              int n_in = std::min(vnodes.size() - 1, inputs[in]);
-              ne = add_edge(vnodes[n_in], rmap[target(e, g_src)], _g);
-             }
-            else if (out != -1)
-             {
-              int n_out = std::min(vnodes.size() - 1, outputs[out]);
-              ne = add_edge(rmap[source(e, g_src)], vnodes[n_out], _g);
-             }
-            else
-             {
-              assert(rmap.find(source(e, g_src)) != rmap.end());
-              assert(rmap.find(target(e, g_src)) != rmap.end());
-              ne = add_edge(rmap[source(e, g_src)], rmap[target(e, g_src)], _g);
-             }
-            _g[ne.first] = g_src[e];
-           }
-
-          _init_io();
-         }
-
-      // remove the connection with a weigth that is smaller (in absolute value) to the threshold 
-      // !!! WARNING
-      // this method will destroy your neural network...
-      int remove_low_weights(float threshold)
-      {
-	int nb_removed = 0;
-	std::vector<edge_desc_t> to_remove;
-	BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-          {
-	    if (fabs(_g[e].get_weight()) < threshold)
-	      to_remove.push_back(e);
-          }
-	for (size_t i = 0; i < to_remove.size(); ++i)
-	  remove_edge(to_remove[i], this->_g);
-	return to_remove.size();
-      }
-
-       // remove neurons that are not connected to both one input and
-       // one output (this is NOT callled automatically in NN
-       //
-       // WARNING: if simplify_in is true, this can change the behavior
-       // of neurons since neurons not connected to inputs but connected
-       // to outputs can output a constant value
-       //
-       // principle : keep the neurons that are successors of inputs
-       // and predecessors of outputs
-       void simplify(bool simplify_in = false)
-        {
-         // we need sets and not lists withouh io
-         std::set<vertex_desc_t> all_neurons;
-         BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-          if (!is_input(v) && !is_output(v))
-          all_neurons.insert(v);
-         std::set<vertex_desc_t> out_preds, in_succs;
-
-         // out
-         BOOST_FOREACH(vertex_desc_t v, this->_outputs)
-          {
-           std::set<vertex_desc_t> preds;
-           nn::bfs_pred_visitor<vertex_desc_t> vis(preds);
-           breadth_first_search(boost::make_reverse_graph(_g),
-                 v, color_map(get(&N::_color, _g)).visitor(vis));
-           out_preds.insert(preds.begin(), preds.end());
-          }
-         // in
-         if (simplify_in)
-         BOOST_FOREACH(vertex_desc_t v, this->_inputs)
-          {
-           std::set<vertex_desc_t> succs;
-           nn::bfs_pred_visitor<vertex_desc_t> vis(succs);
-           breadth_first_search(_g,
-                 v, color_map(get(&N::_color, _g)).visitor(vis));
-           in_succs.insert(succs.begin(), succs.end());
-          }
-         else
-         in_succs = all_neurons;
-         // make the intersection of in_succ and out_preds
-         std::set<vertex_desc_t> valid_neurons;
-         std::set_intersection(in_succs.begin(), in_succs.end(),
-               out_preds.begin(), out_preds.end(),
-               std::insert_iterator<std::set<vertex_desc_t> >(valid_neurons,
-                 valid_neurons.begin()));
-         // get the list of neurons that are NOT in valid_neurons
-         std::set<vertex_desc_t> to_remove;
-         std::set_difference(all_neurons.begin(), all_neurons.end(),
-               valid_neurons.begin(), valid_neurons.end(),
-               std::insert_iterator<std::set<vertex_desc_t> >(to_remove,
-                 to_remove.begin()));
-         // remove these neurons
-         BOOST_FOREACH(vertex_desc_t v, to_remove)
-          {
-           clear_vertex(v, _g);
-           remove_vertex(v, _g);
-          }
-        }
-       // fully connect two vectors of neurons
-       void full_connect(const std::vector<vertex_desc_t> v1,
-             const std::vector<vertex_desc_t> v2,
-             const weight_t& w)
-        {
-         BOOST_FOREACH(vertex_desc_t x, v1)
-          BOOST_FOREACH(vertex_desc_t y, v2)
-          this->add_connection(x, y, w);
-        }
-
-       // 1 to 1 connection
-       void connect(const std::vector<vertex_desc_t> v1,
-             const std::vector<vertex_desc_t> v2,
-             const weight_t& w)
-        {
-         assert(v1.size() == v2.size());
-         for (size_t i = 0; i < v1.size(); ++i)
-         this->add_connection(v1[i], v2[i], w);
-        }
-     protected:
-       // attributes
-       graph_t _g;
-       vertex_list_t _inputs;
-       vertex_list_t _outputs;
-       std::vector<io_t> _outf;
-       int _neuron_counter;
-       bool _init_done;
-
-       // methods
-       void _write_dot(std::ostream& ofs)
-        {
-         ofs << "digraph G {" << std::endl;
-         BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
-          {
-           ofs << this->_g[v].get_id();
-           ofs << " [label=\""<<this->_g[v].get_id()<<"\"";
-           // ofs << " af"<< this->_g[v].get_afparams();
-           // ofs << "| pf"<< this->_g[v].get_pfparams() <<"\"";
-           if (is_input(v) || is_output(v))
-	     ofs<<" shape=doublecircle";
-	   
-           ofs <<"]"<< std::endl;
-          }
-         BGL_FORALL_EDGES_T(e, this->_g, graph_t)
-          {
-           ofs << this->_g[source(e, this->_g)].get_id()
-            << " -> " << this->_g[target(e, this->_g)].get_id()
-	       << "[label=\"" << _g[e].get_weight() << "\"]" << std::endl;
-          }
-         ofs << "}" << std::endl;
-        }
-
-       void _activate(vertex_desc_t n)
-        {
-         using namespace boost;
-         if (_g[n].get_fixed()) return;
-
-         in_edge_it_t in, in_end;
-         unsigned i = 0;
-         for (tie(in, in_end) = in_edges(n, _g); in != in_end; ++in, ++i)
-         _g[n].set_input(i, _g[source(*in, _g)].get_current_output());
-
-         _g[n].activate();
-        }
-
-       void _set_in(const std::vector<io_t>& inf)
-        {
-         assert(inf.size() == _inputs.size());
-         if (inf.size()>0) {
-           unsigned i = 0;
-           for (typename vertex_list_t::const_iterator it = _inputs.begin();
-                 it != _inputs.end(); ++it, ++i)
-            {
-             _g[*it].set_current_output(inf[i]);
-             _g[*it].set_next_output(inf[i]);
-            }
-         }
-        }
-       void _set_out()
-        {
-         unsigned i = 0;
-         for (typename vertex_list_t::const_iterator it = _outputs.begin();
-               it != _outputs.end(); ++it, ++i)
-         _outf[i] = _g[*it].get_current_output();
-        }
-       void _step(const std::vector<io_t>& inf)
-        {
-         assert(_init_done);
-         // in
-         _set_in(inf);
-
-         // activate
-         std::pair<vertex_it_t, vertex_it_t> vp;
-         for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-         _activate(*vp.first);
-
-         // step
-         for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-         _g[*vp.first].step();
-
-         // out
-         _set_out();
-        }
-
-    void _name_io()
-    {
-      int i=0;
-      BOOST_FOREACH(vertex_desc_t v, _inputs)
-	{
-	  _g[v]._id = std::string("i") + boost::lexical_cast<std::string>(i);
-	  ++i;
-	}
-      i = 0;
-      BOOST_FOREACH(vertex_desc_t v, _outputs) {
-	_g[v]._id = std::string("o") + boost::lexical_cast<std::string>(i);
-	++i;
-      }
-    }
-    void _init()
-    {
-      //      BOOST_MPL_ASSERT((boost::mpl::is_same<N::weight_t, C::weight_t>));
-      // BOOST_MPL_ASSERT((boost::mpl::is_same<Pot::weight_t, C::weight_t>));
-      _outf.clear();
-      in_edge_it_t in, in_end;
-      std::pair<vertex_it_t, vertex_it_t> vp;
-      int k = 0;
-      for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-	{
-	  vertex_desc_t n = *vp.first;
-	  _g[n].set_in_degree(in_degree(n, _g));
-	  _g[n].set_id(boost::lexical_cast<std::string>(k++));
-	  unsigned i = 0;
-	  for (tie(in, in_end) = in_edges(n, _g); in != in_end; ++in, ++i)
-	    _g[n].set_weight(i, _g[*in].get_weight());
-	}
-      _outf.resize(_outputs.size());
-      BOOST_FOREACH(vertex_desc_t v, _inputs)
-	{
-	  _g[v].set_fixed();
-	  _g[v].set_current_output(N::zero());
-	}
-      // init to 0
-      for (vp = boost::vertices(_g); vp.first != vp.second; ++vp.first)
-	_g[*vp.first].init();
-      _init_io();
-      _name_io();
-      _init_done = true;
-    }
-    void _init_io()
-    {
-      BGL_FORALL_VERTICES_T(v, _g, graph_t)
-	{
-	  if (_g[v].get_in() != -1)
-	    {
-	      assert(_g[v].get_in() < (int)_inputs.size());
-	      _inputs[_g[v].get_in()] = v;
-	    }
-	  if (_g[v].get_out() != -1)
-	    {
-	      assert(_g[v].get_out() < (int)_outputs.size());
-	      _outputs[_g[v].get_out()] = v;
-	    }
-	}
-    }
-  };
-}
-
-#endif
-
-
-

modules/dnns_easily_fooled/sferes/modules/nn2/params.hpp

@@ -1,93 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#ifndef _NN_PARAMS_HPP_
-#define _NN_PARAMS_HPP_
-namespace nn
-{
-  namespace params
-  {
-    struct Dummy
-    {
-      friend std::ostream& operator<<(std::ostream& output, const Dummy& e);
-      Dummy() : _x(42) {}
-      void mutate() {}
-      void random() {}
-      void develop() {}
-      size_t size() const { return 0; }
-      float data(size_t i) const { return 0.0f;}
-      float& operator[](size_t i) { return _x; }
-      float operator[](size_t i) const { return _x; }
-      template<typename A>
-      void serialize(A& ar, unsigned int v) {}
-      typedef float type_t;
-    protected:
-      float _x;
-    };
-
-    std::ostream& operator<<(std::ostream& output, const Dummy& e) {
-      return output;
-    }
-    template<int S>
-    struct Vectorf
-    {
-      typedef float type_t;
-      BOOST_STATIC_CONSTEXPR int s=S;
-      Vectorf() : _data(S) {}
-      // magic cast !
-      template<typename T>
-      Vectorf(const T& v) :
-	_data(S)
-      {
-      	assert(v.size() == S);
-      	for (size_t i = 0; i < v.size(); ++i)
-      	  _data[i] = v.data(i);
-      }
-      float data(size_t i) const { assert(i < S) ; return _data[i]; }
-      float& operator[](size_t i) { return _data[i]; }
-      float operator[](size_t i) const { return _data[i]; }
-
-      size_t size() const { return _data.size(); }
-      void mutate() {}
-      void random() {}
-      void develop() {}
-    protected:
-      std::vector<float> _data;
-    };
-  }
-
-
-
-}
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/pf.hpp

@@ -1,168 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#ifndef NN_PF_HPP
-#define NN_PF_HPP
-
-#include <cstdlib>
-#include <sferes/misc/rand.hpp>
-
-#include "params.hpp"
-#include "trait.hpp"
-
-// potential functions (weighted sum, leaky integrator, etc.)
-namespace nn
-{
-  template<typename W = float, typename P = params::Dummy>
-  class Pf
-  {
-    public:
-      typedef P params_t;
-      typedef W weight_t;
-      const params_t& get_params() const { return _params; }
-      params_t& get_params() { return _params; }
-      void set_params(const params_t& params) { _params = params; }
-      void init() {}
-       // weights are stored in the pf function for efficiency reasons
-      void set_nb_weights(size_t n) { _weights.resize(n); }
-      void set_weight(size_t i, const W& w) { assert(i < _weights.size()); _weights[i] = w; }
-      const std::valarray<W>& get_weights() const { return _weights; }
-       // main function
-      template<typename IO>
-      float operator() (const typename trait<IO>::vector_t & inputs) const { return 0.0f; }
-    protected:
-      params_t _params;
-       // cache weights
-      std::valarray<W> _weights;
-  };
-
-  template<typename W = float>
-  struct PfWSum : public Pf<W>
-  {
-    typedef params::Dummy params_t;
-    typedef W weight_t;
-    void init()
-    {
-      _w_cache.resize(this->_weights.size());
-      for (size_t i = 0; i < this->_weights.size(); ++i)
-        _w_cache[i] = trait<weight_t>::single_value(this->_weights[i]);
-    }
-    float operator() (const trait<float>::vector_t & inputs) const
-    {
-      assert(inputs.size() == _w_cache.size());
-       //std::cout<<"in:"<<inputs.transpose()<<" w:"<<_w_cache.transpose()<<"=>"<<
-       //_w_cache.dot(inputs)<<std::endl;
-      if (inputs.size() == 0)
-        return 0.0f;
-#ifdef EIGEN3_ENABLED
-      return _w_cache.dot(inputs);
-#else
- #warning "No EIGEN3 -> no vectorization of pwfsum"
-      return (_w_cache * inputs).sum();
-#endif
-    }
-    protected:
-      trait<float>::vector_t _w_cache;
-  };
-
-
-
-   // Ijsspert's coupled non-linear oscillators
-   // see : Learning to Move in Modular Robots using Central Pattern
-   // Generators and Online Optimization, 2008
-   // Main parameters:
-   // - phi_i: phase lag
-   // - r_i: amplitude
-   // - x_i: offset
-   // - _omega: frequency
-  template<typename P>
-  struct PfIjspeert : public Pf<std::pair<float, float>, P>
-  {
-    typedef std::pair<float, float> weight_t;
-    typedef P params_t;
-    BOOST_STATIC_CONSTEXPR float dt = 0.01;
-    BOOST_STATIC_CONSTEXPR float a_r = 20.0f;
-    BOOST_STATIC_CONSTEXPR float a_x = 20.0f;
-    void set_r(float r) { _r = r; }
-    void set_x(float x) { _x = x; }
-    void set_omega(float o) { _omega = o; }
-    float get_theta_i() const { return _theta_i; }
-    void init()
-    {
-      _phi_i = 0; //sferes::misc::rand<float>();
-      _r_i = 0; //sferes::misc::rand<float>();
-      _x_i = 0; //sferes::misc::rand<float>();
-      _theta_i = 0;
-      _phi_i_d = 0; _r_i_d = 0; _x_i_d = 0;
-    }
-     // depends on r_j and phi_j
-    weight_t operator() (const trait<weight_t>::vector_t & inputs)
-    {
-      _phi_i_d = _omega;
-      for (size_t j = 0; j < inputs.size(); ++j)
-      {
-        float r_j = inputs[j].first;
-        float phi_j = inputs[j].second;
-        float w_ij = this->_weights[j].first;
-        float phi_ij = this->_weights[j].second;
-        std::cout << "phi_ij:" << phi_ij << " " << inputs.size() << std::endl;
-        _phi_i_d += w_ij * r_j * sin(phi_j - phi_ij - _phi_i);
-      }
-      float r_i_dd = a_r * (a_r / 4 * (_r - _r_i) - _r_i_d);
-      float x_i_dd = a_r * (a_r / 4 * (_x - _x_i) - _x_i_d);
-
-       // integrate
-      _r_i_d += r_i_dd * dt;
-      _r_i += _r_i_d * dt;
-      _x_i_d += x_i_dd * dt;
-      _x_i += _x_i_d * dt;
-      _phi_i += _phi_i_d * dt;
-
-       // result
-      _theta_i = _x_i + _r_i *cos(_phi_i);
-      return std::make_pair(_r_i, _phi_i);
-    }
-    private:
-       // states
-      float _phi_i, _r_i, _theta_i, _x_i;
-       // states dot
-      float _phi_i_d, _r_i_d, _x_i_d;
-       // parameters
-      float _r, _x, _omega;
-  };
-
-}
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/phen_dnn.hpp

@@ -1,79 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef PHEN_DNN_HPP
-#define PHEN_DNN_HPP
-
-#include <map>
-#include <sferes/phen/indiv.hpp>
-#include "gen_dnn.hpp"
-
-namespace sferes
-{
-  namespace phen
-  {
-    SFERES_INDIV(Dnn, Indiv)
-      {
-      public:
-	void develop()
-	{
-	  // develop the parameters
-	  BGL_FORALL_VERTICES_T(v, this->gen().get_graph(), 
-				typename nn_t::graph_t)
-	    {
-	      this->gen().get_graph()[v].get_afparams().develop();
-	      this->gen().get_graph()[v].get_pfparams().develop();
-	    }
-	  BGL_FORALL_EDGES_T(e, this->gen().get_graph(), 
-			     typename nn_t::graph_t)
-	    {
-	      this->gen().get_graph()[e].get_weight().develop();
-	    }
-	  // init everything
-	  this->_gen.init();	  
-	}
-	void show(std::ostream& os) {  this->gen().write(os);  }
-	typedef typename Gen::nn_t nn_t;
-	nn_t& nn() { return this->gen(); }
-	const nn_t& nn() const { return this->gen(); }
-      protected:
-      };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/phen_hyper_nn.hpp

@@ -1,308 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef PHEN_HYPER_NN_HPP
-#define PHEN_HYPER_NN_HPP
-
-#include <map>
-#include <sferes/phen/indiv.hpp>
-#include <modules/nn2/nn.hpp>
-
-#include <modules/nn2/params.hpp>
-#include "gen_hyper_nn.hpp"
-
-
-namespace sferes
-{
-  namespace phen
-  {
-    namespace hnn
-    {
-      class Pos
-      {
-        public:
-          Pos() {
-          }
-          Pos(float x, float y, float z) : _x(x), _y(y), _z(z) {
-          }
-          float dist(const Pos& p) const
-          {
-            float x = _x - p._x;
-            float y = _y - p._y;
-            float z = _z - p._z;
-            return sqrt(x * x + y * y + z * z);
-          }
-          float x() const {
-            return _x;
-          }
-          float y() const {
-            return _y;
-          }
-          float z() const {
-            return _z;
-          }
-
-          template<class Archive>
-          void serialize(Archive& ar, const unsigned int version)
-          {
-            ar& BOOST_SERIALIZATION_NVP(_x);
-            ar& BOOST_SERIALIZATION_NVP(_y);
-            ar& BOOST_SERIALIZATION_NVP(_z);
-          }
-          bool operator == (const Pos &p)
-          { return _x == p._x && _y == p._y && _z == p._z; }
-        protected:
-          float _x, _y, _z;
-      };
-    }
-
-     // hyperneat-inspired phenotype, based on a cppn
-    SFERES_INDIV(HyperNn, Indiv)
-    {
-      public:
-        typedef Gen gen_t;
-        typedef typename Params::hyper_nn::neuron_t neuron_t;
-        typedef typename Params::hyper_nn::connection_t connection_t;
-        typedef typename nn::NN<neuron_t, connection_t> nn_t;
-        typedef typename nn_t::vertex_desc_t v_d_t;
-        typedef typename gen_t::nn_t gen_nn_t;
-        SFERES_CONST size_t nb_pfparams = Params::hyper_nn::nb_pfparams;
-        SFERES_CONST size_t nb_afparams = Params::hyper_nn::nb_afparams;
-        SFERES_CONST size_t nb_cppn_inputs = 2 + 2;
-        SFERES_CONST size_t nb_cppn_outputs = 2;
-
-        void develop()
-        {
-          this->_nn = nn_t();
-          this->gen().init();
-           // develop the parameters
-          BGL_FORALL_VERTICES_T(v, this->gen().get_graph(),
-                                typename gen_t::nn_t::graph_t)
-          {
-            this->gen().get_graph()[v].get_afparams().develop();
-            this->gen().get_graph()[v].get_pfparams().develop();
-          }
-          BGL_FORALL_EDGES_T(e, this->gen().get_graph(),
-                             typename gen_t::nn_t::graph_t)
-          this->gen().get_graph()[e].get_weight().develop();
-          assert(nb_cppn_inputs == this->gen().get_nb_inputs());
-          assert(nb_cppn_outputs == this->gen().get_nb_outputs());
-
-          _all_neurons.clear();
-
-          size_t d = this->gen().get_depth();
-           // create the nn
-          _nn.set_nb_inputs(Params::hyper_nn::nb_inputs);
-          _nn.set_nb_outputs(Params::hyper_nn::nb_outputs);
-          SFERES_CONST size_t skip =
-            Params::hyper_nn::nb_inputs
-            + Params::hyper_nn::nb_outputs;
-          SFERES_CONST size_t skip_total =
-            Params::hyper_nn::nb_inputs
-            + Params::hyper_nn::nb_outputs
-            + Params::hyper_nn::nb_hidden;
-
-          BGL_FORALL_VERTICES_T(v, _nn.get_graph(), typename nn_t::graph_t)
-           /**/ _all_neurons.push_back(v);
-
-           // hidden neurons
-          for (size_t i = 0; i < Params::hyper_nn::nb_hidden; ++i)
-          {
-            v_d_t v = _nn.add_neuron(boost::lexical_cast<std::string>(i));
-            _all_neurons.push_back(v);
-          }
-
-          assert(_all_neurons.size() ==
-                 Params::hyper_nn::substrate_size() / 2
-                 - Params::hyper_nn::nb_pfparams
-                 - Params::hyper_nn::nb_afparams);
-
-           // build the coordinate map
-          for (size_t i = 0; i < _all_neurons.size() * 2; i += 2)
-            this->_coords_map[_all_neurons[i / 2]] =
-              hnn::Pos(Params::hyper_nn::substrate(i),
-                       Params::hyper_nn::substrate(i + 1), 0);
-
-           // afparams and pfparams
-          for (size_t i = 0; i < _all_neurons.size(); ++i)
-          {
-            typename neuron_t::pf_t::params_t pfparams;
-             // we put pfparams & afparams in [0:1]
-             //for (size_t k = 0; k < nb_pfparams; ++k)
-             //{
-             // pfparams[k] = cppn_value(skip_total + k, i, false, 1) / 2.0f + 0.5f;
-             // std::cout << " " << pfparams[k] << " ";
-             //}
-            typename neuron_t::af_t::params_t
-              afparams;
-            for (size_t k = 0; k < nb_afparams; ++k)
-            {
-              float b = cppn_value(skip_total + k + nb_pfparams, i,
-                                   k + 1, false) / 2.0f + 0.5f;
-              size_t bi = b * Params::hyper_nn::bias_size();
-              bi = std::min(bi, Params::hyper_nn::bias_size() - 1);
-              afparams[k] = Params::hyper_nn::bias(bi);
-            }
-            _nn.get_graph()[_all_neurons[i]].
-            set_pfparams(pfparams);
-            _nn.get_graph()[_all_neurons[i]].
-            set_afparams(afparams);
-          }
-           // create connections
-          for (size_t i = 0; i < skip_total * 2; i += 2)
-            for (size_t j = 0; j < skip_total * 2; j += 2)
-              if (!_nn.is_input(_all_neurons[j / 2]))
-              {
-                float w = cppn_value(i, j, 0, true);
-                float ws = w >= 0 ? 1 : -1;
-                ws *=
-                  (fabs(w) -
-                   Params::hyper_nn::conn_threshold) / (1 - Params::hyper_nn::conn_threshold);
-                 // TODO generalize this
-                 // ws is in [-1, 1] TODO : no guarantee that ws is in [-1;1]
-                size_t wi = (int) ((ws / 2.0 + 0.5) * Params::hyper_nn::weights_size());
-                wi = std::min(wi, Params::hyper_nn::weights_size() - 1);
-                float wf = Params::hyper_nn::weights(wi);
-                typename connection_t::weight_t weight = typename connection_t::weight_t(wf);
-                if (fabs(w) >
-                    Params::hyper_nn::conn_threshold)
-                  _nn.add_connection(_all_neurons[i / 2],
-                                     _all_neurons[j / 2],
-                                     weight);
-              }
-          this->_nn.init();
-        }
-
-        float cppn_value(size_t i, size_t j,
-                         size_t n, bool ff = false)
-        {
-          assert(i < Params::hyper_nn::substrate_size());
-          assert(j < Params::hyper_nn::substrate_size());
-          assert(i + 1 < Params::hyper_nn::substrate_size());
-          assert(j + 1 < Params::hyper_nn::substrate_size());
-          assert(n < nb_cppn_outputs);
-          std::vector<float> in(nb_cppn_inputs);
-          this->gen().init();
-          in[0] = Params::hyper_nn::substrate(i);
-          in[1] = Params::hyper_nn::substrate(i + 1);
-          in[2] = Params::hyper_nn::substrate(j);
-          in[3] = Params::hyper_nn::substrate(j + 1);
-          if (in[1] == in[3])
-            return 0;
-          if (ff && (in[1] > in[3] || fabs(in[1] - in[3]) > Params::hyper_nn::max_y))
-            return 0;
-          for (size_t k = 0; k < this->gen().get_depth(); ++k)
-            this->gen().step(in);
-          return this->gen().get_outf(n);
-        }
-
-        void write_svg(std::ostream& ofs)
-        {
-           //_nn.write(os);
-           //std::ofstream ofs("/tmp/nn.svg");
-          ofs << "<svg width=\"200px\" height=\"200px\" viewbox=\"-500 -500 500 500\">";
-          for (size_t i = 0; i < _all_neurons.size() * 2; i += 2)
-          {
-            float x = Params::hyper_nn::substrate(i);
-            float y = Params::hyper_nn::substrate(i + 1);
-            ofs << "<circle cx=\"" << x * 80 + 100
-                << "\" cy=\"" << y * 80 + 100
-                << "\" r=\"2\" fill=\"black\" "
-                << "opacity=\""
-                << 1
-             // << std::max(0.0f, _nn.get_graph()[_all_neurons[i / 2]].get_pfparams()[0])
-                << "\" "
-                << " />" << std::endl;
-          }
-          typedef typename nn_t::graph_t graph_t;
-          typedef typename nn_t::vertex_desc_t v_d_t;
-          const graph_t& g = this->nn().get_graph();
-
-          BGL_FORALL_EDGES_T(e, g, graph_t)
-          {
-            v_d_t src = boost::source(e, g);
-            v_d_t tgt = boost::target(e, g);
-            float x1 = _coords_map[src].x() * 80 + 100;
-            float y1 = _coords_map[src].y() * 80 + 100;
-            float x2 = _coords_map[tgt].x() * 80 + 100;
-            float y2 = _coords_map[tgt].y() * 80 + 100;
-            double weight = g[e].get_weight();
-            ofs << "<line x1=\"" << x1 << "\" y1=\"" << y1 << "\" "
-                << "x2=\"" << x2 << "\" y2=\"" << y2 << "\""
-                << " style=\"stroke:rgb("
-                << (weight > 0 ? "0,255,0" : "255,0,0")
-                << ");stroke-width:" << fabs(weight)
-                << "\"/>"
-                << std::endl;
-          }
-
-          ofs << "</svg>";
-        }
-        nn_t& nn() {
-          return _nn;
-        }
-        const nn_t& nn() const {
-          return _nn;
-        }
-        const std::vector<typename nn_t::vertex_desc_t>&
-        all_neurons() const {
-          return _all_neurons;
-        }
-        float compute_length(float min_length)
-        {
-          float length = 0;
-          BGL_FORALL_EDGES_T(e, _nn.get_graph(), typename nn_t::graph_t)
-          {
-            typename nn_t::vertex_desc_t src = boost::source(e, _nn.get_graph());
-            typename nn_t::vertex_desc_t tgt = boost::target(e, _nn.get_graph());
-            double weight = _nn.get_graph()[e].get_weight();
-            float l = _coords_map[src].dist(_coords_map[tgt]);
-            length += l > min_length ? l : 0;
-          }
-          return length;
-        }
-
-      protected:
-        nn_t _nn;
-        std::vector<typename nn_t::vertex_desc_t> _all_neurons;
-        std::map<typename nn_t::vertex_desc_t, hnn::Pos> _coords_map;
-    };
-  }
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/test_dnn.cpp

@@ -1,219 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#define BOOST_TEST_DYN_LINK 
-#define BOOST_TEST_MODULE dnn
-
-#include <iostream>
-#include <cmath>
-#include <algorithm>
-
-#include <boost/archive/xml_oarchive.hpp>
-#include <boost/archive/xml_iarchive.hpp>
-#include <boost/archive/binary_oarchive.hpp>
-#include <boost/archive/binary_iarchive.hpp>
-
-#include <boost/test/unit_test.hpp>
-#include <boost/serialization/nvp.hpp>
-#include <boost/assign/std/vector.hpp>
-#include <boost/assign/list_of.hpp>
-
-#include <sferes/fit/fitness.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/phen/parameters.hpp>
-
-#include "gen_dnn.hpp"
-#include "phen_dnn.hpp"
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-template<typename T1, typename T2>
-void check_list_equal(const T1& v1, const T2& v2)
-{
-  BOOST_CHECK_EQUAL(v1.size(), v2.size());
-  typename T1::const_iterator it1 = v1.begin();
-  typename T1::const_iterator it2 = v2.begin();
-  for (; it1 != v1.end(); ++it1, ++it2)
-    BOOST_CHECK(fabs(*it1 - *it2) < 1e-3);
-}
- 
-template<typename NN>
-void check_nn_equal(NN& nn1, NN& nn2)
-{
-  nn1.init();
-  nn2.init();
-
-  BOOST_CHECK_EQUAL(nn1.get_nb_inputs(), nn2.get_nb_inputs());
-  BOOST_CHECK_EQUAL(nn1.get_nb_outputs(), nn2.get_nb_outputs());
-  BOOST_CHECK_EQUAL(nn1.get_nb_neurons(), nn2.get_nb_neurons());
-  BOOST_CHECK_EQUAL(nn1.get_nb_connections(), nn2.get_nb_connections());
-//   nn1.write("/tmp/tmp1.dot");
-//   nn2.write("/tmp/tmp2.dot");
-//   std::ifstream ifs1("/tmp/tmp1.dot"), ifs2("/tmp/tmp2.dot");
-//   while(!ifs1.eof() && !ifs2.eof())
-//     {
-//       //if (ifs1.get() != ifs2.get()) exit(1);
-//       BOOST_CHECK_EQUAL((char)ifs1.get(), (char)ifs2.get());
-//     }
-
-  std::pair<typename NN::vertex_it_t, typename NN::vertex_it_t> vp1 = 
-    boost::vertices(nn1.get_graph());
-  std::pair<typename NN::vertex_it_t, typename NN::vertex_it_t> vp2 = 
-    boost::vertices(nn2.get_graph());
-  while (vp1.first != vp1.second)
-    {
-      BOOST_CHECK_EQUAL(nn1.get_graph()[*vp1.first].get_in_degree(),
-			nn2.get_graph()[*vp2.first].get_in_degree());
-      check_list_equal(nn1.get_graph()[*vp1.first].get_afparams(),
-		       nn2.get_graph()[*vp1.first].get_afparams());
-      check_list_equal(nn1.get_graph()[*vp1.first].get_pfparams(),
-		       nn2.get_graph()[*vp1.first].get_pfparams());
-      ++vp1.first;
-      ++vp2.first;
-    }
-
-}
-
- 
-struct Params
-{
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.1f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -5.0f;
-    // minimum value
-    SFERES_CONST float max = 5.0f;
-  };
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs	= 4;
-    SFERES_CONST size_t nb_outputs	= 1;
-    SFERES_CONST size_t min_nb_neurons	= 4;
-    SFERES_CONST size_t max_nb_neurons	= 5;
-    SFERES_CONST size_t min_nb_conns	= 100;
-    SFERES_CONST size_t max_nb_conns	= 101;
-    SFERES_CONST float  max_weight	= 2.0f;
-    SFERES_CONST float  max_bias	= 2.0f;
-
-    SFERES_CONST float m_rate_add_conn	= 1.0f;
-    SFERES_CONST float m_rate_del_conn	= 1.0f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron  = 1.0f;
-    SFERES_CONST float m_rate_del_neuron  = 1.0f;
-
-    SFERES_CONST int io_param_evolving = true;
-    SFERES_CONST init_t init = random_topology;
-  };
-};
-
-BOOST_AUTO_TEST_CASE(direct_gen)
-{
-  using namespace nn;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> bias_t;
-  typedef PfWSum<weight_t> pf_t;
-  typedef AfTanh<bias_t> af_t; 
-
-  sferes::gen::Dnn<Neuron<pf_t, af_t>,  Connection<weight_t>, Params> gen1, gen2, gen3, gen4;
-      
-  gen1.random();
-  gen2.random();
-
-  gen1.cross(gen2, gen3, gen4);
-  gen3.mutate();
-  gen4.mutate();
-  gen2.mutate();
-}
-
-
-
-BOOST_AUTO_TEST_CASE(direct_nn_serialize)
-{
-  srand(0);
-
-  using namespace nn;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> bias_t;
-  typedef PfWSum<weight_t> pf_t;
-  typedef AfTanh<bias_t> af_t; 
-  typedef sferes::gen::Dnn<Neuron<pf_t, af_t>,  Connection<weight_t>, Params> gen_t;
-  typedef phen::Dnn<gen_t, fit::FitDummy<>, Params> phen_t;
-
-
-  typedef boost::archive::binary_oarchive oa_t;
-  typedef boost::archive::binary_iarchive ia_t;
-
-  for (size_t i = 0; i < 10; ++i) 
-    {
-      phen_t indiv[3];
-      indiv[0].random();
-      indiv[0].mutate();
-      indiv[0].mutate();
-      indiv[0].mutate();
-      indiv[0].nn().init();
-      {
-	std::ofstream ofs("/tmp/serialize_nn1.bin", std::ios::binary);
-	oa_t oa(ofs); 
-	oa & indiv[0];
-      }
-      {
-	std::ifstream ifs("/tmp/serialize_nn1.bin", std::ios::binary);
-	ia_t ia(ifs);
-	ia & indiv[1];
-      }
-      indiv[2].nn() = indiv[0].nn();
-      using namespace boost::assign;
-      std::vector<float> in = list_of(0.5f)(1.0f)(-0.25f)(1.101f);
-      for (size_t j = 0; j < 3; ++j)
-	indiv[j].nn().init();
-      for (size_t i = 0; i < 10; ++i)
-	for (size_t j = 0; j < 3; ++j)
-	  indiv[j].nn().step(in);
-      
-      for (size_t j = 1; j < 3; ++j)
-	BOOST_CHECK_CLOSE(indiv[0].nn().get_outf(0), indiv[j].nn().get_outf(0), 1e-5);
-    }
-}
-

modules/dnns_easily_fooled/sferes/modules/nn2/test_dnn_ff.cpp

@@ -1,145 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#define BOOST_TEST_DYN_LINK 
-#define BOOST_TEST_MODULE dnn_ff
-
-#include <boost/archive/xml_oarchive.hpp>
-#include <boost/archive/xml_iarchive.hpp>
-#include <boost/archive/binary_oarchive.hpp>
-#include <boost/archive/binary_iarchive.hpp>
-
-#include <boost/test/unit_test.hpp>
-#include <boost/serialization/nvp.hpp>
-#include <iostream>
-#include <cmath>
-#include <algorithm>
-#include <boost/graph/depth_first_search.hpp>
-
-#include <sferes/fit/fitness.hpp>
-#include <sferes/gen/evo_float.hpp>
-#include <sferes/phen/parameters.hpp>
-#include "gen_dnn_ff.hpp"
-#include "phen_dnn.hpp"
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-struct Params
-{
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.1f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    // maximum value of parameters
-    SFERES_CONST float min = -5.0f;
-    // minimum value
-    SFERES_CONST float max = 5.0f;
-  };
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs	= 4;
-    SFERES_CONST size_t nb_outputs	= 2;
-    SFERES_CONST size_t min_nb_neurons	= 4;
-    SFERES_CONST size_t max_nb_neurons	= 5;
-    SFERES_CONST size_t min_nb_conns	= 100;
-    SFERES_CONST size_t max_nb_conns	= 101;
-
-    SFERES_CONST float m_rate_add_conn	= 1.0f;
-    SFERES_CONST float m_rate_del_conn	= 0.1f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron  = 1.0f;
-    SFERES_CONST float m_rate_del_neuron  = 1.0f;
-
-    SFERES_CONST int io_param_evolving = true;
-    SFERES_CONST init_t init = ff;
-  };
-};
-
-
-
-struct cycle_detector : public boost::dfs_visitor<>
-{
-  cycle_detector(bool& has_cycle) 
-    : m_has_cycle(has_cycle) { }
-
-  template <class Edge, class Graph>
-  void back_edge(Edge, Graph&) { m_has_cycle = true; }
-protected:
-  bool& m_has_cycle;
-};
-
-BOOST_AUTO_TEST_CASE(direct_nn_ff)
-{
-  srand(time(0));
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> bias_t;
-  typedef nn::PfWSum<weight_t> pf_t;
-  typedef nn::AfTanh<bias_t> af_t; 
-  typedef nn::Neuron<pf_t, af_t> neuron_t;
-  typedef nn::Connection<weight_t> connection_t;
-  typedef gen::DnnFF<neuron_t, connection_t, Params> gen_t;
-  typedef phen::Dnn<gen_t, fit::FitDummy<Params>, Params> phen_t;
-  phen_t i;
-  i.random();
-  i.develop();
-  std::vector<float> in(4);
-  std::fill(in.begin(), in.end(), 0);
-  i.nn().step(in);
-  BOOST_CHECK_EQUAL(i.nn().get_nb_inputs(),  4);
-  BOOST_CHECK_EQUAL(i.nn().get_nb_outputs(), 2);
-  BOOST_CHECK_EQUAL(i.nn().get_nb_neurons(), 6);
-  BOOST_CHECK_EQUAL(i.nn().get_nb_connections(), 8);
-  std::ofstream ofs("/tmp/nn.dot");
-  i.nn().write(ofs);
-  for (size_t k = 0; k < 40; ++k)
-    i.mutate();
-  std::ofstream ofs2("/tmp/nn2.dot");
-  i.nn().write(ofs2);
-  bool has_cycle = false;
-  cycle_detector vis(has_cycle);
-  boost::depth_first_search(i.nn().get_graph(), 
-			    boost::color_map(get(&phen_t::nn_t::neuron_t::_color, 
-						 i.nn().get_graph())).visitor(vis));
-  BOOST_CHECK(!has_cycle);
-
-}
-

modules/dnns_easily_fooled/sferes/modules/nn2/test_hyper_nn.cpp

@@ -1,240 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#define BOOST_TEST_DYN_LINK
-#define BOOST_TEST_MODULE dnn
-
-#include <boost/test/unit_test.hpp>
-#include <iostream>
-#include <sferes/fit/fitness.hpp>
-#include <sferes/phen/parameters.hpp>
-
-#include "gen_dnn.hpp"
-#include "gen_hyper_nn.hpp"
-#include "phen_hyper_nn.hpp"
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-struct Params1
-{
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs = 3;
-    SFERES_CONST size_t nb_outputs = 1;
-
-    SFERES_CONST float m_rate_add_conn = 1.0f;
-    SFERES_CONST float m_rate_del_conn = 0.3f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron = 1.0f;
-    SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-    SFERES_CONST init_t init = ff;
-  };
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.1f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -2.0f;
-    SFERES_CONST float max = 2.0f;
-  };
-
-  struct cppn
-  {
-     // params of the CPPN
-    struct sampled
-    {
-      SFERES_ARRAY(float, values, 0, 1, 2);
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.25f;
-      SFERES_CONST bool ordered = false;
-    };
-    struct evo_float
-    {
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.1f;
-      SFERES_CONST mutation_t mutation_type = polynomial;
-      SFERES_CONST cross_over_t cross_over_type = sbx;
-      SFERES_CONST float eta_m = 15.0f;
-      SFERES_CONST float eta_c = 15.0f;
-    };
-  };
-};
-
-
-struct Params2
-{
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs = 4;
-    SFERES_CONST size_t nb_outputs = 1;
-
-    SFERES_CONST float m_rate_add_conn = 1.0f;
-    SFERES_CONST float m_rate_del_conn = 0.3f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron = 1.0f;
-    SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-    SFERES_CONST float weight_sigma = 0.5f;
-    SFERES_CONST float vect_sigma = 0.5f;
-    SFERES_CONST float m_rate_weight = 1.0f;
-    SFERES_CONST float m_rate_fparams = 1.0f;
-    SFERES_CONST init_t init = ff;
-  };
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.1f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -2.0f;
-    SFERES_CONST float max = 2.0f;
-  };
-
-  struct cppn
-  {
-     // params of the CPPN
-    struct sampled
-    {
-      SFERES_ARRAY(float, values, 0, 1, 2);
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.25f;
-      SFERES_CONST bool ordered = false;
-    };
-    struct evo_float
-    {
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.1f;
-      SFERES_CONST mutation_t mutation_type = polynomial;
-      SFERES_CONST cross_over_t cross_over_type = sbx;
-      SFERES_CONST float eta_m = 15.0f;
-      SFERES_CONST float eta_c = 15.0f;
-    };
-  };
-  struct hyper_nn
-  {
-    SFERES_ARRAY(float, substrate,
-                 0.2f, 0.2f, // in 1
-                 0.2f, 0.8f, // in 2
-                 0.5f, 0.5f, // out 1
-                 0.8f, 0.8f, // hidden 1
-                 0.8f, 0.2f, // hidden 2
-                 0.2f, 0.5f, // hidden 3
-                 0.5f, 0.2f  // hidden 4
-                 );
-    SFERES_ARRAY(float, weights, -1, 0, 1);
-    SFERES_ARRAY(float, bias, -1, 0, 1);
-    SFERES_CONST size_t nb_inputs = 2;
-    SFERES_CONST size_t nb_outputs = 1;
-    SFERES_CONST size_t nb_hidden = 4;
-    SFERES_CONST size_t nb_pfparams = 0;
-    SFERES_CONST size_t nb_afparams = 1;
-    SFERES_CONST float conn_threshold = 0.2f;
-    SFERES_CONST float max_y = 10.0f;
-    typedef nn::Neuron<nn::PfWSum<>,
-                       nn::AfTanh<nn::params::Vectorf<1> > > neuron_t;
-    typedef nn::Connection<> connection_t;
-  };
-};
-
-
-BOOST_AUTO_TEST_CASE(gen_cppn)
-{
-  srand(time(0));
-  typedef phen::Parameters<gen::EvoFloat<1, Params1>, fit::FitDummy<>, Params1> weight_t;
-  typedef gen::HyperNn<weight_t, Params1> cppn_t;
-
-  cppn_t gen1, gen2, gen3, gen4;
-
-  gen1.random();
-  for (size_t i = 0; i < 20; ++i)
-    gen1.mutate();
-  gen1.init();
-  BOOST_CHECK(gen1.get_depth() >= 1);
-  std::ofstream ofs2("/tmp/nn.dot");
-  gen1.write(ofs2);
-
-   //  generate a picture
-  char*pic = new char[256 * 256];
-  std::vector<float> in(3);
-  in[0] = 1;
-  for (size_t i = 0; i < 256; ++i)
-    for (size_t j = 0; j < 256; ++j)
-    {
-      in[1] = i / 128.0f - 1.0;
-      in[2] = j / 128.0f - 1.0;
-      for (size_t k = 0; k < gen1.get_depth(); ++k)
-        gen1.step(in);
-      pic[256 * i + j] = (int)(gen1.get_outf(0) * 256 + 255);
-    }
-  std::ofstream ofs("/tmp/pic.pgm");
-  ofs << "P5" << std::endl;
-  ofs << "256 256" << std::endl;
-  ofs << "255" << std::endl;
-  ofs.write(pic, 256 * 256);
-}
-
-
-BOOST_AUTO_TEST_CASE(phen_hyper_nn)
-{
-  srand(time(0));
-  typedef fit::FitDummy<> fit_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params2>, fit::FitDummy<>, Params2> weight_t;
-  typedef gen::HyperNn<weight_t, Params2> gen_t;
-  typedef phen::HyperNn<gen_t, fit_t, Params2> phen_t;
-
-  phen_t indiv;
-  indiv.random();
-  for (size_t i = 0; i < 5; ++i)
-    indiv.mutate();
-  std::ofstream ofs("/tmp/nn_substrate.svg");
-  indiv.develop();
-  indiv.show(ofs);
-  // BOOST_CHECK_EQUAL(indiv.nn().get_nb_neurons(), 7);
-
-}
-

modules/dnns_easily_fooled/sferes/modules/nn2/test_hyper_nn_anh.cpp

@@ -1,263 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-#define BOOST_TEST_DYN_LINK
-#define BOOST_TEST_MODULE dnn
-
-#include <boost/test/unit_test.hpp>
-#include <iostream>
-#include <sferes/fit/fitness.hpp>
-#include <sferes/phen/parameters.hpp>
-
-#include "gen_dnn.hpp"
-#include "gen_hyper_nn.hpp"
-#include "phen_hyper_nn.hpp"
-
-#include <boost/algorithm/string/join.hpp>
-#include <vector>
-
-using namespace sferes;
-using namespace sferes::gen::dnn;
-using namespace sferes::gen::evo_float;
-
-struct Params1
-{
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs = 3;
-    SFERES_CONST size_t nb_outputs = 1;
-
-    SFERES_CONST float m_rate_add_conn = 1.0f;
-    SFERES_CONST float m_rate_del_conn = 0.3f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron = 1.0f;
-    SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-    SFERES_CONST init_t init = ff;
-  };
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.5f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -2.0f;
-    SFERES_CONST float max = 2.0f;
-  };
-
-  struct cppn
-  {
-     // params of the CPPN
-    struct sampled
-    {
-      SFERES_ARRAY(float, values, 0, 1, 2);
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.25f;
-      SFERES_CONST bool ordered = false;
-    };
-    struct evo_float
-    {
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.1f;
-      SFERES_CONST mutation_t mutation_type = polynomial;
-      SFERES_CONST cross_over_t cross_over_type = sbx;
-      SFERES_CONST float eta_m = 15.0f;
-      SFERES_CONST float eta_c = 15.0f;
-    };
-  };
-};
-
-
-struct Params2
-{
-  struct dnn
-  {
-    SFERES_CONST size_t nb_inputs = 4;
-    SFERES_CONST size_t nb_outputs = 1;
-
-    SFERES_CONST float m_rate_add_conn = 1.0f;
-    SFERES_CONST float m_rate_del_conn = 0.3f;
-    SFERES_CONST float m_rate_change_conn = 1.0f;
-    SFERES_CONST float m_rate_add_neuron = 1.0f;
-    SFERES_CONST float m_rate_del_neuron = 0.2f;
-
-    SFERES_CONST float weight_sigma = 0.5f;
-    SFERES_CONST float vect_sigma = 0.5f;
-    SFERES_CONST float m_rate_weight = 1.0f;
-    SFERES_CONST float m_rate_fparams = 1.0f;
-    SFERES_CONST init_t init = ff;
-  };
-  struct evo_float
-  {
-    SFERES_CONST float mutation_rate = 0.1f;
-    SFERES_CONST float cross_rate = 0.1f;
-    SFERES_CONST mutation_t mutation_type = polynomial;
-    SFERES_CONST cross_over_t cross_over_type = sbx;
-    SFERES_CONST float eta_m = 15.0f;
-    SFERES_CONST float eta_c = 15.0f;
-  };
-  struct parameters
-  {
-    SFERES_CONST float min = -2.0f;
-    SFERES_CONST float max = 2.0f;
-  };
-
-  struct cppn
-  {
-     // params of the CPPN
-    struct sampled
-    {
-      SFERES_ARRAY(float, values, 0, 1, 2);
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.25f;
-      SFERES_CONST bool ordered = false;
-    };
-    struct evo_float
-    {
-      SFERES_CONST float mutation_rate = 0.1f;
-      SFERES_CONST float cross_rate = 0.1f;
-      SFERES_CONST mutation_t mutation_type = polynomial;
-      SFERES_CONST cross_over_t cross_over_type = sbx;
-      SFERES_CONST float eta_m = 15.0f;
-      SFERES_CONST float eta_c = 15.0f;
-    };
-  };
-  struct hyper_nn
-  {
-    SFERES_ARRAY(float, substrate,
-                 0.2f, 0.2f, // in 1
-                 0.2f, 0.8f, // in 2
-                 0.5f, 0.5f, // out 1
-                 0.8f, 0.8f, // hidden 1
-                 0.8f, 0.2f, // hidden 2
-                 0.2f, 0.5f, // hidden 3
-                 0.5f, 0.2f  // hidden 4
-                 );
-    SFERES_ARRAY(float, weights, -1, 0, 1);
-    SFERES_ARRAY(float, bias, -1, 0, 1);
-    SFERES_CONST size_t nb_inputs = 2;
-    SFERES_CONST size_t nb_outputs = 1;
-    SFERES_CONST size_t nb_hidden = 4;
-    SFERES_CONST size_t nb_pfparams = 0;
-    SFERES_CONST size_t nb_afparams = 1;
-    SFERES_CONST float conn_threshold = 0.2f;
-    SFERES_CONST float max_y = 10.0f;
-    typedef nn::Neuron<nn::PfWSum<>,
-                       nn::AfTanh<nn::params::Vectorf<1> > > neuron_t;
-    typedef nn::Connection<> connection_t;
-  };
-};
-
-
-BOOST_AUTO_TEST_CASE(gen_cppn)
-{
-	int nb_images = 0;
-
-	for (; nb_images < 10; ++nb_images)
-		{
-
-//			time_t seconds = time (nb_images);
-			srand (nb_images);
-
-			std::string ts = boost::lexical_cast<std::string> (nb_images);
-
-			typedef phen::Parameters<gen::EvoFloat<1, Params1>, fit::FitDummy<>,
-					Params1> weight_t;
-			typedef gen::HyperNn<weight_t, Params1> cppn_t;
-
-			cppn_t gen1, gen2, gen3, gen4;
-
-			gen1.random ();
-//			for (size_t i = 0; i < 20; ++i)
-//				gen1.mutate ();
-			gen1.init ();
-			BOOST_CHECK(gen1.get_depth () >= 1);
-//			std::ofstream ofs2 ("./nn.dot");
-//			gen1.write (ofs2);
-
-			//  generate a picture
-			char*pic = new char[256 * 256];
-			std::vector<float> in (3);
-			in[0] = 1;
-			for (size_t i = 0; i < 256; ++i)
-				for (size_t j = 0; j < 256; ++j)
-					{
-						in[1] = i / 128.0f - 1.0;
-						in[2] = j / 128.0f - 1.0;
-						for (size_t k = 0; k < gen1.get_depth (); ++k)
-							gen1.step (in);
-						pic[256 * i + j] = (int) (gen1.get_outf (0) * 256 + 255);
-					}
-
-			std::vector < std::string > list;
-			list.push_back ("/home/anh/workspace/sferes/tmp/");
-			list.push_back ("image_");
-			list.push_back (ts);
-			list.push_back (".pgm");
-			std::string joined = boost::algorithm::join (list, "");
-
-			std::ofstream ofs (joined.c_str ());
-			ofs << "P5" << std::endl;
-			ofs << "256 256" << std::endl;
-			ofs << "255" << std::endl;
-			ofs.write (pic, 256 * 256);
-
-		}
-}
-
-/*
-BOOST_AUTO_TEST_CASE(phen_hyper_nn)
-{
-  srand(time(0));
-  typedef fit::FitDummy<> fit_t;
-  typedef phen::Parameters<gen::EvoFloat<1, Params2>, fit::FitDummy<>, Params2> weight_t;
-  typedef gen::HyperNn<weight_t, Params2> gen_t;
-  typedef phen::HyperNn<gen_t, fit_t, Params2> phen_t;
-
-  phen_t indiv;
-  indiv.random();
-  for (size_t i = 0; i < 5; ++i)
-    indiv.mutate();
-  std::ofstream ofs("/tmp/nn_substrate.svg");
-  indiv.develop();
-  indiv.show(ofs);
-  // BOOST_CHECK_EQUAL(indiv.nn().get_nb_neurons(), 7);
-
-}
-*/

modules/dnns_easily_fooled/sferes/modules/nn2/test_mlp.cpp

@@ -1,62 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-#define BOOST_TEST_DYN_LINK 
-#define BOOST_TEST_MODULE nn_mlp
-
-
-#include <boost/test/unit_test.hpp>
-
-#include <iostream>
-#include <cmath>
-#include <algorithm>
-#include "mlp.hpp"
-
-BOOST_AUTO_TEST_CASE(nn_elman)
-{
-  using namespace nn;
-  Mlp<Neuron<PfWSum<>, AfSigmoidNoBias<> >, Connection<> > nn(2, 3, 2);
-  nn.init();
-  BOOST_CHECK_EQUAL(nn.get_nb_neurons(), 3 + 3 + 2);
-  BOOST_CHECK_EQUAL(nn.get_nb_connections(), 9 + 6 + 2);
-  std::vector<float> in(2); 
-  in[0] = 1.0f;
-  in[1] = 0.2f;
-  for (unsigned i = 0; i < 1000; ++i)
-    nn.step(in);
-  float out1 = nn.get_outf(0);
-  float out2 = nn.get_outf(1);
-  
-}

modules/dnns_easily_fooled/sferes/modules/nn2/test_nn.cpp

@@ -1,138 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-#define BOOST_TEST_DYN_LINK 
-#define BOOST_TEST_MODULE nn
-
-
-#include <boost/test/unit_test.hpp>
-#include <boost/timer.hpp>
-#include <iostream>
-#include <cmath>
-#include <algorithm>
-#include "nn.hpp"
-
-BOOST_AUTO_TEST_CASE(nn_basic)
-{
-  using namespace nn;
-  
-  NN<Neuron<PfWSum<>, AfTanh<float> >, Connection<> > nn1, nn2, nn3;
- 
-  nn1.set_nb_inputs(1);
-  nn1.set_nb_outputs(2);
-  nn1.full_connect(nn1.get_inputs(), nn1.get_outputs(), 1.0f);
-  nn1.init();
-  std::vector<float> in(1); 
-  in[0] = 1.0f;
-  for (size_t i = 0; i < 200; ++i)
-    nn1.step(in);
-
-  float out = nn1.get_outf(0);
-  BOOST_CHECK_CLOSE((double)out, tanh(5.0 * 1.0f), 1e-5);
-  std::ofstream ofs("/tmp/test.dot");
-  nn1.write(ofs);
-  
-  // check memory usage
-  nn2 = nn1;
-  for (size_t i = 0; i < 2000; ++i)
-    {
-      nn3 = nn2;
-      nn2 = nn1;
-      nn1 = nn3;
-    }
-  BOOST_CHECK_EQUAL(nn3.get_nb_connections(), nn1.get_nb_connections());
-  BOOST_CHECK_EQUAL(nn3.get_nb_neurons(), nn1.get_nb_neurons());
-  BOOST_CHECK_EQUAL(nn3.get_nb_inputs(), nn1.get_nb_inputs());
-  BOOST_CHECK_EQUAL(nn3.get_nb_outputs(), nn1.get_nb_outputs());
-  
-}
-
-
-BOOST_AUTO_TEST_CASE(nn_remove_small_weights)
-{
-  using namespace nn;
-  NN<Neuron<PfWSum<>, AfTanh<float> >, Connection<> > nn;
-
-  nn.set_nb_inputs(3);
-  nn.set_nb_outputs(3);
-  nn.add_connection_w(nn.get_input(0), nn.get_output(0), 0.5);
-  nn.add_connection_w(nn.get_input(1), nn.get_output(0), 0.25);
-  nn.add_connection_w(nn.get_input(2), nn.get_output(2), -0.25);
-  nn.add_connection_w(nn.get_input(0), nn.get_output(2), 0.05);
-  nn.add_connection_w(nn.get_input(2), nn.get_output(1), -0.05);
-  nn.init();
-  BOOST_CHECK_EQUAL(nn.get_nb_connections(), 5);
-  int k = nn.remove_low_weights(0.1);
-  std::cout << k << std::endl;
-  BOOST_CHECK_EQUAL(nn.get_nb_connections(), 3);
-}
-
-
-BOOST_AUTO_TEST_CASE(nn_speed)
-{
-  using namespace nn;
-
-  typedef NN<Neuron<PfWSum<>, AfTanh<float> >, Connection<> > nn_t;
-  nn_t nn;
-  
-  nn.set_nb_inputs(40000);
-  nn.set_nb_outputs(4);
-  typedef std::vector<nn_t::vertex_desc_t> layer_t;
-  //std::vector<layer_t> layers;
-  //  layers.push_back(nn.get_inputs());
-  // for (size_t i = 0; i < 10; ++i)
-  //   {
-  //     layer_t layer;
-  //     for (size_t j = 0; j < 10; ++j)
-  // 	layer.push_back(nn.add_neuron("n"));
-  //     layers.push_back(layer);
-  //   }
-  // layers.push_back(nn.get_outputs());
-  // for (size_t i = 0; i < layers.size() - 1; ++i)
-  //   nn.full_connect(layers[i], layers[i + 1], 1.0);
-  nn.full_connect(nn.get_inputs(), nn.get_outputs(), 0.25);
-  
-  nn.init();
-  boost::timer timer;
-  std::vector<float> in(40000); 
-  std::fill(in.begin(), in.end(), 0.10f);
-  for (size_t i = 0; i < 100; ++i)
-    nn.step(in);
-  std::cout<<"timer (1000 iterations):" << timer.elapsed() << std::endl;
-  std::ofstream ofs("/tmp/test.dot");
-  nn.write(ofs);
-
-}

modules/dnns_easily_fooled/sferes/modules/nn2/test_osc.cpp

@@ -1,93 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//|
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//|
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-#define BOOST_TEST_DYN_LINK
-#define BOOST_TEST_MODULE nn_osc
-
-
-#include <boost/test/unit_test.hpp>
-
-#include <iostream>
-#include <cmath>
-#include <algorithm>
-#include "nn.hpp"
-
-BOOST_AUTO_TEST_CASE(nn_osc)
-{
-  using namespace nn;
-  typedef std::pair<float, float> weight_t;
-  typedef PfIjspeert<params::Vectorf<3> > pf_t;
-  typedef AfDirectT<weight_t> af_t;
-  typedef Neuron<pf_t, af_t, weight_t> neuron_t;
-  typedef Connection<weight_t, weight_t> connection_t;
-  typedef NN<neuron_t, connection_t> nn_t;
-  typedef nn_t::vertex_desc_t vertex_desc_t;
-  nn_t nn;
-  std::vector<vertex_desc_t> vs;
-
-  float omega = 0.6;
-  float x = 0;
-  float r = 0.59;
-  float phi_ij = 0.81;
-  for (size_t i = 0; i < 5; ++i)
-    {
-      vertex_desc_t v = nn.add_neuron(boost::lexical_cast<std::string>(i));
-      nn.get_neuron_by_vertex(v).get_pf().set_omega(omega);
-      nn.get_neuron_by_vertex(v).get_pf().set_x(x);
-      nn.get_neuron_by_vertex(v).get_pf().set_r(r);
-      vs.push_back(v);
-    }
-
-  for (size_t i = 0; i < 4; ++i)
-    {
-      nn.add_connection(vs[i], vs[i + 1], std::make_pair(5, phi_ij));
-      nn.add_connection(vs[i + 1], vs[i], std::make_pair(5, -phi_ij));
-    }
-
-
-  nn.init();
-  for (size_t s = 0; s < 1000; ++s)
-    {
-      std::vector<weight_t> in;
-      nn.step(in);
-      for (size_t i = 0; i < vs.size(); ++i)
-	std::cout<< i << " "
-		 << nn.get_neuron_by_vertex(vs[i]).get_pf().get_theta_i()
-		 << std::endl;
-    }
-  // you should have beautiful oscillations
-
-}

modules/dnns_easily_fooled/sferes/modules/nn2/trait.hpp

@@ -1,127 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-#ifndef _NN_TRAIT_HPP_
-#define _NN_TRAIT_HPP_
-
-// for std::pair
-#include <map>
-#include <valarray>
-
-#ifdef EIGEN3_ENABLED
-#include <Eigen/Core>
-#endif
-
-#include "params.hpp"
-
-namespace nn
-{
-  template<typename T>
-  struct trait
-  {
-    static T zero() { return T(0.0f); }
-    // a 0 initializer for vectors
-    static T zero(size_t k) { return zero(); }
-    typedef std::valarray<T> vector_t;
-    static size_t size(const T& t) { return t.size(); }
-    static typename T::type_t single_value(const T& t) { assert(t.size() == 1); return t.data(0); }
-  };
-
-  template<>
-  struct trait<params::Dummy>
-  {
-    typedef std::valarray<float> vector_t;
-    static float zero() { return 0.0f; }
-    static float zero(size_t k) { return zero(); }
-    static float single_value(const params::Dummy& t) { return 0.0f; }
-    static size_t size(const params::Dummy&) { return 0; }
-  };
-
-
-  // go with eigen with float (TODO : double)
-  template<>
-  struct trait<float>
-  {
-    typedef Eigen::VectorXf vector_t;
-    static float zero() { return 0.0f; }
-    static vector_t zero(size_t k) { return Eigen::VectorXf::Zero(k); }
-    static float single_value(const float& t) { return t; }
-    static size_t size(const float& t) { return 1; }
-  };
-
-  template<>
-  struct trait<std::pair<float, float> >
-  {
-    typedef std::valarray<std::pair<float, float> > vector_t;
-    static std::pair<float, float> zero() { return std::make_pair(0.0f, 0.0f); }
-    static std::pair<float, float> zero(size_t k) { return zero(); }
-    static float single_value(const std::pair<float, float>& t) { return t.first; }
-    static size_t size(const std::pair<float, float>& t) { return 2; }
-  };
-
-  // useful but wrong place (?)
-  template<typename _CharT, typename _Traits>
-  std::basic_ostream<_CharT, _Traits>&
-  operator<<(std::basic_ostream<_CharT, _Traits>& ofs, const std::pair<float, float>& p)
-  {
-    return ofs<<p.first<<" "<<p.second;
-  }
-
- template<typename _CharT, typename _Traits>
-  std::basic_ostream<_CharT, _Traits>&
- operator<<(std::basic_ostream<_CharT, _Traits>& ofs, const std::vector<float>& p)
-  {
-    for (size_t i = 0; i < p.size(); ++i)
-      ofs<<p[i]<<" ";
-    return ofs;
-  }
-
-
-  template<typename T1, typename T2>
-  std::istream& operator>>(std::istream& ifs, std::pair<T1, T2>& p)
-  {
-    T1 t1;
-    T2 t2;
-    ifs >> t1;
-    ifs >> t2;
-    return std::make_pair(t1, t2);
-  }
- 
-}
-
-
-#endif

modules/dnns_easily_fooled/sferes/modules/nn2/wscript

@@ -1,114 +0,0 @@
-#! /usr/bin/env python
-#| This file is a part of the sferes2 framework.
-#| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-#| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-#|
-#| This software is a computer program whose purpose is to facilitate
-#| experiments in evolutionary computation and evolutionary robotics.
-#| 
-#| This software is governed by the CeCILL license under French law
-#| and abiding by the rules of distribution of free software.  You
-#| can use, modify and/ or redistribute the software under the terms
-#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-#| following URL "http://www.cecill.info".
-#| 
-#| As a counterpart to the access to the source code and rights to
-#| copy, modify and redistribute granted by the license, users are
-#| provided only with a limited warranty and the software's author,
-#| the holder of the economic rights, and the successive licensors
-#| have only limited liability.
-#|
-#| In this respect, the user's attention is drawn to the risks
-#| associated with loading, using, modifying and/or developing or
-#| reproducing the software by the user in light of its specific
-#| status of free software, that may mean that it is complicated to
-#| manipulate, and that also therefore means that it is reserved for
-#| developers and experienced professionals having in-depth computer
-#| knowledge. Users are therefore encouraged to load and test the
-#| software's suitability as regards their requirements in conditions
-#| enabling the security of their systems and/or data to be ensured
-#| and, more generally, to use and operate it in the same conditions
-#| as regards security.
-#|
-#| The fact that you are presently reading this means that you have
-#| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-
-import os
-
-def set_options(blah) : pass
-
-def configure(blah): pass
-
-def build(bld):
-    print ("Entering directory `" + os.getcwd() + "/modules/'")
-    test_nn = bld.new_task_gen('cxx', 'program')
-    test_nn.source = 'test_nn.cpp'
-    test_nn.includes = '. ../../'
-    test_nn.uselib_local = ''
-    test_nn.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK'
-    test_nn.target = 'test_nn'
-    test_nn.unit_test = 1
-
-    test_dnn = bld.new_task_gen('cxx', 'program')
-    test_dnn.source = 'test_dnn.cpp'
-    test_dnn.includes = '. ../../'
-    test_dnn.uselib_local = 'sferes2'
-    test_dnn.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_dnn.target = 'test_dnn'
-    test_dnn.unit_test = 1
-
-    test_mlp = bld.new_task_gen('cxx', 'program')
-    test_mlp.source = 'test_mlp.cpp'
-    test_mlp.includes = '. ../../'
-    test_mlp.uselib_local = 'sferes2'
-    test_mlp.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_mlp.target = 'test_mlp'
-    test_mlp.unit_test = 1
-
-    test_esn = bld.new_task_gen('cxx', 'program')
-    test_esn.source = 'test_hyper_nn.cpp'
-    test_esn.includes = '. ../../'
-    test_esn.uselib_local = 'sferes2'
-    test_esn.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_esn.target = 'test_hyper_nn'
-    test_esn.unit_test = 1
-
-
-    test_esn = bld.new_task_gen('cxx', 'program')
-    test_esn.source = 'test_dnn_ff.cpp'
-    test_esn.includes = '. ../../'
-    test_esn.uselib_local = 'sferes2'
-    test_esn.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_esn.target = 'test_dnn_ff'
-    test_esn.unit_test = 1
-
-
-
-    test_osc = bld.new_task_gen('cxx', 'program')
-    test_osc.source = 'test_osc.cpp'
-    test_osc.includes = '. ../../'
-    test_osc.uselib_local = 'sferes2'
-    test_osc.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_osc.target = 'test_osc'
-    test_osc.unit_test = 1
-
-
-    bench_nn = bld.new_task_gen('cxx', 'program')
-    bench_nn.source = 'bench_nn.cpp'
-    bench_nn.includes = '. ../../'
-    bench_nn.uselib_local = 'sferes2'
-    bench_nn.uselib = 'EIGEN3 BOOST_GRAPH BOOST'
-    bench_nn.target = 'bench_nn'
-    
-    # Added tests by Anh --------------------------------------------------------------------
-    test_esn = bld.new_task_gen('cxx', 'program')
-    test_esn.source = 'test_hyper_nn_anh.cpp'
-    test_esn.includes = '. ../../'
-    test_esn.uselib_local = 'sferes2'
-    test_esn.uselib = 'EIGEN3 BOOST BOOST_GRAPH BOOST_UNIT_TEST_FRAMEWORK BOOST_SERIALIZATION'
-    test_esn.target = 'test_hyper_nn_anh'
-    test_esn.unit_test = 1
-

modules/dnns_easily_fooled/sferes/mpi.py

@@ -1,43 +0,0 @@
-#! /usr/bin/env python
-# encoding: utf-8
-# JB Mouret - 2009
-
-"""
-Quick n dirty mpi detection
-"""
-
-import os, glob, types
-import Options, Configure
-
-
-def detect_mpi(conf):
-	env = conf.env
-	opt = Options.options
-
-	conf.env['LIB_MPI'] = ''
-	conf.env['MPI_FOUND'] = False
-	if Options.options.no_mpi :
-		return
-	if Options.options.mpi:
-		conf.env['CPPPATH_MPI'] = Options.options.mpi + '/include'
-		conf.env['LIBPATH_MPI'] = Options.options.mpi + '/lib'
-	else:
-		conf.env['CPPPATH_MPI'] = ['/usr/include/mpi', '/usr/local/include/mpi', '/usr/include', '/usr/local/include']
-		conf.env['LIBPATH_MPI'] = ['/usr/lib', '/usr/local/lib', '/usr/lib/openmpi']
-
-	res = Configure.find_file('mpi.h', conf.env['CPPPATH_MPI'] )
-	conf.check_message('header','mpi.h', (res != '') , res)
-	if (res == '') :
-		return 0
-	conf.env['MPI_FOUND'] = True
-	conf.env['LIB_MPI'] = ['mpi_cxx','mpi']
-	return 1
-
-def detect(conf):
-	return detect_mpi(conf)
-
-def set_options(opt):
-	opt.add_option("--no-mpi",
-		       default=False, action='store_true',
-		       help='disable mpi', dest='no_mpi')
-	opt.add_option('--mpi', type='string', help='path to mpi', dest='mpi')

modules/dnns_easily_fooled/sferes/ode.py

@@ -1,32 +0,0 @@
-
-#! /usr/bin/env python
-# encoding: utf-8
-# JB Mouret - 2009
-
-"""
-Quick n dirty ODE detection
-"""
-
-import os, glob, types
-import Options, Configure, config_c
-
-import commands
-
-def detect_ode(conf):
-    env = conf.env
-    opt = Options.options
-    ret = conf.find_program('ode-config')
-    conf.check_message_1('Checking for ODE (optional)')
-    if not ret:
-        conf.check_message_2('not found', 'YELLOW')
-        return 0
-    conf.check_message_2('ok')
-    res = commands.getoutput('ode-config --cflags --libs')
-    config_c.parse_flags(res, 'ODE', env)
-    return 1
-
-def detect(conf):
-    return detect_ode(conf)
-
-def set_options(opt):
-    pass

modules/dnns_easily_fooled/sferes/scripts/add_license.sh

@@ -1,17 +0,0 @@
-#!/bin/sh
-set -x
-PATHS='sferes scripts tests modules/nn modules/cartpole'
-FILES=`find $PATHS |egrep "(.hpp|.cpp)$"`
-for i in $FILES; do
-    grep -v "//|" $i >/tmp/file
-    cp scripts/license_cpp.txt $i
-    cat /tmp/file >> $i
-done
-
-FILES=`find $PATHS |egrep "(wscript|.py)$"`
-FILES="$FILES wscript"
-for i in $FILES; do
-    grep -v "#|" $i|grep -v "#!" >/tmp/file
-    cat scripts/license_py.txt >> $i
-    cat /tmp/file >> $i
-done

modules/dnns_easily_fooled/sferes/scripts/error_parser.rb

@@ -1,72 +0,0 @@
-#!/usr/bin/ruby
-## error_parser.rb
-## Login : <mandor@ithaqua>
-## Started on  Fri Apr  4 12:28:15 2008 mandor
-## $Id$
-## 
-## Copyright (C) 2008 mandor
-
-
-
-#| This file is a part of the sferes2 framework.
-#| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-#| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-#|
-#| This software is a computer program whose purpose is to facilitate
-#| experiments in evolutionary computation and evolutionary robotics.
-#| 
-#| This software is governed by the CeCILL license under French law
-#| and abiding by the rules of distribution of free software.  You
-#| can use, modify and/ or redistribute the software under the terms
-#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-#| following URL "http://www.cecill.info".
-#| 
-#| As a counterpart to the access to the source code and rights to
-#| copy, modify and redistribute granted by the license, users are
-#| provided only with a limited warranty and the software's author,
-#| the holder of the economic rights, and the successive licensors
-#| have only limited liability.
-#|
-#| In this respect, the user's attention is drawn to the risks
-#| associated with loading, using, modifying and/or developing or
-#| reproducing the software by the user in light of its specific
-#| status of free software, that may mean that it is complicated to
-#| manipulate, and that also therefore means that it is reserved for
-#| developers and experienced professionals having in-depth computer
-#| knowledge. Users are therefore encouraged to load and test the
-#| software's suitability as regards their requirements in conditions
-#| enabling the security of their systems and/or data to be ensured
-#| and, more generally, to use and operate it in the same conditions
-#| as regards security.
-#|
-#| The fact that you are presently reading this means that you have
-#| had knowledge of the CeCILL license and that you accept its terms.
-
-
-
-def parse(line, level)
-  stack = [""]
-  if !line.include?("instantiated") and (line.include?("error") || line.include?("erreur")) then
-    line.scan(/./) { |c|
-      if c == '<' then
-        print "#{stack[0]}<" if stack.length < level + 2 
-        stack[0] = ""
-        stack.unshift("")
-      elsif c == '>' then
-        print stack[0] if stack.length < level + 2 
-        stack.shift()
-        print ">"
-      else     
-        stack[0] += c if stack[0]
-      end      
-    }
-    print stack[0] if stack[0]
-    print "\n"
-
-  end
-end
-
-level = ARGV[0].to_i
-while $stdin.gets do
-  parse($_, level)
-end

modules/dnns_easily_fooled/sferes/scripts/license_cpp.txt

@@ -1,34 +0,0 @@
-//| This file is a part of the sferes2 framework.
-//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-//|
-//| This software is a computer program whose purpose is to facilitate
-//| experiments in evolutionary computation and evolutionary robotics.
-//| 
-//| This software is governed by the CeCILL license under French law
-//| and abiding by the rules of distribution of free software.  You
-//| can use, modify and/ or redistribute the software under the terms
-//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-//| following URL "http://www.cecill.info".
-//| 
-//| As a counterpart to the access to the source code and rights to
-//| copy, modify and redistribute granted by the license, users are
-//| provided only with a limited warranty and the software's author,
-//| the holder of the economic rights, and the successive licensors
-//| have only limited liability.
-//|
-//| In this respect, the user's attention is drawn to the risks
-//| associated with loading, using, modifying and/or developing or
-//| reproducing the software by the user in light of its specific
-//| status of free software, that may mean that it is complicated to
-//| manipulate, and that also therefore means that it is reserved for
-//| developers and experienced professionals having in-depth computer
-//| knowledge. Users are therefore encouraged to load and test the
-//| software's suitability as regards their requirements in conditions
-//| enabling the security of their systems and/or data to be ensured
-//| and, more generally, to use and operate it in the same conditions
-//| as regards security.
-//|
-//| The fact that you are presently reading this means that you have
-//| had knowledge of the CeCILL license and that you accept its terms.
-

modules/dnns_easily_fooled/sferes/scripts/license_py.txt

@@ -1,35 +0,0 @@
-#! /usr/bin/env python
-#| This file is a part of the sferes2 framework.
-#| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
-#| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
-#|
-#| This software is a computer program whose purpose is to facilitate
-#| experiments in evolutionary computation and evolutionary robotics.
-#| 
-#| This software is governed by the CeCILL license under French law
-#| and abiding by the rules of distribution of free software.  You
-#| can use, modify and/ or redistribute the software under the terms
-#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
-#| following URL "http://www.cecill.info".
-#| 
-#| As a counterpart to the access to the source code and rights to
-#| copy, modify and redistribute granted by the license, users are
-#| provided only with a limited warranty and the software's author,
-#| the holder of the economic rights, and the successive licensors
-#| have only limited liability.
-#|
-#| In this respect, the user's attention is drawn to the risks
-#| associated with loading, using, modifying and/or developing or
-#| reproducing the software by the user in light of its specific
-#| status of free software, that may mean that it is complicated to
-#| manipulate, and that also therefore means that it is reserved for
-#| developers and experienced professionals having in-depth computer
-#| knowledge. Users are therefore encouraged to load and test the
-#| software's suitability as regards their requirements in conditions
-#| enabling the security of their systems and/or data to be ensured
-#| and, more generally, to use and operate it in the same conditions
-#| as regards security.
-#|
-#| The fact that you are presently reading this means that you have
-#| had knowledge of the CeCILL license and that you accept its terms.
-

modules/dnns_easily_fooled/sferes/setup.sh

@@ -1,11 +0,0 @@
-#!/bin/bash
-
-# Setup the exp/images experiment
-cd exp/images/
-./build_wscript.sh
-cd ../../
-
-dir=$(echo $(pwd))
-#echo "Please set LOCAL_RUN in $dir/exp/images/settings.h"
-#vim $dir/exp/images/settings.h
-echo "Done setting up."

modules/dnns_easily_fooled/sferes/sferes.py

@@ -1,390 +0,0 @@
-import sys, os
-import subprocess
-import commands
-
-json_ok = True
-try:
-   import simplejson
-except:
-   json_ok = False
-   print "WARNING simplejson not found some function may not work"
-
-import glob
-#import xml.etree.cElementTree as etree
-import Options
-
-
-
-def create_variants(bld, source, uselib_local, target, 
-                    uselib, variants, includes=". ../../", 
-                    cxxflags='',
-                    json=''):
-   # the basic one
-   #   tgt = bld.new_task_gen('cxx', 'program')
-   #   tgt.source = source
-   #   tgt.includes = includes
-   #   tgt.uselib_local = uselib_local
-   #   tgt.uselib = uselib
-   #   tgt.target = target
-   # the variants
-   c_src = bld.path.abspath() + '/'
-   for v in variants:
-      # create file
-      suff = ''
-      for d in v.split(' '): suff += d.lower() + '_'
-      tmp = source.replace('.cpp', '')
-      src_fname = tmp + '_' + suff[0:len(suff) - 1] + '.cpp'
-      f = open(c_src + src_fname, 'w')
-      f.write("// THIS IS A GENERATED FILE - DO NOT EDIT\n")
-      for d in v.split(' '): f.write("#define " + d + "\n")
-      f.write("#line 1 \"" + c_src + source + "\"\n")
-      code = open(c_src + source, 'r')
-      for line in code: f.write(line)
-      bin_name = src_fname.replace('.cpp', '')
-      bin_name = os.path.basename(bin_name)
-      # create build
-      tgt = bld.new_task_gen('cxx', 'program')
-      tgt.source = src_fname
-      tgt.includes = includes
-      tgt.uselib_local = uselib_local
-      tgt.uselib = uselib
-      tgt.target = bin_name
-      tgt.cxxflags = cxxflags
-
-
-def create_exp(name):
-   ws_tpl = """
-#! /usr/bin/env python
-def build(bld):
-    obj = bld.new_task_gen('cxx', 'program')
-    obj.source = '@exp.cpp'
-    obj.includes = '. ../../'
-    obj.uselib_local = 'sferes2'
-    obj.uselib = ''
-    obj.target = '@exp'
-    obj.uselib_local = 'sferes2'
-"""
-   os.mkdir('exp/' + name)
-   os.system("cp examples/ex_ea.cpp exp/" + name + "/" + name + ".cpp")
-   wscript = open('exp/' + name + "/wscript", "w")
-   wscript.write(ws_tpl.replace('@exp', name))
-   
-
-def parse_modules():
-   if (not os.path.exists("modules.conf")):
-      return []
-   mod = open("modules.conf")
-   modules = []
-   for i in mod:
-      if i[0] != '#' and len(i) != 1:
-         modules += ['modules/' + i[0:len(i)-1]]
-   return modules
-
-def qsub(conf_file):
-   tpl = """
-#! /bin/sh
-#? nom du job affiche
-#PBS -N @exp
-#PBS -o stdout
-#PBS -b stderr
-#PBS -M @email
-# maximum execution time
-#PBS -l walltime=@wall_time
-# mail parameters
-#PBS -m abe
-# number of nodes
-#PBS -l nodes=@nb_cores:ppn=@ppn
-#PBS -l pmem=5200mb -l mem=5200mb
-export LD_LIBRARY_PATH=@ld_lib_path
-exec @exec
-"""
-   if os.environ.has_key('LD_LIBRARY_PATH'):
-      ld_lib_path = os.environ['LD_LIBRARY_PATH']
-   else:
-      ld_lib_path = "''"
-   home = os.environ['HOME']
-   print 'LD_LIBRARY_PATH=' + ld_lib_path
-    # parse conf
-   conf = simplejson.load(open(conf_file))
-   exps = conf['exps']
-   nb_runs = conf['nb_runs']
-   res_dir = conf['res_dir']
-   bin_dir = conf['bin_dir']
-   wall_time = conf['wall_time']
-   use_mpi = "false"
-   try: use_mpi = conf['use_mpi']
-   except: use_mpi = "false"
-   try: nb_cores = conf['nb_cores']
-   except: nb_cores = 1
-   try: args = conf['args']
-   except: args = ''
-   email = conf['email']
-   if (use_mpi == "true"): 
-      ppn = '1'
-      mpirun = 'mpirun'
-   else:
-      nb_cores = 1; 
-      ppn = '8'
-      mpirun = ''
-   
-   for i in range(0, nb_runs):
-      for e in exps:
-         directory = res_dir + "/" + e + "/exp_" + str(i) 
-         try:
-            os.makedirs(directory)
-         except:
-            print "WARNING, dir:" + directory + " not be created"
-         subprocess.call('cp ' + bin_dir + '/' + e + ' ' + directory, shell=True)
-         fname = home + "/tmp/" + e + "_" + str(i) + ".job"
-         f = open(fname, "w")
-         f.write(tpl
-                 .replace("@exp", e)
-                 .replace("@email", email)
-                 .replace("@ld_lib_path", ld_lib_path)
-                 .replace("@wall_time", wall_time)
-                 .replace("@dir", directory)
-                 .replace("@nb_cores", str(nb_cores))
-                 .replace("@ppn", ppn)
-                 .replace("@exec", mpirun + ' ' + directory + '/' + e + ' ' + args))
-         f.close()
-         s = "qsub -d " + directory + " " + fname
-         print "executing:" + s
-         retcode = subprocess.call(s, shell=True, env=None)
-         print "qsub returned:" + str(retcode)
-
-def loadleveler(conf_file):
-   tpl = """
-# @ job_name=<name>
-# @ output = $(job_name).$(jobid) 
-# @ error = $(output) 
-# @ job_type = serial 
-# @ class = <class> 
-# @ resources=ConsumableMemory(<memory>) ConsumableCpus(<cpu>)
-# @ queue
-export LD_LIBRARY_PATH=<ld_library_path>
-cd <initial_dir>
-./<exec>
-"""
-   if os.environ.has_key('LD_LIBRARY_PATH'):
-      ld_lib_path = os.environ['LD_LIBRARY_PATH']
-   else:
-      ld_lib_path = "''"
-   home = os.environ['HOME']
-   print 'LD_LIBRARY_PATH=' + ld_lib_path
-    # parse conf
-   conf = simplejson.load(open(conf_file))
-   jobname = conf['jobname']
-   exps = conf['exps']
-   nb_runs = conf['nb_runs']
-   res_dir = conf['res_dir']
-   bin_dir = conf['bin_dir']
-   jobclass = conf['class']
-   try:
-      memory=conf['memory']
-   except:
-      memory=3000
-   try:
-      cpu=conf['cpu']
-   except:
-      cpu=1
-   
-   for i in range(0, nb_runs):
-      for e in exps:
-         directory = res_dir + "/" + e + "/exp_" + str(i) 
-         try:
-            os.makedirs(directory)
-         except:
-            print "WARNING, dir:" + directory + " cannot be created"
-         subprocess.call('cp ' + bin_dir + '/' + e + ' ' + directory, shell=True)
-         try:
-            os.makedirs(home+"/tmp")
-         except:
-            pass
-         fname = home + "/tmp/" + e + "_" + str(i) + ".job"
-         f = open(fname, "w")
-         f.write(tpl
-                 .replace("<name>", jobname)
-                 .replace("<ld_library_path>", ld_lib_path)
-                 .replace("<class>", jobclass)
-                 .replace("<initial_dir>", directory)
-                 .replace("<memory>", str(memory))
-                 .replace("<cpu>", str(cpu))
-                 .replace("<exec>", e))
-         f.close()
-         s = "llsubmit "+ fname
-         print "executing:" + s
-         retcode = subprocess.call(s, shell=True, env=None)
-         print "llsubmit returned:" + str(retcode)
-
-
-
-
-def time_travel(conf_file):
-   print 'time_travel, conf = ' + conf_file
-   conf = simplejson.load(open(conf_file))
-   dir = conf['dir']
-   # get the diff
-   patch = glob.glob(dir + '/*.diff')[0].split('/')
-   patch = patch[len(patch) - 1]
-   version = patch[0:len(patch) - len('.diff')]
-   cwd = os.getcwd()
-   patch = cwd + '/' + dir + '/' + patch
-   # checkout
-   print 'svn co -r ' + version
-   os.system('cd ' + dir + ' && svn -r ' + version + ' co https://webia.lip6.fr:2004/svn/robur/sferes2')
-   os.system('cd ' + dir + '/sferes2 && patch -p0 < '+ patch)
-   os.chdir(cwd)
-
-
-def get_gen(x):
-   g1 = x.split('_')
-   gen1 = int(g1[len(g1)-1])
-   return gen1
-
-
-def get_exe(conf_file):
-    return os.path.split(conf_file.replace('.json', ''))[1]
-
-
-def compare_gen(x, y):
-    return get_gen(x) - get_gen(y)
-
-
-def kill(conf_file):
-   print 'kill, conf =' + conf_file
-   exe = get_exe(conf_file)
-   conf = simplejson.load(open(conf_file))
-   machines = conf['machines']
-   if conf['debug'] == 1:
-      exe += '_debug'
-   else:
-      exe += '_opt'
-   print 'kill '+ exe
-   for m in machines:
-       print m
-       s = "ssh -o CheckHostIP=false -f " + m + \
-           " killall -9 " + exe
-       print s
-       os.system(s)
-
-
-
-def status(conf):
-   # parse configuration
-   print 'status, conf = ' + conf
-   conf = simplejson.load(open(conf))
-   exp = conf['exp']
-   dir = conf['dir']
-
-   exps = glob.glob(dir + '/exp_*/')
-   total = 0.0
-   for i in exps:
-      glist = glob.glob(i + '*/gen_*')
-      glist.sort(cmp=compare_gen)
-      last = glist[len(glist) - 1]
-      last_gen = get_gen(last)
-      r = ''
-      try:
-         tree = etree.parse(last)
-         l = tree.find("//x/_pareto_front/item/px/_fit/_objs")
-         if l == None:
-            l = tree.find("//x/_best/px/_fit/_value")
-            total += float(l.text)
-            r = l.text
-         else:
-            l = l[1:len(l)]
-            total +=  float(l[0].text)
-            for k in l:
-               r +=  k.text + ' '
-         print i + ' :\t' + str(last_gen) + '\t=> ' + r
-      except:
-        print "error"
-   total /= len(exps)
-   print "=> " + str(total)
-
-
-def get_exp(conf_file):
-   conf = simplejson.load(open(conf_file))
-   return conf['exp'].sub('exp/', '')
-
-def launch_exp(conf_file):
-   print '--- launch exp ---'
-
-   # parse configuration
-   print 'launch, conf = ' + conf_file
-   conf = simplejson.load(open(conf_file))
-   machines = conf['machines']
-   nb_runs = conf['nb_runs']
-   exp = conf['exp']
-   directory = conf['dir']
-   debug = conf['debug']
-   
-   args = ""
-   if 'args' in conf : args=conf['args']
-   print 'exp = ' + exp
-   print 'dir = ' + directory
-   print 'nb_runs = ' + str(nb_runs)
-   print 'debug = ' + str(debug)
-   print 'machines =' + str(machines)
-   print 'args =' + str(args)
-
-   # copy binaries (debug and opt) & json file
-   exe = get_exe(conf_file)
-   try:
-      os.makedirs(directory + '/bin')
-      os.system('cp ' + 'build/default/' + exp +'/'+exe+ ' ' + directory + '/bin/' + exe + '_opt')
-      os.system('cp ' + 'build/debug/' + exp +'/'+exe+ ' ' + directory + '/bin/' + exe + '_debug')
-      print conf
-      print directory
-      os.system('cp ' + conf_file + ' ' + directory)
-      # create directories
-      for i in range(0, nb_runs * len(machines)):
-         os.makedirs(directory + '/exp_' + str(i))
-   except:
-      print '/!\ files exist, I cannot replace them'
-      return
-   print 'dirs created'
-
-   # make a svn diff
-   status, version = commands.getstatusoutput('svnversion') 
-   if version[len(version)-1] == 'M':
-      version = version[0:len(version)-1]
-   os.system('svn diff >' + directory + '/' + version + '.diff')
-   print 'diff done [version=' + version + ']'
-
-   # run on each machines
-   if debug == 1:
-      exe = exe + '_debug'
-   else:
-      exe = exe + '_opt'
-   if os.environ.has_key('LD_LIBRARY_PATH'):
-      ld_lib_path = os.environ['LD_LIBRARY_PATH']
-   else:
-      ld_lib_path = "''"
-   k = 0
-   pids = []
-   for m in machines.iterkeys() :
-      pid = os.fork() 
-      if (pid == 0): #son
-         for i in range(0, machines[m]):
-            if m == 'localhost':
-               s = "export LD_LIBRARY_PATH=" + ld_lib_path + \
-                   " && cd " + os.getcwd() + '/' + directory + '/exp_'+ str(i + k) + \
-                   " && " +  os.getcwd() + '/' + directory + '/bin/' + exe + " " + args + \
-                   " 1> stdout 2> stderr"
-            else:
-               s = "ssh -o CheckHostIP=false " + m + \
-                   " 'export LD_LIBRARY_PATH=" + ld_lib_path + \
-                   " && cd " + os.getcwd() + '/' + directory + '/exp_'+ str(i + k) + \
-                   " && " +  os.getcwd() + '/' + directory + '/bin/' + exe + " " + args + \
-                   " 1> stdout 2> stderr'"
-            print 'run ' + str(i + k) + ' on ' + m
-	    print s
-            ret = subprocess.call(s, shell=True)            
-            print "ret = " + str(ret)
-         exit(0)
-      pids += [pid]
-      k += machines[m]
-   print "waitpid..."
-   for i in pids:
-      os.waitpid(i, 0)