Add multi-Binomial distrib operators in EDO

So as to model vector<vector<bool>> individuals with 2D binomial distributions (as Eigen matrix).

edo/test/CMakeLists.txt

@@ -41,6 +41,7 @@ set(SOURCES
 #  t-dispatcher-round
   t-repairer-modulo
   t-binomial
+  t-binomialmulti
   )
 
 foreach(current ${SOURCES})

edo/test/t-binomialmulti.cpp

@@ -0,0 +1,87 @@
+/*
+The Evolving Distribution Objects framework (EDO) is a template-based,
+ANSI-C++ evolutionary computation library which helps you to write your
+own estimation of distribution algorithms.
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+
+Copyright (C) 2013 Thales group
+*/
+/*
+Authors:
+    Johann Dréo <johann.dreo@thalesgroup.com>
+*/
+
+#include <vector>
+#include <iostream>
+#include <string>
+#include <cmath>
+
+#include <eo>
+#include <edo>
+#include <ga.h> // for Bools
+
+
+#ifdef WITH_EIGEN
+#include <Eigen/Dense>
+
+// NOTE: a typedef on eoVector does not work, because of readFrom on a vector AtomType
+// typedef eoVector<eoMinimizingFitness, std::vector<bool> > Bools;
+class Bools : public std::vector<std::vector<bool> >, public EO<double>
+{
+    public:
+        typedef std::vector<bool> AtomType;
+};
+
+int main(int ac, char** av)
+{
+    eoParser parser(ac, av);
+
+    std::string        section("Algorithm parameters");
+    unsigned int popsize = parser.createParam((unsigned int)100000, "popSize", "Population Size", 'P', section).value(); // P
+    unsigned int rows = parser.createParam((unsigned int)2, "lines", "Lines number", 'l', section).value(); // l
+    unsigned int cols = parser.createParam((unsigned int)3, "columns", "Columns number", 'c', section).value(); // c
+    double proba = parser.createParam((double)0.5, "proba", "Probability to estimate", 'b', section).value(); // b
+
+    if( parser.userNeedsHelp() ) {
+            parser.printHelp(std::cout);
+            exit(1);
+    }
+
+    make_help(parser);
+
+    std::cout << "Init distrib" << std::endl;
+    Eigen::MatrixXd initd = Eigen::MatrixXd::Constant(rows,cols,proba);
+    edoBinomialMulti<Bools> distrib( initd );
+    std::cout << distrib << std::endl;
+
+    edoEstimatorBinomialMulti<Bools> estimate;
+    edoSamplerBinomialMulti<Bools> sample;
+
+    std::cout << "Sample a pop from the init distrib" << std::endl;
+    eoPop<Bools> pop; pop.reserve(popsize);
+    for( unsigned int i=0; i < popsize; ++i ) {
+        pop.push_back( sample( distrib ) );
+    }
+
+    std::cout << "Estimate a distribution from the sampled pop" << std::endl;
+    distrib = estimate( pop );
+    std::cout << distrib << std::endl;
+
+    std::cout << "Estimated initial proba = " << distrib.mean() << std::endl;
+}
+
+
+#endif // WITH_EIGEN