Add multi-Binomial distrib operators in EDO

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

edo/src/edoEstimatorBinomialMulti.h

@@ -0,0 +1,97 @@
+/*
+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>
+*/
+
+#ifndef _edoEstimatorBinomialMulti_h
+#define _edoEstimatorBinomialMulti_h
+
+#include "edoBinomialMulti.h"
+#include "edoEstimator.h"
+
+#ifdef WITH_EIGEN
+#include <Eigen/Dense>
+
+/** An estimator for edoBinomialMulti
+ *
+ * @ingroup Estimators
+ * @ingroup Binomial
+ */
+template< class EOT, class D = edoBinomialMulti<EOT> >
+class edoEstimatorBinomialMulti : public edoEstimator<D>
+{
+    protected:
+        D eot2d( EOT from, unsigned int rows, unsigned int cols ) // FIXME maybe more elegant with Eigen::Map?
+        {
+            assert( rows > 0 );
+            assert( from.size() == rows );
+            assert( cols > 0 );
+
+            D to( Eigen::MatrixXd(rows, cols) );
+            for( unsigned int i=0; i < rows; ++i ) {
+                assert( from[i].size() == cols );
+                for( unsigned int j=0; j < cols; ++j ) {
+                    to(i,j) = from[i][j];
+                }
+            }
+
+            return to;
+        }
+
+    public:
+        /** The expected EOT interface is the same as an Eigen3::MatrixXd.
+         */
+        D operator()( eoPop<EOT>& pop )
+        {
+            unsigned int popsize = pop.size();
+            assert(popsize > 0);
+
+            unsigned int rows = pop[0].size();
+            assert( rows > 0 );
+            unsigned int cols = pop[0][0].size();
+            assert( cols > 0 );
+
+            D probas( D::Zero(rows, cols) );
+
+            // We still need a loop over pop, because it is an eoVector
+            for (unsigned int i = 0; i < popsize; ++i) {
+                D indiv = eot2d( pop[i], rows, cols );
+                assert( indiv.rows() == rows && indiv.cols() == cols );
+
+                // the EOT matrix should be filled with 1 or 0 only
+                assert( indiv.sum() <= popsize );
+
+                probas += indiv / popsize;
+
+                // sum and scalar product, no size pb expected
+                assert( probas.rows() == rows && probas.cols() == cols );
+            }
+
+            return probas;
+        }
+};
+
+#endif // WITH_EIGEN
+#endif // !_edoEstimatorBinomialMulti_h
+