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Refactor edoBinomialMulti to allow more complex data structures

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Refactor distribution, sampler and estimator related to the multi-binomial distribution.
This introduce tomic methods which may be overloaded for data structures more complex than eoReal of vector of bool (the
default implentation).
This commit is contained in:
Johann Dreo 2013-04-18 10:11:32 +02:00 • committed by LPTK
commit 5caa49012f
3 changed files with 89 additions and 42 deletions
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31
edo/src/edoSamplerBinomialMulti.h
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@ -44,6 +44,24 @@ template< class EOT, class D = edoBinomialMulti<EOT> >
class edoSamplerBinomialMulti : public edoSampler<D>
{
public:
typedef typename EOT::AtomType AtomType;
/** Called if the sampler draw the item at (i,j)
*
* The default implementation is to push back a true boolean.
* If you have a more complex data structure, you can just overload this.
*/
virtual void make_true( AtomType & atom, unsigned int i, unsigned int j )
{
atom.push_back( 1 );
}
/** @see make_true */
virtual void make_false( AtomType & atom, unsigned int i, unsigned int j )
{
atom.push_back( 0 );
}
EOT sample( D& distrib )
{
unsigned int rows = distrib.rows();
@ -52,17 +70,22 @@ public:
assert(cols > 0);
// The point we want to draw
// x = {x1, x2, ..., xn}
// X = {x1, x2, ..., xn}
// with xn a container of booleans
EOT solution;
// Sampling all dimensions
for( unsigned int i = 0; i < rows; ++i ) {
typename EOT::AtomType vec;
AtomType atom;
for( unsigned int j = 0; j < cols; ++j ) {
// Toss a coin, biased by the proba of being 1.
vec.push_back( rng.flip( distrib(i,j) ) );
if( rng.flip( distrib(i,j) ) ) {
make_true( atom, i, j );
} else {
make_false( atom, i, j );
}
}
solution.push_back( vec );
solution.push_back( atom );
}
return solution;