/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    GNU General Public License Version 3.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin subgraph_sparsity.cpp$$
$spell
	Subgraph
$$

$section Subgraph Dependency Sparsity Patterns: Example and Test$$


$code
$srcfile%example/sparse/subgraph_sparsity.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++
# include <cppad/cppad.hpp>

bool subgraph_sparsity(void)
{	bool ok = true;
	using CppAD::AD;
	typedef CPPAD_TESTVECTOR(size_t)     SizeVector;
	typedef CppAD::sparse_rc<SizeVector> sparsity;
	//
	// domain space vector
	size_t n = 2;
	CPPAD_TESTVECTOR(AD<double>) ax(n);
	ax[0] = 0.;
	ax[1] = 1.;

	// declare independent variables and start recording
	CppAD::Independent(ax);

	// range space vector
	size_t m = 3;
	CPPAD_TESTVECTOR(AD<double>) ay(m);
	ay[0] = ax[0];
	ay[1] = ax[0] * ax[1];
	ay[2] = ax[1];

	// create f: x -> y and stop tape recording
	CppAD::ADFun<double> f(ax, ay);



	// compute sparsite pattern for F'(x)
	CPPAD_TESTVECTOR(bool) select_domain(n), select_range(m);
	for(size_t j = 0; j < n; j++)
		select_domain[j] = true;
	for(size_t i = 0; i < m; i++)
		select_range[i] = true;
	bool transpose       = false;
	sparsity pattern_out;
	f.subgraph_sparsity(select_domain, select_range, transpose, pattern_out);

	// check sparsity pattern
	size_t nnz = pattern_out.nnz();
	ok        &= nnz == 4;
	ok        &= pattern_out.nr() == m;
	ok        &= pattern_out.nc() == n;
	{	// check results
		const SizeVector& row( pattern_out.row() );
		const SizeVector& col( pattern_out.col() );
		SizeVector col_major = pattern_out.col_major();
		//
		ok &= row[ col_major[0] ] ==  0  && col[ col_major[0] ] ==  0;
		ok &= row[ col_major[1] ] ==  1  && col[ col_major[1] ] ==  0;
		ok &= row[ col_major[2] ] ==  1  && col[ col_major[2] ] ==  1;
		ok &= row[ col_major[3] ] ==  2  && col[ col_major[3] ] ==  1;
	}
	// note that the transpose of the identity is the identity
	transpose     = true;
	f.subgraph_sparsity(select_domain, select_range, transpose, pattern_out);
	//
	nnz  = pattern_out.nnz();
	ok  &= nnz == 4;
	ok  &= pattern_out.nr() == n;
	ok  &= pattern_out.nc() == m;
	{	// check results
		const SizeVector& row( pattern_out.row() );
		const SizeVector& col( pattern_out.col() );
		SizeVector row_major = pattern_out.row_major();
		//
		ok &= col[ row_major[0] ] ==  0  && row[ row_major[0] ] ==  0;
		ok &= col[ row_major[1] ] ==  1  && row[ row_major[1] ] ==  0;
		ok &= col[ row_major[2] ] ==  1  && row[ row_major[2] ] ==  1;
		ok &= col[ row_major[3] ] ==  2  && row[ row_major[3] ] ==  1;
	}
	return ok;
}
// END C++
