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1 : // rbOOmit: An implementation of the Certified Reduced Basis method. 2 : // Copyright (C) 2009, 2010 David J. Knezevic 3 : 4 : // This file is part of rbOOmit. 5 : 6 : // rbOOmit is free software; you can redistribute it and/or 7 : // modify it under the terms of the GNU Lesser General Public 8 : // License as published by the Free Software Foundation; either 9 : // version 2.1 of the License, or (at your option) any later version. 10 : 11 : // rbOOmit is distributed in the hope that it will be useful, 12 : // but WITHOUT ANY WARRANTY; without even the implied warranty of 13 : // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 14 : // Lesser General Public License for more details. 15 : 16 : // You should have received a copy of the GNU Lesser General Public 17 : // License along with this library; if not, write to the Free Software 18 : // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA 19 : 20 : // Local includes 21 : #include "libmesh/rb_theta.h" 22 : #include "libmesh/rb_parameters.h" 23 : #include "libmesh/int_range.h" 24 : 25 : namespace libMesh 26 : { 27 : 28 28491042 : Number RBTheta::evaluate(const RBParameters & mu) 29 : { 30 : // The RBTheta::evaluate() API is not general enough to handle the 31 : // multi-sample RBParameters case, and you must therefore call 32 : // RBTheta::evaluate_vec() instead. 33 28491042 : libmesh_error_msg_if(mu.n_samples() > 1, 34 : "You should only call the evaluate_vec() API when using multi-sample RBParameters objects."); 35 : 36 28491042 : return 1.; 37 : } 38 : 39 : std::vector<Number> 40 0 : RBTheta::evaluate_vec(const std::vector<RBParameters> & mus) 41 : { 42 : // Eventual return value 43 0 : std::vector<Number> result; 44 : 45 0 : for (const auto & mu : mus) 46 : { 47 : // Backwards-compatible behavior: for single-sample RBParameters objects, we fall back on 48 : // calling the scalar evaluate() function for this RBTheta object, which may have been 49 : // overridden by the user. 50 0 : if (mu.n_samples() == 1) 51 0 : result.push_back( this->evaluate(mu) ); 52 : else 53 : { 54 : // For multi-sample RBParameters objects, all we can do is return 55 : // mu.n_samples() copies of 1 here at the base class level. 56 0 : result.insert(result.end(), /*count=*/mu.n_samples(), /*val=*/1.); 57 : } 58 : } 59 : 60 0 : return result; 61 : } 62 : 63 : }