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include/roboptim/core/manifold-map/decorator/dispatcher.hh
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| // Copyright (C) 2015 by Félix Darricau, AIST, CNRS, EPITA | ||
| // Grégoire Duchemin, AIST, CNRS, EPITA | ||
| // | ||
| // This file is part of the roboptim. | ||
| // | ||
| // roboptim 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 3 of the License, or | ||
| // (at your option) any later version. | ||
| // | ||
| // roboptim 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 roboptim. If not, see <http://www.gnu.org/licenses/>. | ||
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| #ifndef ROBOPTIM_CORE_MANIFOLD_MAP_DECORATOR_DISPATCHER_HH | ||
| # define ROBOPTIM_CORE_MANIFOLD_MAP_DECORATOR_DISPATCHER_HH | ||
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| # include <manifolds/Manifold.h> | ||
| # include <manifolds/RealSpace.h> | ||
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| namespace roboptim | ||
| { | ||
| template <typename U> class FunctionOnManifold; | ||
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| template <typename U, typename T> | ||
| struct Dispatcher; | ||
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| template <typename U> | ||
| struct Dispatcher <U, roboptim::EigenMatrixDense> | ||
| { | ||
| static void unmapGradient(const FunctionOnManifold<U>* instance, | ||
| typename U::gradient_ref gradient) | ||
| { | ||
| for (long i = 0; i < instance->mappingFromFunctionSize_; ++i) | ||
| { | ||
| gradient(static_cast<long>(instance->mappingFromFunction_[i])) = instance->mappedGradient_(i); | ||
| } | ||
| } | ||
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| static void jacobian(const FunctionOnManifold<U>* instance, | ||
| typename U::jacobian_ref jacobian) | ||
| { | ||
| for (long j = 0; j < jacobian.rows(); ++j) | ||
| { | ||
| instance->fct_->gradient(instance->mappedGradient_, instance->mappedInput_, j); | ||
| Dispatcher<U, roboptim::EigenMatrixDense>::unmapGradient(instance, jacobian.row(j)); | ||
| } | ||
| } | ||
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| static void applyDiff(const FunctionOnManifold<U>* instance) | ||
| { | ||
| instance->manifold_->applyDiffRetractation(instance->tangentMappedJacobian, instance->mappedJacobian_, instance->mappedInput_); | ||
| } | ||
| }; | ||
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| template <typename U> | ||
| struct Dispatcher <U, roboptim::EigenMatrixSparse> | ||
| { | ||
| static void unmapGradient(const FunctionOnManifold<U>* instance, | ||
| typename U::gradient_ref gradient) | ||
| { | ||
| assert(gradient.cols() == instance->inputSize()); | ||
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| for (int i = 0; i < instance->mappingFromFunctionSize_; ++i) | ||
| { | ||
| gradient.coeffRef(static_cast<int>(instance->mappingFromFunction_[i])) = instance->mappedGradient_.coeffRef(i); | ||
| } | ||
| } | ||
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| static void jacobian(const FunctionOnManifold<U>* instance, | ||
| typename U::jacobian_ref jacobian) | ||
| { | ||
| assert(jacobian.cols() == instance->inputSize()); | ||
| assert(jacobian.rows() == instance->outputSize()); | ||
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| for (int j = 0; j < jacobian.rows(); ++j) | ||
| { | ||
| instance->fct_->gradient(instance->mappedGradient_, instance->mappedInput_, j); | ||
| for (int i = 0; i < instance->mappingFromFunctionSize_; ++i) | ||
| { | ||
| jacobian.coeffRef(j,static_cast<int>(instance->mappingFromFunction_[i])) = instance->mappedGradient_.coeffRef(i); | ||
| } | ||
| } | ||
| } | ||
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| static void applyDiff(const FunctionOnManifold<U>* instance) | ||
| { | ||
| if (instance->manifold_->getTypeId() != mnf::RealSpace(1).getTypeId()) | ||
| throw std::runtime_error("No Sparse support for non RealSpaces manifolds"); | ||
| else | ||
| instance->tangentMappedJacobian = instance->mappedJacobian_; | ||
| } | ||
| }; | ||
| } // end of namespace roboptim | ||
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| #endif //! ROBOPTIM_CORE_MANIFOLD_MAP_DECORATOR_DISPATCHER_HH |
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