/*! * * * \brief - * * \author O. Krause * \date 2010-01-01 * * * \par Copyright 1995-2017 Shark Development Team * *

* This file is part of Shark. * * * Shark 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. * * Shark 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 Shark. If not, see . * */ #ifndef SHARK_ML_MODEL_CMAC_H #define SHARK_ML_MODEL_CMAC_H #include #include #include #include namespace shark{ //! //! \brief The CMACMap class represents a linear combination of piecewise constant functions //! //! when a point is fed into the CMAC, it is first mapped into a vector of binary features. //! For this purpose the inputspace is divided into several tilings. Every tiling produces a bitstring where an element //! is 1 if the point lies inside the tile, 0 otherwise. The concatenation of all tilings forms the feature vector which is then fed //! into a linear function. //! Usually the CMAC is only good for low dimensional input data since the size of the featurevector grows exponentially with the //! number of dimensions. //! class CMACMap :public AbstractModel{ protected: ///offset of the position of every tiling RealMatrix m_offset; ///coordinate offset for every dimension in the Array std::vector m_dimOffset; ///lower bound and tileWidth for every Dimension RealMatrix m_tileBounds; ///number of tilings std::size_t m_tilings; std::size_t m_parametersPerTiling; Shape m_inputShape; std::size_t m_inputSize; Shape m_outputShape; ///The parameters of the model RealVector m_parameters; ///calculates the index in the parameter vector for the activated feature in the tiling SHARK_EXPORT_SYMBOL std::size_t getArrayIndexForTiling(std::size_t indexOfTiling,RealVector const& point)const; ///returns an index in the parameter array for each activated feature SHARK_EXPORT_SYMBOL std::vector getIndizes(blas::dense_vector_adaptor const& point)const; public: ///\brief construct the CMAC SHARK_EXPORT_SYMBOL CMACMap(); /// \brief From INameable: return the class name. std::string name() const { return "CMACMap"; } ///\brief initializes the structure of the cmac. it uses the same lower and upper bound for every input dimension. default is [0,1] /// ///\param inputs Shape of the input dimensions ///\param outputs Shape of the input dimensions ///\param numberOfTilings number of Tilings to be created ///\param numberOfTiles amount of tiles per dimension ///\param lower lower bound of input values ///\param upper upper bound of input values ///\param randomTiles flag specifying whether distance between tiles is regular or randomized SHARK_EXPORT_SYMBOL void setStructure(Shape const& inputs, Shape const& outputs, std::size_t numberOfTilings, std::size_t numberOfTiles, double lower = 0., double upper = 1.,bool randomTiles = false); ///\brief initializes the structure of the cmac /// ///\param inputs number of input dimensions ///\param outputs number of output dimensions ///\param numberOfTilings number of Tilings to be created ///\param numberOfTiles amount of tiles per dimension ///\param bounds lower and upper bounts for every input dimension. every row consists of (lower,upper) ///\param randomTiles flag specifying whether distance between tiles is regular or randomized SHARK_EXPORT_SYMBOL void setStructure(Shape const& inputs, Shape const& outputs, std::size_t numberOfTilings, std::size_t numberOfTiles, RealMatrix const& bounds,bool randomTiles = false); ///\brief Returns the expected shape of the input Shape inputShape() const{ return m_inputShape; } ///\brief Returns the shape of the output Shape outputShape() const{ return m_outputShape; } virtual RealVector parameterVector()const{ return m_parameters; } virtual void setParameterVector(RealVector const& newParameters){ SIZE_CHECK(numberOfParameters() == newParameters.size()); m_parameters=newParameters; } virtual std::size_t numberOfParameters()const{ return m_parameters.size(); } boost::shared_ptr createState()const{ return boost::shared_ptr(new EmptyState()); } using AbstractModel::eval; SHARK_EXPORT_SYMBOL void eval(const RealMatrix& patterns,RealMatrix& outputs)const; void eval(const RealMatrix& patterns,RealMatrix& outputs, State& state)const{ eval(patterns,outputs); } SHARK_EXPORT_SYMBOL void weightedParameterDerivative( RealMatrix const& pattern, BatchOutputType const& outputs, RealMatrix const& coefficients, State const& state, RealVector& gradient)const; /// From ISerializable, reads a model from an archive SHARK_EXPORT_SYMBOL void read( InArchive & archive ); /// From ISerializable, writes a model to an archive SHARK_EXPORT_SYMBOL void write( OutArchive & archive ) const; }; } #endif