removed dead code

include/xerus/algorithms/crossApproximation.h

-// Xerus - A General Purpose Tensor Library
-// Copyright (C) 2014-2017 Benjamin Huber and Sebastian Wolf. 
-// 
-// Xerus is free software: you can redistribute it and/or modify
-// it under the terms of the GNU Affero General Public License as published
-// by the Free Software Foundation, either version 3 of the License,
-// or (at your option) any later version.
-// 
-// Xerus 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 Affero General Public License for more details.
-// 
-// You should have received a copy of the GNU Affero General Public License
-// along with Xerus. If not, see <http://www.gnu.org/licenses/>.
-//
-// For further information on Xerus visit https://libXerus.org 
-// or contact us at contact@libXerus.org.
-
-/**
- * @file
- * @brief Header file for the cross approximation algorithm and its variants.
- */
-
-#pragma once
-
-#include "../ttNetwork.h"
-#include "../performanceData.h"
-#include "../measurments.h"
-
-
-namespace xerus {
-// 	TTTensor cross_approximation(const Tensor& _input, const std::vector<size_t> &_ranks);
-}
-

src/xerus/algorithms/crossApproximation.cpp

-// Xerus - A General Purpose Tensor Library
-// Copyright (C) 2014-2017 Benjamin Huber and Sebastian Wolf. 
-// 
-// Xerus is free software: you can redistribute it and/or modify
-// it under the terms of the GNU Affero General Public License as published
-// by the Free Software Foundation, either version 3 of the License,
-// or (at your option) any later version.
-// 
-// Xerus 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 Affero General Public License for more details.
-// 
-// You should have received a copy of the GNU Affero General Public License
-// along with Xerus. If not, see <http://www.gnu.org/licenses/>.
-//
-// For further information on Xerus visit https://libXerus.org 
-// or contact us at contact@libXerus.org.
-
-/**
- * @file
- * @brief Implementation of the ALS variants.
- */
-
-#include <xerus/algorithms/als.h>
-#include <xerus/basic.h>
-#include <xerus/tensorNetwork.h>
- 
-#include <xerus/indexedTensor_tensor_factorisations.h>
-
-namespace xerus {
-	///@brief Creates a set of @a _num unique random index tuples, using the @a _dimensions starting at @a _position +1.
-// 	std::vector<std::vector<size_t>> create_random_tuples(const std::vector<size_t>& _dimensions, const size_t _position, const size_t _num) {
-// 		const size_t N = _dimensions.size()-_position;
-// 		
-// 		std::mt19937_64 rnd(rd());
-// 		std::vector<std::uniform_int_distribution<size_t>> dists;
-// 		for(size_t d = 0; d < N; ++d) {
-// 			dists.emplace_back(0, _dimensions[_position+d]);
-// 		}
-// 
-// 		std::vector<std::vector<size_t>> tuples(_num, std::vector<size_t>(N, 0));
-// 		
-// 		for(size_t i = 0; i < _num; ++i) {
-// 			// Create a random tuple.
-// 			for(size_t d = 0; d < N; ++d) {
-// 				tuples[i][d] = dists[d](rnd);
-// 			}
-// 			
-// 			// Redo this one if it is non unique.
-// 			for(size_t j = 0; j < i; ++j) {
-// 				if(tuples[i] == tuples[j]) { --i; break; }
-// 			}
-// 		}
-// 		
-// 		return tuples;
-// 	}
-// 	
-// 	TTTensor cross_approximation(const Tensor& _input, const std::vector<size_t> &_ranks) {
-// 		std::mt19937_64 rnd(rd());
-// 		
-// 		TTTensor reconstruction(_input.dimensions.size());
-// 		
-// 		
-// 		std::vector<std::vector<size_t>> leftTuples;
-// 		
-// 		reconstruction.set_component(0, Tensor({1, _input.dimensions[0], _ranks[0]}, Tensor::Representation::Dense, Tensor::Initialisation::None));
-// 		std::vector<std::vector<size_t>> tuples = create_random_tuples(_input.dimensions, 0, _ranks[0]);
-// 		
-// 		for(size_t i = 0; i < _input.dimensions[0]; ++i) {
-// 			for(size_t j = 0; j < tuples.size(); ++j) {
-// 				std::vector<size_t> index(1, i);
-// 				index.insert(index.end(), tuples[j].begin(), tuples[j].end());
-// 				reconstruction.component(0)[{0,i,j}] = _input[index];
-// 			}
-// 		}
-// 		
-// 		std::uniform_int_distribution<size_t> dist(0, _input.dimensions[0]);
-// 		
-// 		for(size_t i = 0; i < _ranks[0]; ++i) {
-// 			leftTuples.emplace_back(dist(rnd));
-// 		}
-// 		
-// 		for(size_t position = 0; position < _input.degree()-1; ++position) {
-// // 			std::vector<std::vector<size_t>> tuples = create_random_tuples(_input.dimensions, position, _ranks[position]);
-// 			
-// // 			reconstruction.set_component(position, Tensor({_ranks[position-1], _input.dimensions[position], _ranks[position]}, Tensor::Initialisation::None));
-// 		}
-// 		
-// 		return reconstruction;
-// 	}
-	
-} // namespace xerus