Line data    Source code

       1             : /*************************************************************************
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      12             :  *
      13             :  * Copyright (C) Akiel Aries, <akiel@akiel.org>, et al.
      14             :  *
      15             :  * This software is licensed as described in the file LICENSE, which
      16             :  * you should have received as part of this distribution. The terms
      17             :  * among other details are referenced in the official documentation
      18             :  * seen here : https://akielaries.github.io/openGPMP/ along with
      19             :  * important files seen in this project.
      20             :  *
      21             :  * You may opt to use, copy, modify, merge, publish, distribute
      22             :  * and/or sell copies of the Software, and permit persons to whom
      23             :  * the Software is furnished to do so, under the terms of the
      24             :  * LICENSE file. As this is an Open Source effort, all implementations
      25             :  * must be of the same methodology.
      26             :  *
      27             :  *
      28             :  *
      29             :  * This software is distributed on an AS IS basis, WITHOUT
      30             :  * WARRANTY OF ANY KIND, either express or implied.
      31             :  *
      32             :  ************************************************************************/
      33             : #include <openGPMP/linalg/tensor.hpp>
      34             : #include <stdexcept>
      35             : 
      36             : // Constructors
      37             : 
      38           0 : gpmp::linalg::Tensor::Tensor() : data_({{{}}}), dimensions_{0, 0, 0} {
      39           0 : }
      40             : 
      41           0 : gpmp::linalg::Tensor::Tensor(const std::vector<size_t> &dimensions) {
      42           0 :     if (dimensions.empty()) {
      43           0 :         throw std::invalid_argument("Error: Tensor dimensions are empty.");
      44             :     }
      45             : 
      46             :     // Assign values to dimensions_
      47           0 :     for (size_t i = 0; i < 3; ++i) {
      48           0 :         dimensions_[i] = dimensions[i];
      49             :     }
      50             : 
      51           0 :     size_t totalSize = 1;
      52           0 :     for (size_t dim : dimensions) {
      53           0 :         totalSize *= dim;
      54             :     }
      55           0 :     data_ = std::vector<std::vector<std::vector<double>>>(
      56             :         dimensions_[0],
      57           0 :         std::vector<std::vector<double>>(
      58             :             dimensions_[1],
      59           0 :             std::vector<double>(dimensions_[2], 0.0)));
      60           0 : }
      61             : 
      62           0 : gpmp::linalg::Tensor::Tensor(
      63           0 :     const std::vector<std::vector<std::vector<double>>> &data)
      64           0 :     : data_(data) {
      65           0 :     if (data.empty() || data[0].empty()) {
      66           0 :         throw std::invalid_argument("Error: Tensor data is empty.");
      67             :     }
      68             : 
      69           0 :     dimensions_[0] = data.size();
      70           0 :     dimensions_[1] = data[0].size();
      71           0 :     dimensions_[2] = data[0][0].size();
      72           0 : }
      73             : 
      74             : gpmp::linalg::Tensor
      75           0 : gpmp::linalg::Tensor::add(const gpmp::linalg::Tensor &other) const {
      76           0 :     if (dimensions_[0] != other.dimensions_[0] ||
      77           0 :         dimensions_[1] != other.dimensions_[1] ||
      78           0 :         dimensions_[2] != other.dimensions_[2]) {
      79           0 :         throw std::invalid_argument(
      80           0 :             "Error: Tensor dimensions do not match for addition.");
      81             :     }
      82             : 
      83           0 :     gpmp::linalg::Tensor result;
      84           0 :     result.data_ = data_;
      85             : 
      86           0 :     for (size_t i = 0; i < dimensions_[0]; ++i) {
      87           0 :         for (size_t j = 0; j < dimensions_[1]; ++j) {
      88           0 :             for (size_t k = 0; k < dimensions_[2]; ++k) {
      89           0 :                 result.data_[i][j][k] += other.data_[i][j][k];
      90             :             }
      91             :         }
      92             :     }
      93             : 
      94           0 :     return result;
      95           0 : }
      96             : 
      97           0 : gpmp::linalg::Tensor gpmp::linalg::Tensor::multiply(double scalar) const {
      98           0 :     gpmp::linalg::Tensor result;
      99           0 :     result.data_ = data_;
     100             : 
     101           0 :     for (size_t i = 0; i < dimensions_[0]; ++i) {
     102           0 :         for (size_t j = 0; j < dimensions_[1]; ++j) {
     103           0 :             for (size_t k = 0; k < dimensions_[2]; ++k) {
     104           0 :                 result.data_[i][j][k] *= scalar;
     105             :             }
     106             :         }
     107             :     }
     108             : 
     109           0 :     return result;
     110           0 : }
     111             : 
     112           0 : gpmp::linalg::Tensor gpmp::linalg::Tensor::multiply(const Tensor &other) const {
     113           0 :     if (dimensions_[2] != other.dimensions_[1]) {
     114           0 :         throw std::invalid_argument(
     115           0 :             "Error: Tensor dimensions do not match for multiplication.");
     116             :     }
     117             : 
     118           0 :     gpmp::linalg::Tensor result;
     119           0 :     result.dimensions_[0] = dimensions_[0];
     120           0 :     result.dimensions_[1] = dimensions_[1];
     121           0 :     result.dimensions_[2] = other.dimensions_[2];
     122             : 
     123           0 :     result.data_ = std::vector<std::vector<std::vector<double>>>(
     124           0 :         dimensions_[0],
     125           0 :         std::vector<std::vector<double>>(
     126           0 :             dimensions_[1],
     127           0 :             std::vector<double>(other.dimensions_[2], 0.0)));
     128             : 
     129           0 :     for (size_t i = 0; i < dimensions_[0]; ++i) {
     130           0 :         for (size_t j = 0; j < dimensions_[1]; ++j) {
     131           0 :             for (size_t k = 0; k < other.dimensions_[2]; ++k) {
     132           0 :                 for (size_t l = 0; l < dimensions_[2]; ++l) {
     133           0 :                     result.data_[i][j][k] +=
     134           0 :                         data_[i][j][l] * other.data_[l][j][k];
     135             :                 }
     136             :             }
     137             :         }
     138             :     }
     139             : 
     140           0 :     return result;
     141           0 : }
     142             : 
     143           0 : double gpmp::linalg::Tensor::get(const std::vector<size_t> &indices) const {
     144           0 :     if (indices.size() != 3) {
     145           0 :         throw std::out_of_range(
     146           0 :             "Error: Invalid number of indices for tensor access.");
     147             :     }
     148             : 
     149           0 :     for (size_t i = 0; i < 3; ++i) {
     150           0 :         if (indices[i] >= dimensions_[i]) {
     151           0 :             throw std::out_of_range(
     152           0 :                 "Error: Index out of bounds for tensor access.");
     153             :         }
     154             :     }
     155             : 
     156           0 :     return data_[indices[0]][indices[1]][indices[2]];
     157             : }
     158             : 
     159           0 : void gpmp::linalg::Tensor::set(const std::vector<size_t> &indices,
     160             :                                double value) {
     161           0 :     if (indices.size() != 3) {
     162           0 :         throw std::out_of_range(
     163           0 :             "Error: Invalid number of indices for tensor access.");
     164             :     }
     165             : 
     166           0 :     for (size_t i = 0; i < 3; ++i) {
     167           0 :         if (indices[i] >= dimensions_[i]) {
     168           0 :             throw std::out_of_range(
     169           0 :                 "Error: Index out of bounds for tensor access.");
     170             :         }
     171             :     }
     172             : 
     173           0 :     data_[indices[0]][indices[1]][indices[2]] = value;
     174           0 : }
     175             : 
     176           0 : void gpmp::linalg::Tensor::display() const {
     177           0 :     for (size_t i = 0; i < dimensions_[0]; ++i) {
     178           0 :         for (size_t j = 0; j < dimensions_[1]; ++j) {
     179           0 :             for (size_t k = 0; k < dimensions_[2]; ++k) {
     180           0 :                 std::cout << data_[i][j][k] << " ";
     181             :             }
     182           0 :             std::cout << "\n";
     183             :         }
     184           0 :         std::cout << "\n";
     185             :     }
     186           0 : }