77 lines
2.8 KiB
C++
77 lines
2.8 KiB
C++
/********************************************************
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* ██████╗ ██████╗████████╗██╗
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* ██╔════╝ ██╔════╝╚══██╔══╝██║
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* ██║ ███╗██║ ██║ ██║
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* ██║ ██║██║ ██║ ██║
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* ╚██████╔╝╚██████╗ ██║ ███████╗
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* ╚═════╝ ╚═════╝ ╚═╝ ╚══════╝
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* Geophysical Computational Tools & Library (GCTL)
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*
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* Copyright (c) 2022 Yi Zhang (yizhang-geo@zju.edu.cn)
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*
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* GCTL is distributed under a dual licensing scheme. You can redistribute
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* it and/or modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation, either version 2
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* of the License, or (at your option) any later version. You should have
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* received a copy of the GNU Lesser General Public License along with this
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* program. If not, see <http://www.gnu.org/licenses/>.
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*
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* If the terms and conditions of the LGPL v.2. would prevent you from using
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* the GCTL, please consider the option to obtain a commercial license for a
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* fee. These licenses are offered by the GCTL's original author. As a rule,
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* licenses are provided "as-is", unlimited in time for a one time fee. Please
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* send corresponding requests to: yizhang-geo@zju.edu.cn. Please do not forget
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* to include some description of your company and the realm of its activities.
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* Also add information on how to contact you by electronic and paper mail.
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******************************************************/
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#include "olayer_rmse.h"
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gctl::rmse::rmse() {}
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gctl::rmse::~rmse() {}
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void gctl::rmse::evaluation(const matrix<double> &prev_layer_data, const matrix<double> &target)
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{
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int nobs = prev_layer_data.col_size();
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int nvar = prev_layer_data.row_size();
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if (target.col_size() != nobs || target.row_size() != nvar)
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{
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throw std::invalid_argument("[gctl::rmse] Target data have incorrect dimension.");
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}
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// Compute the derivative of the input of this layer
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// L = 0.5 * ||yhat - y||^2
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// in = yhat
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// d(L) / d(in) = yhat - y
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der_in_.resize(nvar, nobs);
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int i, j;
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#pragma omp parallel for private (i, j) schedule(guided)
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for (i = 0; i < nvar; i++)
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{
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for (j = 0; j < nobs; j++)
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{
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der_in_[i][j] = prev_layer_data[i][j] - target[i][j];
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}
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}
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return;
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}
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double gctl::rmse::loss_value() const
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{
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// L = 0.5 * ||yhat - y||^2
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double n = der_in_.norm(L2);
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return 0.5*power2(n)/der_in_.col_size();
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}
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std::string gctl::rmse::get_output_name() const
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{
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return "RegressionMSE";
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}
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gctl::olayer_type_e gctl::rmse::get_output_type() const
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{
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return RegressionMSE;
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} |