125 lines
4.6 KiB
C++
125 lines
4.6 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) 2023 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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#ifndef _GCTL_LOSS_FUNC_H
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#define _GCTL_LOSS_FUNC_H
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// library's head files
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#include "gctl/core.h"
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namespace gctl
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{
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/**
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* @brief 损失函数对象，可计算L1范数, L2范数平方，Lp范数定义的数据拟合差及相应的模型偏导数（按数据个数归一化）。
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* 损失函数的定义为：Phi = Lp(d - d^tar)^2/num(d)
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*/
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class loss_func
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{
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public:
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loss_func(); ///< 构造函数
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/**
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* @brief 构造函数
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*
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* @param tar 数据拟合差目标
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* @param n_type 拟合差函数范数类型
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* @param p Lp范数的阶次
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* @param eps Lp范数分母内的小值（防止分母变为奇异值）
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*/
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loss_func(const array<double> &tar, norm_type_e n_type, double p = 2.0, double eps = 1e-16);
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virtual ~loss_func(); ///< 析构函数
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/**
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* @brief 初始化函数
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*
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* @param tar 数据拟合差目标
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* @param n_type 拟合差函数范数类型
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* @param p Lp范数的阶次
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* @param eps Lp范数分母内的小值（防止分母变为奇异值）
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*/
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void init(const array<double> &tar, norm_type_e n_type, double p = 2.0, double eps = 1e-16);
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/**
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* @brief 设置目标数据的不确定度
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*
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* @param uncer 不确定度
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*/
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void set_uncertainty(double uncer);
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/**
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* @brief 设置目标数据的不确定度
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*
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* @param uncer 不确定度数组，长度与目标数据一致
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*/
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void set_uncertainty(const array<double> &uncer);
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/**
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* @brief 计算单个输入模型数据的拟合差，同时将计算值累计至内部变量
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*
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* @param inp 输入数据值
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* @param id 输入数据的索引
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* @return 单个数据拟合差值
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*/
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double evaluate(double inp, int id);
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/**
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* @brief 计算输入模型的数据拟合差与模型梯度
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*
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* @param x 输入模型，长度与目标数据相等
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* @param g 数据拟合差相对于模型的梯度
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* @return 数据拟合差值
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*/
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double evaluate(const array<double> &x, array<double> &g);
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/**
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* @brief 返回内置的数据拟合差函数值，然后将值重设为0
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*
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* @return 累计的数据拟合差
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*/
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double get_loss();
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/**
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* @brief 计算数据拟合差函数相对于单个输入模型数据的梯度
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*
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* @param inp 输入数据值
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* @param id 输入数据的索引
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* @return 单个数据拟合差函数的梯度
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*/
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double gradient(double inp, int id);
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private:
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bool init_;
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double loss_, eps_, p_;
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unsigned int tnum_;
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norm_type_e ntype_;
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array<double> tars_, diff_;
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array<double> us_;
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};
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};
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#endif // _GCTL_LOSS_FUNC_H
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