SHOGUN  4.1.0
 全部  命名空间 文件 函数 变量 类型定义 枚举 枚举值 友元 宏定义  
ExponentialLoss.cpp
浏览该文件的文档.
1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Parijat Mazumdar
4  * All rights reserved.
5  *
6  * Redistribution and use in source and binary forms, with or without
7  * modification, are permitted provided that the following conditions are met:
8  *
9  * 1. Redistributions of source code must retain the above copyright notice, this
10  * list of conditions and the following disclaimer.
11  * 2. Redistributions in binary form must reproduce the above copyright notice,
12  * this list of conditions and the following disclaimer in the documentation
13  * and/or other materials provided with the distribution.
14  *
15  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
16  * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
17  * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
18  * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
19  * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
20  * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
21  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
22  * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
23  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
24  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
25  *
26  * The views and conclusions contained in the software and documentation are those
27  * of the authors and should not be interpreted as representing official policies,
28  * either expressed or implied, of the Shogun Development Team.
29  */
30 
33 
34 using namespace shogun;
35 
37 {
38  return loss(prediction*label);
39 }
40 
42 {
43  return CMath::exp(-z);
44 }
45 
47 {
48  return -label*loss(prediction,label);
49 }
50 
52 {
53  return -loss(z);
54 }
55 
57 {
58  return label*label*loss(prediction,label);
59 }
60 
62 {
63  return loss(z);
64 }
65 
67 {
69  return 0;
70 }
71 
73 {
75  return 0;
76 }
double norm(double *v, double p, int n)
Definition: epph.cpp:452
virtual float64_t get_square_grad(float64_t prediction, float64_t label)
#define SG_NOTIMPLEMENTED
Definition: SGIO.h:139
virtual float64_t get_update(float64_t prediction, float64_t label, float64_t eta_t, float64_t norm)
double float64_t
Definition: common.h:50
float64_t first_derivative(float64_t prediction, float64_t label)
float64_t second_derivative(float64_t prediction, float64_t label)
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
float64_t loss(float64_t prediction, float64_t label)
static float64_t exp(float64_t x)
Definition: Math.h:621

SHOGUN 机器学习工具包 - 项目文档