1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
|
#ifndef INCLUDED_SLS_ALP_REGRESSION
#define INCLUDED_SLS_ALP_REGRESSION
/* $Id: $
* ===========================================================================
*
* PUBLIC DOMAIN NOTICE
* National Center for Biotechnology Information
*
* This software/database is a "United States Government Work" under the
* terms of the United States Copyright Act. It was written as part of
* the author's offical duties as a United States Government employee and
* thus cannot be copyrighted. This software/database is freely available
* to the public for use. The National Library of Medicine and the U.S.
* Government have not placed any restriction on its use or reproduction.
*
* Although all reasonable efforts have been taken to ensure the accuracy
* and reliability of the software and data, the NLM and the U.S.
* Government do not and cannot warrant the performance or results that
* may be obtained by using this software or data. The NLM and the U.S.
* Government disclaim all warranties, express or implied, including
* warranties of performance, merchantability or fitness for any particular
* purpose.
*
* Please cite the author in any work or product based on this material.
*
* ===========================================================================*/
/*****************************************************************************
File name: sls_alp_regression.hpp
Author: Sergey Sheetlin
Contents: Regression methods
******************************************************************************/
#include "sls_basic.hpp"
#include <complex>
#include <iostream>
#include <map>
#include <vector>
#include <fstream>
#include <float.h>
#include <algorithm>
namespace Sls {
typedef double function_type(double x_,void* func_number_);
class alp_reg{
public:
alp_reg(//constructor
);
~alp_reg();//destructor
static void find_tetta_general(
function_type *func_,
void* func_pointer_,
double a_,//[a,b] is the interval for search of equation solution
double b_,
long int n_partition_,
double eps_,
std::vector<double> &res_);
static double find_single_tetta_general(
function_type *func_,
void* func_pointer_,
double a_,//[a,b] is the interval for search of equation solution
double b_,
double eps_);
static void correction_of_errors(
double *errors_,
long int number_of_elements_);
static void robust_regression_sum_with_cut_LSM(
long int min_length_,
long int number_of_elements_,
double *values_,
double *errors_,
bool cut_left_tail_,
bool cut_right_tail_,
double y_,
double &beta0_,
double &beta1_,
double &beta0_error_,
double &beta1_error_,
long int &k1_opt_,
long int &k2_opt_,
bool &res_was_calculated_);
static double function_for_robust_regression_sum_with_cut_LSM(
double *values_,
double *errors_,
long int number_of_elements_,
long int k_start_,
double c_,
double &beta0_,
double &beta1_,
double &beta0_error_,
double &beta1_error_,
bool &res_was_calculated_);
static void robust_regression_sum_with_cut_LSM_beta1_is_defined(
long int min_length_,
long int number_of_elements_,
double *values_,
double *errors_,
bool cut_left_tail_,
bool cut_right_tail_,
double y_,
double &beta0_,
double beta1_,
double &beta0_error_,
double beta1_error_,
long int &k1_opt_,
long int &k2_opt_,
bool &res_was_calculated_);
static double function_for_robust_regression_sum_with_cut_LSM_beta1_is_defined(
double *values_,
double *errors_,
long int number_of_elements_,
long int k_start_,
double c_,
double &beta0_,
double beta1_,
double &beta0_error_,
double beta1_error_,
bool &res_was_calculated_);
inline static double sqrt_for_errors(
double x_)
{
if(x_<=0)
{
return 0.0;
}
else
{
return sqrt(x_);
};
}
static double median(
long int dim_,
double *array_);
static double robust_sum(
double *values,
long int dim,
long int N_points,
bool *&remove_flag);
};
}
#endif
|
