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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
|
/*
* Copyright (c) 2017, Miroslav Stoyanov
*
* This file is part of
* Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
*
* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
* and the following disclaimer in the documentation and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
* or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
* OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
* OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* UT-BATTELLE, LLC AND THE UNITED STATES GOVERNMENT MAKE NO REPRESENTATIONS AND DISCLAIM ALL WARRANTIES, BOTH EXPRESSED AND IMPLIED.
* THERE ARE NO EXPRESS OR IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY PATENT,
* COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS, OR THAT THE SOFTWARE WILL ACCOMPLISH THE INTENDED RESULTS OR THAT THE SOFTWARE OR ITS USE WILL NOT RESULT IN INJURY OR DAMAGE.
* THE USER ASSUMES RESPONSIBILITY FOR ALL LIABILITIES, PENALTIES, FINES, CLAIMS, CAUSES OF ACTION, AND COSTS AND EXPENSES, CAUSED BY, RESULTING FROM OR ARISING OUT OF,
* IN WHOLE OR IN PART THE USE, STORAGE OR DISPOSAL OF THE SOFTWARE.
*/
#ifndef __TASMANIAN_DREAM_SAMPLE_WRAPC_CPP
#define __TASMANIAN_DREAM_SAMPLE_WRAPC_CPP
#include "tsgDreamSample.hpp"
namespace TasDREAM{
using tsg_dream_pdf = void (*)(int, int, const double[], double[], int*);
using tsg_dream_domain = int (*)(int, const double[]);
using tsg_dream_iupdate = void (*)(int, double[], int*);
using tsg_dream_dupdate = double (*)();
using tsg_dream_random = double (*)();
std::function<bool(const std::vector<double> &x)>
getSpecifiedDomain(int num_dimensions, void *domain_grid, double *domain_lower, double *domain_upper, tsg_dream_domain domain_callback){
if (domain_grid != nullptr){
return reinterpret_cast<TasGrid::TasmanianSparseGrid*>(domain_grid)->getDomainInside();
}else if (domain_upper != nullptr){
return hypercube(std::vector<double>(domain_lower, domain_lower + num_dimensions),
std::vector<double>(domain_upper, domain_upper + num_dimensions));
}else{
return [=](std::vector<double> const &x)->
bool{
return (domain_callback((int) x.size(), x.data()) != 0);
};
}
}
std::function<double(void)>
getSpecifiedDifferentialUpdate(int dupdate_percent, tsg_dream_dupdate dupdate_callback){
if (dupdate_percent >= 0){
return [=]()->double{ return double(dupdate_percent) / 100.0; };
}else{
return [=]()->double{ return dupdate_callback(); };
}
}
extern "C"{
void tsgGenUniformSamples(int num_dimensions, int num_samples, double const lower[], double const upper[],
const char* random_type, int random_seed, tsg_dream_random random_callback, double *samples){
std::minstd_rand park_miller((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed);
std::uniform_real_distribution<double> unif(0.0, 1.0);
srand((unsigned int) ((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed));
std::string rtype(random_type);
auto randgen = [&]()->
std::function<double(void)>{
if (rtype == "default"){
return [&]()->double{ return tsgCoreUniform01(); };
}else if (rtype == "minstd_rand"){
return [&]()->double{ return unif(park_miller); };
}else{
return [&]()->double{ return random_callback(); };
}
}();
std::vector<double> result = TasDREAM::genUniformSamples(Utils::copyArray(lower, num_dimensions),
Utils::copyArray(upper, num_dimensions),
num_samples, randgen);
std::copy(result.begin(), result.end(), samples);
}
void tsgGenGaussianSamples(int num_dimensions, int num_samples, double const mean[], double const deviation[],
const char* random_type, int random_seed, tsg_dream_random random_callback, double *samples){
std::minstd_rand park_miller((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed);
std::uniform_real_distribution<double> unif(0.0, 1.0);
srand((unsigned int) ((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed));
std::string rtype(random_type);
auto randgen = [&]()->
std::function<double(void)>{
if (rtype == "default"){
return [&]()->double{ return tsgCoreUniform01(); };
}else if (rtype == "minstd_rand"){
return [&]()->double{ return unif(park_miller); };
}else{
return [&]()->double{ return random_callback(); };
}
}();
std::vector<double> result = TasDREAM::genGaussianSamples(Utils::copyArray(mean, num_dimensions),
Utils::copyArray(deviation, num_dimensions),
num_samples, randgen);
std::copy(result.begin(), result.end(), samples);
}
void tsgDreamSample(int form,
int num_burnup, int num_collect,
tsg_dream_pdf distribution,
void* state_pntr,
void *domain_grid, double domain_lower[], double dommain_upper[], tsg_dream_domain domain_callback,
const char* iupdate_type, double iupdate_magnitude, tsg_dream_iupdate iupdate_callback,
int dupdate_percent, tsg_dream_dupdate dupdate_callback,
const char* random_type, int random_seed, tsg_dream_random random_callback, int *err){
*err = 1;
TasmanianDREAM& state = *reinterpret_cast<TasmanianDREAM*>(state_pntr);
int num_dimensions = (int) state.getNumDimensions();
auto domain = getSpecifiedDomain(num_dimensions, domain_grid, domain_lower, dommain_upper, domain_callback);
TypeDistribution dist = IO::getDistributionString(iupdate_type);
auto diff_update = getSpecifiedDifferentialUpdate(dupdate_percent, dupdate_callback);
std::minstd_rand park_miller((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed);
std::uniform_real_distribution<double> unif(0.0, 1.0);
srand((unsigned int) ((random_seed == -1) ? static_cast<long unsigned>(std::time(nullptr)) : random_seed));
std::string rtype(random_type);
auto randgen = [&]()->
std::function<double(void)>{
if (rtype == "default"){
return [&]()->double{ return tsgCoreUniform01(); };
}else if (rtype == "minstd_rand"){
return [&]()->double{ return unif(park_miller); };
}else{
return [&]()->double{ return random_callback(); };
}
}();
try{
if (dist == dist_null){
if (IO::intToForm(form) == regform){
SampleDREAM<regform>(num_burnup, num_collect, [&](const std::vector<double> &candidates, std::vector<double> &values)->
void{
int num_samples = (int) candidates.size() / num_dimensions;
int error_code = 0;
distribution(num_samples, num_dimensions, candidates.data(), values.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in tsgDreamSample()");
}, domain, state, [&](std::vector<double> &x)->
void{
int error_code = 0;
iupdate_callback((int) x.size(), x.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in tsgDreamSample()");
}, diff_update, randgen);
}else{
SampleDREAM<logform>(num_burnup, num_collect, [&](const std::vector<double> &candidates, std::vector<double> &values)->
void{
int num_samples = (int) candidates.size() / num_dimensions;
int error_code = 0;
distribution(num_samples, num_dimensions, candidates.data(), values.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in tsgDreamSample()");
}, domain, state, [&](std::vector<double> &x)->
void{
int error_code = 0;
iupdate_callback((int) x.size(), x.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in tsgDreamSample()");
}, diff_update, randgen);
}
}else{
if (IO::intToForm(form) == regform){
SampleDREAM<regform>(num_burnup, num_collect, [&](const std::vector<double> &candidates, std::vector<double> &values)->
void{
int num_samples = (int) candidates.size() / num_dimensions;
int error_code = 0;
distribution(num_samples, num_dimensions, candidates.data(), values.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in tsgDreamSample()");
}, domain, state, dist, iupdate_magnitude, diff_update, randgen);
}else{
SampleDREAM<logform>(num_burnup, num_collect, [&](const std::vector<double> &candidates, std::vector<double> &values)->
void{
int num_samples = (int) candidates.size() / num_dimensions;
int error_code = 0;
distribution(num_samples, num_dimensions, candidates.data(), values.data(), &error_code);
if (error_code != 0) throw std::runtime_error("The Python callback returned an error in analysis()");
}, domain, state, dist, iupdate_magnitude, diff_update, randgen);
}
}
*err = 0; // success
}catch(std::runtime_error &){} // *err will remain 1
}
}
}
#endif
|
