-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathgfun_v2.m
389 lines (309 loc) · 15.8 KB
/
gfun_v2.m
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
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
function [ G, grad_g ] = gfun_v2(lsf,x,grad_flag,probdata,analysisopt,gfundata,femodel,randomfield)
global nfun
% Takes a default value for analysisopt.ffdpara, if not defined in input file
if ~isfield(analysisopt,'ffdpara')
switch lower(gfundata(lsf).evaluator)
case 'basic'
analysisopt.ffdpara = 1000;
otherwise
analysisopt.ffdpara = 50;
end
end
nrv = size(x,1);
nx = size(x,2);
if isfield(gfundata(lsf),'thetag')
nthetag = size(gfundata(lsf).thetag,2);
else
nthetag = 0;
end
% If multi_proc option not specified for 'basic' limit-state function, the g-function
% is supposed to be of non-vectorized type and a sequential process is forced
if ~isfield(analysisopt,'multi_proc') && gfundata(lsf).evaluator == 'basic'
analysisopt.multi_proc = 0;
end
switch analysisopt.multi_proc
% sequential calls
case 0
if strcmp(grad_flag, 'no')
if nx > 1
if isfield(gfundata(lsf),'ng')
ng = gfundata(lsf).ng;
else
ng = 1;
end
G = zeros(ng,nx);
grad_g = zeros(1,nx);
for i = 1:nx
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g, dummy ] = gfunbasic_v2(lsf,x(:,i),'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x(:,i),''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
g = gfunwithbulge(lsf,x(:,i),probdata,gfundata,g);
end
G(:,i) = g;
grad_g(i) = dummy;
end
elseif nthetag > 1
G = zeros(1,nthetag);
original_thetag = gfundata(lsf).thetag;
for i = 1:nthetag
gfundata(lsf).thetag = original_thetag(:,i);
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g, ~ ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
G(i) = g;
end
grad_g = 0;
else
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ G, dummy ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ G, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
G = gfunwithbulge(lsf,x,probdata,gfundata,G);
end
grad_g = dummy;
end
% <--->
elseif strcmp(grad_flag, 'ffd')
allx = x;
G = zeros(1,nx);
grad_g = zeros(nrv,nx);
for i = 1:nx
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g, ~ ] = gfunbasic_v2(lsf,allx(:,i),'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,allx(:,i),''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
g = gfunwithbulge(lsf,allx(:,i),probdata,gfundata,g);
end
G(i) = g;
marg = probdata.marg;
original_x = allx(:,i);
for j = 1:nrv
x = original_x;
h = marg(j,3)/analysisopt.ffdpara;
x(j) = x(j) + h;
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g_a_step_ahead, ~ ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g_a_step_ahead, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
g_a_step_ahead = gfunwithbulge(lsf,x,probdata,gfundata,g_a_step_ahead);
end
grad_g(j,i) = (g_a_step_ahead - g)/h;
end
end
elseif strcmp(grad_flag, 'ddm')
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ G, grad_g ] = gfunbasic_v2(lsf,x,'yes','yes',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ G, grad_g ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''yes'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
% <--->
% =====================================================================================================================================================
% EXTENSION - Arpad Rozsas, 2017-April-18
% mixed 'ffd' and 'ddm' calculation of the gradient
%
% add check of input! if 'ffd_ddm' is selected!!
% what gfunwithbulge is doing, left intact for now
elseif strcmp(grad_flag, 'ffd_ddm')
allx = x;
G = nan(1,nx);
grad_g = nan(nrv,nx);
for i = 1:nx
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g, ~ ] = gfunbasic_v2(lsf,allx(:,i),'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,allx(:,i),''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
g = gfunwithbulge(lsf,allx(:,i),probdata,gfundata,g);
end
G(i) = g;
marg = probdata.marg;
original_x = allx(:,i);
gradient_expression = gfundata(lsf).dgdq;
for j = 1:nrv
x = original_x;
% use 'ffd' to estimate the partial derivative
if any(isnan(gradient_expression{j}))
h = marg(j,3)/analysisopt.ffdpara;
x(j) = x(j) + h;
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ g_a_step_ahead, ~ ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,gfundata);
nfun = nfun+1;
otherwise
eval(['[ g_a_step_ahead, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
g_a_step_ahead = gfunwithbulge(lsf,x,probdata,gfundata,g_a_step_ahead);
end
grad_g(j,i) = (g_a_step_ahead - g)/h;
% use 'ddm' with the user specified partial derivative
% only the gradient is needed as G is already
% available
else
% evaluates the gradient multiple times
% not elegant, but it is cheap with the current
% formulation, later might be extended, if the
% gradient is expensive
[ ~, grad_g_tmp ] = gfunbasic_v2(lsf,x,'no','yes',probdata,analysisopt,gfundata);
logi_idx_exp = ~cellfun(@(C) isnumeric(C) && any(isnan(C(:))), gfundata(lsf).dgdq);
grad_g(logi_idx_exp) = grad_g_tmp(logi_idx_exp);
end
end
end
% =====================================================================================================================================================
% <--->
else
disp('ERROR: Invalid method for gradient computations');
end
% simultaneous calls
case 1
block_size = analysisopt.block_size;
if strcmp(grad_flag,'no')
if nx > 1
if isfield(gfundata(lsf),'ng')
ng = gfundata(lsf).ng;
else
ng = 1;
end
G = zeros(ng,nx);
dummy = zeros(1,nx);
k = 0;
while k < nx
block_size = min( block_size, nx-k );
blockx = x(:,(k+1):(k+block_size));
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ blockG, blockdummy ] = gfunbasic_v2(lsf,blockx,'yes','no',probdata,analysisopt,gfundata);
otherwise
eval(['[ blockG, blockdummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,blockx,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
blockG = gfunwithbulge(lsf,blockx,probdata,gfundata,blockG);
end
G(:,(k+1):(k+block_size)) = blockG;
dummy(1,(k+1):(k+block_size)) = blockdummy;
k = k + block_size;
end
grad_g = dummy;
switch lower(gfundata(lsf).evaluator)
case 'basic'
nfun = nfun+nx;
end
elseif nthetag > 1
G = zeros(1,nthetag);
dummy = zeros(1,nthetag);
k = 0;
while k < nthetag
block_size = min( block_size, nthetag-k );
blockgfundata = gfundata;
blockgfundata(lsf).thetag = blockgfundata(lsf).thetag(:,(k+1):(k+block_size));
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ blockG, blockdummy ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,blockgfundata);
otherwise
eval(['[ blockG, blockdummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,blockgfundata,femodel,randomfield);']);
end
G(1,(k+1):(k+block_size)) = blockG;
dummy(1,(k+1):(k+block_size)) = blockdummy;
k = k + block_size;
end
grad_g = dummy;
switch lower(gfundata(lsf).evaluator)
case 'basic'
nfun = nfun+nthetag;
end
else
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ G, dummy ] = gfunbasic_v2(lsf,x,'yes','no',probdata,analysisopt,gfundata);
otherwise
eval(['[ G, dummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
G = gfunwithbulge(lsf,x,probdata,gfundata,G);
end
grad_g = dummy;
switch lower(gfundata(lsf).evaluator)
case 'basic'
nfun = nfun+1;
end
end
elseif strcmp(grad_flag, 'ffd')
allx = zeros(nrv,nx*(1+nrv));
allx(:,1:(1+nrv):(1+(nx-1)*(1+nrv))) = x;
allh = zeros(1,nrv);
marg = probdata.marg;
original_x = x;
for j = 1:nrv
x = original_x;
allh(j) = marg(j,3)/analysisopt.ffdpara;
x(j,:) = x(j,:) + allh(j)*ones(1,nx);
allx(:,(1+j):(1+nrv):(1+j+(nx-1)*(1+nrv))) = x;
end
allG = zeros(1,nx*(1+nrv));
k = 0;
while k < nx*(1+nrv)
block_size = min( block_size, nx*(1+nrv)-k );
blockx = allx(:,(k+1):(k+block_size));
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ blockG, blockdummy ] = gfunbasic_v2(lsf,blockx,'yes','no',probdata,analysisopt,gfundata);
otherwise
eval(['[ blockG, blockdummy ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,blockx,''no'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
if isfield(gfundata(lsf),'bulge') && ( gfundata(lsf).bulge == 1 )
blockG = gfunwithbulge(lsf,blockx,probdata,gfundata,blockG);
end
allG(1,(k+1):(k+block_size)) = blockG;
k = k + block_size;
end
G = allG(1:(1+nrv):(1+(nx-1)*(1+nrv)));
grad_g = zeros(nrv,nx);
for j = 1:nrv
grad_g(j,:) = ( allG((1+j):(1+nrv):((1+j)+(nx-1)*(1+nrv))) - G ) / allh(j);
end
switch lower(gfundata(lsf).evaluator)
case 'basic'
nfun = nfun+nx*(1+nrv);
end
elseif grad_flag == 'ddm'
switch lower(gfundata(lsf).evaluator)
case 'basic'
[ G, grad_g ] = gfunbasic(lsf,x,'yes',probdata,analysisopt,gfundata);
otherwise
eval(['[ G, grad_g ] = gfun' lower(gfundata(lsf).evaluator) '(lsf,x,''yes'',probdata,analysisopt,gfundata,femodel,randomfield);']);
end
switch lower(gfundata(lsf).evaluator)
case 'basic'
nfun = nfun+1;
end
end
otherwise
disp('multi_proc option of analysisopt incorrectly defined!');
end