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main.cpp @ 1363

Revision 1362, 20.3 kB (checked in by sindj, 14 years ago)
Pridani soft apriorna, boj s integraly. JS

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1
2	/*!
3	\file
4	\brief Robust
5	\author Vasek Smidl
6
7	*/
8
9	#include "estim/arx.h"
10	#include "robustlib.h"
11	#include <vector>
12	#include <iostream>
13	#include <fstream>
14	#include <itpp/itsignal.h>
15	#include "windows.h"
16	#include "ddeml.h"
17	#include "stdio.h"
18
19	//#include "DDEClient.h"
20	//#include <conio.h>
21
22
23	using namespace itpp;
24	using namespace bdm;
25
26	//const int emlig_size = 2;
27	//const int utility_constant = 5;
28
29	const int max_model_order = 2;
30
31
32
33	HDDEDATA CALLBACK DdeCallback(
34	UINT uType, // Transaction type.
35	UINT uFmt, // Clipboard data format.
36	HCONV hconv, // Handle to the conversation.
37	HSZ hsz1, // Handle to a string.
38	HSZ hsz2, // Handle to a string.
39	HDDEDATA hdata, // Handle to a global memory object.
40	DWORD dwData1, // Transaction-specific data.
41	DWORD dwData2) // Transaction-specific data.
42	{
43	return 0;
44	}
45
46	void DDERequest(DWORD idInst, HCONV hConv, char* szItem)
47	{
48	HSZ hszItem = DdeCreateStringHandle(idInst, szItem, 0);
49	HDDEDATA hData = DdeClientTransaction(NULL,0,hConv,hszItem,CF_TEXT,
50	XTYP_REQUEST,TIMEOUT_ASYNC , NULL);
51	if (hData==NULL)
52	{
53	printf("Request failed: %s\n", szItem);
54	}
55
56	if (hData==0)
57	{
58	printf("Request failed: %s\n", szItem);
59	}
60	}
61
62	class model
63	{
64
65
66	public:
67	list<pair<int,int>> ar_components;
68
69	// Best thing would be to inherit the two models from a single souce, this is planned, but now structurally
70	// problematic.
71	RARX* my_rarx;
72	ARXwin* my_arx;
73
74	bool has_constant;
75	int window_size;
76	int predicted_channel;
77	mat* data_matrix;
78
79	model(list<pair<int,int>> ar_components,
80	bool robust,
81	bool has_constant,
82	int window_size,
83	int predicted_channel,
84	mat* data_matrix)
85	{
86	this->ar_components.insert(this->ar_components.begin(),ar_components.begin(),ar_components.end());
87	this->has_constant = has_constant;
88	this->window_size = window_size;
89	this->predicted_channel = predicted_channel;
90	this->data_matrix = data_matrix;
91
92	if(robust)
93	{
94	if(has_constant)
95	{
96	my_rarx = new RARX(ar_components.size()+1,window_size,true);
97	my_arx = NULL;
98	}
99	else
100	{
101	my_rarx = new RARX(ar_components.size(),window_size,false);
102	my_arx = NULL;
103	}
104	}
105	else
106	{
107	my_rarx = NULL;
108	my_arx = new ARXwin();
109	mat V0;
110
111	if(has_constant)
112	{
113	V0 = 0.01 * eye(ar_components.size()+2);
114	V0(0,0) = 1;
115	my_arx->set_constant(true);
116
117	}
118	else
119	{
120
121	V0 = 0.01 * eye(ar_components.size()+1);
122	V0(0,0) = 1;
123	my_arx->set_constant(false);
124
125	}
126
127	my_arx->set_statistics(1, V0);
128	my_arx->set_parameters(window_size);
129	my_arx->validate();
130	}
131	}
132
133	void data_update(int time)
134	{
135	vec data_vector;
136	for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
137	{
138	data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
139	}
140
141	if(my_rarx!=NULL)
142	{
143	data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
144	my_rarx->bayes(data_vector);
145	}
146	else
147	{
148	vec pred_vec;
149	pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
150	my_arx->bayes(pred_vec,data_vector);
151	}
152	}
153
154	static list<list<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels)
155	{
156	list<list<pair<int,int>>> created_model_types;
157
158	if(max_order == 1)
159	{
160	for(int channel = 0;channel<number_of_channels;channel++)
161	{
162	list<pair<int,int>> returned_type;
163	returned_type.push_back(pair<int,int>(channel,1));
164	created_model_types.push_back(returned_type);
165	}
166
167	return created_model_types;
168	}
169	else
170	{
171	created_model_types = possible_models_recurse(max_order-1,number_of_channels);
172	list<list<pair<int,int>>> returned_types;
173
174	for(list<list<pair<int,int>>>::iterator model_ref = created_model_types.begin();model_ref!=created_model_types.end();model_ref++)
175	{
176
177	for(int order = 1; order<=max_order; order++)
178	{
179	for(int channel = 0;channel<number_of_channels;channel++)
180	{
181	list<pair<int,int>> returned_type;
182	pair<int,int> new_pair = pair<int,int>(channel,order);
183	if(find((model_ref).begin(),(model_ref).end(),new_pair)==(*model_ref).end())
184	{
185	returned_type.insert(returned_type.begin(),(model_ref).begin(),(model_ref).end());
186	returned_type.push_back(new_pair);
187	returned_types.push_back(returned_type);
188	}
189	}
190	}
191	}
192
193	created_model_types.insert(created_model_types.end(),returned_types.begin(),returned_types.end());
194
195	return created_model_types;
196	}
197	}
198	};
199
200
201
202
203	int main ( int argc, char* argv[] ) {
204
205	itpp::Laplace_RNG LapRNG = Laplace_RNG();
206
207	/*
208	char szApp[] = "MT4";
209	char szTopic[] = "ASK";
210	char szItem1[] = "EURUSD";
211
212	//DDE Initialization
213	DWORD idInst=0;
214	UINT iReturn;
215	iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback,
216	APPCLASS_STANDARD \| APPCMD_CLIENTONLY, 0 );
217	if (iReturn!=DMLERR_NO_ERROR)
218	{
219	printf("DDE Initialization Failed: 0x%04x\n", iReturn);
220	Sleep(1500);
221	return 0;
222	}
223
224	/*
225	//Start DDE Server and wait for it to become idle.
226	HINSTANCE hRet = ShellExecute(0, "open", szTopic, 0, 0, SW_SHOWNORMAL);
227	if ((int)hRet < 33)
228	{
229	printf("Unable to Start DDE Server: 0x%04x\n", hRet);
230	Sleep(1500); DdeUninitialize(idInst);
231	return 0;
232	}
233	Sleep(1000);
234	*/
235
236	/*
237	//DDE Connect to Server using given AppName and topic.
238	HSZ hszApp, hszTopic;
239	HCONV hConv;
240	hszApp = DdeCreateStringHandle(idInst, szApp, 0);
241	hszTopic = DdeCreateStringHandle(idInst, szTopic, 0);
242	hConv = DdeConnect(idInst, hszApp, hszTopic, NULL);
243	DdeFreeStringHandle(idInst, hszApp);
244	DdeFreeStringHandle(idInst, hszTopic);
245	if (hConv == NULL)
246	{
247	printf("DDE Connection Failed.\n");
248	Sleep(1500); DdeUninitialize(idInst);
249	return 0;
250	}
251
252	//Execute commands/requests specific to the DDE Server.
253
254	DDERequest(idInst, hConv, szItem1);
255
256	//DDE Disconnect and Uninitialize.
257	//DdeDisconnect(hConv);
258	//DdeUninitialize(idInst);
259
260	Sleep(300000);
261	Sleep(3000);
262	*/
263
264	/*
265	// EXPERIMENT: 100 AR model generated time series of length of 30 from y_t=0.95y_(t-1)+0.05y_(t-2)+0.2*e_t,
266	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
267	// variance of location parameter estimators and compare it to the classical setup.
268	vector<vector<vector<string>>> string_lists;
269	string_lists.push_back(vector<vector<string>>());
270	string_lists.push_back(vector<vector<string>>());
271	string_lists.push_back(vector<vector<string>>());
272
273	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
274
275
276	for(int i = 0;i<3;i++)
277	{
278	ifstream myfile(file_strings[i]);
279	if (myfile.is_open())
280	{
281	while ( myfile.good() )
282	{
283	string line;
284	getline(myfile,line);
285
286	vector<string> parsed_line;
287	while(line.find(',') != string::npos)
288	{
289	int loc = line.find(',');
290	parsed_line.push_back(line.substr(0,loc));
291	line.erase(0,loc+1);
292	}
293
294	string_lists[i].push_back(parsed_line);
295	}
296	myfile.close();
297	}
298	}
299
300	for(int j = 0;j<string_lists.size();j++)
301	{
302
303	for(int i = 0;i<string_lists[j].size()-1;i++)
304	{
305	vector<vec> conditions;
306	//emlig* emliga = new emlig(2);
307	RARX* my_rarx = new RARX(2,30);
308
309	for(int k = 1;k<string_lists[j][i].size();k++)
310	{
311	vec condition;
312	//condition.ins(0,1);
313	condition.ins(0,string_lists[j][i][k]);
314	conditions.push_back(condition);
315
316	//cout << "orig:" << condition << endl;
317
318	if(conditions.size()>1)
319	{
320	conditions[k-2].ins(0,string_lists[j][i][k]);
321
322	}
323
324	if(conditions.size()>2)
325	{
326	conditions[k-3].ins(0,string_lists[j][i][k]);
327
328	//cout << "modi:" << conditions[k-3] << endl;
329
330	my_rarx->bayes(conditions[k-3]);
331
332
333	//if(k>5)
334	//{
335	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
336	//}
337
338	}
339
340	}
341
342	//emliga->step_me(0);
343	/*
344	ofstream myfile;
345	myfile.open("c:\\robust_ar1.txt",ios::app);
346	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
347	myfile.close();
348
349	myfile.open("c:\\robust_ar2.txt",ios::app);
350	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
351	myfile.close();
352
353
354	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
355	cout << "Step: " << i << endl;
356	}
357
358	cout << "One experiment finished." << endl;
359
360	ofstream myfile;
361	myfile.open("c:\\robust_ar1.txt",ios::app);
362	myfile << endl;
363	myfile.close();
364
365	myfile.open("c:\\robust_ar2.txt",ios::app);
366	myfile << endl;
367	myfile.close();
368	}*/
369
370
371
372	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
373	// y_t=0.95y_(t-1)+0.05y_(t-2)+0.2*e_t, where e_t is normally, student(4) and cauchy distributed. It
374	// can be compared to the classical setup.
375
376
377	vector<vector<string>> strings;
378
379	char* file_string = "c:\\data";
380
381	char dfstring[80];
382	strcpy(dfstring,file_string);
383	strcat(dfstring,".txt");
384
385
386	mat data_matrix;
387	ifstream myfile(dfstring);
388	if (myfile.is_open())
389	{
390	string line;
391	while(getline(myfile,line))
392	{
393	vec data_vector;
394	while(line.find(',') != string::npos)
395	{
396	int loc2 = line.find('\n');
397	int loc = line.find(',');
398	data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));
399	line.erase(0,loc+1);
400	}
401
402	data_matrix.ins_row(data_matrix.rows(),data_vector);
403	}
404
405	myfile.close();
406	}
407	else
408	{
409	cout << "Can't open data file!" << endl;
410	}
411
412	list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows());
413
414	list<model*> models;
415	for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
416	{
417	models.push_back(new model((*model_type),true,false,30,0,&data_matrix));
418	models.push_back(new model((*model_type),false,false,30,0,&data_matrix));
419	}
420
421	mat result_lognc;
422
423	for(int time = max_model_order;20;time++) //time<data_matrix.cols()
424	{
425	vec cur_res_lognc;
426
427	for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
428	{
429	(*model_ref)->data_update(time);
430	if((*model_ref)->my_rarx!=NULL)
431	{
432	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc);
433	}
434	else
435	{
436	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc());
437	}
438	}
439
440	result_lognc.ins_col(result_lognc.cols(),cur_res_lognc);
441
442	cout << "Updated." << endl;
443
444	/*
445	vector<vec> conditions;
446	//emlig* emliga = new emlig(2);
447	RARX* my_rarx = new RARX(2,10,false);
448
449
450	mat V0 = 0.0001 * eye ( 3 );
451	ARX* my_arx = new ARX(0.85);
452	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
453	my_arx->set_constant ( false );
454	my_arx->validate();
455
456
457	for(int k = 1;k<strings[j].size();k++)
458	{
459	vec condition;
460	//condition.ins(0,1);
461	condition.ins(0,strings[j][k]);
462	conditions.push_back(condition);
463
464	//cout << "orig:" << condition << endl;
465
466	if(conditions.size()>1)
467	{
468	conditions[k-2].ins(0,strings[j][k]);
469
470	}
471
472	if(conditions.size()>2)
473	{
474	conditions[k-3].ins(0,strings[j][k]);
475
476	// cout << "Condition:" << conditions[k-3] << endl;
477
478	my_rarx->bayes(conditions[k-3]);
479	//my_rarx->posterior->step_me(1);
480
481	vec cond_vec;
482	cond_vec.ins(0,conditions[k-3][0]);
483
484	my_arx->bayes(cond_vec,conditions[k-3].right(2));
485
486	/*
487	if(k>8)
488	{
489	//my_rarx->posterior->step_me(0);
490
491	//mat samples = my_rarx->posterior->sample_mat(10);
492
493	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000);
494
495	//cout << imp_samples.first << endl;
496
497	vec sample_prediction;
498	vec averaged_params = zeros(imp_samples.second.rows());
499	for(int t = 0;t<imp_samples.first.size();t++)
500	{
501	vec lap_sample = conditions[k-3].left(2);
502	//lap_sample.ins(lap_sample.size(),1.0);
503
504	lap_sample.ins(0,LapRNG());
505
506	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
507
508	averaged_params += imp_samples.first[t]*imp_samples.second.get_col(t);
509	}
510
511	averaged_params = averaged_params(1/(imp_samples.firstones(imp_samples.first.size())));
512
513	// cout << "Averaged estimated parameters: " << averaged_params << endl;
514
515	vec sample_pow = sample_prediction;
516
517	// cout << sample_prediction << endl;
518	vec poly_coefs;
519	double prediction;
520	bool stop_iteration = false;
521	int en = 1;
522	do
523	{
524	double poly_coef = imp_samples.firstsample_pow/(imp_samples.firstones(imp_samples.first.size()));
525
526	if(en==1)
527	{
528	prediction = poly_coef;
529	}
530
531	poly_coef = poly_coefenfact(utility_constant-2+en)/fact(utility_constant-2);
532
533	if(abs(poly_coef)>numeric_limits<double>::epsilon())
534	{
535	sample_pow = elem_mult(sample_pow,sample_prediction);
536	poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
537	}
538	else
539	{
540	stop_iteration = true;
541	}
542
543	en++;
544
545	if(en>20)
546	{
547	stop_iteration = true;
548	}
549	}
550	while(!stop_iteration);
551
552	/*
553	ofstream myfile_coef;
554
555	myfile_coef.open("c:\\coefs.txt",ios::app);
556
557	for(int t = 0;t<poly_coefs.size();t++)
558	{
559	myfile_coef << poly_coefs[t] << ",";
560	}
561
562	myfile_coef << endl;
563	myfile_coef.close();
564	*/
565
566	//cout << "Coefficients: " << poly_coefs << endl;
567
568	/*
569	vec bas_coef = vec("1.0 2.0 -8.0");
570	cout << "Coefs: " << bas_coef << endl;
571	cvec actions2 = roots(bas_coef);
572	cout << "Roots: " << actions2 << endl;
573	*/
574
575	/*
576
577	cvec actions = roots(poly_coefs);
578
579
580	bool is_max = false;
581	for(int t = 0;t<actions.size();t++)
582	{
583	if(actions[t].imag() == 0)
584	{
585	double second_derivative = 0;
586	for(int q = 1;q<poly_coefs.size();q++)
587	{
588	second_derivative+=poly_coefs[q]pow(actions[t].real(),q-1)q;
589	}
590
591	if(second_derivative<0)
592	{
593	cout << "Action:" << actions[t].real() << endl;
594
595	is_max = true;
596	}
597	}
598	}
599
600	if(!is_max)
601	{
602	cout << "No maximum." << endl;
603	}
604
605	// cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
606
607	/*
608	double prediction = 0;
609	for(int s = 1;s<samples.rows();s++)
610	{
611
612	double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols();
613
614	prediction += avg_parameter*conditions[k-3][s-1];
615
616
617
618	/*
619	ofstream myfile;
620	char fstring[80];
621	strcpy(fstring,file_strings[j]);
622
623	char es[5];
624	strcat(fstring,itoa(s,es,10));
625
626	strcat(fstring,"_res.txt");
627
628
629	myfile.open(fstring,ios::app);
630
631	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
632	myfile << avg_parameter;
633
634	if(k!=strings[j].size()-1)
635	{
636	myfile << ",";
637	}
638	else
639	{
640	myfile << endl;
641	}
642	myfile.close();
643	*/
644
645
646	//}
647
648	// cout << "Prediction: "<< prediction << endl;
649	/*
650	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
651	double prediction2 = pred_mat->mean()[0];
652	*/
653
654	ofstream myfile;
655	char fstring[80];
656	strcpy(fstring,file_string);
657
658	strcat(fstring,"lognc.txt");
659
660	myfile.open(fstring,ios::app);
661
662	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
663
664	for(int i = 0;i<cur_res_lognc.size();i++)
665	{
666	myfile << cur_res_lognc[i] << ',';
667	}
668
669	myfile << endl;
670
671	myfile.close();
672
673	/*
674	myfile.open(f2string,ios::app);
675	myfile << prediction2;
676
677	if(k!=strings[j].size()-1)
678	{
679	myfile << ",";
680	}
681	else
682	{
683	myfile << endl;
684	}
685	myfile.close();
686	////
687
688	}
689	} */
690
691	//emliga->step_me(0);
692	/*
693	ofstream myfile;
694	myfile.open("c:\\robust_ar1.txt",ios::app);
695	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
696	myfile.close();
697
698	myfile.open("c:\\robust_ar2.txt",ios::app);
699	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
700	myfile.close();
701
702
703	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
704	cout << "Step: " << i << endl;*/
705	//}
706
707
708	}
709
710
711	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
712	// with maximization of logarithm of one-step ahead wealth.
713
714
715
716	/*
717	cout << "One experiment finished." << endl;
718
719	ofstream myfile;
720	myfile.open("c:\\robust_ar1.txt",ios::app);
721	myfile << endl;
722	myfile.close();
723
724	myfile.open("c:\\robust_ar2.txt",ios::app);
725	myfile << endl;
726	myfile.close();*/
727
728
729	//emlig* emlig1 = new emlig(emlig_size);
730
731	//emlig1->step_me(0);
732	//emlig* emlig2 = new emlig(emlig_size);
733
734	/*
735	emlig1->set_correction_factors(4);
736
737	for(int j = 0;j<emlig1->correction_factors.size();j++)
738	{
739	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
740	{
741	cout << j << " ";
742
743	for(int i=0;i<(*vec_ref).size();i++)
744	{
745	cout << (*vec_ref)[i];
746	}
747
748	cout << endl;
749	}
750	}*/
751
752	/*
753	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
754
755	emlig1->add_condition(condition5);
756	//emlig1->step_me(0);
757
758
759	vec condition1a = "-1.0 1.02 0.5";
760	//vec condition1b = "1.0 1.0 1.01";
761	emlig1->add_condition(condition1a);
762	//emlig2->add_condition(condition1b);
763
764	vec condition2a = "-0.3 1.7 1.5";
765	//vec condition2b = "-1.0 1.0 1.0";
766	emlig1->add_condition(condition2a);
767	//emlig2->add_condition(condition2b);
768
769	vec condition3a = "0.5 -1.01 1.0";
770	//vec condition3b = "0.5 -1.01 1.0";
771
772	emlig1->add_condition(condition3a);
773	//emlig2->add_condition(condition3b);
774
775	vec condition4a = "-0.5 -1.0 1.0";
776	//vec condition4b = "-0.5 -1.0 1.0";
777
778	emlig1->add_condition(condition4a);
779	//cout << "************************************************" << endl;
780	//emlig2->add_condition(condition4b);
781	//cout << "************************************************" << endl;
782
783	//cout << emlig1->minimal_vertex->get_coordinates();
784
785	//emlig1->remove_condition(condition3a);
786	//emlig1->step_me(0);
787	//emlig1->remove_condition(condition2a);
788	//emlig1->remove_condition(condition1a);
789	//emlig1->remove_condition(condition5);
790
791
792	//emlig1->step_me(0);
793	//emlig2->step_me(0);
794
795
796	// DA SE POUZIT PRO VYPIS DO SOUBORU
797	// emlig1->step_me(0);
798
799	//emlig1->remove_condition(condition1);
800
801
802
803
804
805	/*
806	for(int i = 0;i<100;i++)
807	{
808	cout << endl << "Step:" << i << endl;
809
810	double condition[emlig_size+1];
811
812	for(int k = 0;k<=emlig_size;k++)
813	{
814	condition[k] = (rand()-RAND_MAX/2)/1000.0;
815	}
816
817
818	vec* condition_vec = new vec(condition,emlig_size+1);
819	emlig1->add_condition(*condition_vec);
820
821	/*
822	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
823	{
824	cout << ((toprow*)toprow_ref)->probability << endl;
825	}
826	*/
827	/*
828	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
829
830	/*
831	if(i-emlig1->number_of_parameters >= 0)
832	{
833	pause(30);
834	}
835	*/
836
837	// emlig1->step_me(i);
838
839	/*
840	vector<int> sizevector;
841	for(int s = 0;s<=emlig1->number_of_parameters;s++)
842	{
843	sizevector.push_back(emlig1->statistic_rowsize(s));
844	}
845	*/
846	//}
847
848
849
850
851	/*
852	emlig1->step_me(1);
853
854	vec condition = "2.0 0.0 1.0";
855
856	emlig1->add_condition(condition);
857
858	vector<int> sizevector;
859	for(int s = 0;s<=emlig1->number_of_parameters;s++)
860	{
861	sizevector.push_back(emlig1->statistic_rowsize(s));
862	}
863
864	emlig1->step_me(2);
865
866	condition = "2.0 1.0 0.0";
867
868	emlig1->add_condition(condition);
869
870	sizevector.clear();
871	for(int s = 0;s<=emlig1->number_of_parameters;s++)
872	{
873	sizevector.push_back(emlig1->statistic_rowsize(s));
874	}
875	*/
876
877	return 0;
878	}
879
880

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