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main.cpp @ 1379

Revision 1379, 24.9 kB (checked in by sindj, 14 years ago)
Prepracovani integrace v robustlib.cpp, zjednoduseni, zesymetrizovani. Nevim jestli funguje, nutno overit a vyzkouset testovani hypotez a integraci na jednicku (u normalizacnich faktoru).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	const double apriorno=0.005;
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_ADVSTART,TIMEOUT_ASYNC , NULL); //TIMEOUT_ASYNC
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	DWORD WINAPI ThrdFunc( LPVOID n )
63	{
64	return 0;
65	}
66	*/
67
68	class model
69	{
70	public:
71	set<pair<int,int>> ar_components;
72
73	// Best thing would be to inherit the two models from a single souce, this is planned, but now structurally
74	// problematic.
75	RARX* my_rarx; //vzmenovane parametre pre triedu model
76	ARXwin* my_arx;
77
78	bool has_constant;
79	int window_size; //musi byt vacsia ako pocet krokov ak to nema ovplyvnit
80	int predicted_channel;
81	mat* data_matrix;
82
83	model(set<pair<int,int>> ar_components, //funkcie treidz model-konstruktor
84	bool robust,
85	bool has_constant,
86	int window_size,
87	int predicted_channel,
88	mat* data_matrix)
89	{
90	this->ar_components.insert(ar_components.begin(),ar_components.end());
91	this->has_constant = has_constant;
92	this->window_size = window_size;
93	this->predicted_channel = predicted_channel;
94	this->data_matrix = data_matrix;
95
96	if(robust)
97	{
98	if(has_constant)
99	{
100	my_rarx = new RARX(ar_components.size()+1,window_size,true);
101	my_arx = NULL;
102	}
103	else
104	{
105	my_rarx = new RARX(ar_components.size(),window_size,false);
106	my_arx = NULL;
107	}
108	}
109	else
110	{
111	my_rarx = NULL;
112	my_arx = new ARXwin();
113	mat V0;
114
115	if(has_constant)
116	{
117	V0 = apriorno * eye(ar_components.size()+2); //aj tu konst
118	V0(0,0) = 1;
119	my_arx->set_constant(true);
120	}
121	else
122	{
123
124	V0 = apriorno * eye(ar_components.size()+1);//menit konstantu
125	V0(0,0) = 1;
126	my_arx->set_constant(false);
127
128	}
129
130	my_arx->set_statistics(1, V0, V0.rows()+1);
131	my_arx->set_parameters(window_size);
132	my_arx->validate();
133	}
134	}
135
136	void data_update(int time) //vlozime cas a ono vlozi do data_vector podmineky(conditions) a predikce, ktore pouzije do bayes
137	{
138	vec data_vector;
139	for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
140	{ //ar?iterator ide len od 1 pod 2, alebo niekedy len 1
141	data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
142	// do data vector vlozi pre dany typ regresoru prislusne cisla z data_matrix. Ale ako? preco time-ar_iterator->second?
143	}
144	if(my_rarx!=NULL)
145	{ //pre robusr priradi az tu do data_vector aj rpedikciu
146	data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
147	my_rarx->bayes(data_vector);
148	}
149	else
150	{
151	vec pred_vec;//tu sa predikcia zadava zvlast
152	pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
153	my_arx->bayes(pred_vec,data_vector);
154	}
155	}
156
157	pair<vec,vec> predict(int sample_size, int time, itpp::Laplace_RNG* LapRNG) //nerozumiem, ale vraj to netreba, nepouziva to
158	{
159	vec condition_vector;
160	for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
161	{
162	condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1));
163	}
164
165	if(my_rarx!=NULL)
166	{
167	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size);
168
169	//cout << imp_samples.first << endl;
170
171	vec sample_prediction;
172	for(int t = 0;t<sample_size;t++)
173	{
174	vec lap_sample = condition_vector;
175
176	if(has_constant)
177	{
178	lap_sample.ins(lap_sample.size(),1.0);
179	}
180
181	lap_sample.ins(0,(*LapRNG)());
182
183	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
184	}
185
186	return pair<vec,vec>(imp_samples.first,sample_prediction);
187	}
188	else
189	{
190	mat samples = my_arx->posterior().sample_mat(sample_size);
191
192	vec sample_prediction;
193	for(int t = 0;t<sample_size;t++)
194	{
195	vec gau_sample = condition_vector;
196
197	if(has_constant)
198	{
199	gau_sample.ins(gau_sample.size(),1.0);
200	}
201
202	gau_sample.ins(0,randn());
203
204	sample_prediction.ins(0,gau_sample*samples.get_col(t));
205	}
206
207	return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction);
208	}
209
210	}
211
212
213	static set<set<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels)
214	{
215	set<set<pair<int,int>>> created_model_types;
216
217	if(max_order == 1)//ukoncovacia vetva
218	{
219	for(int channel = 0;channel<number_of_channels;channel++)//pre AR 1 model vytvori kombinace kanalov v prvom kroku poyadu
220	{
221	set<pair<int,int>> returned_type;
222	returned_type.insert(pair<int,int>(channel,1)); //??
223	created_model_types.insert(returned_type);
224	}
225
226	return created_model_types;
227	}
228	else
229	{
230	created_model_types = possible_models_recurse(max_order-1,number_of_channels);//tu uz mame ulozene kombinace o jeden krok dozadu //rekuryivne volanie
231	set<set<pair<int,int>>> returned_types;
232
233	for(set<set<pair<int,int>>>::iterator model_ref = created_model_types.begin();model_ref!=created_model_types.end();model_ref++)
234	{
235
236	for(int order = 1; order<=max_order; order++)
237	{
238	for(int channel = 0;channel<number_of_channels;channel++)
239	{
240	set<pair<int,int>> returned_type;
241	pair<int,int> new_pair = pair<int,int>(channel,order);//??
242	if(find((model_ref).begin(),(model_ref).end(),new_pair)==(*model_ref).end()) //??
243	{
244	returned_type.insert((model_ref).begin(),(model_ref).end()); //co vlozi na zaciatok retuned_type?
245	returned_type.insert(new_pair);
246
247
248	returned_types.insert(returned_type);
249	}
250	}
251	}
252	}
253
254	created_model_types.insert(returned_types.begin(),returned_types.end());
255
256	return created_model_types;
257	}
258	}
259	};
260
261
262
263
264	int main ( int argc, char* argv[] ) {
265
266	/*
267	DWORD Id;
268	HANDLE hThrd = CreateThread( NULL, 0, (LPTHREAD_START_ROUTINE)ThrdFunc, (LPVOID)1, 0, &Id);
269
270	if ( !hThrd )
271	{
272	cout<<"Error Creating Threads,,,,.exiting"<<endl;
273	return -1;
274	}
275	Sleep ( 100 );
276
277
278	char szApp[] = "MT4";
279	char szTopic[] = "QUOTE";
280	char szItem1[] = "EURUSD";
281
282	//DDE Initialization
283	DWORD idInst=0;
284	UINT iReturn;
285	iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback,
286	APPCLASS_STANDARD \| APPCMD_CLIENTONLY, 0 );
287	if (iReturn!=DMLERR_NO_ERROR)
288	{
289	printf("DDE Initialization Failed: 0x%04x\n", iReturn);
290	Sleep(1500);
291	return 0;
292	}
293
294	//DDE Connect to Server using given AppName and topic.
295	HSZ hszApp, hszTopic;
296	HCONV hConv;
297	hszApp = DdeCreateStringHandle(idInst, szApp, 0);
298	hszTopic = DdeCreateStringHandle(idInst, szTopic, 0);
299	hConv = DdeConnect(idInst, hszApp, hszTopic, NULL);
300	//DdeFreeStringHandle(idInst, hszApp);
301	//DdeFreeStringHandle(idInst, hszTopic);
302	if (hConv == NULL)
303	{
304	printf("DDE Connection Failed.\n");
305	Sleep(1500); DdeUninitialize(idInst);
306	return 0;
307	}
308
309	//Execute commands/requests specific to the DDE Server.
310
311	DDERequest(idInst, hConv, szItem1);
312
313	while(1)
314	{
315	MSG msg;
316	BOOL MsgReturn = GetMessage ( &msg , NULL , 0 , 0 );
317
318	if(MsgReturn)
319	{
320	TranslateMessage(&msg);
321	DispatchMessage(&msg);
322	}
323	}
324
325	//DDE Disconnect and Uninitialize.
326	DdeDisconnect(hConv);
327	DdeUninitialize(idInst);
328	*/
329
330
331
332	/*
333	// 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,
334	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
335	// variance of location parameter estimators and compare it to the classical setup.
336	vector<vector<vector<string>>> string_lists;
337	string_lists.push_back(vector<vector<string>>());
338	string_lists.push_back(vector<vector<string>>());
339	string_lists.push_back(vector<vector<string>>());
340
341	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
342
343
344	for(int i = 0;i<3;i++)
345	{
346	ifstream myfile(file_strings[i]);
347	if (myfile.is_open())
348	{
349	while ( myfile.good() )
350	{
351	string line;
352	getline(myfile,line);
353
354	vector<string> parsed_line;
355	while(line.find(',') != string::npos)
356	{
357	int loc = line.find(',');
358	parsed_line.push_back(line.substr(0,loc));
359	line.erase(0,loc+1);
360	}
361
362	string_lists[i].push_back(parsed_line);
363	}
364	myfile.close();
365	}
366	}
367
368	for(int j = 0;j<string_lists.size();j++)
369	{
370
371	for(int i = 0;i<string_lists[j].size()-1;i++)
372	{
373	vector<vec> conditions;
374	//emlig* emliga = new emlig(2);
375	RARX* my_rarx = new RARX(2,30);
376
377	for(int k = 1;k<string_lists[j][i].size();k++)
378	{
379	vec condition;
380	//condition.ins(0,1);
381	condition.ins(0,string_lists[j][i][k]);
382	conditions.push_back(condition);
383
384	//cout << "orig:" << condition << endl;
385
386	if(conditions.size()>1)
387	{
388	conditions[k-2].ins(0,string_lists[j][i][k]);
389
390	}
391
392	if(conditions.size()>2)
393	{
394	conditions[k-3].ins(0,string_lists[j][i][k]);
395
396	//cout << "modi:" << conditions[k-3] << endl;
397
398	my_rarx->bayes(conditions[k-3]);
399
400
401	//if(k>5)
402	//{
403	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
404	//}
405
406	}
407
408	}
409
410	//emliga->step_me(0);
411	/*
412	ofstream myfile;
413	myfile.open("c:\\robust_ar1.txt",ios::app);
414	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
415	myfile.close();
416
417	myfile.open("c:\\robust_ar2.txt",ios::app);
418	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
419	myfile.close();
420
421
422	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
423	cout << "Step: " << i << endl;
424	}
425
426	cout << "One experiment finished." << endl;
427
428	ofstream myfile;
429	myfile.open("c:\\robust_ar1.txt",ios::app);
430	myfile << endl;
431	myfile.close();
432
433	myfile.open("c:\\robust_ar2.txt",ios::app);
434	myfile << endl;
435	myfile.close();
436	}*/
437
438	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
439	// 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
440	// can be compared to the classical setup.
441
442	itpp::Laplace_RNG LapRNG = Laplace_RNG();
443
444	vector<vector<string>> strings;
445
446	char* file_string = "c:\\ar_normal_single"; // "c:\\dataTYClosePercDiff"; //
447
448	char dfstring[80];
449	strcpy(dfstring,file_string);
450	strcat(dfstring,".txt");
451
452
453	mat data_matrix;
454	ifstream myfile(dfstring);
455	if (myfile.is_open())
456	{
457	string line;
458	while(getline(myfile,line))
459	{
460	vec data_vector;
461	while(line.find(',') != string::npos) //zmenil som ciarku za medzeru
462	{
463	line.erase(0,1); // toto som sem pridal
464	int loc2 = line.find('\n');
465	int loc = line.find(',');
466	data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));
467	line.erase(0,loc+1);
468	}
469
470	data_matrix.ins_row(data_matrix.rows(),data_vector);
471	}
472
473	myfile.close();
474	}
475	else
476	{
477	cout << "Can't open data file!" << endl;
478	}
479
480	//konec nacitavania dat
481	set<set<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows()); //volanie funkce kde robi kombinace modelov
482	//to priradime do model_types, data_matrix.row urcuje pocet kanalov dat
483	vector<model*> models;
484	for(set<set<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
485	{// prechadza rozne typy kanalov, a poctu regresorov
486	for(int window_size = 15;window_size < 16;window_size++)
487	{
488	models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix)); // to su len konstruktory, len inicializujeme rozne typy
489	models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix));
490	models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix));
491	models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix));
492	}
493
494	//set<pair<int,int>> empty_list;
495	//models.push_back(new model(empty_list,false,true,100,0,&data_matrix));
496	}
497
498	mat result_lognc;
499	// mat result_preds;
500
501	for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols()
502	{ //pocet stlpcov data_matrix je pocet casovych krokov
503	vec cur_res_lognc;
504	// vec preds;
505	vector<string> nazvy;
506	for(vector<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
507	{//posuvam s apo models, co je pole modelov urobene o cyklus vyssie. Teda som v case time a robim to tam pre vsetky typy modelov, kombinace regresorov
508	(*model_ref)->data_update(time); //pozret sa preco je toto tu nutne
509
510	cout << "Updated." << endl;
511	//if (time = max_model_order) nazvy.push_back(models.model_ref]);// ako by som mohol dostat nazov modelu?
512
513	if((*model_ref)->my_rarx!=NULL) //vklada normalizacnz faktor do cur_res_lognc
514	{
515	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc);
516	}
517	else
518	{
519	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc());
520	}
521
522	// pair<vec,vec> predictions = (*model_ref)->predict(200,time,&LapRNG);
523
524	// preds.ins(preds.size(),(predictions.firstpredictions.second)/(predictions.firstones(predictions.first.size())));
525	// preds.ins(0,data_matrix.get(0,time+1));
526
527	}
528
529	result_lognc.ins_col(result_lognc.cols(),cur_res_lognc);
530	// result_preds.ins_col(result_preds.cols(),preds);
531
532	// cout << "Updated." << endl;
533
534	/*
535	vector<vec> conditions;
536	//emlig* emliga = new emlig(2);
537	RARX* my_rarx = new RARX(2,10,false);
538
539
540	mat V0 = 0.0001 * eye ( 3 );
541	ARX* my_arx = new ARX(0.85);
542	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
543	my_arx->set_constant ( false );
544	my_arx->validate();
545
546
547	for(int k = 1;k<strings[j].size();k++)
548	{
549	vec condition;
550	//condition.ins(0,1);
551	condition.ins(0,strings[j][k]);
552	conditions.push_back(condition);
553
554	//cout << "orig:" << condition << endl;
555
556	if(conditions.size()>1)
557	{
558	conditions[k-2].ins(0,strings[j][k]);
559
560	}
561
562	if(conditions.size()>2)
563	{
564	conditions[k-3].ins(0,strings[j][k]);
565
566	// cout << "Condition:" << conditions[k-3] << endl;
567
568	my_rarx->bayes(conditions[k-3]);
569	//my_rarx->posterior->step_me(1);
570
571	vec cond_vec;
572	cond_vec.ins(0,conditions[k-3][0]);
573
574	my_arx->bayes(cond_vec,conditions[k-3].right(2));
575
576	/*
577	if(k>8)
578	{
579	//my_rarx->posterior->step_me(0);
580
581	//mat samples = my_rarx->posterior->sample_mat(10);
582
583	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000);
584
585	//cout << imp_samples.first << endl;
586
587	vec sample_prediction;
588	vec averaged_params = zeros(imp_samples.second.rows());
589	for(int t = 0;t<imp_samples.first.size();t++)
590	{
591	vec lap_sample = conditions[k-3].left(2);
592	//lap_sample.ins(lap_sample.size(),1.0);
593
594	lap_sample.ins(0,LapRNG());
595
596	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
597
598	averaged_params += imp_samples.first[t]*imp_samples.second.get_col(t);
599	}
600
601	averaged_params = averaged_params(1/(imp_samples.firstones(imp_samples.first.size())));
602
603	// cout << "Averaged estimated parameters: " << averaged_params << endl;
604
605	vec sample_pow = sample_prediction;
606
607	// cout << sample_prediction << endl;
608	vec poly_coefs;
609	double prediction;
610	bool stop_iteration = false;
611	int en = 1;
612	do
613	{
614	double poly_coef = imp_samples.firstsample_pow/(imp_samples.firstones(imp_samples.first.size()));
615
616	if(en==1)
617	{
618	prediction = poly_coef;
619	}
620
621	poly_coef = poly_coefenfact(utility_constant-2+en)/fact(utility_constant-2);
622
623	if(abs(poly_coef)>numeric_limits<double>::epsilon())
624	{
625	sample_pow = elem_mult(sample_pow,sample_prediction);
626	poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
627	}
628	else
629	{
630	stop_iteration = true;
631	}
632
633	en++;
634
635	if(en>20)
636	{
637	stop_iteration = true;
638	}
639	}
640	while(!stop_iteration);
641
642	/*
643	ofstream myfile_coef;
644
645	myfile_coef.open("c:\\coefs.txt",ios::app);
646
647	for(int t = 0;t<poly_coefs.size();t++)
648	{
649	myfile_coef << poly_coefs[t] << ",";
650	}
651
652	myfile_coef << endl;
653	myfile_coef.close();
654	*/
655
656	//cout << "Coefficients: " << poly_coefs << endl;
657
658	/*
659	vec bas_coef = vec("1.0 2.0 -8.0");
660	cout << "Coefs: " << bas_coef << endl;
661	cvec actions2 = roots(bas_coef);
662	cout << "Roots: " << actions2 << endl;
663	*/
664
665	/*
666
667	cvec actions = roots(poly_coefs);
668
669
670	bool is_max = false;
671	for(int t = 0;t<actions.size();t++)
672	{
673	if(actions[t].imag() == 0)
674	{
675	double second_derivative = 0;
676	for(int q = 1;q<poly_coefs.size();q++)
677	{
678	second_derivative+=poly_coefs[q]pow(actions[t].real(),q-1)q;
679	}
680
681	if(second_derivative<0)
682	{
683	cout << "Action:" << actions[t].real() << endl;
684
685	is_max = true;
686	}
687	}
688	}
689
690	if(!is_max)
691	{
692	cout << "No maximum." << endl;
693	}
694
695	// cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
696
697	/*
698	double prediction = 0;
699	for(int s = 1;s<samples.rows();s++)
700	{
701
702	double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols();
703
704	prediction += avg_parameter*conditions[k-3][s-1];
705
706
707
708	/*
709	ofstream myfile;
710	char fstring[80];
711	strcpy(fstring,file_strings[j]);
712
713	char es[5];
714	strcat(fstring,itoa(s,es,10));
715
716	strcat(fstring,"_res.txt");
717
718
719	myfile.open(fstring,ios::app);
720
721	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
722	myfile << avg_parameter;
723
724	if(k!=strings[j].size()-1)
725	{
726	myfile << ",";
727	}
728	else
729	{
730	myfile << endl;
731	}
732	myfile.close();
733	*/
734
735
736	//}
737
738	// cout << "Prediction: "<< prediction << endl;
739	/*
740	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
741	double prediction2 = pred_mat->mean()[0];
742	*/
743
744
745	ofstream myfile;
746	char fstring[80];
747	strcpy(fstring,file_string);
748
749	strcat(fstring,"lognc.txt");
750
751	myfile.open(fstring,ios::app);
752
753	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
754
755	if(time == max_model_order)
756	{
757	for(int i = 0;i<cur_res_lognc.size();i++)
758	{
759	for(set<pair<int,int>>::iterator ar_ref = models[i]->ar_components.begin();ar_ref != models[i]->ar_components.end();ar_ref++)
760	{
761	myfile << (ar_ref).second << (ar_ref).first;
762	}
763
764	myfile << ".";
765
766	if(models[i]->my_arx == NULL)
767	{
768	myfile << "1";
769	}
770	else
771	{
772	myfile << "0";
773	}
774
775	if(models[i]->has_constant)
776	{
777	myfile << "1";
778	}
779	else
780	{
781	myfile << "0";
782	}
783
784	myfile << ",";
785	}
786
787	myfile << endl;
788	}
789
790	for(int i = 0;i<cur_res_lognc.size();i++)
791	{
792	myfile << cur_res_lognc[i] << ' ';//zmenil som ciarku ze medzeru
793	}
794
795	myfile << endl;
796
797	myfile.close();
798	}
799	/*
800	myfile.open(f2string,ios::app);
801	myfile << prediction2;
802
803	if(k!=strings[j].size()-1)
804	{
805	myfile << ",";
806	}
807	else
808	{
809	myfile << endl;
810	}
811	myfile.close();
812	////
813
814	}
815	} */
816
817	//emliga->step_me(0);
818	/*
819	ofstream myfile;
820	myfile.open("c:\\robust_ar1.txt",ios::app);
821	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
822	myfile.close();
823
824	myfile.open("c:\\robust_ar2.txt",ios::app);
825	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
826	myfile.close();
827
828
829	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
830	cout << "Step: " << i << endl;*/
831	//}
832
833
834	//}
835
836
837	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
838	// with maximization of logarithm of one-step ahead wealth.
839
840
841
842	/*
843	cout << "One experiment finished." << endl;
844
845	ofstream myfile;
846	myfile.open("c:\\robust_ar1.txt",ios::app);
847	myfile << endl;
848	myfile.close();
849
850	myfile.open("c:\\robust_ar2.txt",ios::app);
851	myfile << endl;
852	myfile.close();*/
853
854
855	//emlig* emlig1 = new emlig(emlig_size);
856
857	//emlig1->step_me(0);
858	//emlig* emlig2 = new emlig(emlig_size);
859
860	/*
861	emlig1->set_correction_factors(4);
862
863	for(int j = 0;j<emlig1->correction_factors.size();j++)
864	{
865	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
866	{
867	cout << j << " ";
868
869	for(int i=0;i<(*vec_ref).size();i++)
870	{
871	cout << (*vec_ref)[i];
872	}
873
874	cout << endl;
875	}
876	}*/
877
878	/*
879	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
880
881	emlig1->add_condition(condition5);
882	//emlig1->step_me(0);
883
884
885	vec condition1a = "-1.0 1.02 0.5";
886	//vec condition1b = "1.0 1.0 1.01";
887	emlig1->add_condition(condition1a);
888	//emlig2->add_condition(condition1b);
889
890	vec condition2a = "-0.3 1.7 1.5";
891	//vec condition2b = "-1.0 1.0 1.0";
892	emlig1->add_condition(condition2a);
893	//emlig2->add_condition(condition2b);
894
895	vec condition3a = "0.5 -1.01 1.0";
896	//vec condition3b = "0.5 -1.01 1.0";
897
898	emlig1->add_condition(condition3a);
899	//emlig2->add_condition(condition3b);
900
901	vec condition4a = "-0.5 -1.0 1.0";
902	//vec condition4b = "-0.5 -1.0 1.0";
903
904	emlig1->add_condition(condition4a);
905	//cout << "************************************************" << endl;
906	//emlig2->add_condition(condition4b);
907	//cout << "************************************************" << endl;
908
909	//cout << emlig1->minimal_vertex->get_coordinates();
910
911	//emlig1->remove_condition(condition3a);
912	//emlig1->step_me(0);
913	//emlig1->remove_condition(condition2a);
914	//emlig1->remove_condition(condition1a);
915	//emlig1->remove_condition(condition5);
916
917
918	//emlig1->step_me(0);
919	//emlig2->step_me(0);
920
921
922	// DA SE POUZIT PRO VYPIS DO SOUBORU
923	// emlig1->step_me(0);
924
925	//emlig1->remove_condition(condition1);
926
927
928
929
930
931	/*
932	for(int i = 0;i<100;i++)
933	{
934	cout << endl << "Step:" << i << endl;
935
936	double condition[emlig_size+1];
937
938	for(int k = 0;k<=emlig_size;k++)
939	{
940	condition[k] = (rand()-RAND_MAX/2)/1000.0;
941	}
942
943
944	vec* condition_vec = new vec(condition,emlig_size+1);
945	emlig1->add_condition(*condition_vec);
946
947	/*
948	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
949	{
950	cout << ((toprow*)toprow_ref)->probability << endl;
951	}
952	*/
953	/*
954	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
955
956	/*
957	if(i-emlig1->number_of_parameters >= 0)
958	{
959	pause(30);
960	}
961	*/
962
963	// emlig1->step_me(i);
964
965	/*
966	vector<int> sizevector;
967	for(int s = 0;s<=emlig1->number_of_parameters;s++)
968	{
969	sizevector.push_back(emlig1->statistic_rowsize(s));
970	}
971	*/
972	//}
973
974
975
976
977	/*
978	emlig1->step_me(1);
979
980	vec condition = "2.0 0.0 1.0";
981
982	emlig1->add_condition(condition);
983
984	vector<int> sizevector;
985	for(int s = 0;s<=emlig1->number_of_parameters;s++)
986	{
987	sizevector.push_back(emlig1->statistic_rowsize(s));
988	}
989
990	emlig1->step_me(2);
991
992	condition = "2.0 1.0 0.0";
993
994	emlig1->add_condition(condition);
995
996	sizevector.clear();
997	for(int s = 0;s<=emlig1->number_of_parameters;s++)
998	{
999	sizevector.push_back(emlig1->statistic_rowsize(s));
1000	}
1001	*/
1002
1003	return 0;
1004	}
1005
1006

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