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main.cpp @ 1356

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Oprava dalsich drobnych chyb (pridavani otcovske podminky do totalne neutralnich poly). Jeste nejake zbyva odstranit. 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
16	using namespace itpp;
17	using namespace bdm;
18
19	const int emlig_size = 2;
20	const int utility_constant = 10;
21
22
23	int main ( int argc, char* argv[] ) {
24
25	itpp::Laplace_RNG LapRNG = Laplace_RNG();
26
27	/*
28	// 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,
29	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
30	// variance of location parameter estimators and compare it to the classical setup.
31	vector<vector<vector<string>>> string_lists;
32	string_lists.push_back(vector<vector<string>>());
33	string_lists.push_back(vector<vector<string>>());
34	string_lists.push_back(vector<vector<string>>());
35
36	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
37
38
39	for(int i = 0;i<3;i++)
40	{
41	ifstream myfile(file_strings[i]);
42	if (myfile.is_open())
43	{
44	while ( myfile.good() )
45	{
46	string line;
47	getline(myfile,line);
48
49	vector<string> parsed_line;
50	while(line.find(',') != string::npos)
51	{
52	int loc = line.find(',');
53	parsed_line.push_back(line.substr(0,loc));
54	line.erase(0,loc+1);
55	}
56
57	string_lists[i].push_back(parsed_line);
58	}
59	myfile.close();
60	}
61	}
62
63	for(int j = 0;j<string_lists.size();j++)
64	{
65
66	for(int i = 0;i<string_lists[j].size()-1;i++)
67	{
68	vector<vec> conditions;
69	//emlig* emliga = new emlig(2);
70	RARX* my_rarx = new RARX(2,30);
71
72	for(int k = 1;k<string_lists[j][i].size();k++)
73	{
74	vec condition;
75	//condition.ins(0,1);
76	condition.ins(0,string_lists[j][i][k]);
77	conditions.push_back(condition);
78
79	//cout << "orig:" << condition << endl;
80
81	if(conditions.size()>1)
82	{
83	conditions[k-2].ins(0,string_lists[j][i][k]);
84
85	}
86
87	if(conditions.size()>2)
88	{
89	conditions[k-3].ins(0,string_lists[j][i][k]);
90
91	//cout << "modi:" << conditions[k-3] << endl;
92
93	my_rarx->bayes(conditions[k-3]);
94
95
96	//if(k>5)
97	//{
98	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
99	//}
100
101	}
102
103	}
104
105	//emliga->step_me(0);
106	/*
107	ofstream myfile;
108	myfile.open("c:\\robust_ar1.txt",ios::app);
109	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
110	myfile.close();
111
112	myfile.open("c:\\robust_ar2.txt",ios::app);
113	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
114	myfile.close();
115
116
117	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
118	cout << "Step: " << i << endl;
119	}
120
121	cout << "One experiment finished." << endl;
122
123	ofstream myfile;
124	myfile.open("c:\\robust_ar1.txt",ios::app);
125	myfile << endl;
126	myfile.close();
127
128	myfile.open("c:\\robust_ar2.txt",ios::app);
129	myfile << endl;
130	myfile.close();
131	}*/
132
133
134	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
135	// 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
136	// can be compared to the classical setup.
137
138
139	vector<vector<string>> strings;
140
141	char* file_strings[3] = {"c:\\dataCDClosePercDiff","c:\\ar_student_single","c:\\ar_cauchy_single"};
142
143	for(int i = 0;i<3;i++)
144	{
145	char dfstring[80];
146	strcpy(dfstring,file_strings[i]);
147	strcat(dfstring,".txt");
148
149	ifstream myfile(dfstring);
150	if (myfile.is_open())
151	{
152	string line;
153	getline(myfile,line);
154
155	vector<string> parsed_line;
156	while(line.find(',') != string::npos)
157	{
158	int loc = line.find(',');
159	parsed_line.push_back(line.substr(0,loc));
160	line.erase(0,loc+1);
161	}
162
163	strings.push_back(parsed_line);
164
165	myfile.close();
166	}
167	}
168
169	for(int j = 0;j<strings.size();j++)
170	{
171	vector<vec> conditions;
172	//emlig* emliga = new emlig(2);
173	RARX* my_rarx = new RARX(2,10,false);
174
175
176	mat V0 = 0.0001 * eye ( 3 );
177	ARX* my_arx = new ARX(0.85);
178	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
179	my_arx->set_constant ( false );
180	my_arx->validate();
181
182
183	for(int k = 1;k<strings[j].size();k++)
184	{
185	vec condition;
186	//condition.ins(0,1);
187	condition.ins(0,strings[j][k]);
188	conditions.push_back(condition);
189
190	//cout << "orig:" << condition << endl;
191
192	if(conditions.size()>1)
193	{
194	conditions[k-2].ins(0,strings[j][k]);
195
196	}
197
198	if(conditions.size()>2)
199	{
200	conditions[k-3].ins(0,strings[j][k]);
201
202	// cout << "Condition:" << conditions[k-3] << endl;
203
204	my_rarx->bayes(conditions[k-3]);
205	//my_rarx->posterior->step_me(1);
206
207	vec cond_vec;
208	cond_vec.ins(0,conditions[k-3][0]);
209
210	my_arx->bayes(cond_vec,conditions[k-3].right(2));
211
212
213	if(k>8)
214	{
215	//my_rarx->posterior->step_me(0);
216
217	//mat samples = my_rarx->posterior->sample_mat(10);
218
219	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000);
220
221	//cout << imp_samples.first << endl;
222
223	vec sample_prediction;
224	for(int t = 0;t<imp_samples.first.size();t++)
225	{
226	vec lap_sample = conditions[k-3].left(2);
227	//lap_sample.ins(lap_sample.size(),1.0);
228
229	lap_sample.ins(0,LapRNG());
230
231	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
232	}
233
234
235	vec sample_pow = sample_prediction;
236
237	// cout << sample_prediction << endl;
238	vec poly_coefs;
239	double prediction;
240	bool stop_iteration = false;
241	int en = 1;
242	do
243	{
244	double poly_coef = imp_samples.firstsample_pow/(imp_samples.firstones(imp_samples.first.size()));
245
246	if(en==1)
247	{
248	prediction = poly_coef;
249	}
250
251	poly_coef = poly_coefenfact(utility_constant-2+en)/fact(utility_constant-2);
252
253	if(abs(poly_coef)>numeric_limits<double>::epsilon())
254	{
255	sample_pow = elem_mult(sample_pow,sample_prediction);
256	poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
257	}
258	else
259	{
260	stop_iteration = true;
261	}
262
263	en++;
264
265	if(en>20)
266	{
267	stop_iteration = true;
268	}
269	}
270	while(!stop_iteration);
271
272	/*
273	ofstream myfile_coef;
274
275	myfile_coef.open("c:\\coefs.txt",ios::app);
276
277	for(int t = 0;t<poly_coefs.size();t++)
278	{
279	myfile_coef << poly_coefs[t] << ",";
280	}
281
282	myfile_coef << endl;
283	myfile_coef.close();
284	*/
285
286	//cout << "Coefficients: " << poly_coefs << endl;
287
288	/*
289	vec bas_coef = vec("1.0 2.0 -8.0");
290	cout << "Coefs: " << bas_coef << endl;
291	cvec actions2 = roots(bas_coef);
292	cout << "Roots: " << actions2 << endl;
293	*/
294
295
296
297	cvec actions = roots(poly_coefs);
298
299
300	bool is_max = false;
301	for(int t = 0;t<actions.size();t++)
302	{
303	if(actions[t].imag() == 0)
304	{
305	double second_derivative = 0;
306	for(int q = 1;q<poly_coefs.size();q++)
307	{
308	second_derivative+=poly_coefs[q]pow(actions[t].real(),q-1)q;
309	}
310
311	if(second_derivative<0)
312	{
313	cout << "Action:" << actions[t].real() << endl;
314
315	is_max = true;
316	}
317	}
318	}
319
320	if(!is_max)
321	{
322	cout << "No maximum." << endl;
323	}
324
325	// cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
326
327	/*
328	double prediction = 0;
329	for(int s = 1;s<samples.rows();s++)
330	{
331
332	double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols();
333
334	prediction += avg_parameter*conditions[k-3][s-1];
335
336
337
338	/*
339	ofstream myfile;
340	char fstring[80];
341	strcpy(fstring,file_strings[j]);
342
343	char es[5];
344	strcat(fstring,itoa(s,es,10));
345
346	strcat(fstring,"_res.txt");
347
348
349	myfile.open(fstring,ios::app);
350
351	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
352	myfile << avg_parameter;
353
354	if(k!=strings[j].size()-1)
355	{
356	myfile << ",";
357	}
358	else
359	{
360	myfile << endl;
361	}
362	myfile.close();
363	*/
364
365
366	//}
367
368	// cout << "Prediction: "<< prediction << endl;
369
370	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
371	double prediction2 = pred_mat->mean()[0];
372
373
374	ofstream myfile;
375	char fstring[80];
376	char f2string[80];
377	strcpy(fstring,file_strings[j]);
378	strcpy(f2string,fstring);
379
380	strcat(fstring,"pred.txt");
381	strcat(f2string,"2pred.txt");
382
383
384	myfile.open(fstring,ios::app);
385
386	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
387	myfile << prediction;
388
389	if(k!=strings[j].size()-1)
390	{
391	myfile << ",";
392	}
393	else
394	{
395	myfile << endl;
396	}
397	myfile.close();
398
399
400	myfile.open(f2string,ios::app);
401	myfile << prediction2;
402
403	if(k!=strings[j].size()-1)
404	{
405	myfile << ",";
406	}
407	else
408	{
409	myfile << endl;
410	}
411	myfile.close();
412	//*/
413
414	}
415	}
416
417	//emliga->step_me(0);
418	/*
419	ofstream myfile;
420	myfile.open("c:\\robust_ar1.txt",ios::app);
421	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
422	myfile.close();
423
424	myfile.open("c:\\robust_ar2.txt",ios::app);
425	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
426	myfile.close();
427
428
429	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
430	cout << "Step: " << i << endl;*/
431	}
432
433
434	}
435
436
437	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
438	// with maximization of logarithm of one-step ahead wealth.
439
440
441
442	/*
443	cout << "One experiment finished." << endl;
444
445	ofstream myfile;
446	myfile.open("c:\\robust_ar1.txt",ios::app);
447	myfile << endl;
448	myfile.close();
449
450	myfile.open("c:\\robust_ar2.txt",ios::app);
451	myfile << endl;
452	myfile.close();*/
453
454
455	//emlig* emlig1 = new emlig(emlig_size);
456
457	//emlig1->step_me(0);
458	//emlig* emlig2 = new emlig(emlig_size);
459
460	/*
461	emlig1->set_correction_factors(4);
462
463	for(int j = 0;j<emlig1->correction_factors.size();j++)
464	{
465	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
466	{
467	cout << j << " ";
468
469	for(int i=0;i<(*vec_ref).size();i++)
470	{
471	cout << (*vec_ref)[i];
472	}
473
474	cout << endl;
475	}
476	}*/
477
478	/*
479	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
480
481	emlig1->add_condition(condition5);
482	//emlig1->step_me(0);
483
484
485	vec condition1a = "-1.0 1.02 0.5";
486	//vec condition1b = "1.0 1.0 1.01";
487	emlig1->add_condition(condition1a);
488	//emlig2->add_condition(condition1b);
489
490	vec condition2a = "-0.3 1.7 1.5";
491	//vec condition2b = "-1.0 1.0 1.0";
492	emlig1->add_condition(condition2a);
493	//emlig2->add_condition(condition2b);
494
495	vec condition3a = "0.5 -1.01 1.0";
496	//vec condition3b = "0.5 -1.01 1.0";
497
498	emlig1->add_condition(condition3a);
499	//emlig2->add_condition(condition3b);
500
501	vec condition4a = "-0.5 -1.0 1.0";
502	//vec condition4b = "-0.5 -1.0 1.0";
503
504	emlig1->add_condition(condition4a);
505	//cout << "************************************************" << endl;
506	//emlig2->add_condition(condition4b);
507	//cout << "************************************************" << endl;
508
509	//cout << emlig1->minimal_vertex->get_coordinates();
510
511	//emlig1->remove_condition(condition3a);
512	//emlig1->step_me(0);
513	//emlig1->remove_condition(condition2a);
514	//emlig1->remove_condition(condition1a);
515	//emlig1->remove_condition(condition5);
516
517
518	//emlig1->step_me(0);
519	//emlig2->step_me(0);
520
521
522	// DA SE POUZIT PRO VYPIS DO SOUBORU
523	// emlig1->step_me(0);
524
525	//emlig1->remove_condition(condition1);
526
527
528
529
530
531	/*
532	for(int i = 0;i<100;i++)
533	{
534	cout << endl << "Step:" << i << endl;
535
536	double condition[emlig_size+1];
537
538	for(int k = 0;k<=emlig_size;k++)
539	{
540	condition[k] = (rand()-RAND_MAX/2)/1000.0;
541	}
542
543
544	vec* condition_vec = new vec(condition,emlig_size+1);
545	emlig1->add_condition(*condition_vec);
546
547	/*
548	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
549	{
550	cout << ((toprow*)toprow_ref)->probability << endl;
551	}
552	*/
553	/*
554	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
555
556	/*
557	if(i-emlig1->number_of_parameters >= 0)
558	{
559	pause(30);
560	}
561	*/
562
563	// emlig1->step_me(i);
564
565	/*
566	vector<int> sizevector;
567	for(int s = 0;s<=emlig1->number_of_parameters;s++)
568	{
569	sizevector.push_back(emlig1->statistic_rowsize(s));
570	}
571	*/
572	//}
573
574
575
576
577	/*
578	emlig1->step_me(1);
579
580	vec condition = "2.0 0.0 1.0";
581
582	emlig1->add_condition(condition);
583
584	vector<int> sizevector;
585	for(int s = 0;s<=emlig1->number_of_parameters;s++)
586	{
587	sizevector.push_back(emlig1->statistic_rowsize(s));
588	}
589
590	emlig1->step_me(2);
591
592	condition = "2.0 1.0 0.0";
593
594	emlig1->add_condition(condition);
595
596	sizevector.clear();
597	for(int s = 0;s<=emlig1->number_of_parameters;s++)
598	{
599	sizevector.push_back(emlig1->statistic_rowsize(s));
600	}
601	*/
602
603	return 0;
604	}
605
606

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