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main.cpp @ 1357

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Opravena chyba s uvolnovanim pameti pri ruseni ve for_merging poli a dalsi drobne nedostatky. JS

Line
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 = 5;
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:\\dataADClosePercDiff","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
170
171	for(int j = 0;j<strings.size();j++)
172	{
173	vector<vec> conditions;
174	//emlig* emliga = new emlig(2);
175	RARX* my_rarx = new RARX(2,8,false);
176
177
178	mat V0 = 0.0001 * eye ( 3 );
179	ARX* my_arx = new ARX(0.85);
180	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
181	my_arx->set_constant ( false );
182	my_arx->validate();
183
184
185	for(int k = 1;k<strings[j].size();k++)
186	{
187	vec condition;
188	//condition.ins(0,1);
189	condition.ins(0,strings[j][k]);
190	conditions.push_back(condition);
191
192	//cout << "orig:" << condition << endl;
193
194	if(conditions.size()>1)
195	{
196	conditions[k-2].ins(0,strings[j][k]);
197
198	}
199
200	if(conditions.size()>2)
201	{
202	conditions[k-3].ins(0,strings[j][k]);
203
204	// cout << "Condition:" << conditions[k-3] << endl;
205
206	my_rarx->bayes(conditions[k-3]);
207	//my_rarx->posterior->step_me(1);
208
209	vec cond_vec;
210	cond_vec.ins(0,conditions[k-3][0]);
211
212	my_arx->bayes(cond_vec,conditions[k-3].right(2));
213
214
215	if(k>8)
216	{
217	//my_rarx->posterior->step_me(0);
218
219	//mat samples = my_rarx->posterior->sample_mat(10);
220
221	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000);
222
223	//cout << imp_samples.first << endl;
224
225	vec sample_prediction;
226	for(int t = 0;t<imp_samples.first.size();t++)
227	{
228	vec lap_sample = conditions[k-3].left(2);
229	//lap_sample.ins(lap_sample.size(),1.0);
230
231	lap_sample.ins(0,LapRNG());
232
233	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
234	}
235
236
237	vec sample_pow = sample_prediction;
238
239	// cout << sample_prediction << endl;
240	vec poly_coefs;
241	double prediction;
242	bool stop_iteration = false;
243	int en = 1;
244	do
245	{
246	double poly_coef = imp_samples.firstsample_pow/(imp_samples.firstones(imp_samples.first.size()));
247
248	if(en==1)
249	{
250	prediction = poly_coef;
251	}
252
253	poly_coef = poly_coefenfact(utility_constant-2+en)/fact(utility_constant-2);
254
255	if(abs(poly_coef)>numeric_limits<double>::epsilon())
256	{
257	sample_pow = elem_mult(sample_pow,sample_prediction);
258	poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
259	}
260	else
261	{
262	stop_iteration = true;
263	}
264
265	en++;
266
267	if(en>20)
268	{
269	stop_iteration = true;
270	}
271	}
272	while(!stop_iteration);
273
274	/*
275	ofstream myfile_coef;
276
277	myfile_coef.open("c:\\coefs.txt",ios::app);
278
279	for(int t = 0;t<poly_coefs.size();t++)
280	{
281	myfile_coef << poly_coefs[t] << ",";
282	}
283
284	myfile_coef << endl;
285	myfile_coef.close();
286	*/
287
288	//cout << "Coefficients: " << poly_coefs << endl;
289
290	/*
291	vec bas_coef = vec("1.0 2.0 -8.0");
292	cout << "Coefs: " << bas_coef << endl;
293	cvec actions2 = roots(bas_coef);
294	cout << "Roots: " << actions2 << endl;
295	*/
296
297
298
299	cvec actions = roots(poly_coefs);
300
301
302	bool is_max = false;
303	for(int t = 0;t<actions.size();t++)
304	{
305	if(actions[t].imag() == 0)
306	{
307	double second_derivative = 0;
308	for(int q = 1;q<poly_coefs.size();q++)
309	{
310	second_derivative+=poly_coefs[q]pow(actions[t].real(),q-1)q;
311	}
312
313	if(second_derivative<0)
314	{
315	cout << "Action:" << actions[t].real() << endl;
316
317	is_max = true;
318	}
319	}
320	}
321
322	if(!is_max)
323	{
324	cout << "No maximum." << endl;
325	}
326
327	// cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
328
329	/*
330	double prediction = 0;
331	for(int s = 1;s<samples.rows();s++)
332	{
333
334	double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols();
335
336	prediction += avg_parameter*conditions[k-3][s-1];
337
338
339
340	/*
341	ofstream myfile;
342	char fstring[80];
343	strcpy(fstring,file_strings[j]);
344
345	char es[5];
346	strcat(fstring,itoa(s,es,10));
347
348	strcat(fstring,"_res.txt");
349
350
351	myfile.open(fstring,ios::app);
352
353	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
354	myfile << avg_parameter;
355
356	if(k!=strings[j].size()-1)
357	{
358	myfile << ",";
359	}
360	else
361	{
362	myfile << endl;
363	}
364	myfile.close();
365	*/
366
367
368	//}
369
370	// cout << "Prediction: "<< prediction << endl;
371
372	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
373	double prediction2 = pred_mat->mean()[0];
374
375
376	ofstream myfile;
377	char fstring[80];
378	char f2string[80];
379	strcpy(fstring,file_strings[j]);
380	strcpy(f2string,fstring);
381
382	strcat(fstring,"pred.txt");
383	strcat(f2string,"2pred.txt");
384
385
386	myfile.open(fstring,ios::app);
387
388	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
389	myfile << prediction;
390
391	if(k!=strings[j].size()-1)
392	{
393	myfile << ",";
394	}
395	else
396	{
397	myfile << endl;
398	}
399	myfile.close();
400
401
402	myfile.open(f2string,ios::app);
403	myfile << prediction2;
404
405	if(k!=strings[j].size()-1)
406	{
407	myfile << ",";
408	}
409	else
410	{
411	myfile << endl;
412	}
413	myfile.close();
414	//*/
415
416	}
417	}
418
419	//emliga->step_me(0);
420	/*
421	ofstream myfile;
422	myfile.open("c:\\robust_ar1.txt",ios::app);
423	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
424	myfile.close();
425
426	myfile.open("c:\\robust_ar2.txt",ios::app);
427	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
428	myfile.close();
429
430
431	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
432	cout << "Step: " << i << endl;*/
433	}
434
435
436	}
437
438
439	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
440	// with maximization of logarithm of one-step ahead wealth.
441
442
443
444	/*
445	cout << "One experiment finished." << endl;
446
447	ofstream myfile;
448	myfile.open("c:\\robust_ar1.txt",ios::app);
449	myfile << endl;
450	myfile.close();
451
452	myfile.open("c:\\robust_ar2.txt",ios::app);
453	myfile << endl;
454	myfile.close();*/
455
456
457	//emlig* emlig1 = new emlig(emlig_size);
458
459	//emlig1->step_me(0);
460	//emlig* emlig2 = new emlig(emlig_size);
461
462	/*
463	emlig1->set_correction_factors(4);
464
465	for(int j = 0;j<emlig1->correction_factors.size();j++)
466	{
467	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
468	{
469	cout << j << " ";
470
471	for(int i=0;i<(*vec_ref).size();i++)
472	{
473	cout << (*vec_ref)[i];
474	}
475
476	cout << endl;
477	}
478	}*/
479
480	/*
481	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
482
483	emlig1->add_condition(condition5);
484	//emlig1->step_me(0);
485
486
487	vec condition1a = "-1.0 1.02 0.5";
488	//vec condition1b = "1.0 1.0 1.01";
489	emlig1->add_condition(condition1a);
490	//emlig2->add_condition(condition1b);
491
492	vec condition2a = "-0.3 1.7 1.5";
493	//vec condition2b = "-1.0 1.0 1.0";
494	emlig1->add_condition(condition2a);
495	//emlig2->add_condition(condition2b);
496
497	vec condition3a = "0.5 -1.01 1.0";
498	//vec condition3b = "0.5 -1.01 1.0";
499
500	emlig1->add_condition(condition3a);
501	//emlig2->add_condition(condition3b);
502
503	vec condition4a = "-0.5 -1.0 1.0";
504	//vec condition4b = "-0.5 -1.0 1.0";
505
506	emlig1->add_condition(condition4a);
507	//cout << "************************************************" << endl;
508	//emlig2->add_condition(condition4b);
509	//cout << "************************************************" << endl;
510
511	//cout << emlig1->minimal_vertex->get_coordinates();
512
513	//emlig1->remove_condition(condition3a);
514	//emlig1->step_me(0);
515	//emlig1->remove_condition(condition2a);
516	//emlig1->remove_condition(condition1a);
517	//emlig1->remove_condition(condition5);
518
519
520	//emlig1->step_me(0);
521	//emlig2->step_me(0);
522
523
524	// DA SE POUZIT PRO VYPIS DO SOUBORU
525	// emlig1->step_me(0);
526
527	//emlig1->remove_condition(condition1);
528
529
530
531
532
533	/*
534	for(int i = 0;i<100;i++)
535	{
536	cout << endl << "Step:" << i << endl;
537
538	double condition[emlig_size+1];
539
540	for(int k = 0;k<=emlig_size;k++)
541	{
542	condition[k] = (rand()-RAND_MAX/2)/1000.0;
543	}
544
545
546	vec* condition_vec = new vec(condition,emlig_size+1);
547	emlig1->add_condition(*condition_vec);
548
549	/*
550	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
551	{
552	cout << ((toprow*)toprow_ref)->probability << endl;
553	}
554	*/
555	/*
556	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
557
558	/*
559	if(i-emlig1->number_of_parameters >= 0)
560	{
561	pause(30);
562	}
563	*/
564
565	// emlig1->step_me(i);
566
567	/*
568	vector<int> sizevector;
569	for(int s = 0;s<=emlig1->number_of_parameters;s++)
570	{
571	sizevector.push_back(emlig1->statistic_rowsize(s));
572	}
573	*/
574	//}
575
576
577
578
579	/*
580	emlig1->step_me(1);
581
582	vec condition = "2.0 0.0 1.0";
583
584	emlig1->add_condition(condition);
585
586	vector<int> sizevector;
587	for(int s = 0;s<=emlig1->number_of_parameters;s++)
588	{
589	sizevector.push_back(emlig1->statistic_rowsize(s));
590	}
591
592	emlig1->step_me(2);
593
594	condition = "2.0 1.0 0.0";
595
596	emlig1->add_condition(condition);
597
598	sizevector.clear();
599	for(int s = 0;s<=emlig1->number_of_parameters;s++)
600	{
601	sizevector.push_back(emlig1->statistic_rowsize(s));
602	}
603	*/
604
605	return 0;
606	}
607
608

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