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main.cpp @ 1337

Revision 1337, 11.6 kB (checked in by sindj, 13 years ago)
Zacatky experimentovani s knihovnou robustlib. 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/signal/poly.h>
15
16	using namespace itpp;
17	using namespace bdm;
18
19	const int emlig_size = 2;
20
21
22	int main ( int argc, char* argv[] ) {
23
24	itpp::Laplace_RNG LapRNG = Laplace_RNG();
25
26	/*
27	// 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,
28	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
29	// variance of location parameter estimators and compare it to the classical setup.
30	vector<vector<vector<string>>> string_lists;
31	string_lists.push_back(vector<vector<string>>());
32	string_lists.push_back(vector<vector<string>>());
33	string_lists.push_back(vector<vector<string>>());
34
35	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
36
37
38	for(int i = 0;i<3;i++)
39	{
40	ifstream myfile(file_strings[i]);
41	if (myfile.is_open())
42	{
43	while ( myfile.good() )
44	{
45	string line;
46	getline(myfile,line);
47
48	vector<string> parsed_line;
49	while(line.find(',') != string::npos)
50	{
51	int loc = line.find(',');
52	parsed_line.push_back(line.substr(0,loc));
53	line.erase(0,loc+1);
54	}
55
56	string_lists[i].push_back(parsed_line);
57	}
58	myfile.close();
59	}
60	}
61
62	for(int j = 0;j<string_lists.size();j++)
63	{
64
65	for(int i = 0;i<string_lists[j].size()-1;i++)
66	{
67	vector<vec> conditions;
68	//emlig* emliga = new emlig(2);
69	RARX* my_rarx = new RARX(2,30);
70
71	for(int k = 1;k<string_lists[j][i].size();k++)
72	{
73	vec condition;
74	//condition.ins(0,1);
75	condition.ins(0,string_lists[j][i][k]);
76	conditions.push_back(condition);
77
78	//cout << "orig:" << condition << endl;
79
80	if(conditions.size()>1)
81	{
82	conditions[k-2].ins(0,string_lists[j][i][k]);
83
84	}
85
86	if(conditions.size()>2)
87	{
88	conditions[k-3].ins(0,string_lists[j][i][k]);
89
90	//cout << "modi:" << conditions[k-3] << endl;
91
92	my_rarx->bayes(conditions[k-3]);
93
94
95	//if(k>5)
96	//{
97	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
98	//}
99
100	}
101
102	}
103
104	//emliga->step_me(0);
105	/*
106	ofstream myfile;
107	myfile.open("c:\\robust_ar1.txt",ios::app);
108	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
109	myfile.close();
110
111	myfile.open("c:\\robust_ar2.txt",ios::app);
112	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
113	myfile.close();
114
115
116	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
117	cout << "Step: " << i << endl;
118	}
119
120	cout << "One experiment finished." << endl;
121
122	ofstream myfile;
123	myfile.open("c:\\robust_ar1.txt",ios::app);
124	myfile << endl;
125	myfile.close();
126
127	myfile.open("c:\\robust_ar2.txt",ios::app);
128	myfile << endl;
129	myfile.close();
130	}*/
131
132
133	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
134	// 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
135	// can be compared to the classical setup.
136
137
138	vector<vector<string>> strings;
139
140	char* file_strings[3] = {"c:\\dataADClosePercDiff", "c:\\ar_student_single","c:\\ar_cauchy_single"};
141
142	for(int i = 0;i<3;i++)
143	{
144	char dfstring[80];
145	strcpy(dfstring,file_strings[i]);
146	strcat(dfstring,".txt");
147
148	ifstream myfile(dfstring);
149	if (myfile.is_open())
150	{
151	string line;
152	getline(myfile,line);
153
154	vector<string> parsed_line;
155	while(line.find(',') != string::npos)
156	{
157	int loc = line.find(',');
158	parsed_line.push_back(line.substr(0,loc));
159	line.erase(0,loc+1);
160	}
161
162	strings.push_back(parsed_line);
163
164	myfile.close();
165	}
166	}
167
168	for(int j = 0;j<strings.size();j++)
169	{
170	vector<vec> conditions;
171	//emlig* emliga = new emlig(2);
172	RARX* my_rarx = new RARX(2,30,false);
173
174	/*
175	mat V0 = 0.0001 * eye ( 3 );
176	ARX* my_arx = new ARX(0.97);
177	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
178	my_arx->set_constant ( false );
179	my_arx->validate();
180	*/
181
182	for(int k = 1;k<170;k++)
183	{
184	vec condition;
185	//condition.ins(0,1);
186	condition.ins(0,strings[j][k]);
187	conditions.push_back(condition);
188
189	//cout << "orig:" << condition << endl;
190
191	if(conditions.size()>1)
192	{
193	conditions[k-2].ins(0,strings[j][k]);
194
195	}
196
197	if(conditions.size()>2)
198	{
199	conditions[k-3].ins(0,strings[j][k]);
200
201	// cout << "modi:" << conditions[k-3] << endl;
202
203	my_rarx->bayes(conditions[k-3]);
204
205	vec cond_vec;
206	cond_vec.ins(0,conditions[k-3][0]);
207
208	// my_arx->bayes(cond_vec,conditions[k-3].right(2));
209
210
211	if(k>5)
212	{
213	//my_rarx->posterior->step_me(0);
214
215	mat samples = my_rarx->posterior->sample_mat(50);
216
217	vec sample_prediction;
218	for(int t = 0;t<50;t++)
219	{
220	vec lap_sample = conditions[k-3].left(2);
221
222	lap_sample.ins(0,LapRNG());
223
224	sample_prediction.ins(0,lap_sample*samples.get_col(t));
225	}
226
227	vec sample_pow = sample_prediction;
228	vec poly_coefs;
229	bool stop_iteration = false;
230	int en = 0;
231	do
232	{
233	double poly_coef = ones(sample_pow.size())*sample_pow/sample_pow.size();
234
235	if(abs(poly_coef)>numeric_limits<double>::epsilon())
236	{
237	sample_pow = elem_mult(sample_pow,sample_prediction);
238	poly_coefs.ins(poly_coefs.size(),pow(-1,en)*poly_coef);
239	}
240	else
241	{
242	stop_iteration = true;
243	}
244
245	en++;
246	}
247	while(!stop_iteration);
248
249	cvec action =
250
251
252	cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
253
254	double prediction = 0;
255	for(int s = 1;s<samples.rows();s++)
256	{
257
258	double avg_parameter = samples.get_row(s)*ones(samples.cols())/samples.cols();
259
260	prediction += avg_parameter*conditions[k-3][s-1];
261
262
263
264	/*
265	ofstream myfile;
266	char fstring[80];
267	strcpy(fstring,file_strings[j]);
268
269	char es[5];
270	strcat(fstring,itoa(s,es,10));
271
272	strcat(fstring,"_res.txt");
273
274
275	myfile.open(fstring,ios::app);
276
277	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
278	myfile << avg_parameter;
279
280	if(k!=strings[j].size()-1)
281	{
282	myfile << ",";
283	}
284	else
285	{
286	myfile << endl;
287	}
288	myfile.close();
289	*/
290	}
291
292	/*
293	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
294	double prediction2 = pred_mat->mean()[0];
295	*/
296
297	ofstream myfile;
298	char fstring[80];
299	//char f2string[80];
300	strcpy(fstring,file_strings[j]);
301	//strcpy(f2string,fstring);
302
303	strcat(fstring,"pred.txt");
304	//strcat(f2string,"2pred.txt");
305
306
307	myfile.open(fstring,ios::app);
308
309	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
310	myfile << prediction;
311
312	if(k!=strings[j].size()-1)
313	{
314	myfile << ",";
315	}
316	else
317	{
318	myfile << endl;
319	}
320	myfile.close();
321
322	/*
323	myfile.open(f2string,ios::app);
324	myfile << prediction2;
325
326	if(k!=strings[j].size()-1)
327	{
328	myfile << ",";
329	}
330	else
331	{
332	myfile << endl;
333	}
334	myfile.close();
335	*/
336
337	}
338	}
339
340	//emliga->step_me(0);
341	/*
342	ofstream myfile;
343	myfile.open("c:\\robust_ar1.txt",ios::app);
344	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
345	myfile.close();
346
347	myfile.open("c:\\robust_ar2.txt",ios::app);
348	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
349	myfile.close();
350
351
352	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
353	cout << "Step: " << i << endl;*/
354	}
355
356
357	}
358
359
360	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
361	// with maximization of logarithm of one-step ahead wealth.
362
363
364
365	/*
366	cout << "One experiment finished." << endl;
367
368	ofstream myfile;
369	myfile.open("c:\\robust_ar1.txt",ios::app);
370	myfile << endl;
371	myfile.close();
372
373	myfile.open("c:\\robust_ar2.txt",ios::app);
374	myfile << endl;
375	myfile.close();*/
376
377
378	//emlig* emlig1 = new emlig(emlig_size);
379
380	//emlig1->step_me(0);
381	//emlig* emlig2 = new emlig(emlig_size);
382
383	/*
384	emlig1->set_correction_factors(4);
385
386	for(int j = 0;j<emlig1->correction_factors.size();j++)
387	{
388	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
389	{
390	cout << j << " ";
391
392	for(int i=0;i<(*vec_ref).size();i++)
393	{
394	cout << (*vec_ref)[i];
395	}
396
397	cout << endl;
398	}
399	}*/
400
401	/*
402	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
403
404	emlig1->add_condition(condition5);
405	//emlig1->step_me(0);
406
407
408	vec condition1a = "-1.0 1.02 0.5";
409	//vec condition1b = "1.0 1.0 1.01";
410	emlig1->add_condition(condition1a);
411	//emlig2->add_condition(condition1b);
412
413	vec condition2a = "-0.3 1.7 1.5";
414	//vec condition2b = "-1.0 1.0 1.0";
415	emlig1->add_condition(condition2a);
416	//emlig2->add_condition(condition2b);
417
418	vec condition3a = "0.5 -1.01 1.0";
419	//vec condition3b = "0.5 -1.01 1.0";
420
421	emlig1->add_condition(condition3a);
422	//emlig2->add_condition(condition3b);
423
424	vec condition4a = "-0.5 -1.0 1.0";
425	//vec condition4b = "-0.5 -1.0 1.0";
426
427	emlig1->add_condition(condition4a);
428	//cout << "************************************************" << endl;
429	//emlig2->add_condition(condition4b);
430	//cout << "************************************************" << endl;
431
432	//cout << emlig1->minimal_vertex->get_coordinates();
433
434	//emlig1->remove_condition(condition3a);
435	//emlig1->step_me(0);
436	//emlig1->remove_condition(condition2a);
437	//emlig1->remove_condition(condition1a);
438	//emlig1->remove_condition(condition5);
439
440
441	//emlig1->step_me(0);
442	//emlig2->step_me(0);
443
444
445	// DA SE POUZIT PRO VYPIS DO SOUBORU
446	// emlig1->step_me(0);
447
448	//emlig1->remove_condition(condition1);
449
450
451
452
453
454	/*
455	for(int i = 0;i<100;i++)
456	{
457	cout << endl << "Step:" << i << endl;
458
459	double condition[emlig_size+1];
460
461	for(int k = 0;k<=emlig_size;k++)
462	{
463	condition[k] = (rand()-RAND_MAX/2)/1000.0;
464	}
465
466
467	vec* condition_vec = new vec(condition,emlig_size+1);
468	emlig1->add_condition(*condition_vec);
469
470	/*
471	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
472	{
473	cout << ((toprow*)toprow_ref)->probability << endl;
474	}
475	*/
476	/*
477	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
478
479	/*
480	if(i-emlig1->number_of_parameters >= 0)
481	{
482	pause(30);
483	}
484	*/
485
486	// emlig1->step_me(i);
487
488	/*
489	vector<int> sizevector;
490	for(int s = 0;s<=emlig1->number_of_parameters;s++)
491	{
492	sizevector.push_back(emlig1->statistic_rowsize(s));
493	}
494	*/
495	//}
496
497
498
499
500	/*
501	emlig1->step_me(1);
502
503	vec condition = "2.0 0.0 1.0";
504
505	emlig1->add_condition(condition);
506
507	vector<int> sizevector;
508	for(int s = 0;s<=emlig1->number_of_parameters;s++)
509	{
510	sizevector.push_back(emlig1->statistic_rowsize(s));
511	}
512
513	emlig1->step_me(2);
514
515	condition = "2.0 1.0 0.0";
516
517	emlig1->add_condition(condition);
518
519	sizevector.clear();
520	for(int s = 0;s<=emlig1->number_of_parameters;s++)
521	{
522	sizevector.push_back(emlig1->statistic_rowsize(s));
523	}
524	*/
525
526	return 0;
527	}
528
529

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