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Changeset 1370

Timestamp:

05/13/11 19:03:16 (14 years ago)

Author:

sindj

Message:

Pripojeni na realna data. Prvni real time system. JS

Files:

: 1 modified

applications/robust/main.cpp (modified) (16 diffs)

Legend:

: Unmodified
: Added
: Removed

applications/robust/main.cpp

r1367	r1370
16	16	#include "ddeml.h"
17	17	#include "stdio.h"
	18	#include <time.h>
18	19
19	20	//#include "DDEClient.h"
…	…
28	29
29	30	const int max_model_order = 2;
30
31
32		/*
	31	const int data_count = 5;
	32
	33	static vec avg_vec;
	34
33	35	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.
	36	UINT uType, // Transaction type.
	37	UINT uFmt, // Clipboard data format.
	38	HCONV hconv, // Handle to the conversation.
	39	HSZ hsz1, // Handle to a string.
	40	HSZ hsz2, // Handle to a string.
	41	HDDEDATA hdata, // Handle to a global memory object.
	42	DWORD dwData1, // Transaction-specific data.
	43	DWORD dwData2) // Transaction-specific data.
42	44	{
43		return 0;
	45	char * pData;
	46	DWORD data_len;
	47
	48	switch (uType)
	49	{
	50	case XTYP_ADVDATA:
	51
	52	if ( hdata == NULL )
	53	cout << "No data in the message." << endl;
	54	else
	55	{
	56	pData = (char*)DdeAccessData( hdata, &data_len );
	57
	58	avg_vec.ins(0,atof(pData));
	59
	60	cout << pData << endl;
	61
	62	DdeUnaccessData( hdata );
	63	}
	64	}
	65	return (HDDEDATA) NULL;
	66
44	67	}
	68
	69
45	70
46	71	void DDERequest(DWORD idInst, HCONV hConv, char* szItem)
…	…
64	89	return 0;
65	90	}
66		*/
	91
67	92
68	93	class model
…	…
77	102
78	103	bool has_constant;
	104	int order;
79	105	int window_size;
80	106	int predicted_channel;
	107	int number_of_updates;
	108	double prior_lognc;
	109	vec lognc_history;
81	110	mat* data_matrix;
82	111
…	…
85	114	bool has_constant,
86	115	int window_size,
87		int predicted_channel,
	116	int predicted_channel,
88	117	mat* data_matrix)
89	118	{
90	119	this->ar_components.insert(this->ar_components.begin(),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;
	120	this->has_constant = has_constant;
	121	this->window_size = window_size;
	122	this->predicted_channel = predicted_channel;
	123	this->data_matrix = data_matrix;
	124	number_of_updates = 0;
	125
	126	order = 0;
	127	for(list<pair<int,int>>::iterator ar_ref = ar_components.begin();ar_ref!=ar_components.end();ar_ref++)
	128	{
	129	if((*ar_ref).second > order)
	130	{
	131	order = (*ar_ref).second;
	132	}
	133	}
95	134
96	135	if(robust)
…	…
106	145	my_arx = NULL;
107	146	}
	147
	148	prior_lognc = my_rarx->posterior->log_nc;
108	149	}
109	150	else
…	…
116	157	{
117	158	V0 = 0.001 * eye(ar_components.size()+2);
118		V0(0,0) = 0;
	159	V0(0,0) = 1;
119	160	my_arx->set_constant(true);
120	161
…	…
124	165
125	166	V0 = 0.001 * eye(ar_components.size()+1);
126		V0(0,0) = 0;
	167	V0(0,0) = 1;
127	168	my_arx->set_constant(false);
128	169
…	…
132	173	my_arx->set_parameters(window_size);
133	174	my_arx->validate();
	175
	176	prior_lognc = my_arx->posterior().lognc();
134	177	}
135	178	}
…	…
137	180	void data_update(int time)
138	181	{
139		vec data_vector;
140		for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
141		{
142		data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
143		}
144
145		if(my_rarx!=NULL)
146		{
147		data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
148		my_rarx->bayes(data_vector);
	182	if(time > order)
	183	{
	184	vec data_vector;
	185	for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
	186	{
	187	data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
	188	}
	189
	190	// cout << "Update cond: " << data_vector << endl;
	191
	192	double cur_lognc;
	193	if(my_rarx!=NULL)
	194	{
	195	data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
	196	my_rarx->bayes(data_vector);
	197	cur_lognc = my_rarx->posterior->log_nc;
	198	}
	199	else
	200	{
	201	vec pred_vec;
	202	pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
	203	my_arx->bayes(pred_vec,data_vector);
	204	cur_lognc = my_arx->posterior().lognc();
	205	}
	206
	207	number_of_updates++;
	208
	209	if(number_of_updates>window_size)
	210	{
	211	lognc_history.ins(lognc_history.size(),cur_lognc-prior_lognc);
	212	}
149	213	}
150	214	else
151	215	{
152		vec pred_vec;
153		pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
154		my_arx->bayes(pred_vec,data_vector);
	216	lognc_history.ins(lognc_history.size(),0);
155	217	}
156	218	}
…	…
158	220	pair<vec,vec> predict(int sample_size, int time, itpp::Laplace_RNG* LapRNG)
159	221	{
160		vec condition_vector;
161		for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
162		{
163		condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1));
164		}
165
166		if(my_rarx!=NULL)
167		{
168		pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size);
169
170		//cout << imp_samples.first << endl;
	222	if(time > order)
	223	{
	224	vec condition_vector;
	225	for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
	226	{
	227	condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1));
	228	}
	229
	230	// cout << "Prediction cond: " << condition_vector << endl;
	231
	232	if(my_rarx!=NULL)
	233	{
	234	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size);
	235
	236	//cout << "Point estimate: " << (imp_samples.secondimp_samples.first)/(imp_samples.firstones(imp_samples.first.size())) << endl;
	237
	238	vec sample_prediction;
	239	for(int t = 0;t<sample_size;t++)
	240	{
	241	vec lap_sample = condition_vector;
	242
	243	if(has_constant)
	244	{
	245	lap_sample.ins(lap_sample.size(),1.0);
	246	}
	247
	248	lap_sample.ins(0,(*LapRNG)());
	249
	250	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
	251	}
	252
	253	return pair<vec,vec>(imp_samples.first,sample_prediction);
	254	}
	255	else
	256	{
	257	mat samples = my_arx->posterior().sample_mat(sample_size);
	258
	259	//cout << "Point estimate: " << (samples*ones(samples.cols()))/samples.cols() << endl;
	260
	261	// cout << samples.get_col(1) << endl;
	262
	263	vec sample_prediction;
	264	for(int t = 0;t<sample_size;t++)
	265	{
	266	vec gau_sample = condition_vector;
	267
	268	if(has_constant)
	269	{
	270	gau_sample.ins(gau_sample.size(),1.0);
	271	}
	272
	273	gau_sample.ins(gau_sample.size(),randn());
	274
	275	sample_prediction.ins(0,gau_sample*samples.get_col(t));
	276	}
	277
	278	return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction);
	279	}
	280	}
	281	else
	282	{
171	283
172		vec sample_prediction;
173		for(int t = 0;t<sample_size;t++)
174		{
175		vec lap_sample = condition_vector;
176
177		if(has_constant)
178		{
179		lap_sample.ins(lap_sample.size(),1.0);
180		}
181
182		lap_sample.ins(0,(*LapRNG)());
183
184		sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
185		}
186
187		return pair<vec,vec>(imp_samples.first,sample_prediction);
188		}
189		else
190		{
191		mat samples = my_arx->posterior().sample_mat(sample_size);
192
193		vec sample_prediction;
194		for(int t = 0;t<sample_size;t++)
195		{
196		vec gau_sample = condition_vector;
197
198		if(has_constant)
199		{
200		gau_sample.ins(gau_sample.size(),1.0);
201		}
202
203		gau_sample.ins(0,randn());
204
205		sample_prediction.ins(0,gau_sample*samples.get_col(t));
206		}
207
208		return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction);
	284	return pair<vec,vec>(zeros(1),ones(1));
209	285	}
210	286
…	…
261	337
262	338
	339
	340
	341
263	342	int main ( int argc, char* argv[] ) {
264	343
	344	vec data_vec;
	345	mat data_matrix;
	346	DWORD Id;
	347
	348	char szApp[] = "MT4";
	349	char szTopic[] = "BID";
	350	char szItem[] = "EURUSD";
	351	char* itRef = &szItem[0];
	352
	353	char* file_string = "c:\\rtdata";
	354
	355	ofstream myfile;
	356	char fstring[80];
	357	strcpy(fstring,file_string);
	358	strcat(fstring,itRef);
	359	strcat(fstring,"lognc.txt");
	360
	361	list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,1);
	362
	363	int max_window = 50;
	364	int min_window = 1;
	365
	366	list<model*> models;
	367	for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
	368	{
	369	for(int window_size = min_window;window_size < max_window;window_size++)
	370	{
	371	//models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix));
	372	models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix));
	373	//models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix));
	374	//models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix));
	375	}
	376	}
	377
	378	mat result_lognc;
	379	mat result_preds;
	380
265	381	/*
266		~~DWORD Id;~~
267	382	HANDLE hThrd = CreateThread( NULL, 0, (LPTHREAD_START_ROUTINE)ThrdFunc, (LPVOID)1, 0, &Id);
268
269		if ( !hThrd )
	383	if ( !hThrd )
270	384	{
271	385	cout<<"Error Creating Threads,,,,.exiting"<<endl;
…	…
273	387	}
274	388	Sleep ( 100 );
275
276
277		char szApp[] = "MT4";
278		char szTopic[] = "QUOTE";
279		char szItem1[] = "EURUSD";
	389	*/
	390
	391
280	392
281	393	//DDE Initialization
282		DWORD idInst=0;
	394	DWORD idInst = 0;
283	395	UINT iReturn;
284	396	iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback,
…	…
308	420	//Execute commands/requests specific to the DDE Server.
309	421
310		DDERequest(idInst, hConv, szItem1);
311
	422	DDERequest(idInst, hConv, szItem);
	423
	424	itpp::Laplace_RNG LapRNG = Laplace_RNG();
	425
	426	// time_t current_time = time(0);
	427
312	428	while(1)
313	429	{
314		MSG msg;
315		BOOL MsgReturn = GetMessage ( &msg , NULL , 0 , 0 );
316
317		if(MsgReturn)
318		{
319		TranslateMessage(&msg);
320		DispatchMessage(&msg);
321		}
322		}
323
	430	while(avg_vec.size() < data_count)
	431	{
	432	MSG msg;
	433	BOOL MsgReturn = GetMessage ( &msg , NULL , 0 , 0 );
	434
	435	if(MsgReturn)
	436	{
	437	TranslateMessage(&msg);
	438	DispatchMessage(&msg);
	439	}
	440	Sleep(500);
	441	}
	442
	443
	444	data_vec.ins(data_vec.size(),avg_vec*ones(avg_vec.size())/avg_vec.size());
	445	data_matrix = mat(data_vec).T();
	446	avg_vec.del(0,avg_vec.size()-1);
	447
	448	vector<double> test_lognc;
	449	vec preds;
	450
	451	for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
	452	{
	453	(*model_ref)->data_update(data_vec.size()-1);
	454
	455	if((*model_ref)->lognc_history.size()==0)
	456	{
	457	test_lognc.push_back(0);
	458	}
	459	else
	460	{
	461	test_lognc.push_back((ones((model_ref)->lognc_history.size())(model_ref)->lognc_history)/(model_ref)->lognc_history.size()(max_window/(model_ref)->window_size));
	462	}
	463
	464	pair<vec,vec> predictions = (*model_ref)->predict(500,data_vec.size()-1,&LapRNG);
	465
	466	preds.ins(preds.size(),(predictions.firstpredictions.second)/(predictions.firstones(predictions.first.size())));
	467
	468	}
	469
	470	//preds.ins(0,data_matrix.get(0,data_vec.size()));
	471
	472	result_preds.ins_col(result_preds.cols(),preds);
	473
	474	myfile.open(fstring,ios::app);
	475
	476	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
	477
	478	for(int i = 0;i<test_lognc.size();i++)
	479	{
	480	myfile << test_lognc[i] << ',';
	481	}
	482
	483	myfile << endl;
	484
	485	myfile.close();
	486
	487	}
	488
324	489	//DDE Disconnect and Uninitialize.
325	490	DdeDisconnect(hConv);
326	491	DdeUninitialize(idInst);
327		*/
328
329
330
331		/*
332		// 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,
333		// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
334		// variance of location parameter estimators and compare it to the classical setup.
335		vector<vector<vector<string>>> string_lists;
336		string_lists.push_back(vector<vector<string>>());
337		string_lists.push_back(vector<vector<string>>());
338		string_lists.push_back(vector<vector<string>>());
339
340		char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
341
342
343		for(int i = 0;i<3;i++)
344		{
345		ifstream myfile(file_strings[i]);
	492
	493	return 0;
	494	}
	495
	496	/*
	497	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
	498	// 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
	499	// can be compared to the classical setup.
	500
	501	vector<vector<string>> strings;
	502
	503	char* file_string = "c:\\dataGCClosePercDiff";
	504
	505	char dfstring[80];
	506	strcpy(dfstring,file_string);
	507	strcat(dfstring,".txt");
	508
	509
	510	mat data_matrix;
	511	ifstream myfile(dfstring);
346	512	if (myfile.is_open())
347		{
348		while ( myfile.good() )
349		{
350		string line;
351		getline(myfile,line);
352
353		vector<string> parsed_line;
	513	{
	514	string line;
	515	while(getline(myfile,line))
	516	{
	517	vec data_vector;
354	518	while(line.find(',') != string::npos)
355	519	{
356		int loc = line.find(',');
357		parsed_line.push_back(line.substr(0,loc));
	520	int loc2 = line.find('\n');
	521	int loc = line.find(',');
	522	data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));
358	523	line.erase(0,loc+1);
359		}
360
361		~~string_lists[i].push_back(parsed_line);~~
362		}
363		~~myfile.close();~~
364		}
365		}
366
367		~~for(int j = 0;j<string_lists.size();j++)~~
368		{
369
370		~~for(int i = 0;i<string_lists[j].size()-1;i++)~~
371		{
372		~~vector<vec> conditions;~~
373		~~//emlig* emliga = new emlig(2);~~
374		~~RARX* my_rarx = new RARX(2,30);~~
375
376		~~for(int k = 1;k<string_lists[j][i].size();k++)~~
377		{
378		~~vec condition;~~
379		~~//condition.ins(0,1);~~
380		~~condition.ins(0,string_lists[j][i][k]);~~
381		~~conditions.push_back(condition);~~
382
383		~~//cout << "orig:" << condition << endl;~~
384
385		~~if(conditions.size()>1)~~
386		{
387		~~conditions[k-2].ins(0,string_lists[j][i][k]);~~
388
389	524	}
390	525
391		if(conditions.size()>2)
	526	data_matrix.ins_row(data_matrix.rows(),data_vector);
	527	}
	528
	529	myfile.close();
	530	}
	531	else
	532	{
	533	cout << "Can't open data file!" << endl;
	534	}
	535	*/
	536
	537
	538	// cout << "Updated." << endl;
	539
	540	/*
	541	// 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,
	542	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
	543	// variance of location parameter estimators and compare it to the classical setup.
	544	vector<vector<vector<string>>> string_lists;
	545	string_lists.push_back(vector<vector<string>>());
	546	string_lists.push_back(vector<vector<string>>());
	547	string_lists.push_back(vector<vector<string>>());
	548
	549	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
	550
	551
	552	for(int i = 0;i<3;i++)
	553	{
	554	ifstream myfile(file_strings[i]);
	555	if (myfile.is_open())
	556	{
	557	while ( myfile.good() )
392	558	{
393		conditions[k-3].ins(0,string_lists[j][i][k]);
394
395		//cout << "modi:" << conditions[k-3] << endl;
396
397		my_rarx->bayes(conditions[k-3]);
398
399
400		//if(k>5)
401		//{
402		// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
403		//}
404
405		}
	559	string line;
	560	getline(myfile,line);
	561
	562	vector<string> parsed_line;
	563	while(line.find(',') != string::npos)
	564	{
	565	int loc = line.find(',');
	566	parsed_line.push_back(line.substr(0,loc));
	567	line.erase(0,loc+1);
	568	}
	569
	570	string_lists[i].push_back(parsed_line);
	571	}
	572	myfile.close();
	573	}
	574	}
	575
	576	for(int j = 0;j<string_lists.size();j++)
	577	{
	578
	579	for(int i = 0;i<string_lists[j].size()-1;i++)
	580	{
	581	vector<vec> conditions;
	582	//emlig* emliga = new emlig(2);
	583	RARX* my_rarx = new RARX(2,30);
	584
	585	for(int k = 1;k<string_lists[j][i].size();k++)
	586	{
	587	vec condition;
	588	//condition.ins(0,1);
	589	condition.ins(0,string_lists[j][i][k]);
	590	conditions.push_back(condition);
	591
	592	//cout << "orig:" << condition << endl;
	593
	594	if(conditions.size()>1)
	595	{
	596	conditions[k-2].ins(0,string_lists[j][i][k]);
	597
	598	}
	599
	600	if(conditions.size()>2)
	601	{
	602	conditions[k-3].ins(0,string_lists[j][i][k]);
	603
	604	//cout << "modi:" << conditions[k-3] << endl;
	605
	606	my_rarx->bayes(conditions[k-3]);
	607
	608
	609	//if(k>5)
	610	//{
	611	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
	612	//}
	613
	614	}
	615
	616	}
	617
	618	//emliga->step_me(0);
	619	/*
	620	ofstream myfile;
	621	myfile.open("c:\\robust_ar1.txt",ios::app);
	622	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
	623	myfile.close();
	624
	625	myfile.open("c:\\robust_ar2.txt",ios::app);
	626	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
	627	myfile.close();
406	628
407		}
408
409		//emliga->step_me(0);
410		/*
	629
	630	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
	631	cout << "Step: " << i << endl;
	632	}
	633
	634	cout << "One experiment finished." << endl;
	635
411	636	ofstream myfile;
412	637	myfile.open("c:\\robust_ar1.txt",ios::app);
413		myfile << ~~my_rarx->minimal_vertex->get_coordinates()[0] << ";"~~;
	638	myfile << endl;
414	639	myfile.close();
415	640
416	641	myfile.open("c:\\robust_ar2.txt",ios::app);
417		myfile << e~~mliga->minimal_vertex->get_coordinates()[1] << ";"~~;
	642	myfile << endl;
418	643	myfile.close();
419
420
421		cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
422		cout << "Step: " << i << endl;
423		}
424
425		cout << "One experiment finished." << endl;
426
427		ofstream myfile;
428		myfile.open("c:\\robust_ar1.txt",ios::app);
429		myfile << endl;
430		myfile.close();
431
432		myfile.open("c:\\robust_ar2.txt",ios::app);
433		myfile << endl;
434		myfile.close();
435		}*/
436
437		// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
438		// 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
439		// can be compared to the classical setup.
440
441		itpp::Laplace_RNG LapRNG = Laplace_RNG();
442
443		vector<vector<string>> strings;
444
445		char* file_string = "c:\\dataADClosePercDiff";
446
447		char dfstring[80];
448		strcpy(dfstring,file_string);
449		strcat(dfstring,".txt");
450
451
452		mat data_matrix;
453		ifstream myfile(dfstring);
454		if (myfile.is_open())
455		{
456		string line;
457		while(getline(myfile,line))
458		{
459		vec data_vector;
460		while(line.find(',') != string::npos)
461		{
462		int loc2 = line.find('\n');
463		int loc = line.find(',');
464		data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));
465		line.erase(0,loc+1);
466		}
467
468		data_matrix.ins_row(data_matrix.rows(),data_vector);
469		}
470
471		myfile.close();
472		}
473		else
474		{
475		cout << "Can't open data file!" << endl;
476		}
477
478
479		list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows());
480
481		list<model*> models;
482		for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
483		{
484		for(int window_size = 30;window_size < 31;window_size++)
485		{
486		models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix));
487		models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix));
488		models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix));
489		models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix));
490		}
491		}
492
493		mat result_lognc;
494		mat result_preds;
495
496		for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols()
497		{
498		vec cur_res_lognc;
499		vec preds;
500
501		for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
502		{
503		(*model_ref)->data_update(time);
504
505		if((*model_ref)->my_rarx!=NULL)
506		{
507		cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc);
508		}
509		else
510		{
511		cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc());
512		}
513
514		pair<vec,vec> predictions = (*model_ref)->predict(500,time,&LapRNG);
515
516		preds.ins(preds.size(),(predictions.firstpredictions.second)/(predictions.firstones(predictions.first.size())));
517		}
518
519		preds.ins(0,data_matrix.get(0,time+1));
520
521		result_lognc.ins_col(result_lognc.cols(),cur_res_lognc);
522		result_preds.ins_col(result_preds.cols(),preds);
523
524		// cout << "Updated." << endl;
525
	644	}*/
	645
526	646	/*
527	647	vector<vec> conditions;
…	…
735	855
736	856
737		ofstream myfile;
738		char fstring[80];
739		strcpy(fstring,file_string);
740
741		strcat(fstring,"preds.txt");
742
743		myfile.open(fstring,ios::app);
744
745		// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
746
747		for(int i = 0;i<preds.size();i++)
748		{
749		myfile << preds[i] << ',';
750		}
751
752		myfile << endl;
753
754		myfile.close();
755		}
	857
756	858	/*
757	859	myfile.open(f2string,ios::app);
…	…
958	1060	*/
959	1061
960		return 0;
961		}
962
963
964
	1062
	1063
	1064

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applications/robust/main.cpp

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