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main.cpp @ 1362

Revision 1362, 20.3 kB (checked in by sindj, 13 years ago)
Pridani soft apriorna, boj s integraly. JS

Rev	Line
[976]	1
	2	/*!
	3	\file
	4	\brief Robust
	5	\author Vasek Smidl
	6
	7	*/
	8
[1337]	9	#include "estim/arx.h"
[976]	10	#include "robustlib.h"
[1216]	11	#include <vector>
[1284]	12	#include <iostream>
[1282]	13	#include <fstream>
[1338]	14	#include <itpp/itsignal.h>
[1361]	15	#include "windows.h"
	16	#include "ddeml.h"
	17	#include "stdio.h"
[1282]	18
[1361]	19	//#include "DDEClient.h"
	20	//#include <conio.h>
[1358]	21
[1361]	22
[1208]	23	using namespace itpp;
[1337]	24	using namespace bdm;
[976]	25
[1361]	26	//const int emlig_size = 2;
	27	//const int utility_constant = 5;
[1268]	28
[1361]	29	const int max_model_order = 2;
[1272]	30
[1361]	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_REQUEST,TIMEOUT_ASYNC , NULL);
	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	class model
	63	{
[976]	64
[1337]	65
[1361]	66	public:
	67	list<pair<int,int>> ar_components;
[1358]	68
[1361]	69	// Best thing would be to inherit the two models from a single souce, this is planned, but now structurally
	70	// problematic.
	71	RARX* my_rarx;
	72	ARXwin* my_arx;
	73
	74	bool has_constant;
	75	int window_size;
	76	int predicted_channel;
	77	mat* data_matrix;
	78
	79	model(list<pair<int,int>> ar_components,
	80	bool robust,
	81	bool has_constant,
	82	int window_size,
	83	int predicted_channel,
	84	mat* data_matrix)
[1358]	85	{
[1361]	86	this->ar_components.insert(this->ar_components.begin(),ar_components.begin(),ar_components.end());
	87	this->has_constant = has_constant;
	88	this->window_size = window_size;
	89	this->predicted_channel = predicted_channel;
	90	this->data_matrix = data_matrix;
	91
	92	if(robust)
	93	{
	94	if(has_constant)
	95	{
	96	my_rarx = new RARX(ar_components.size()+1,window_size,true);
	97	my_arx = NULL;
	98	}
	99	else
	100	{
	101	my_rarx = new RARX(ar_components.size(),window_size,false);
	102	my_arx = NULL;
	103	}
	104	}
	105	else
	106	{
	107	my_rarx = NULL;
	108	my_arx = new ARXwin();
	109	mat V0;
	110
	111	if(has_constant)
	112	{
[1362]	113	V0 = 0.01 * eye(ar_components.size()+2);
	114	V0(0,0) = 1;
[1361]	115	my_arx->set_constant(true);
	116
	117	}
	118	else
	119	{
	120
[1362]	121	V0 = 0.01 * eye(ar_components.size()+1);
	122	V0(0,0) = 1;
[1361]	123	my_arx->set_constant(false);
	124
	125	}
	126
	127	my_arx->set_statistics(1, V0);
	128	my_arx->set_parameters(window_size);
	129	my_arx->validate();
	130	}
[1358]	131	}
[1361]	132
	133	void data_update(int time)
[1358]	134	{
[1361]	135	vec data_vector;
	136	for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
	137	{
	138	data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
	139	}
[1358]	140
[1361]	141	if(my_rarx!=NULL)
[1358]	142	{
[1361]	143	data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
	144	my_rarx->bayes(data_vector);
[1358]	145	}
	146	else
	147	{
[1361]	148	vec pred_vec;
	149	pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
	150	my_arx->bayes(pred_vec,data_vector);
	151	}
	152	}
	153
	154	static list<list<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels)
	155	{
	156	list<list<pair<int,int>>> created_model_types;
	157
	158	if(max_order == 1)
	159	{
	160	for(int channel = 0;channel<number_of_channels;channel++)
[1358]	161	{
[1361]	162	list<pair<int,int>> returned_type;
	163	returned_type.push_back(pair<int,int>(channel,1));
	164	created_model_types.push_back(returned_type);
[1358]	165	}
[1361]	166
	167	return created_model_types;
	168	}
	169	else
	170	{
	171	created_model_types = possible_models_recurse(max_order-1,number_of_channels);
	172	list<list<pair<int,int>>> returned_types;
	173
	174	for(list<list<pair<int,int>>>::iterator model_ref = created_model_types.begin();model_ref!=created_model_types.end();model_ref++)
	175	{
	176
	177	for(int order = 1; order<=max_order; order++)
[1358]	178	{
[1361]	179	for(int channel = 0;channel<number_of_channels;channel++)
	180	{
	181	list<pair<int,int>> returned_type;
	182	pair<int,int> new_pair = pair<int,int>(channel,order);
	183	if(find((model_ref).begin(),(model_ref).end(),new_pair)==(*model_ref).end())
	184	{
	185	returned_type.insert(returned_type.begin(),(model_ref).begin(),(model_ref).end());
	186	returned_type.push_back(new_pair);
	187	returned_types.push_back(returned_type);
	188	}
	189	}
[1358]	190	}
	191	}
[1361]	192
	193	created_model_types.insert(created_model_types.end(),returned_types.begin(),returned_types.end());
	194
	195	return created_model_types;
	196	}
[1358]	197	}
[1361]	198	};
	199
	200
	201
	202
	203	int main ( int argc, char* argv[] ) {
[1358]	204
[1361]	205	itpp::Laplace_RNG LapRNG = Laplace_RNG();
	206
[1300]	207	/*
[1361]	208	char szApp[] = "MT4";
	209	char szTopic[] = "ASK";
	210	char szItem1[] = "EURUSD";
	211
	212	//DDE Initialization
	213	DWORD idInst=0;
	214	UINT iReturn;
	215	iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback,
	216	APPCLASS_STANDARD \| APPCMD_CLIENTONLY, 0 );
	217	if (iReturn!=DMLERR_NO_ERROR)
	218	{
	219	printf("DDE Initialization Failed: 0x%04x\n", iReturn);
	220	Sleep(1500);
	221	return 0;
	222	}
	223
	224	/*
	225	//Start DDE Server and wait for it to become idle.
	226	HINSTANCE hRet = ShellExecute(0, "open", szTopic, 0, 0, SW_SHOWNORMAL);
	227	if ((int)hRet < 33)
	228	{
	229	printf("Unable to Start DDE Server: 0x%04x\n", hRet);
	230	Sleep(1500); DdeUninitialize(idInst);
	231	return 0;
	232	}
	233	Sleep(1000);
	234	*/
	235
	236	/*
	237	//DDE Connect to Server using given AppName and topic.
	238	HSZ hszApp, hszTopic;
	239	HCONV hConv;
	240	hszApp = DdeCreateStringHandle(idInst, szApp, 0);
	241	hszTopic = DdeCreateStringHandle(idInst, szTopic, 0);
	242	hConv = DdeConnect(idInst, hszApp, hszTopic, NULL);
	243	DdeFreeStringHandle(idInst, hszApp);
	244	DdeFreeStringHandle(idInst, hszTopic);
	245	if (hConv == NULL)
	246	{
	247	printf("DDE Connection Failed.\n");
	248	Sleep(1500); DdeUninitialize(idInst);
	249	return 0;
	250	}
	251
	252	//Execute commands/requests specific to the DDE Server.
	253
	254	DDERequest(idInst, hConv, szItem1);
	255
	256	//DDE Disconnect and Uninitialize.
	257	//DdeDisconnect(hConv);
	258	//DdeUninitialize(idInst);
	259
	260	Sleep(300000);
	261	Sleep(3000);
	262	*/
	263
	264	/*
[1301]	265	// 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,
	266	// where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the
	267	// variance of location parameter estimators and compare it to the classical setup.
[1282]	268	vector<vector<vector<string>>> string_lists;
	269	string_lists.push_back(vector<vector<string>>());
	270	string_lists.push_back(vector<vector<string>>());
	271	string_lists.push_back(vector<vector<string>>());
[1186]	272
[1282]	273	char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
[1268]	274
[1282]	275
	276	for(int i = 0;i<3;i++)
	277	{
	278	ifstream myfile(file_strings[i]);
	279	if (myfile.is_open())
	280	{
	281	while ( myfile.good() )
	282	{
	283	string line;
	284	getline(myfile,line);
	285
	286	vector<string> parsed_line;
	287	while(line.find(',') != string::npos)
	288	{
	289	int loc = line.find(',');
	290	parsed_line.push_back(line.substr(0,loc));
	291	line.erase(0,loc+1);
	292	}
	293
	294	string_lists[i].push_back(parsed_line);
	295	}
	296	myfile.close();
	297	}
	298	}
	299
	300	for(int j = 0;j<string_lists.size();j++)
	301	{
[1301]	302
[1284]	303	for(int i = 0;i<string_lists[j].size()-1;i++)
[1282]	304	{
	305	vector<vec> conditions;
[1301]	306	//emlig* emliga = new emlig(2);
	307	RARX* my_rarx = new RARX(2,30);
	308
[1282]	309	for(int k = 1;k<string_lists[j][i].size();k++)
	310	{
	311	vec condition;
	312	//condition.ins(0,1);
	313	condition.ins(0,string_lists[j][i][k]);
	314	conditions.push_back(condition);
	315
	316	//cout << "orig:" << condition << endl;
	317
	318	if(conditions.size()>1)
	319	{
	320	conditions[k-2].ins(0,string_lists[j][i][k]);
	321
	322	}
	323
	324	if(conditions.size()>2)
	325	{
	326	conditions[k-3].ins(0,string_lists[j][i][k]);
	327
	328	//cout << "modi:" << conditions[k-3] << endl;
	329
[1301]	330	my_rarx->bayes(conditions[k-3]);
[1282]	331
[1299]	332
	333	//if(k>5)
	334	//{
	335	// cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
	336	//}
	337
	338	}
	339
[1282]	340	}
	341
	342	//emliga->step_me(0);
[1301]	343	/*
[1284]	344	ofstream myfile;
	345	myfile.open("c:\\robust_ar1.txt",ios::app);
[1301]	346	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
[1284]	347	myfile.close();
	348
	349	myfile.open("c:\\robust_ar2.txt",ios::app);
	350	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
	351	myfile.close();
[1301]	352
[1284]	353
[1282]	354	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
	355	cout << "Step: " << i << endl;
	356	}
	357
	358	cout << "One experiment finished." << endl;
[1284]	359
	360	ofstream myfile;
	361	myfile.open("c:\\robust_ar1.txt",ios::app);
	362	myfile << endl;
	363	myfile.close();
	364
	365	myfile.open("c:\\robust_ar2.txt",ios::app);
	366	myfile << endl;
	367	myfile.close();
[1301]	368	}*/
	369
[1361]	370
[1301]	371
	372	// EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from
	373	// 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
	374	// can be compared to the classical setup.
	375
	376
	377	vector<vector<string>> strings;
	378
[1361]	379	char* file_string = "c:\\data";
[1301]	380
[1361]	381	char dfstring[80];
	382	strcpy(dfstring,file_string);
	383	strcat(dfstring,".txt");
[1337]	384
[1361]	385
	386	mat data_matrix;
	387	ifstream myfile(dfstring);
	388	if (myfile.is_open())
	389	{
	390	string line;
	391	while(getline(myfile,line))
[1301]	392	{
[1361]	393	vec data_vector;
[1301]	394	while(line.find(',') != string::npos)
	395	{
[1361]	396	int loc2 = line.find('\n');
	397	int loc = line.find(',');
	398	data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));
[1301]	399	line.erase(0,loc+1);
[1361]	400	}
[1301]	401
[1361]	402	data_matrix.ins_row(data_matrix.rows(),data_vector);
	403	}
	404
	405	myfile.close();
[1282]	406	}
[1361]	407	else
	408	{
	409	cout << "Can't open data file!" << endl;
	410	}
	411
	412	list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows());
[1357]	413
[1361]	414	list<model*> models;
	415	for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
	416	{
	417	models.push_back(new model((*model_type),true,false,30,0,&data_matrix));
	418	models.push_back(new model((*model_type),false,false,30,0,&data_matrix));
	419	}
[1282]	420
[1361]	421	mat result_lognc;
	422
	423	for(int time = max_model_order;20;time++) //time<data_matrix.cols()
	424	{
	425	vec cur_res_lognc;
	426
	427	for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
	428	{
	429	(*model_ref)->data_update(time);
	430	if((*model_ref)->my_rarx!=NULL)
	431	{
	432	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc);
	433	}
	434	else
	435	{
	436	cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc());
	437	}
	438	}
	439
	440	result_lognc.ins_col(result_lognc.cols(),cur_res_lognc);
	441
	442	cout << "Updated." << endl;
	443
	444	/*
[1301]	445	vector<vec> conditions;
	446	//emlig* emliga = new emlig(2);
[1358]	447	RARX* my_rarx = new RARX(2,10,false);
[1337]	448
[1338]	449
[1337]	450	mat V0 = 0.0001 * eye ( 3 );
[1349]	451	ARX* my_arx = new ARX(0.85);
[1337]	452	my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
	453	my_arx->set_constant ( false );
	454	my_arx->validate();
[1338]	455
[1301]	456
[1338]	457	for(int k = 1;k<strings[j].size();k++)
[1301]	458	{
	459	vec condition;
	460	//condition.ins(0,1);
	461	condition.ins(0,strings[j][k]);
	462	conditions.push_back(condition);
	463
	464	//cout << "orig:" << condition << endl;
	465
	466	if(conditions.size()>1)
	467	{
	468	conditions[k-2].ins(0,strings[j][k]);
	469
	470	}
	471
	472	if(conditions.size()>2)
	473	{
	474	conditions[k-3].ins(0,strings[j][k]);
	475
[1349]	476	// cout << "Condition:" << conditions[k-3] << endl;
[1301]	477
	478	my_rarx->bayes(conditions[k-3]);
[1338]	479	//my_rarx->posterior->step_me(1);
[1337]	480
	481	vec cond_vec;
	482	cond_vec.ins(0,conditions[k-3][0]);
	483
[1338]	484	my_arx->bayes(cond_vec,conditions[k-3].right(2));
[1301]	485
[1361]	486	/*
[1346]	487	if(k>8)
[1301]	488	{
[1324]	489	//my_rarx->posterior->step_me(0);
	490
[1346]	491	//mat samples = my_rarx->posterior->sample_mat(10);
[1343]	492
[1346]	493	pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000);
[1343]	494
[1346]	495	//cout << imp_samples.first << endl;
[1336]	496
[1337]	497	vec sample_prediction;
[1358]	498	vec averaged_params = zeros(imp_samples.second.rows());
[1346]	499	for(int t = 0;t<imp_samples.first.size();t++)
[1337]	500	{
	501	vec lap_sample = conditions[k-3].left(2);
[1346]	502	//lap_sample.ins(lap_sample.size(),1.0);
[1337]	503
	504	lap_sample.ins(0,LapRNG());
	505
[1346]	506	sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));
[1358]	507
	508	averaged_params += imp_samples.first[t]*imp_samples.second.get_col(t);
[1337]	509	}
	510
[1358]	511	averaged_params = averaged_params(1/(imp_samples.firstones(imp_samples.first.size())));
	512
	513	// cout << "Averaged estimated parameters: " << averaged_params << endl;
[1338]	514
[1358]	515	vec sample_pow = sample_prediction;
[1343]	516
	517	// cout << sample_prediction << endl;
[1337]	518	vec poly_coefs;
[1346]	519	double prediction;
[1337]	520	bool stop_iteration = false;
[1343]	521	int en = 1;
[1337]	522	do
	523	{
[1346]	524	double poly_coef = imp_samples.firstsample_pow/(imp_samples.firstones(imp_samples.first.size()));
[1337]	525
[1346]	526	if(en==1)
	527	{
	528	prediction = poly_coef;
	529	}
	530
[1343]	531	poly_coef = poly_coefenfact(utility_constant-2+en)/fact(utility_constant-2);
	532
[1337]	533	if(abs(poly_coef)>numeric_limits<double>::epsilon())
	534	{
	535	sample_pow = elem_mult(sample_pow,sample_prediction);
[1343]	536	poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
[1337]	537	}
	538	else
	539	{
	540	stop_iteration = true;
	541	}
	542
	543	en++;
[1343]	544
	545	if(en>20)
	546	{
	547	stop_iteration = true;
	548	}
[1337]	549	}
	550	while(!stop_iteration);
	551
[1343]	552	/*
	553	ofstream myfile_coef;
	554
	555	myfile_coef.open("c:\\coefs.txt",ios::app);
	556
	557	for(int t = 0;t<poly_coefs.size();t++)
	558	{
	559	myfile_coef << poly_coefs[t] << ",";
	560	}
	561
	562	myfile_coef << endl;
	563	myfile_coef.close();
	564	*/
	565
[1349]	566	//cout << "Coefficients: " << poly_coefs << endl;
[1338]	567
[1343]	568	/*
	569	vec bas_coef = vec("1.0 2.0 -8.0");
	570	cout << "Coefs: " << bas_coef << endl;
	571	cvec actions2 = roots(bas_coef);
	572	cout << "Roots: " << actions2 << endl;
	573	*/
	574
[1361]	575	/*
[1346]	576
[1338]	577	cvec actions = roots(poly_coefs);
[1343]	578
	579
[1338]	580	bool is_max = false;
	581	for(int t = 0;t<actions.size();t++)
	582	{
[1343]	583	if(actions[t].imag() == 0)
[1338]	584	{
[1343]	585	double second_derivative = 0;
	586	for(int q = 1;q<poly_coefs.size();q++)
	587	{
	588	second_derivative+=poly_coefs[q]pow(actions[t].real(),q-1)q;
	589	}
	590
	591	if(second_derivative<0)
	592	{
	593	cout << "Action:" << actions[t].real() << endl;
	594
	595	is_max = true;
	596	}
[1338]	597	}
	598	}
[1301]	599
[1338]	600	if(!is_max)
	601	{
	602	cout << "No maximum." << endl;
	603	}
	604
	605	// cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;
	606
[1346]	607	/*
[1337]	608	double prediction = 0;
	609	for(int s = 1;s<samples.rows();s++)
[1336]	610	{
	611
[1346]	612	double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols();
[1337]	613
	614	prediction += avg_parameter*conditions[k-3][s-1];
	615
[1336]	616
[1337]	617
	618	/*
[1336]	619	ofstream myfile;
	620	char fstring[80];
	621	strcpy(fstring,file_strings[j]);
[1301]	622
[1336]	623	char es[5];
	624	strcat(fstring,itoa(s,es,10));
	625
	626	strcat(fstring,"_res.txt");
	627
	628
	629	myfile.open(fstring,ios::app);
	630
	631	//myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
	632	myfile << avg_parameter;
	633
	634	if(k!=strings[j].size()-1)
	635	{
	636	myfile << ",";
	637	}
	638	else
	639	{
	640	myfile << endl;
	641	}
	642	myfile.close();
[1337]	643	*/
	644
[1338]	645
[1346]	646	//}
	647
	648	// cout << "Prediction: "<< prediction << endl;
[1361]	649	/*
[1337]	650	enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
	651	double prediction2 = pred_mat->mean()[0];
[1361]	652	*/
[1337]	653
	654	ofstream myfile;
[1361]	655	char fstring[80];
	656	strcpy(fstring,file_string);
[1337]	657
[1361]	658	strcat(fstring,"lognc.txt");
[1337]	659
	660	myfile.open(fstring,ios::app);
	661
[1338]	662	// myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
[1337]	663
[1361]	664	for(int i = 0;i<cur_res_lognc.size();i++)
[1337]	665	{
[1361]	666	myfile << cur_res_lognc[i] << ',';
[1337]	667	}
[1361]	668
	669	myfile << endl;
	670
[1337]	671	myfile.close();
	672
[1361]	673	/*
[1337]	674	myfile.open(f2string,ios::app);
	675	myfile << prediction2;
	676
	677	if(k!=strings[j].size()-1)
	678	{
	679	myfile << ",";
	680	}
	681	else
	682	{
	683	myfile << endl;
	684	}
	685	myfile.close();
[1361]	686	////
[1337]	687
[1319]	688	}
[1361]	689	} */
[1301]	690
	691	//emliga->step_me(0);
	692	/*
	693	ofstream myfile;
	694	myfile.open("c:\\robust_ar1.txt",ios::app);
	695	myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
	696	myfile.close();
	697
	698	myfile.open("c:\\robust_ar2.txt",ios::app);
	699	myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
	700	myfile.close();
	701
	702
	703	cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
	704	cout << "Step: " << i << endl;*/
[1361]	705	//}
[1337]	706
	707
[1301]	708	}
[1337]	709
	710
	711	// EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
	712	// with maximization of logarithm of one-step ahead wealth.
	713
	714
[1301]	715
	716	/*
	717	cout << "One experiment finished." << endl;
	718
	719	ofstream myfile;
	720	myfile.open("c:\\robust_ar1.txt",ios::app);
	721	myfile << endl;
	722	myfile.close();
	723
	724	myfile.open("c:\\robust_ar2.txt",ios::app);
	725	myfile << endl;
	726	myfile.close();*/
[1300]	727
[1301]	728
	729	//emlig* emlig1 = new emlig(emlig_size);
	730
	731	//emlig1->step_me(0);
	732	//emlig* emlig2 = new emlig(emlig_size);
[1300]	733
[1267]	734	/*
	735	emlig1->set_correction_factors(4);
[1266]	736
[1267]	737	for(int j = 0;j<emlig1->correction_factors.size();j++)
	738	{
	739	for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
	740	{
[1268]	741	cout << j << " ";
	742
[1267]	743	for(int i=0;i<(*vec_ref).size();i++)
	744	{
	745	cout << (*vec_ref)[i];
	746	}
	747
	748	cout << endl;
	749	}
[1268]	750	}*/
	751
[1301]	752	/*
[1300]	753	vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";
	754
[1299]	755	emlig1->add_condition(condition5);
[1301]	756	//emlig1->step_me(0);
	757
	758
	759	vec condition1a = "-1.0 1.02 0.5";
[1300]	760	//vec condition1b = "1.0 1.0 1.01";
[1301]	761	emlig1->add_condition(condition1a);
[1300]	762	//emlig2->add_condition(condition1b);
[1267]	763
[1301]	764	vec condition2a = "-0.3 1.7 1.5";
[1300]	765	//vec condition2b = "-1.0 1.0 1.0";
[1301]	766	emlig1->add_condition(condition2a);
[1300]	767	//emlig2->add_condition(condition2b);
[1234]	768
[1301]	769	vec condition3a = "0.5 -1.01 1.0";
[1300]	770	//vec condition3b = "0.5 -1.01 1.0";
[1280]	771
[1301]	772	emlig1->add_condition(condition3a);
[1300]	773	//emlig2->add_condition(condition3b);
[1280]	774
[1301]	775	vec condition4a = "-0.5 -1.0 1.0";
[1300]	776	//vec condition4b = "-0.5 -1.0 1.0";
	777
[1301]	778	emlig1->add_condition(condition4a);
[1300]	779	//cout << "************************************************" << endl;
	780	//emlig2->add_condition(condition4b);
	781	//cout << "************************************************" << endl;
	782
[1299]	783	//cout << emlig1->minimal_vertex->get_coordinates();
[1300]	784
[1301]	785	//emlig1->remove_condition(condition3a);
	786	//emlig1->step_me(0);
	787	//emlig1->remove_condition(condition2a);
	788	//emlig1->remove_condition(condition1a);
	789	//emlig1->remove_condition(condition5);
	790
[1275]	791
[1299]	792	//emlig1->step_me(0);
	793	//emlig2->step_me(0);
	794
[1282]	795
	796	// DA SE POUZIT PRO VYPIS DO SOUBORU
[1275]	797	// emlig1->step_me(0);
	798
	799	//emlig1->remove_condition(condition1);
	800
[1301]	801
[1275]	802
	803
[1301]	804
[1275]	805	/*
[1282]	806	for(int i = 0;i<100;i++)
[1219]	807	{
[1282]	808	cout << endl << "Step:" << i << endl;
[1208]	809
[1268]	810	double condition[emlig_size+1];
[1220]	811
[1268]	812	for(int k = 0;k<=emlig_size;k++)
	813	{
[1272]	814	condition[k] = (rand()-RAND_MAX/2)/1000.0;
[1268]	815	}
	816
[1216]	817
[1268]	818	vec* condition_vec = new vec(condition,emlig_size+1);
[1219]	819	emlig1->add_condition(*condition_vec);
[1271]	820
[1272]	821	/*
	822	for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
	823	{
	824	cout << ((toprow*)toprow_ref)->probability << endl;
	825	}
	826	*/
[1275]	827	/*
[1271]	828	cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
[1268]	829
[1272]	830	/*
[1271]	831	if(i-emlig1->number_of_parameters >= 0)
	832	{
	833	pause(30);
	834	}
[1272]	835	*/
[1219]	836
[1271]	837	// emlig1->step_me(i);
[1219]	838
[1272]	839	/*
[1219]	840	vector<int> sizevector;
	841	for(int s = 0;s<=emlig1->number_of_parameters;s++)
	842	{
	843	sizevector.push_back(emlig1->statistic_rowsize(s));
	844	}
[1272]	845	*/
[1275]	846	//}
	847
[1219]	848
	849
	850
	851	/*
	852	emlig1->step_me(1);
	853
	854	vec condition = "2.0 0.0 1.0";
	855
[1208]	856	emlig1->add_condition(condition);
	857
[1216]	858	vector<int> sizevector;
	859	for(int s = 0;s<=emlig1->number_of_parameters;s++)
	860	{
	861	sizevector.push_back(emlig1->statistic_rowsize(s));
	862	}
	863
[1219]	864	emlig1->step_me(2);
[1216]	865
[1219]	866	condition = "2.0 1.0 0.0";
[1216]	867
	868	emlig1->add_condition(condition);
	869
	870	sizevector.clear();
	871	for(int s = 0;s<=emlig1->number_of_parameters;s++)
	872	{
	873	sizevector.push_back(emlig1->statistic_rowsize(s));
	874	}
[1219]	875	*/
[1216]	876
[976]	877	return 0;
	878	}
	879
[1282]	880

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