C++ mt19937::seed方法代码示例

static void init_rand_gen()
{
	int fd = open("/dev/urandom", O_RDONLY);
	if (fd != -1) {
		uint32_t seed;
		read(fd, &seed, sizeof(uint32_t));
		g_mt_rand.seed(seed);
		close(fd);
	}
	else {
		g_mt_rand.seed(time(NULL));
	}
}

int main() {

	gen.seed(static_cast<unsigned int>(std::time(0)));

	const int beam_size = 3;
	const int vocab_size = 10;
	const int start_symbol = 0;
	const int end_symbol = 1;
	const int max_decoding_length = 30;
	const int min_decoding_length = 10;
	const int source_length = 10;
	const precision penalty = 3;

	decoder<precision> d(beam_size,vocab_size,start_symbol,end_symbol,
		max_decoding_length,min_decoding_length,penalty,"");

	Eigen::Matrix<precision,Eigen::Dynamic, Eigen::Dynamic> outputDist;
	Eigen::Matrix<precision, 1, Eigen::Dynamic> normalization;
	outputDist.resize(vocab_size,beam_size);
	normalization.resize(1,beam_size);

	init_output_dist(outputDist,normalization);

	d.init_decoder();

	for(int i=0; i<20; i++) {
		init_output_dist(outputDist,normalization);
		d.expand_hypothesis(outputDist);
		d.print_current_hypotheses();
	}
	d.finish_current_hypotheses(outputDist);
	d.print_current_hypotheses();
}

void UnsyncedRNG::Seed(unsigned seed)
{
#ifdef USE_BOOST_RNG
	rng.seed(seed);
#else
	randSeed = seed;
#endif
}

	void seedrand()
	{
		randfast.seed(time(0));
		randfast64.seed(time(0));
	}

int main(int argc, char** argv) {

    build_odes();
    return 0;

    //    build_odes();
    //    return 0;

    ptime begin = microsec_clock::local_time();

    random::mt19937 rng;
    random::uniform_real_distribution<> uni(-1, 1);

    int seed = lexical_cast<int>(argv[1]);

    if (seed != -1) {

        double Wi = lexical_cast<double>(argv[2]);
        double Wf = lexical_cast<double>(argv[3]);

        double mu = lexical_cast<double>(argv[4]);

        double Ui = UWi(Wi);

        double D = lexical_cast<double>(argv[5]);

        double taui = lexical_cast<double>(argv[6]);
        double tauf = lexical_cast<double>(argv[7]);
        int ntaus = lexical_cast<int>(argv[8]);

        int numthreads = lexical_cast<int>(argv[9]);

        int resi = lexical_cast<int>(argv[10]);

        //        int integrator = lexical_cast<int>(argv[11]);
        std::string intg = argv[11];
        double dt = lexical_cast<double>(argv[12]);

#ifdef AMAZON
        //    path resdir("/home/ubuntu/Results/Canonical Transformation Dynamical Gutzwiller");
        path resdir("/home/ubuntu/Dropbox/Amazon EC2/Simulation Results/CTDG");
#else
        path resdir("/Users/Abuenameh/Documents/Simulation Results/Canonical Transformation Dynamical Gutzwiller 2");
        //        path resdir("/Users/Abuenameh/Documents/Simulation Results/Dynamical Gutzwiller Hartmann Comparison");
#endif
        if (!exists(resdir)) {
            cerr << "Results directory " << resdir << " does not exist!" << endl;
            exit(1);
        }
        ostringstream oss;
        oss << "res." << resi << ".txt";
        path resfile = resdir / oss.str();
        //#ifndef AMAZON
        while (exists(resfile)) {
            resi++;
            oss.str("");
            oss << "res." << resi << ".txt";
            resfile = resdir / oss.str();
        }
        //#endif
        if (seed < 0) {
            resi = seed;
            oss.str("");
            oss << "res." << resi << ".txt";
            resfile = resdir / oss.str();
        }
        vector<double> xi(L, 1);
        rng.seed(seed);
        if (seed > -1) {
            for (int j = 0; j < L; j++) {
                xi[j] = (1 + D * uni(rng));
            }
        }

        //        double Ui = UWi(Wi);
        double mui = mu * Ui;

        filesystem::ofstream os(resfile);
        printMath(os, "int", resi, intg);
        printMath(os, "seed", resi, seed);
        printMath(os, "Delta", resi, D);
        printMath(os, "dt", resi, dt);
        printMath(os, "mures", resi, mui);
        printMath(os, "Ures", resi, Ui);
        printMath(os, "xires", resi, xi);
        os << flush;

        printMath(os, "Wires", resi, Wi);
        printMath(os, "Wfres", resi, Wf);
        os << flush;

        cout << "Res: " << resi << endl;

        struct shm_remove {

            shm_remove() {
                shared_memory_object::remove("SharedMemory");
            }

            ~shm_remove() {
//.........这里部分代码省略.........

worker_input* initialize(double Wi, double Wf, double mu, vector<double>& xi, managed_shared_memory& segment) {

    SX f = SX::sym("f", 2 * L * dim);
    SX dU = SX::sym("dU", L);
    SX J = SX::sym("J", L);
    SX U0 = SX::sym("U0");

    U0 = UW(Wi);
    for (int i = 0; i < L; i++) {
        J[i] = JWij(Wi * xi[i], Wi * xi[mod(i + 1)]);
        dU[i] = UW(Wi * xi[i]) - U0;
    }

    SX E = energy(f, J, U0, dU, mu).real();

    SX g = SX::sym("g", L);
    for (int i = 0; i < L; i++) {
        g[i] = 0;
        for (int n = 0; n < dim; n++) {
            g[i] += f[2 * (i * dim + n)] * f[2 * (i * dim + n)] + f[2 * (i * dim + n) + 1] * f[2 * (i * dim + n) + 1];
        }
    }

    SXFunction nlp("nlp", nlpIn("x", f), nlpOut("f", E, "g", g));
    NlpSolver solver("solver", "ipopt", nlp, make_dict("hessian_approximation", "limited-memory", "linear_solver", "ma86", "print_level", 0, "print_time", false, "obj_scaling_factor", 1));

    boost::random::mt19937 rng;
    boost::random::uniform_real_distribution<> uni(-1, 1);

    vector<double> xrand(2 * L*dim, 1);
    rng.seed();
    for (int i = 0; i < 2 * L * dim; i++) {
        xrand[i] = uni(rng);
    }

    map<string, DMatrix> arg;
    arg["lbx"] = -1;
    arg["ubx"] = 1;
    arg["x0"] = xrand;
    arg["lbg"] = 1;
    arg["ubg"] = 1;

    map<string, DMatrix> res = solver(arg);
    vector<double> x0 = res["x"].nonzeros();
    //        vector<double> x0 = xrand;
    //    cout << "x0 = " << ::math(x0) << endl;
    //    cout << "E0 = " << ::math(res["f"].toScalar()) << endl;

    vector<complex<double>> x0i(dim);
    for (int i = 0; i < L; i++) {
        for (int n = 0; n <= nmax; n++) {
            x0i[n] = complex<double>(x0[2 * (i * dim + n)], x0[2 * (i * dim + n) + 1]);
        }
        double nrm = sqrt(abs(dot(x0i, x0i)));
        for (int n = 0; n <= nmax; n++) {
            x0[2 * (i * dim + n)] /= nrm;
            x0[2 * (i * dim + n) + 1] /= nrm;
        }
    }
    //    cout << "nlp: " << ::math(nlp(vector<DMatrix>{x0, vector<double>()})[0].toScalar()) << endl;

    void_allocator void_alloc(segment.get_segment_manager());
    worker_input* input = segment.construct<worker_input>("input")(void_alloc);
    input->U0 = UW(Wi);
    for (int i = 0; i < L; i++) {
        input->J0.push_back(JWij(Wi * xi[i], Wi * xi[mod(i + 1)]));
    }
    for (int i = 0; i < 2 * L * dim; i++) {
        input->x0.push_back(x0[i]);
    }
    for (int i = 0; i < L; i++) {
        complex_vector f0i(dim, void_alloc);
        for (int n = 0; n <= nmax; n++) {
            f0i[n] = complex<double>(x0[2 * (i * dim + n)], x0[2 * (i * dim + n) + 1]);
        }
        input->f0.push_back(f0i);
    }
    input->Wi = Wi;
    input->Wf = Wf;
    input->mu = mu;
    for (int i = 0; i < L; i++) {
        input->xi.push_back(xi[i]);
    }

    return input;
}

int main(int argc, char** argv) {

    ptime begin = microsec_clock::local_time();

    random::mt19937 rng;
    random::uniform_real_distribution<> uni(-1, 1);

    int seed = lexical_cast<int>(argv[1]);

    double Wi = lexical_cast<double>(argv[2]);
    double Wf = lexical_cast<double>(argv[3]);

    double mu = lexical_cast<double>(argv[4]);

    double Ui = UWi(Wi);

    double D = lexical_cast<double>(argv[5]);

    double taui = lexical_cast<double>(argv[6]);
    double tauf = lexical_cast<double>(argv[7]);
    int ntaus = lexical_cast<int>(argv[8]);
    //    double tf = 2*tau;
    //    int nsteps = lexical_cast<int>(argv[9]);

    double dt = lexical_cast<double>(argv[9]);
    //    int dnsav = lexical_cast<int>(argv[8]);

    //    int nsteps = (int) ceil(2 * tau / dt);
    //    double h = 2 * tau / nsteps;


    int numthreads = lexical_cast<int>(argv[10]);

    int resi = lexical_cast<int>(argv[11]);
    //    int resi = 0;
    //    if (argc > 9) {
    //        resi = lexical_cast<int>(argv[9]);
    //    }

    int subresi = -1;
    int seed2 = 0;
    if (argc > 12) {
        //        subresi = lexical_cast<int>(argv[12]);
        seed2 = lexical_cast<int>(argv[12]);
    }

#ifdef AMAZON
    //    path resdir("/home/ubuntu/Results/Canonical Transformation Dynamical Gutzwiller");
    path resdir("/home/ubuntu/Dropbox/Amazon EC2/Simulation Results/Canonical Transformation Dynamical Gutzwiller 2");
#else
    path resdir("/Users/Abuenameh/Documents/Simulation Results/Canonical Transformation Dynamical Gutzwiller 2");
    //        path resdir("/Users/Abuenameh/Documents/Simulation Results/Dynamical Gutzwiller Hartmann Comparison");
#endif
    if (!exists(resdir)) {
        cerr << "Results directory " << resdir << " does not exist!" << endl;
        exit(1);
    }
    ostringstream oss;
    if (subresi == -1) {
        oss << "res." << resi << ".txt";
    }
    else {
        oss << "res." << resi << "." << subresi << ".txt";
    }
    path resfile = resdir / oss.str();
    //#ifndef AMAZON
    while (exists(resfile)) {
        resi++;
        oss.str("");
        if (subresi == -1) {
            oss << "res." << resi << ".txt";
        }
        else {
            oss << "res." << resi << "." << subresi << ".txt";
        }
        resfile = resdir / oss.str();
    }
    //#endif
    //        if (seed == -1) {
    //            resi = -1;
    if (seed < 0) {
        resi = seed;
        oss.str("");
        oss << "res." << resi << ".txt";
        resfile = resdir / oss.str();
    }
    vector<double> xi(L, 1);
    rng.seed(seed);
    if (seed > -1) {
        for (int j = 0; j < L; j++) {
            xi[j] = (1 + D * uni(rng));
        }
    }

    //        double Ui = UWi(Wi);
    double mui = mu * Ui;

    filesystem::ofstream os(resfile);
    printMath(os, "seed", resi, seed);
    printMath(os, "Delta", resi, D);
//.........这里部分代码省略.........

int Helper::getRandomNumber(const int _min, const int _max)
{
	randomGenerator.seed(std::time(0));
	boost::random::uniform_int_distribution<> dist(_min, _max);
	return dist(randomGenerator);
}

// Initialize the seed of the random number generator
void random_initialize(unsigned int seed) {
    random_gen.seed(seed);
}

	void seed(int seed)
	{
		gen.seed(seed);
	}

	void reset(int min, int max, int seed)
	{
		dist = boost::random::uniform_int_distribution<>(min, max);
		gen.seed(seed);
	}

	RandomInt(int min, int max, int seed)
		: dist(min, max)
	{
		gen.seed(seed);
	}

void init(unsigned int seed, std::string outputDirectory,
		std::string confFileName, bool overwrite, bool saveAll) {

	// Seed random number generator

	rng.seed(seed);

	conf.reset(new EvolverConfiguration());
	if (!conf->init(confFileName)) {
		std::cerr << "Problems parsing the evolution configuration file. Quit."
				<< std::endl;
		exitRobogen(EXIT_FAILURE);
	}

	robotConf = ConfigurationReader::parseConfigurationFile(
			conf->simulatorConfFile);
	if (robotConf == NULL) {
		std::cerr << "Problems parsing the robot configuration file. Quit."
				<< std::endl;
		exitRobogen(EXIT_FAILURE);
	}

	// ---------------------------------------
	// Set up evolution
	// ---------------------------------------

	if (conf->selection == conf->DETERMINISTIC_TOURNAMENT) {
		selector.reset(new DeterministicTournament(conf->tournamentSize, rng));
	} else {
		std::cerr << "Selection type id " << conf->selection << " unknown."
				<< std::endl;
		exitRobogen(EXIT_FAILURE);
	}

	mutator.reset(new Mutator(conf, rng));
	log.reset(new EvolverLog());
	try {
		if (!log->init(conf, robotConf, outputDirectory, overwrite, saveAll)) {
			std::cerr << "Error creating evolver log. Aborting." << std::endl;
			exitRobogen(EXIT_FAILURE);
		}
	} catch (std::exception& e) {
		std::cerr << e.what() << std::endl;
		exitRobogen(EXIT_FAILURE);
	} catch (...) {
		std::cerr << "non standard exception " << std::endl;
		exitRobogen(EXIT_FAILURE);
	}

	// ---------------------------------------
	// parse robot from file & initialize population
	// ---------------------------------------

	boost::shared_ptr<RobotRepresentation> referenceBot(
			new RobotRepresentation());
	bool growBodies = false;
	if (conf->evolutionMode == EvolverConfiguration::BRAIN_EVOLVER
			&& conf->referenceRobotFile.compare("") == 0) {
		std::cerr << "Trying to evolve brain, but no robot file provided."
				<< std::endl;
		exitRobogen(EXIT_FAILURE);
	} else if (conf->referenceRobotFile.compare("") != 0) {
		if (!referenceBot->init(conf->referenceRobotFile)) {
			std::cerr << "Failed interpreting robot from text file"
					<< std::endl;
			exitRobogen(EXIT_FAILURE);
		}
	} else { //doing body evolution and don't have a reference robot
		if (referenceBot->init()) {
			growBodies = true;
		} else {
			std::cerr << "Failed creating base robot for body evolution"
					<< std::endl;
			exitRobogen(EXIT_FAILURE);
		}
	}

	neat = (conf->evolutionaryAlgorithm == EvolverConfiguration::HYPER_NEAT);
	population.reset(new Population());
	if (!population->init(referenceBot, conf->mu, mutator, growBodies,
			(!(conf->useBrainSeed || neat)) ) ) {
		std::cerr << "Error when initializing population!" << std::endl;
		exitRobogen(EXIT_FAILURE);
	}

	if (neat) {
		neatContainer.reset(new NeatContainer(conf, population, seed, rng));
	}

	// ---------------------------------------
	// open sockets for communication with simulator processes
	// ---------------------------------------
#ifndef EMSCRIPTEN
	sockets.resize(conf->sockets.size());
	for (unsigned int i = 0; i < conf->sockets.size(); i++) {
		sockets[i] = new TcpSocket;
#ifndef FAKEROBOTREPRESENTATION_H // do not bother with sockets when using
		// benchmark
		if (!sockets[i]->open(conf->sockets[i].first,
				conf->sockets[i].second)) {
//.........这里部分代码省略.........

/**
 * Set the random seed used by the random functions (Random() and RandInt()).
 * The seed is casted to a 32-bit integer before being given to the random
 * number generator, but a size_t is taken as a parameter for API consistency.
 *
 * @param seed Seed for the random number generator.
 */
inline void RandomSeed(const size_t seed)
{
  randGen.seed((uint32_t) seed);
  srand((unsigned int) seed);
}

int main() {
	string x, y, opcode;
	string cmd;

    boost::random::uniform_int_distribution<> dist32bit(0, 2147483647);
	boost::random::uniform_int_distribution<> dist3bit(0, 4);
	
	vector<pair<string,string> > db;
	
	for(int i = 0; i < 5000; i++) {
		gen.seed(static_cast<unsigned int>(time(0) + getpid()) + i);
		
		int r0 = dist32bit(gen);
		int r1 = dist32bit(gen); 
		int r2 = dist3bit(gen);
		
		//cout << r0 << " " << r1 << " " << r2 << endl;
		
		bitset<32> t0(r0);
		bitset<32> t1(r1);
		bitset<3> t2(r2);
		x = t0.to_string();
		y = t1.to_string();
		opcode = t2.to_string();
		
		//cout << x << " " << y << " " << opcode << endl;
		
		cmd = "vvp a.out +x=" + x + " +y=" + y + " +opcode=" + opcode;	// Program to run or "wrap"
		string out = exec(cmd);
		
		//cout << out;
		
		int index;
		string delay;
		const char* process = out.c_str();
		
		for(int i = strlen(process) - 1; i >= 0; i--) {
			if(process[i] == ' ') { index = i; break; }
		}
		
		for(int i = index + 1; i < strlen(process); i++) {
			delay = delay + process[i];
		}
		
		add(delay, opcode, db);
		
		cout << i << endl;
	}
	
	int j;
	ofstream f("5krandominputs.txt");	// Output file
	for(vector<pair<string,string> >::const_iterator i = db.begin(); i != db.end(); ++i) {
		string first = (*i).first;
		string second = (*i).second;
		trim(first);
		trim(second);
		string w = first + " " + second + '\n';
		f << w;
		int j;
		cout << "writing..." << j++ << endl;
	}
}