C++ xvec_t类代码示例

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本文整理汇总了C++中xvec_t类的典型用法代码示例。如果您正苦于以下问题：C++ xvec_t类的具体用法？C++ xvec_t怎么用？C++ xvec_t使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

在下文中一共展示了xvec_t类的14个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的C++代码示例。

示例1: clone

		double 
SvmSgd::my_evaluateEta(int imin, int imax, const xvec_t &xp, const yvec_t &yp, double eta00)
{
		SvmSgd clone(*this); // take a copy of the current state

		cout << "[my_evaluateEta: clone.wDivisor: ]" << setprecision(12) << clone.wDivisor << " clone.t: " << clone.t << " clone.eta0: " << clone.eta0 << endl; 
		cout << "Trying eta=" << eta00 ;

		assert(imin <= imax);
		double _t = 0;
		double eta = 0;
		for (int i=imin; i<=imax; i++){
				eta = eta00 / (1 + lambda * eta00 * _t);
				//cout << "[my_evaluateEta:] Eta: " << eta << endl;
				clone.trainOne(xp.at(i), yp.at(i), eta);
				_t++;
		}
		double loss = 0;
		double cost = 0;
		for (int i=imin; i<=imax; i++)
				clone.testOne(xp.at(i), yp.at(i), &loss, 0);
		loss = loss / (imax - imin + 1);
		cost = loss + 0.5 * lambda * clone.wnorm();
		cout <<" yields loss " << loss << endl;
		// cout << "Trying eta=" << eta << " yields cost " << cost << endl;
		return cost;
}

开发者ID:shanil-puri，项目名称:SysResearchLab，代码行数:27，代码来源:init_svmsgd.cpp

示例2: clone

double
SvmAisgd::evaluateEta(int imin, int imax, const xvec_t &xp, const yvec_t &yp, double eta)
{
  SvmAisgd clone(*this); // take a copy of the current state
  assert(imin <= imax);
  for (int i=imin; i<=imax; i++)
    clone.trainOne(xp.at(i), yp.at(i), eta, 1.0);
  double loss = 0;
  double cost = 0;
  for (int i=imin; i<=imax; i++)
    clone.testOne(xp.at(i), yp.at(i), &loss, 0);
  loss = loss / (imax - imin + 1);
  cost = loss + 0.5 * lambda * clone.wnorm();
  // cout << "Trying eta=" << eta << " yields cost " << cost << endl;
  return cost;
}

开发者ID:airoldilab，项目名称:ai-sgd，代码行数:16，代码来源:svmaisgd.cpp

示例3: loadmult_datafile_sub

static void
loadmult_datafile_sub(istream &f, bool binary, const char *fname, 
                  xvec_t &xp, yvec_t &yp, int &maxdim, int maxrows)
{
  cout << "# Reading file " << fname << endl;
  if (! f.good())
    assertfail("Cannot open " << fname);

  int pcount = 0;
  while (f.good() && maxrows--)
    {
	double y;
      	SVector x;
	y = (f.get());
        x.load(f);
      
      if (f.good())
        {          
          xp.push_back(x);
          yp.push_back(y);
          pcount += 1;
          if (x.size() > maxdim)
            maxdim = x.size();
        }
    }
  cout << "# Read " << pcount << " examples." << endl;
  

}

开发者ID:yqxian，项目名称:zsl，代码行数:29，代码来源:data_mult.cpp

示例4: assert

void 
SvmSgd::test(int imin, int imax, 
             const xvec_t &xp, const yvec_t &yp, 
             const char *prefix)

{
  cout << prefix << "Testing on [" << imin << ", " << imax << "]." << endl;
  assert(imin <= imax);
  int nerr = 0;
  double cost = 0;
  for (int i=imin; i<=imax; i++)
    {
      const SVector &x = xp.at(i);
      double y = yp.at(i);
      double wx = dot(w,x);
      double z = y * (wx + bias);
      if (z <= 0)
        nerr += 1;
#if LOSS < LOGLOSS
      if (z < 1)
#endif
        cost += loss(z);
    }
  int n = imax - imin + 1;
  double loss = cost / n;
  cost = loss + 0.5 * lambda * dot(w,w);
  cout << prefix << setprecision(4)
       << "Misclassification: " << (double)nerr * 100.0 / n << "%." << endl;
  cout << prefix << setprecision(12) 
       << "Cost: " << cost << "." << endl;
  cout << prefix << setprecision(12) 
       << "Loss: " << loss << "." << endl;
}

开发者ID:AnryYang，项目名称:cpp_algorithms，代码行数:33，代码来源:svmsgd2.cpp

示例5: assert

/// Perform a test pass
void
SvmAisgd::test(int imin, int imax, const xvec_t &xp, const yvec_t &yp, const char *prefix)
{
  cout << prefix << "Testing on [" << imin << ", " << imax << "]." << endl;
  assert(imin <= imax);
  double nerr = 0;
  double loss = 0;
  for (int i=imin; i<=imax; i++)
    testOne(xp.at(i), yp.at(i), &loss, &nerr);
  nerr = nerr / (imax - imin + 1);
  loss = loss / (imax - imin + 1);
  double cost = loss + 0.5 * lambda * anorm();
  cout << prefix
       << "Loss=" << setprecision(12) << loss
       << " Cost=" << setprecision(12) << cost
       << " Misclassification=" << setprecision(4) << 100 * nerr << "%."
       << endl;
}

开发者ID:airoldilab，项目名称:ai-sgd，代码行数:19，代码来源:svmaisgd.cpp

示例6: generator

/// Perform a SAG training epoch
void 
SvmSag::trainSag(int imin, int imax, const xvec_t &xp, const yvec_t &yp, const char *prefix)
{
  cout << prefix << "Training on [" << imin << ", " << imax << "]." << endl;
  assert(imin <= imax);
  assert(imin >= sdimin);
  assert(imax <= sdimax);
  assert(eta > 0);
  uniform_int_generator generator(imin, imax);
  for (int i=imin; i<=imax; i++)
    {
      int ii = generator(); 
      trainOne(xp.at(ii), yp.at(ii), eta, ii);
      t += 1;
    }
  cout << prefix << setprecision(6) << "wNorm=" << wnorm();
#if BIAS
  cout << " wBias=" << wBias;
#endif
  cout << endl;
}

开发者ID:DavidGrangier，项目名称:svmsparse，代码行数:22，代码来源:svmsag.cpp

示例7: assert

/// Perform a training epoch
void
SvmSgd::train(int imin, int imax, const xvec_t &xp, const yvec_t &yp, const char *prefix)
{
#if VERBOSE
  cout << prefix << "Training on [" << imin << ", " << imax << "]." << endl;
#endif
  assert(imin <= imax);
  assert(eta0 > 0);
  for (int i=imin; i<=imax; i++)
    {
      double eta = eta0 / (1 + lambda * eta0 * t);
      trainOne(xp.at(i), yp.at(i), eta);
      t += 1;
    }
#if VERBOSE
  cout << prefix << setprecision(6) << "wNorm=" << wnorm();
#if BIAS
  cout << " wBias=" << wBias;
#endif
  cout << endl;
#endif
}

开发者ID:DavidGrangier，项目名称:svmsparse，代码行数:23，代码来源:svmsgd.cpp

示例8: assert

/// Perform initial training epoch
void 
SvmSag::trainInit(int imin, int imax, const xvec_t &xp, const yvec_t &yp, const char *prefix)
{
  cout << prefix << "Training on [" << imin << ", " << imax << "]." << endl;
  assert(imin <= imax);
  assert(eta > 0);
  assert(m == 0);
  sd.resize(imax - imin + 1);
  sdimin = imin;
  sdimax = imax;
  for (int i=imin; i<=imax; i++)
    {
      m += 1;
      trainOne(xp.at(i), yp.at(i), eta, i);
      t += 1;
    }
  cout << prefix << setprecision(6) << "wNorm=" << wnorm();
#if BIAS
  cout << " wBias=" << wBias;
#endif
  cout << endl;
}

开发者ID:DavidGrangier，项目名称:svmsparse，代码行数:23，代码来源:svmsag.cpp

示例9: setprecision

/// Perform a training epoch
		void 
SvmSgd::train(int imin, int imax, const xvec_t &xp, const yvec_t &yp, const char *prefix)
{
		cout << prefix << "Training on [" << imin << ", " << imax << "]." << endl;
		assert(imin <= imax);
		assert(eta0 > 0);

		//cout << "wDivisor: " << wDivisor << "  wBias: " << wBias<< endl;
		for (int i=imin; i<=imax; i++)
		{
				double eta = eta0 / (1 + lambda * eta0 * t);
				//cout << "[my_evaluateEta:] Eta: " << eta << endl;
				trainOne(xp.at(i), yp.at(i), eta);
				t += 1;
		}

		//cout << "\nAfter training: \n  wDivisor: " << wDivisor << "  wBias: " << wBias<< endl;
		cout << prefix << setprecision(6) << "wNorm=" << wnorm();
#if BIAS
		cout << " wBias=" << wBias;
#endif
		cout << endl;
}

开发者ID:shanil-puri，项目名称:SysResearchLab，代码行数:24，代码来源:init_svmsgd.cpp

示例10: c

void 
SvmSgd::calibrate(int imin, int imax, 
                const xvec_t &xp, const yvec_t &yp)
{
  cout << "Estimating sparsity and bscale." << endl;
  int j;

  // compute average gradient size
  double n = 0;
  double m = 0;
  double r = 0;
  FVector c(w.size());
  for (j=imin; j<=imax && m<=1000; j++,n++)
    {
      const SVector &x = xp.at(j);
      n += 1;
      r += x.npairs();
      const SVector::Pair *p = x;
      while (p->i >= 0 && p->i < c.size())
        {
          double z = c.get(p->i) + fabs(p->v);
          c.set(p->i, z);
          m = max(m, z);
          p += 1;
        }
    }

  // bias update scaling
  bscale = m/n;

  // compute weight decay skip
  skip = (int) ((8 * n * w.size()) / r);
  cout << " using " << n << " examples." << endl;
  cout << " skip: " << skip 
       << " bscale: " << setprecision(6) << bscale << endl;
}

开发者ID:AnryYang，项目名称:cpp_algorithms，代码行数:36，代码来源:svmsgd2.cpp

示例11: main

int main(int argc, const char **argv)
{
  parse(argc, argv);
  config(argv[0]);
  if (trainfile)
    load_datafile(trainfile, xtrain, ytrain, dims, normalize, maxtrain);
  if (testfile)
    load_datafile(testfile, xtest, ytest, dims, normalize);
  cout << "# Number of features " << dims << "." << endl;
  // prepare svm
  int imin = 0;
  int imax = xtrain.size() - 1;
  int tmin = 0;
  int tmax = xtest.size() - 1;
  // heuristic determination of averaging start point
  int avgfrom = fabs(avgstart) * (imax - imin + 1);
  avgfrom = (avgstart < 0 && dims < avgfrom) ? dims : avgfrom;
  // create
  SvmAisgd svm(dims, lambda, avgfrom);
  Timer timer;
  // determine eta0 using sample
  int smin = 0;
  int smax = imin + min(1000, imax);
  timer.start();
  svm.determineEta0(smin, smax, xtrain, ytrain);
  timer.stop();
  // train
  for(int i=0; i<epochs; i++)
    {
      cout << "--------- Epoch " << i+1 << "." << endl;
      timer.start();
      svm.train(imin, imax, xtrain, ytrain);
      timer.stop();
      cout << "Total training time " << setprecision(6)
           << timer.elapsed() << " secs." << endl;
      svm.test(imin, imax, xtrain, ytrain, "train: ");
      if (tmax >= tmin)
        svm.test(tmin, tmax, xtest, ytest, "test:  ");
    }
  svm.renorm();
  // Linear classifier is in svm.a and svm.aBias
  return 0;
}

开发者ID:airoldilab，项目名称:ai-sgd，代码行数:43，代码来源:svmaisgd.cpp

示例12: main

int main(int argc, const char **argv)
{
  parse(argc, argv);
  config(argv[0]);
  if (trainfile)
    load_datafile(trainfile, xtrain, ytrain, dims, normalize, maxtrain);
  if (testfile)
    load_datafile(testfile, xtest, ytest, dims, normalize);
  cout << "# Number of features " << dims << "." << endl;
  // prepare svm
  int imin = 0;
  int imax = xtrain.size() - 1;
  int tmin = 0;
  int tmax = xtest.size() - 1;
  SvmSag svm(dims, lambda);
  Timer timer;
  // determine eta0 using sample
  int smin = 0;
  int smax = imin + min(1000, imax);
  timer.start();
  if (eta > 0)
    svm.setEta(eta);
  else
    svm.determineEta(smin, smax, xtrain, ytrain);
  timer.stop();
  // train
  for(int i=0; i<epochs; i++)
    {
      cout << "--------- Epoch " << i+1 << "." << endl;
      timer.start();
      if (i == 0)
        svm.trainInit(imin, imax, xtrain, ytrain);
      else
        svm.trainSag(imin, imax, xtrain, ytrain);
      timer.stop();
      cout << "Total training time " << setprecision(6) 
           << timer.elapsed() << " secs." << endl;
      svm.test(imin, imax, xtrain, ytrain, "train: ");
      if (tmax >= tmin)
        svm.test(tmin, tmax, xtest, ytest, "test:  ");
    }
  return 0;
}

开发者ID:DavidGrangier，项目名称:svmsparse，代码行数:43，代码来源:svmsag.cpp

示例13: svm

int 
main(int argc, const char **argv)
{
  parse(argc, argv);
  cout << "Loss=" << lossname 
       << " Bias=" << BIAS 
       << " RegBias=" << REGULARIZEBIAS
       << " Lambda=" << lambda
       << endl;

  // load training set
  load(trainfile.c_str(), xtrain, ytrain);
  cout << "Number of features " << dim << "." << endl;
  int imin = 0;
  int imax = xtrain.size() - 1;
  if (trainsize > 0 && imax >= trainsize)
    imax = imin + trainsize -1;
  // prepare svm
  SvmSgd svm(dim, lambda);
  Timer timer;

  // load testing set
  if (! testfile.empty())
    load(testfile.c_str(), xtest, ytest);
  int tmin = 0;
  int tmax = xtest.size() - 1;

  svm.calibrate(imin, imax, xtrain, ytrain);
  for(int i=0; i<epochs; i++)
    {
      
      cout << "--------- Epoch " << i+1 << "." << endl;
      timer.start();
      svm.train(imin, imax, xtrain, ytrain);
      timer.stop();
      cout << "Total training time " << setprecision(6) 
           << timer.elapsed() << " secs." << endl;
      svm.test(imin, imax, xtrain, ytrain, "train: ");
      if (tmax >= tmin)
        svm.test(tmin, tmax, xtest, ytest, "test:  ");
    }
}

开发者ID:AnryYang，项目名称:cpp_algorithms，代码行数:42，代码来源:svmsgd2.cpp

示例14: main

int main(int argc, const char **argv)
{
		parse(argc, argv);
		config(argv[0]);
		if (trainfile)
				load_datafile(trainfile, xtrain, ytrain, dims, normalize, maxtrain);
		if (testfile)
				load_datafile(testfile, xtest, ytest, dims, normalize);
		cout << "# Number of features " << dims << "." << endl;
		// prepare svm
		int imin = 0;
		int imax = xtrain.size() - 1;
		int tmin = 0;
		int tmax = xtest.size() - 1;
		SvmSgd svm(dims, lambda);
		Timer timer;
		// determine eta0 using sample
		int smin = 0;
		int smax = imin + min(1000, imax);
		// train

		Timer totalTimer, overheadtimer, exetimer;

		totalTimer.start();
		//winnie, initial wDivisor and wBias

		timeval t1, t4, t5, t6, t7, t8;
		overheadtimer.start();
		if(sample_file){
				int sample_size = 500;
				int bin_num = 20;
				int num_compare = 49;
				int dimension = dims - 1; //The first feature is the classification, does not count in sampling.


				gettimeofday(&t1, NULL);

				Sampling<double> selector(imax, 0 ,
								dimension, sample_size, bin_num, num_compare);

				selector.do_sampling(sample_file);
				gettimeofday(&t4, NULL);
				selector.calc_ecdf();		
				gettimeofday(&t5, NULL);
				//	std::cout << "test init kmeans " << std::endl;
				//step3a, do database search
				if(num_compare <= 0){
						cerr << "Number of comparison is less than or equal to 0" << endl;
						return -1;
				}
				else{
						//TODO: make the comparison choose the second best result, when we use data base that contains dataset itself

						//winnie, prerun several iterations, and log some information
						int prerun_iters = 3;
						FVector old_w(dimension);

						svm.determineEta0(smin, smax, xtrain, ytrain);

						for(int i = 0; i < prerun_iters; i ++ ){

								svm.get_w(old_w);
								svm.train(imin, imax, xtrain, ytrain);
								//svm.test(imin, imax, xtrain, ytrain, "train: ");

						}
						//get the idx of dimensions	

						map <double, int> delta_w;
						svm.get_delta_w(old_w, delta_w);
						//int reducedDimNum[8] = {1, 2, 3, 4, 8, 16, 32, 50};
						int reducedDimNum[8] = {1, 3, 8, 16, 32, 50, 64, 128};
						int selected_id[9];
						multimap <double, int> error_dim;  //error value and dimension
						for(int iout = 0; iout < 8; iout++){

								vector<int> reducedDimIdx(reducedDimNum[iout]);
								map<double, int>::reverse_iterator rmit = delta_w.rbegin();
								double sum_value = 0;
								for(int i = 0; i < reducedDimNum[iout]; i ++){
										reducedDimIdx[i] = rmit->second;
										sum_value += rmit->first;
										//cout << "reduceDim: " << reducedDimIdx[i] << " value " << rmit->first << endl;
										rmit++;
								}
								cout << "sum_value: " << sum_value << " with dim num: " << reducedDimNum[iout] << endl;


								//cout << "distancetype: " << distancetype << endl;
								selected_id[iout] = selector.search_database('b', database_file, distancetype, reducedDimIdx);

								gettimeofday(&t6, NULL);
								if(selected_id[iout] < 0)
										return -1;
								else{
										std::cout << "selected id: " << selected_id[iout] << std::endl;
										//Do data customization, use norm.cpp program, 
										stringstream ss;//create a stringstream
										ss << setfill('0') << setw(2) << selected_id[iout];
										string filename = string(database_dir) + "/" + ss.str() + ".txt";
//.........这里部分代码省略.........

开发者ID:shanil-puri，项目名称:SysResearchLab，代码行数:101，代码来源:init_svmsgd.cpp

注：本文中的xvec_t类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。