fixed conflicts

1 files changed, 0 insertions, 231 deletions

diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
deleted file mode 100644
index 461e6b5f..00000000
--- a/training/mr_optimize_reduce.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <fstream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "optimize.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-
-using namespace std;
-namespace po = boost::program_options;
-
-void SanityCheck(const vector<double>& w) {
-  for (int i = 0; i < w.size(); ++i) {
-    assert(!isnan(w[i]));
-    assert(!isinf(w[i]));
-  }
-}
-
-struct FComp {
-  const vector<double>& w_;
-  FComp(const vector<double>& w) : w_(w) {}
-  bool operator()(int a, int b) const {
-    return fabs(w_[a]) > fabs(w_[b]);
-  }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
-  vector<int> fnums(w.size());
-  for (int i = 0; i < w.size(); ++i)
-    fnums[i] = i;
-  vector<int>::iterator mid = fnums.begin();
-  mid += (w.size() > 10 ? 10 : w.size());
-  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
-  cerr << "TOP FEATURES:";
-  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
-    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
-  }
-  cerr << endl;
-}
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("input_weights,i",po::value<string>(),"Input feature weights file")
-        ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
-        ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
-        ("state,s",po::value<string>(),"Read (and write if output_state is not set) optimizer state from this state file. In the first iteration, the file should not exist.")
-        ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)")
-        ("output_state,S", po::value<string>(), "Output state file (optional override)")
-	("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
-        ("eta,e", po::value<double>()->default_value(0.1), "Learning rate for SGD (eta)")
-        ("gaussian_prior,p","Use a Gaussian prior on the weights")
-        ("means,u", po::value<string>(), "File containing the means for Gaussian prior")
-        ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
-  po::options_description clo("Command line options");
-  clo.add_options()
-        ("config", po::value<string>(), "Configuration file")
-        ("help,h", "Print this help message and exit");
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts);
-  dcmdline_options.add(opts).add(clo);
-  
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("config")) {
-    ifstream config((*conf)["config"].as<string>().c_str());
-    po::store(po::parse_config_file(config, dconfig_options), *conf);
-  }
-  po::notify(*conf);
-
-  if (conf->count("help") || !conf->count("input_weights") || !conf->count("state")) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-
-  const bool use_b64 = conf["input_format"].as<string>() == "b64";
-
-  vector<weight_t> lambdas;
-  Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
-  const string s_obj = "**OBJ**";
-  int num_feats = FD::NumFeats();
-  cerr << "Number of features: " << num_feats << endl;
-  const bool gaussian_prior = conf.count("gaussian_prior");
-  vector<weight_t> means(num_feats, 0);
-  if (conf.count("means")) {
-    if (!gaussian_prior) {
-      cerr << "Don't use --means without --gaussian_prior!\n";
-      exit(1);
-    }
-    Weights::InitFromFile(conf["means"].as<string>(), &means);
-  }
-  boost::shared_ptr<BatchOptimizer> o;
-  const string omethod = conf["optimization_method"].as<string>();
-  if (omethod == "rprop")
-    o.reset(new RPropOptimizer(num_feats));  // TODO add configuration
-  else
-    o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
-  cerr << "Optimizer: " << o->Name() << endl;
-  string state_file = conf["state"].as<string>();
-  {
-    ifstream in(state_file.c_str(), ios::binary);
-    if (in)
-      o->Load(&in);
-    else
-      cerr << "No state file found, assuming ITERATION 1\n";
-  }
-
-  double objective = 0;
-  vector<double> gradient(num_feats, 0);
-  // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
-  // 0<TAB>**OBJ**=1.1;Feat1=1.0;
-  int total_lines = 0;  // TODO - this should be a count of the
-                        // training instances!!
-  while(cin) {
-    string line;
-    getline(cin, line);
-    if (line.empty()) continue;
-    ++total_lines;
-    int feat;
-    double val;
-    size_t i = line.find("\t");
-    assert(i != string::npos);
-    ++i;
-    if (use_b64) {
-      SparseVector<double> g;
-      double obj;
-      if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
-        cerr << "B64 decoder returned error, skipping gradient!\n";
-	cerr << "  START: " << line.substr(0,line.size() > 200 ? 200 : line.size()) << endl;
-	if (line.size() > 200)
-	  cerr << "    END: " << line.substr(line.size() - 200, 200) << endl;
-        cout << "-1\tRESTART\n";
-        exit(99);
-      }
-      objective += obj;
-      const SparseVector<double>& cg = g;
-      for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
-        if (it->first >= num_feats) {
-	  cerr << "Unexpected feature in gradient: " << FD::Convert(it->first) << endl;
-	  abort();
-        }
-        gradient[it->first] -= it->second;
-      }
-    } else {       // text encoding - your gradients will not be accurate!
-      while (i < line.size()) {
-        size_t start = i;
-        while (line[i] != '=' && i < line.size()) ++i;
-        if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
-        string fname = line.substr(start, i - start);
-        if (fname == s_obj) {
-          feat = -1;
-        } else {
-          feat = FD::Convert(line.substr(start, i - start));
-          if (feat >= num_feats) {
-	    cerr << "Unexpected feature in gradient: " << line.substr(start, i - start) << endl;
-	    abort();
-	  }
-        }
-        ++i;
-        start = i;
-        while (line[i] != ';' && i < line.size()) ++i;
-        if (i - start == 0) continue;
-        val = atof(line.substr(start, i - start).c_str());
-        ++i;
-        if (feat == -1) {
-          objective += val;
-        } else {
-          gradient[feat] -= val;
-        }
-      }
-    }
-  }
-
-  if (gaussian_prior) {
-    const double sigsq = conf["sigma_squared"].as<double>();
-    double norm = 0;
-    for (int k = 1; k < lambdas.size(); ++k) {
-      const double& lambda_k = lambdas[k];
-      if (lambda_k) {
-        const double param = (lambda_k - means[k]);
-        norm += param * param;
-        gradient[k] += param / sigsq;
-      }
-    }
-    const double reg = norm / (2.0 * sigsq);
-    cerr << "REGULARIZATION TERM: " << reg << endl;
-    objective += reg;
-  }
-  cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
-  double gnorm = 0;
-  for (int i = 0; i < gradient.size(); ++i)
-    gnorm += gradient[i] * gradient[i];
-  cerr << "  GNORM=" << sqrt(gnorm) << endl;
-  vector<double> old = lambdas;
-  int c = 0;
-  while (old == lambdas) {
-    ++c;
-    if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
-    o->Optimize(objective, gradient, &lambdas);
-    assert(c < 5);
-  }
-  old.clear();
-  SanityCheck(lambdas);
-  ShowLargestFeatures(lambdas);
-  Weights::WriteToFile(conf["output_weights"].as<string>(), lambdas, false);
-
-  const bool conv = o->HasConverged();
-  if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
-  
-  if (conf.count("output_state"))
-    state_file = conf["output_state"].as<string>();
-  ofstream out(state_file.c_str(), ios::binary);
-  cerr << "Writing state to: " << state_file << endl;
-  o->Save(&out);
-  out.close();
-
-  cout << o->EvaluationCount() << "\t" << conv << endl;
-  return 0;
-}