Move non-MaxEnt code out of utils

1. alignment.h, argument_reorder_model.h, src_sentence.h, tree.h,
   tsuruoka_maxent.h -> decoder/ff_const_reorder_common.h.

2. Trainers source files (argument_reorder_model.cc and
   constituent_reorder_model.cc) are moved to training/const_reorder.

1 files changed, 0 insertions, 153 deletions

diff --git a/utils/tsuruoka_maxent.h b/utils/tsuruoka_maxent.h
deleted file mode 100644
index 82da44ff..00000000
--- a/utils/tsuruoka_maxent.h
+++ /dev/null
@@ -1,153 +0,0 @@
-/*
- * tsuruoka_maxent.h
- *
- */
-
-#ifndef TSURUOKA_MAXENT_H_
-#define TSURUOKA_MAXENT_H_
-
-#include <assert.h>
-#include <string.h>
-#include <string>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "stringlib.h"
-#include "maxent.h"
-
-typedef std::unordered_map<std::string, int> Map;
-typedef std::unordered_map<std::string, int>::iterator Iterator;
-
-struct Tsuruoka_Maxent {
-  Tsuruoka_Maxent(const char* pszModelFName) {
-    if (pszModelFName != NULL) {
-      m_pModel = new ME_Model();
-      m_pModel->load_from_file(pszModelFName);
-    } else
-      m_pModel = NULL;
-  }
-
-  ~Tsuruoka_Maxent() {
-    if (m_pModel != NULL) delete m_pModel;
-  }
-
-  void fnTrain(const char* pszInstanceFName, const char* pszAlgorithm,
-               const char* pszModelFName, int /*iNumIteration*/) {
-    assert(strcmp(pszAlgorithm, "l1") == 0 || strcmp(pszAlgorithm, "l2") == 0 ||
-           strcmp(pszAlgorithm, "sgd") == 0 ||
-           strcmp(pszAlgorithm, "SGD") == 0);
-    FILE* fpIn = fopen(pszInstanceFName, "r");
-
-    ME_Model* pModel = new ME_Model();
-
-    char* pszLine = new char[100001];
-    int iNumInstances = 0;
-    int iLen;
-    while (!feof(fpIn)) {
-      pszLine[0] = '\0';
-      fgets(pszLine, 20000, fpIn);
-      if (strlen(pszLine) == 0) {
-        continue;
-      }
-
-      iLen = strlen(pszLine);
-      while (iLen > 0 && pszLine[iLen - 1] > 0 && pszLine[iLen - 1] < 33) {
-        pszLine[iLen - 1] = '\0';
-        iLen--;
-      }
-
-      iNumInstances++;
-
-      ME_Sample* pmes = new ME_Sample();
-
-      char* p = strrchr(pszLine, ' ');
-      assert(p != NULL);
-      p[0] = '\0';
-      p++;
-      std::vector<std::string> vecContext;
-      SplitOnWhitespace(std::string(pszLine), &vecContext);
-
-      pmes->label = std::string(p);
-      for (size_t i = 0; i < vecContext.size(); i++)
-        pmes->add_feature(vecContext[i]);
-      pModel->add_training_sample((*pmes));
-      if (iNumInstances % 100000 == 0)
-        fprintf(stdout, "......Reading #Instances: %1d\n", iNumInstances);
-      delete pmes;
-    }
-    fprintf(stdout, "......Reading #Instances: %1d\n", iNumInstances);
-    fclose(fpIn);
-
-    if (strcmp(pszAlgorithm, "l1") == 0)
-      pModel->use_l1_regularizer(1.0);
-    else if (strcmp(pszAlgorithm, "l2") == 0)
-      pModel->use_l2_regularizer(1.0);
-    else
-      pModel->use_SGD();
-
-    pModel->train();
-    pModel->save_to_file(pszModelFName);
-
-    delete pModel;
-    fprintf(stdout, "......Finished Training\n");
-    fprintf(stdout, "......Model saved as %s\n", pszModelFName);
-    delete[] pszLine;
-  }
-
-  double fnEval(const char* pszContext, const char* pszOutcome) const {
-    std::vector<std::string> vecContext;
-    ME_Sample* pmes = new ME_Sample();
-    SplitOnWhitespace(std::string(pszContext), &vecContext);
-
-    for (size_t i = 0; i < vecContext.size(); i++)
-      pmes->add_feature(vecContext[i]);
-    std::vector<double> vecProb = m_pModel->classify(*pmes);
-    delete pmes;
-    int iLableID = m_pModel->get_class_id(pszOutcome);
-    return vecProb[iLableID];
-  }
-  void fnEval(const char* pszContext,
-              std::vector<std::pair<std::string, double> >& vecOutput) const {
-    std::vector<std::string> vecContext;
-    ME_Sample* pmes = new ME_Sample();
-    SplitOnWhitespace(std::string(pszContext), &vecContext);
-
-    vecOutput.clear();
-
-    for (size_t i = 0; i < vecContext.size(); i++)
-      pmes->add_feature(vecContext[i]);
-    std::vector<double> vecProb = m_pModel->classify(*pmes);
-
-    for (size_t i = 0; i < vecProb.size(); i++) {
-      std::string label = m_pModel->get_class_label(i);
-      vecOutput.push_back(make_pair(label, vecProb[i]));
-    }
-    delete pmes;
-  }
-  void fnEval(const char* pszContext, std::vector<double>& vecOutput) const {
-    std::vector<std::string> vecContext;
-    ME_Sample* pmes = new ME_Sample();
-    SplitOnWhitespace(std::string(pszContext), &vecContext);
-
-    vecOutput.clear();
-
-    for (size_t i = 0; i < vecContext.size(); i++)
-      pmes->add_feature(vecContext[i]);
-    std::vector<double> vecProb = m_pModel->classify(*pmes);
-
-    for (size_t i = 0; i < vecProb.size(); i++) {
-      std::string label = m_pModel->get_class_label(i);
-      vecOutput.push_back(vecProb[i]);
-    }
-    delete pmes;
-  }
-  int fnGetClassId(const std::string& strLabel) const {
-    return m_pModel->get_class_id(strLabel);
-  }
-
- private:
-  ME_Model* m_pModel;
-};
-
-#endif /* TSURUOKA_MAXENT_H_ */