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authorWu, Ke <wuke@cs.umd.edu>2014-12-06 12:17:27 -0500
committerWu, Ke <wuke@cs.umd.edu>2014-12-06 12:17:27 -0500
commit16827862bcc4f04ada087abc255c6604d88076c1 (patch)
tree394f8585409ca2d132fc1400c906c39df43613e6 /utils/tsuruoka_maxent.h
parenta21959213f9b1cc15befae52dbb5091e848de7a1 (diff)
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.
Diffstat (limited to 'utils/tsuruoka_maxent.h')
-rw-r--r--utils/tsuruoka_maxent.h153
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_ */