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#include "trainer.h"
Tsuruoka_Maxent_Trainer::Tsuruoka_Maxent_Trainer()
: const_reorder::Tsuruoka_Maxent(NULL) {}
void Tsuruoka_Maxent_Trainer::fnTrain(const char* pszInstanceFName,
const char* pszAlgorithm,
const char* pszModelFName) {
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;
}
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