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import os
class MyConfig:
''' My Configuration '''
m_current_epoch = {'SegmentEpoch': 1, \
'GenerateEpoch': 1, \
'EstimateEpoch': 1, \
'PruneEpoch': 1, \
'EvaluateEpoch': 1, \
'PrepareEpoch': 1, \
'PopulateEpoch': 1, \
'PartialWordEpoch': 1, \
'NewWordEpoch': 1, \
'MarkPinyinEpoch': 1, \
'MergeSequenceEpoch': 1, \
}
def getEpochs(self):
return self.m_current_epoch
m_trainer_dir = '/media/data/Program/trainer'
def getBaseDir(self):
return self.m_trainer_dir
def getTextDir(self):
return self.m_trainer_dir + os.sep + 'texts'
def getTextIndexDir(self):
return self.m_trainer_dir + os.sep + 'texts' + os.sep + 'index'
def getModelDir(self):
return self.m_trainer_dir + os.sep + 'models'
def getFinalModelDir(self):
return self.m_trainer_dir + os.sep + 'finals'
m_tools_dir = '/media/data/Program/trainer/tools/libpinyin'
def getToolsDir(self):
return self.m_tools_dir
m_evals_dir = '/media/data/Program/trainer/evals/libpinyin'
def getEvalsDir(self):
return self.m_evals_dir
def getEstimatesModel(self):
estimates_model = self.m_tools_dir + '/data/estimates.db'
return estimates_model
def getEstimateIndex(self):
return 'estimate.index'
def getSortedEstimateIndex(self):
return 'estimate.sorted.index'
def getInMemoryFileSystem(self):
return '/dev/shm'
def getEvalsTextFileName(self):
return 'evals2.text'
def getMinimumFileSize(self):
#about 1,200 Chinese characters
minimum_chinese_characters = 1200
minimum_file_size = minimum_chinese_characters * 3 + \
minimum_chinese_characters / 2
return minimum_file_size
#the trained corpus size of model candidates
def getCandidateModelSize(self):
candidate_model_size = 28.5 * 1000 * 1000 * 2
return candidate_model_size
def getModelPostfix(self):
return '.db'
def getCandidateModelName(self, index):
candidate_model_name = "model-candidates-{0}.db"
return candidate_model_name.format(index)
def getMaximumOccursAllowed(self):
return 20
def getMaximumIncreaseRatesAllowed(self):
return 3.
def getReportPostfix(self):
return '.report'
def getSegmentPostfix(self):
return '.segmented'
def getSegmentReportPostfix(self):
return '.segment.report'
#For both index page, item page and binary model file
def getStatusPostfix(self):
return '.status'
def getIndexPostfix(self):
return '.index'
def getTextPostfix(self):
return '.text'
def getBackupPostfix(self):
return '.backup'
def getMergedPostfix(self):
return '.merged'
def getMergedReportPostfix(self):
return '.merged.report'
def getFinalModelFileName(self):
return 'interpolation2.text'
def getFinalStatusFileName(self):
return 'cwd.status'
'''
Word Recognizer Configuration
'''
def getWordRecognizerDir(self):
return self.m_trainer_dir + os.sep + 'words'
def getNgramFileName(self, length):
return str(length) + '-gram.db'
def getWordSep(self):
return " "
def getMaximumCombineNumber(self):
N = 7
assert N >= 2, 'at least bi-gram'
return N
def getPruneMinimumOccurrence(self):
return 1 # prune minimum occurrence
def getWordMinimumOccurrence(self):
return 3 # minimum word occurrence
def getNgramMinimumOccurrence(self):
return 9 # minimum word occurrence in n-gram table
def getPartialWordThreshold(self):
return 0.50 # the first 50% in position
def getNewWordThreshold(self):
return 0.60 # the first 60% in position, subject verb object.
def getMinimumEntropy(self):
return 0.01
def getMaximumIteration(self):
return 20 # roughly around N
def getBigramFileName(self):
return "bigram.db"
def getWordsListFileName(self):
return "words.txt"
def getWordsWithPinyinFileName(self):
return "oldwords.txt"
def getPartialWordFileName(self):
return "partialword.txt"
def getNewWordFileName(self):
return "newword.txt"
def getRecognizedWordFileName(self):
return "recognized.txt"
def getDefaultPinyinTotalFrequency(self):
return 100
def getMinimumPinyinFrequency(self):
return 3
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