import os


class MyConfig:
    ''' My Configuration '''

    m_current_epoch = {'SegmentEpoch': 1, \
                           'GenerateEpoch': 1, \
                           'EstimateEpoch': 1, \
                           'PruneEpoch': 1, \
                           'EvaluateEpoch': 1, \
                           'PrepareEpoch': 2, \
                           'PopulateEpoch': 3, \
                           'PartialWordThresholdEpoch': 4, \
                           'PartialWordEpoch': 5, \
                           'PopulateBigramEpoch': 6, \
                           }

    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 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 = 11.9 * 1024 * 1024 * 1
        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 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 = 5
        assert N >= 2, 'at least bi-gram'
        return N

    def getMinimumOccurrence(self):
        return 3 # minimum word occurrence

    def getPartialWordThreshold(self):
        return 0.10 # the first 10% in position

    def getNewWordThreshold(self):
        return 0.10 / 2 # the first 5% in position

    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"