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			121 lines
		
	
	
		
			4.7 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			121 lines
		
	
	
		
			4.7 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
######################## BEGIN LICENSE BLOCK ########################
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# The Original Code is Mozilla Universal charset detector code.
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#
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# The Initial Developer of the Original Code is
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# Netscape Communications Corporation.
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# Portions created by the Initial Developer are Copyright (C) 2001
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# the Initial Developer. All Rights Reserved.
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#
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# Contributor(s):
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#   Mark Pilgrim - port to Python
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#   Shy Shalom - original C code
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#
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# This library is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# This library is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this library; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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# 02110-1301  USA
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######################### END LICENSE BLOCK #########################
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import sys
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from . import constants
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from .charsetprober import CharSetProber
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from .compat import wrap_ord
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SAMPLE_SIZE = 64
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SB_ENOUGH_REL_THRESHOLD = 1024
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POSITIVE_SHORTCUT_THRESHOLD = 0.95
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NEGATIVE_SHORTCUT_THRESHOLD = 0.05
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SYMBOL_CAT_ORDER = 250
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NUMBER_OF_SEQ_CAT = 4
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POSITIVE_CAT = NUMBER_OF_SEQ_CAT - 1
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#NEGATIVE_CAT = 0
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class SingleByteCharSetProber(CharSetProber):
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    def __init__(self, model, reversed=False, nameProber=None):
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        CharSetProber.__init__(self)
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        self._mModel = model
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        # TRUE if we need to reverse every pair in the model lookup
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        self._mReversed = reversed
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        # Optional auxiliary prober for name decision
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        self._mNameProber = nameProber
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        self.reset()
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    def reset(self):
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        CharSetProber.reset(self)
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        # char order of last character
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        self._mLastOrder = 255
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        self._mSeqCounters = [0] * NUMBER_OF_SEQ_CAT
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        self._mTotalSeqs = 0
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        self._mTotalChar = 0
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        # characters that fall in our sampling range
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        self._mFreqChar = 0
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    def get_charset_name(self):
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        if self._mNameProber:
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            return self._mNameProber.get_charset_name()
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        else:
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            return self._mModel['charsetName']
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    def feed(self, aBuf):
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        if not self._mModel['keepEnglishLetter']:
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            aBuf = self.filter_without_english_letters(aBuf)
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        aLen = len(aBuf)
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        if not aLen:
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            return self.get_state()
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        for c in aBuf:
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            order = self._mModel['charToOrderMap'][wrap_ord(c)]
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            if order < SYMBOL_CAT_ORDER:
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                self._mTotalChar += 1
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            if order < SAMPLE_SIZE:
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                self._mFreqChar += 1
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                if self._mLastOrder < SAMPLE_SIZE:
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                    self._mTotalSeqs += 1
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                    if not self._mReversed:
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                        i = (self._mLastOrder * SAMPLE_SIZE) + order
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                        model = self._mModel['precedenceMatrix'][i]
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                    else:  # reverse the order of the letters in the lookup
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                        i = (order * SAMPLE_SIZE) + self._mLastOrder
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                        model = self._mModel['precedenceMatrix'][i]
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                    self._mSeqCounters[model] += 1
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            self._mLastOrder = order
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        if self.get_state() == constants.eDetecting:
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            if self._mTotalSeqs > SB_ENOUGH_REL_THRESHOLD:
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                cf = self.get_confidence()
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                if cf > POSITIVE_SHORTCUT_THRESHOLD:
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                    if constants._debug:
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                        sys.stderr.write('%s confidence = %s, we have a'
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                                         'winner\n' %
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                                         (self._mModel['charsetName'], cf))
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                    self._mState = constants.eFoundIt
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                elif cf < NEGATIVE_SHORTCUT_THRESHOLD:
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                    if constants._debug:
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                        sys.stderr.write('%s confidence = %s, below negative'
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                                         'shortcut threshhold %s\n' %
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                                         (self._mModel['charsetName'], cf,
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                                          NEGATIVE_SHORTCUT_THRESHOLD))
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                    self._mState = constants.eNotMe
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        return self.get_state()
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    def get_confidence(self):
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        r = 0.01
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        if self._mTotalSeqs > 0:
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            r = ((1.0 * self._mSeqCounters[POSITIVE_CAT]) / self._mTotalSeqs
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                 / self._mModel['mTypicalPositiveRatio'])
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            r = r * self._mFreqChar / self._mTotalChar
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            if r >= 1.0:
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                r = 0.99
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        return r
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