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