120 lines
4.7 KiB
Python
120 lines
4.7 KiB
Python
######################## 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
|