/usr/lib/python2.7/dist-packages/chardet/sbcharsetprober.py is in python-chardet 2.3.0-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | ######################## 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
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