曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.かつては雑草やヨモギと共に雨や露を分かち合っていたが、今では松やヒノキと共に霜や雪に耐えている。曾与蒿藜同雨露,Once sharing rain and dew with weeds and wormwood, now enduring frost and snow with pines and cypresses.终随松柏到冰霜.曾与蒿藜同雨露한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.,终随松柏到冰霜.譖セ荳手珍阯懷酔髮ィ髴イ�檎サ磯囂譚セ譟丞芦蜀ー髴�曾与蒿藜同雨露,鏇句笌钂胯棞鍚岄洦闇诧紝缁堥殢鏉炬煆鍒板啺闇�终随松柏到冰霜.曾与蒿藜同雨露,한때 잡초와 쑥과 함께 비와 이슬을 나누던 곳이 이제는 소나무와 삼나무와 함께 서리와 눈을 견뎌내고 있다.终随松柏到冰霜.曾与蒿藜同雨露,终随松柏到冰霜.
######################## 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