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Diffstat (limited to 'lib/chardet/sbcharsetprober.py')
| -rw-r--r-- | lib/chardet/sbcharsetprober.py | 160 |
1 files changed, 160 insertions, 0 deletions
diff --git a/lib/chardet/sbcharsetprober.py b/lib/chardet/sbcharsetprober.py new file mode 100644 index 0000000..31d70e1 --- /dev/null +++ b/lib/chardet/sbcharsetprober.py @@ -0,0 +1,160 @@ +######################## 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 ######################### + +from collections import namedtuple + +from .charsetprober import CharSetProber +from .enums import CharacterCategory, ProbingState, SequenceLikelihood + +SingleByteCharSetModel = namedtuple( + "SingleByteCharSetModel", + [ + "charset_name", + "language", + "char_to_order_map", + "language_model", + "typical_positive_ratio", + "keep_ascii_letters", + "alphabet", + ], +) + + +class SingleByteCharSetProber(CharSetProber): + SAMPLE_SIZE = 64 + SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2 + POSITIVE_SHORTCUT_THRESHOLD = 0.95 + NEGATIVE_SHORTCUT_THRESHOLD = 0.05 + + def __init__(self, model, is_reversed=False, name_prober=None): + super().__init__() + self._model = model + # TRUE if we need to reverse every pair in the model lookup + self._reversed = is_reversed + # Optional auxiliary prober for name decision + self._name_prober = name_prober + self._last_order = None + self._seq_counters = None + self._total_seqs = None + self._total_char = None + self._control_char = None + self._freq_char = None + self.reset() + + def reset(self): + super().reset() + # char order of last character + self._last_order = 255 + self._seq_counters = [0] * SequenceLikelihood.get_num_categories() + self._total_seqs = 0 + self._total_char = 0 + self._control_char = 0 + # characters that fall in our sampling range + self._freq_char = 0 + + @property + def charset_name(self): + if self._name_prober: + return self._name_prober.charset_name + return self._model.charset_name + + @property + def language(self): + if self._name_prober: + return self._name_prober.language + return self._model.language + + def feed(self, byte_str): + # TODO: Make filter_international_words keep things in self.alphabet + if not self._model.keep_ascii_letters: + byte_str = self.filter_international_words(byte_str) + else: + byte_str = self.remove_xml_tags(byte_str) + if not byte_str: + return self.state + char_to_order_map = self._model.char_to_order_map + language_model = self._model.language_model + for char in byte_str: + order = char_to_order_map.get(char, CharacterCategory.UNDEFINED) + # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but + # CharacterCategory.SYMBOL is actually 253, so we use CONTROL + # to make it closer to the original intent. The only difference + # is whether or not we count digits and control characters for + # _total_char purposes. + if order < CharacterCategory.CONTROL: + self._total_char += 1 + if order < self.SAMPLE_SIZE: + self._freq_char += 1 + if self._last_order < self.SAMPLE_SIZE: + self._total_seqs += 1 + if not self._reversed: + lm_cat = language_model[self._last_order][order] + else: + lm_cat = language_model[order][self._last_order] + self._seq_counters[lm_cat] += 1 + self._last_order = order + + charset_name = self._model.charset_name + if self.state == ProbingState.DETECTING: + if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD: + confidence = self.get_confidence() + if confidence > self.POSITIVE_SHORTCUT_THRESHOLD: + self.logger.debug( + "%s confidence = %s, we have a winner", charset_name, confidence + ) + self._state = ProbingState.FOUND_IT + elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD: + self.logger.debug( + "%s confidence = %s, below negative shortcut threshold %s", + charset_name, + confidence, + self.NEGATIVE_SHORTCUT_THRESHOLD, + ) + self._state = ProbingState.NOT_ME + + return self.state + + def get_confidence(self): + r = 0.01 + if self._total_seqs > 0: + r = ( + ( + self._seq_counters[SequenceLikelihood.POSITIVE] + + 0.25 * self._seq_counters[SequenceLikelihood.LIKELY] + ) + / self._total_seqs + / self._model.typical_positive_ratio + ) + # The more control characters (proportionnaly to the size + # of the text), the less confident we become in the current + # charset. + r = r * (self._total_char - self._control_char) / self._total_char + r = r * self._freq_char / self._total_char + if r >= 1.0: + r = 0.99 + return r |