| ######################## 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 |
