def predict(self,
context_line,
limit=20,
case_insensitive=False,
case_insensitive_smart=False,
accent_insensitive=False,
accent_insensitive_smart=False,
ignore_capitalized=False,
ignore_non_capitalized=False):
""" Find completion/prediction choices. """
LanguageModel = pypredict.LanguageModel
options = 0
if case_insensitive:
options |= LanguageModel.CASE_INSENSITIVE
if case_insensitive_smart:
options |= LanguageModel.CASE_INSENSITIVE_SMART
if accent_insensitive:
options |= LanguageModel.ACCENT_INSENSITIVE
if accent_insensitive_smart:
options |= LanguageModel.ACCENT_INSENSITIVE_SMART
if ignore_capitalized:
options |= LanguageModel.IGNORE_CAPITALIZED
if ignore_non_capitalized:
options |= LanguageModel.IGNORE_NON_CAPITALIZED
context, spans = pypredict.tokenize_context(context_line)
choices = self._get_prediction(self.models, context, limit, options)
_logger.debug("context=" + repr(context))
_logger.debug("choices=" + repr(choices[:5]))
return [x[0] for x in choices]
def tokenize_context(self, text):
""" let the service find the words in text """
tokens = []
if 1:
# avoid the D-Bus round-trip while we can
tokens = pypredict.tokenize_context(text)
else:
tokens = self._call_method("tokenize_context", [], text)
return tokens
def tokenize_context(self, text):
""" let the service find the words in text """
tokens = []
if 1:
# avoid the D-Bus round-trip while we can
tokens = pypredict.tokenize_context(text)
else:
tokens = self._call_method("tokenize_context", [],
text)
return tokens
def predict(self, context_line, limit = 20,
case_insensitive = False,
accent_insensitive = False,
ignore_capitalized = False,
ignore_non_capitalized = False):
""" Find completion/prediction choices. """
LanguageModel = pypredict.LanguageModel
options = 0
if case_insensitive:
options |= LanguageModel.CASE_INSENSITIVE
if accent_insensitive:
options |= LanguageModel.ACCENT_INSENSITIVE
if ignore_capitalized:
options |= LanguageModel.IGNORE_CAPITALIZED
if ignore_non_capitalized:
options |= LanguageModel.IGNORE_NON_CAPITALIZED
context = pypredict.tokenize_context(context_line)
choices = self._get_prediction(self.models, context, limit, options)
_logger.debug("context=" + repr(context))
_logger.debug("choices=" + repr(choices[:5]))
return [x[0] for x in choices]
def tokenize_context(self, text):
""" let the service find the words in text """
return pypredict.tokenize_context(text)
def tokenize_context(self, text):
""" let the service find the words in text """
tokens = pypredict.tokenize_context(text)
return tokens
