class_docstr: Intent classifier which uses a Logistic Regression underneath
class_docstr_tok: ["Intent","classifier","which","uses","a","Logistic","Regression","underneath"]
docstr: Performs intent classification on the provided *text*
  
  Args:
      text (str): Input
      intents_filter (str or list of str): When defined, it will find
          the most likely intent among the list, otherwise it will use
          the whole list of intents defined in the dataset
  
  Returns:
      dict or None: The most likely intent along with its probability or
      *None* if no intent was found
  
  Raises:
      :class:`snips_nlu.exceptions.NotTrained`: When the intent
          classifier is not fitted
docstr_tok: ["Performs","intent","classification","on","the","provided","*","text","*","Args",":","text","(","str",")",":","Input","intents_filter","(","str","or","list","of","str",")",":","When","defined",",","it","will","find","the","most","likely","intent","among","the","list",",","otherwise","it","will","use","the","whole","list","of","intents","defined","in","the","dataset","Returns",":","dict","or","None",":","The","most","likely","intent","along","with","its","probability","or","*","None","*","if","no","intent","was","found","Raises",":",":","class",":","`","snips_nlu.exceptions.NotTrained","`",":","When","the","intent","classifier","is","not","fitted"]
code: def get_intent(self, text, intents_filter=None):
      """Performs intent classification on the provided *text*
  
      Args:
          text (str): Input
          intents_filter (str or list of str): When defined, it will find
              the most likely intent among the list, otherwise it will use
              the whole list of intents defined in the dataset
  
      Returns:
          dict or None: The most likely intent along with its probability or
          *None* if no intent was found
  
      Raises:
          :class:`snips_nlu.exceptions.NotTrained`: When the intent
              classifier is not fitted
  
      """
      return self._get_intents(text, intents_filter)[0]
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import range, str, zip
  from pathlib import Path
  from snips_nlu.common.log_utils import DifferedLoggingMessage, log_elapsed_time
  from snips_nlu.common.utils import check_persisted_path, fitted_required, json_string
  from snips_nlu.constants import LANGUAGE, RES_PROBA
  from snips_nlu.dataset import validate_and_format_dataset
  from snips_nlu.exceptions import LoadingError, _EmptyDatasetUtterancesError
  from snips_nlu.intent_classifier.featurizer import Featurizer
  from snips_nlu.intent_classifier.intent_classifier import IntentClassifier
  from snips_nlu.intent_classifier.log_reg_classifier_utils import build_training_data, get_regularization_factor, text_to_utterance
  from snips_nlu.pipeline.configs import LogRegIntentClassifierConfig
  from snips_nlu.result import intent_classification_result
label_desc: Use image node_id 4 for calling the LogRegIntentClassifier obj's underlying member method code with example usage: obj.get_intent(text, intents_filter) and returns: self