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* and returns
  the list of intents ordered by decreasing probability
  
  The length of the returned list is exactly the number of intents in the
  dataset + 1 for the None intent
  
  Raises:
      :class:`snips_nlu.exceptions.NotTrained`: when the intent
          classifier is not fitted
docstr_tok: ["Performs","intent","classification","on","the","provided","*","text","*","and","returns","the","list","of","intents","ordered","by","decreasing","probability","The","length","of","the","returned","list","is","exactly","the","number","of","intents","in","the","dataset","+","1","for","the","None","intent","Raises",":",":","class",":","`","snips_nlu.exceptions.NotTrained","`",":","when","the","intent","classifier","is","not","fitted"]
code: def get_intents(self, text):
      """Performs intent classification on the provided *text* and returns
      the list of intents ordered by decreasing probability
  
      The length of the returned list is exactly the number of intents in the
      dataset + 1 for the None intent
  
      Raises:
          :class:`snips_nlu.exceptions.NotTrained`: when the intent
              classifier is not fitted
      """
      return self._get_intents(text, intents_filter=None)
  
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 5 for calling the LogRegIntentClassifier obj's underlying member method code with example usage: obj.get_intents(text) and returns: self