class_docstr: Intent classifier which uses a Logistic Regression underneath
class_docstr_tok: ["Intent","classifier","which","uses","a","Logistic","Regression","underneath"]
docstr: Whether or not the intent classifier has already been fitted
docstr_tok: ["Whether","or","not","the","intent","classifier","has","already","been","fitted"]
code: def fitted(self):
      """Whether or not the intent classifier has already been fitted"""
      return self.intent_list is not 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 2 for calling the LogRegIntentClassifier obj's underlying member method code with example usage: obj.fitted() and returns: unknown