class_docstr: Class for learning a locally linear sparse model from perturbed data
class_docstr_tok: ["Class","for","learning","a","locally","linear","sparse","model","from","perturbed","data"]
docstr: Init function
  
  Args:
      kernel_fn: function that transforms an array of distances into an
                  array of proximity values (floats).
      verbose: if true, print local prediction values from linear model.
      random_state: an integer or numpy.RandomState that will be used to
          generate random numbers. If None, the random state will be
          initialized using the internal numpy seed.
docstr_tok: ["Init","function","Args",":","kernel_fn",":","function","that","transforms","an","array","of","distances","into","an","array","of","proximity","values","(","floats",")",".","verbose",":","if","true",",","print","local","prediction","values","from","linear","model",".","random_state",":","an","integer","or","numpy.RandomState","that","will","be","used","to","generate","random","numbers",".","If","None",",","the","random","state","will","be","initialized","using","the","internal","numpy","seed","."]
code: def __init__(self, kernel_fn, verbose=False, random_state=None):
      """Init function
  
      Args:
          kernel_fn: function that transforms an array of distances into an
                      array of proximity values (floats).
          verbose: if true, print local prediction values from linear model.
          random_state: an integer or numpy.RandomState that will be used to
              generate random numbers. If None, the random state will be
              initialized using the internal numpy seed.
      """
      self.kernel_fn = kernel_fn
      self.verbose = verbose
      self.random_state = check_random_state(random_state)
  
file_path: tensorwatch/tensorwatch/saliency/lime/lime_base.py
imports: from __future__ import print_function
  import numpy
  from sklearn.linear_model import Ridge, lars_path
  from sklearn.utils import check_random_state
label_desc: Use image node_id 1 to create a new LimeBase object from inherited base classes: object with example: obj = LimeBase(kernel_fn, verbose, random_state)