class_docstr: Learner for a Generalized Linear Model (GML).
  Not intended for end-users, but for development only.
  It should be sklearn-learn compliant
  
  Parameters
  ----------
  C : `float`, default=1e3
      Level of penalization
  
  penalty : 'none', 'l1', 'l2', 'elasticnet', 'tv', 'binarsity', default='l2'
      The penalization to use. Default 'l2', namely ridge penalization.
  
  solver : 'gd', 'agd', 'bfgs', 'svrg', 'sdca'
      The name of the solver to use
  
  fit_intercept : `bool`, default=True
      If `True`, include an intercept in the model
  
  warm_start : `bool`, default=False
      If true, learning will start from the last reached solution
  
  step : `float`, default=None
      Initial step size used for learning. Used in 'gd', 'agd', 'sgd'
      and 'svrg' solvers
  
  tol : `float`, default=1e-5
      The tolerance of the solver (iterations stop when the stopping
      criterion is below it). By default the solver does ``max_iter``
      iterations
  
  max_iter : `int`, default=100
      Maximum number of iterations of the solver
  
  verbose : `bool`, default=True
      If `True`, we verbose things, otherwise the solver does not
      print anything (but records information in history anyway)
  
  print_every : `int`, default=10
      Print history information when ``n_iter`` (iteration number) is
      a multiple of ``print_every``
  
  record_every : `int`, default=10
      Record history information when ``n_iter`` (iteration number) is
      a multiple of ``record_every``
      
  Other Parameters
  ----------------
  sdca_ridge_strength : `float`, default=1e-3
      It controls the strength of the additional ridge penalization. Used in
      'sdca' solver
  
  elastic_net_ratio : `float`, default=0.95
      Ratio of elastic net mixing parameter with 0 <= ratio <= 1.
      For ratio = 0 this is ridge (L2 squared) regularization
      For ratio = 1 this is lasso (L1) regularization
      For 0 < ratio < 1, the regularization is a linear combination
      of L1 and L2.
      Used in 'elasticnet' penalty
  
  random_state : int seed, RandomState instance, or None (default)
      The seed that will be used by stochastic solvers. Used in 'sgd',
      'svrg', and 'sdca' solvers
      
  blocks_start : `numpy.array`, shape=(n_features,), default=None
      The indices of the first column of each binarized feature blocks. It
      corresponds to the ``feature_indices`` property of the
      ``FeaturesBinarizer`` preprocessing.
      Used in 'binarsity' penalty
      
  blocks_length : `numpy.array`, shape=(n_features,), default=None
      The length of each binarized feature blocks. It corresponds to the
      ``n_values`` property of the ``FeaturesBinarizer`` preprocessing.
      Used in 'binarsity' penalty
  
  Attributes
  ----------
  weights : np.array, shape=(n_features,)
      The learned weights of the model (not including the intercept)
  
  intercept : `float` or None
      The intercept, if ``fit_intercept=True``, otherwise `None`
docstr: Get parameters for this estimator.
  
  Returns
  -------
  params : `dict`
      Parameter names mapped to their values.
docstr_tok: ["Get","parameters","for","this","estimator",".","Returns","--","--","--","-","params",":","`","dict","`","Parameter","names","mapped","to","their","values","."]
code: def get_params(self):
      """
      Get parameters for this estimator.
  
      Returns
      -------
      params : `dict`
          Parameter names mapped to their values.
      """
      dd = {
          "fit_intercept": self.fit_intercept,
          "penalty": self.penalty,
          "C": self.C,
          "solver": self.solver,
          "step": self.step,
          "tol": self.tol,
          "max_iter": self.max_iter,
          "verbose": self.verbose,
          "warm_start": self.warm_start,
          "print_every": self.print_every,
          "record_every": self.record_every,
          "sdca_ridge_strength": self.sdca_ridge_strength,
          "elastic_net_ratio": self.elastic_net_ratio,
          "random_state": self.random_state,
          "blocks_start": self.blocks_start,
          "blocks_length": self.blocks_length,
      }
      return dd
  
file_path: tick/tick/base/learner/learner_glm.py
imports: from warnings import warn
  import numpy
  from tick.base import Base
  from tick.base_model import ModelLipschitz
  from .learner_optim import LearnerOptim
label_desc: Use image node_id 5 for calling the LearnerGLM obj's underlying member method code with example usage: obj.get_params() and returns: dd