class_docstr: Estimator for learning linear regressors by AdaGrad.
  
  Solves the following objective:
  
  .. code-block::
  
      minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
                  + alpha * l1_ratio * ||w||_1
                  + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2
class_docstr_tok: ["Estimator","for","learning","linear","regressors","by","AdaGrad",".","Solves","the","following","objective",":","..","code-block",":",":","minimize_w","1","\/","n_samples","*","\\sum_i","loss","(","w^T","x_i",",","y_i",")","+","alpha","*","l1_ratio","*","||w||_1","+","alpha","*","(","1","-","l1_ratio",")","*","0.5","*","||w||^2_2"]
code: def __init__(
      self,
      eta=1.0,
      alpha=1.0,
      l1_ratio=0,
      loss="squared",
      gamma=1.0,
      epsilon=0,
      n_iter=10,
      shuffle=True,
      callback=None,
      n_calls=None,
      random_state=None,
  ):
      self.eta = eta
      self.alpha = alpha
      self.l1_ratio = l1_ratio
      self.loss = loss
      self.gamma = gamma
      self.epsilon = epsilon
      self.n_iter = n_iter
      self.shuffle = shuffle
      self.callback = callback
      self.n_calls = n_calls
      self.random_state = random_state
  
imports: import numpy
  from sklearn.utils import check_random_state
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .adagrad_fast import _adagrad_fit
  from .sgd_fast import ModifiedHuber
  from .sgd_fast import Hinge
  from .sgd_fast import SmoothHinge
  from .sgd_fast import SquaredHinge
  from .sgd_fast import Log
  from .sgd_fast import SquaredLoss
  from .sgd_fast import EpsilonInsensitive
  from .sgd_fast import Huber
label_desc: Use image node_id 1 to create a new AdaGradRegressor object from inherited base classes: BaseRegressor, _BaseAdagrad with example: obj = AdaGradRegressor(eta, alpha, l1_ratio, loss, gamma, epsilon, n_iter, shuffle, callback, n_calls, random_state)