code: def test_univariate_stochastic_gradient_descent():
      w_exp = np.array([[0.7]])
      b_exp = np.array([0.0])
      sgd_lr = LinearRegression(
          method="sgd",
          minibatches=len(y),
          eta=0.0001,
          epochs=150,
          random_seed=0,
      )
      sgd_lr.fit(X_rm_std, y_std)
      assert_almost_equal(sgd_lr.w_, w_exp, decimal=1)
      assert_almost_equal(sgd_lr.b_, b_exp, decimal=1)
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  from sklearn.base import clone
  from mlxtend.data import boston_housing_data
  from mlxtend.regressor import LinearRegression
label_desc: Use image node_id 9 for calling a global function with example usage: test_univariate_stochastic_gradient_descent() without return types