docstr: ...Test RobustLinearRegression setting of parameter of C
          
docstr_tok: ["...","Test","RobustLinearRegression","setting","of","parameter","of","C"]
code: def test_RobustLinearRegression_penalty_C_sample_intercepts(self):
      """...Test RobustLinearRegression setting of parameter of C"""
      learner = RobustLinearRegression(C_sample_intercepts=self.float_1)
      self.assertEqual(learner.C_sample_intercepts, self.float_1)
      self.assertEqual(
          learner._prox_intercepts_obj.strength, 1.0 / self.float_1
      )
      learner.C_sample_intercepts = self.float_2
      self.assertEqual(learner.C_sample_intercepts, self.float_2)
      self.assertEqual(
          learner._prox_intercepts_obj.strength, 1.0 / self.float_2
      )
  
      msg = "^``C_sample_intercepts`` must be positive, got -1.0$"
      with self.assertRaisesRegex(ValueError, msg):
          RobustLinearRegression(C_sample_intercepts=-1.0)
  
      msg = "^``C_sample_intercepts`` cannot be `None`$"
      with self.assertRaisesRegex(ValueError, msg):
          RobustLinearRegression(C_sample_intercepts=None)
  
      msg = "^``C_sample_intercepts`` cannot be 0.$"
      with self.assertRaisesRegex(ValueError, msg):
          RobustLinearRegression(C_sample_intercepts=0.0)
  
      msg = "^``C_sample_intercepts`` must be a finite number, got inf$"
      with self.assertRaisesRegex(ValueError, msg):
          RobustLinearRegression(C_sample_intercepts=np.inf)
  
imports: import itertools
  import unittest
  import numpy
  from tick.base.inference import InferenceTest
  from tick.robust import RobustLinearRegression
  from tick.simulation import weights_sparse_gauss, features_normal_cov_toeplitz
  from tick.metrics import support_fdp, support_recall
label_desc: Use image node_id 9 for calling the Test obj's underlying member method code with example usage: obj.test_RobustLinearRegression_penalty_C_sample_intercepts() without return types