docstr: Anderson-Darling test for data coming from a particular distribution, for exponential.
docstr_tok: ["Anderson-Darling","test","for","data","coming","from","a","particular","distribution",",","for","exponential","."]
code: def test_linear_exp_parent_dist(self, graph):
      """Anderson-Darling test for data coming from a particular distribution, for exponential."""
      data = generate_continuous_data(
          graph,
          distribution="exponential",
          noise_scale=1,
          n_samples=100000,
          seed=10,
      )
  
      stat, crit, sig = stats.anderson(data[:, 0], "expon")
      assert stat < crit[list(sig).index(5)]
  
imports: import re
  import string
  from itertools import product
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.gaussian_process.kernels import RBF
  from causalnex.structure import StructureModel
  from causalnex.structure.data_generators import gen_stationary_dyn_net_and_df, generate_binary_data, generate_binary_dataframe, generate_categorical_dataframe, generate_continuous_data, generate_continuous_dataframe, generate_count_dataframe, generate_dataframe_dynamic, generate_structure, generate_structure_dynamic
  from tests.structure.data_generators.test_core import calculate_proba
label_desc: Use image node_id 8 for calling the TestGenerateContinuousData obj's underlying member method code with example usage: obj.test_linear_exp_parent_dist(graph) without return types