<jupyter_start><jupyter_text>Inheritance<jupyter_code>P = np.array([[0.9, 0.1, 0],[0, 0.8, 0.2],[0.01, 0, 0.99]])
x=[]
x.append(0)
for i in range(100):
    x.append(np.random.choice([0, 1 ,2], 1, p=P[x[i],:])[0])
print(x)
plt.hist(x)<jupyter_output>[0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
{'fraction_non_alphanumeric': 0.3115942028985507, 'fraction_numerical': 0.23007246376811594, 'mean_word_length': 3.0072463768115942, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '62839', 'license': 'MIT', 'n_tokens_mistral': 455, 'n_tokens_neox': 337, 'n_words': 121, 'path': 'ModelFramework.ipynb', 'repo_name': 'krennt/hmf', 'size': '552'}