<jupyter_start><jupyter_text>Credible estimator applied to Gaussians<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../src")
import numpy as np
import matplotlib.pyplot as plt
import credible_estimator
sampler = lambda N : np.random.randn(N,1)
credible_estimator.credible_ball_estimator(sampler,0.954,0.0,verbose=1,maxiter=1000,maxdrift=1000,tol=1e-3)
sampler = lambda N : np.random.randn(N,10)
r = credible_estimator.credible_ball_estimator(sampler,0.954,0.0,verbose=1,maxiter=1000,maxdrift=1000,tol=1e-3)
np.mean((sampler(100000)**2).sum(axis=1) <= r**2)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1347068145800317, 'fraction_numerical': 0.07290015847860538, 'mean_word_length': 8.723076923076922, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 5, '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': '527421', 'license': 'MIT', 'n_tokens_mistral': 278, 'n_tokens_neox': 252, 'n_words': 38, 'path': 'notebooks/example2.ipynb', 'repo_name': 'DFNaiff/Credible-Ball-Estimator', 'size': '631'}