<jupyter_start><jupyter_text>Side<jupyter_code>legend = ['20', '30', '40']

t_1_20 = [161, 258, 343, 1081]
t_1_30 = [115, 372, 414, 1032]
t_1_40 = [175, 236, 367, 862]
plot_ts([t_1_20, t_1_30, t_1_40], legend, title = "cluster = 1, obstacle = 0")

t_1_20 = [300, 329, 1705, 1931]
t_1_30 = [234, 418, 548, 2112]
t_1_40 = [268, 410, 420, 1022]
plot_ts([t_1_20, t_1_30, t_1_40], legend, title = "cluster = 1, obstacle = 10")

t_1_20 = [266, 370, 863, 2771]
t_1_30 = [317, 623, 994, 3000]
t_1_40 = [307, 580, 1018, 2069]
plot_ts([t_1_20, t_1_30, t_1_40], legend, title = "cluster = 1, obstacle = 20")

t_3_20 = [559, 1231, 2730, 3000]
t_3_30 = [869, 1165, 2999, 3000]
t_3_40 = [833, 834, 970, 3000]
plot_ts([t_3_20, t_3_30, t_3_40], legend, title = "cluster = 3, obstacle = 0")

t_3_20 = [2997, 2998, 2999, 3000]
t_3_30 = [1449, 1962, 2981, 3000]
t_3_40 = [1542, 1633, 2394, 2453]
plot_ts([t_3_20, t_3_30, t_3_40], legend, title = "cluster = 3, obstacle = 10")

t_3_20 = [2997, 2998, 2999, 3000]
t_3_30 = [1461, 2667, 2990, 3000]
t_3_40 = [1534, 2815, 2999, 3000]
plot_ts([t_3_20, t_3_30, t_3_40], legend, title = "cluster = 3, obstacle = 20")<jupyter_output>0.84
0.83
0.86<jupyter_text>Random<jupyter_code>legend = ['basic', 'random']

t_20 = [2997, 2998, 2999, 3000]
random_20 = [454, 1112, 2999, 3000]
plot_ts([t_20, random_20], legend, title = "robots = 20, cluster = 3, obstacle = 10")

t_30 = [1449, 1962, 2981, 3000]
random_30 = [575, 1667, 2999, 3000]
plot_ts([t_30, random_30], legend, title = "robots = 30, cluster = 3, obstacle = 10")

t_40 = [1542, 1633, 2394, 2453]
random_40 = [960, 1278, 2445, 3000]
plot_ts([t_40, random_40], legend, title = "robots = 40, cluster = 3, obstacle = 10")<jupyter_output>0.0
0.36
{'fraction_non_alphanumeric': 0.1765039727582293, 'fraction_numerical': 0.3024971623155505, 'mean_word_length': 4.424615384615384, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '402428', 'license': 'MIT', 'n_tokens_mistral': 1219, 'n_tokens_neox': 860, 'n_words': 213, 'path': 'performance.ipynb', 'repo_name': 'gabri-16/RobotExploration', 'size': '1762'}