<jupyter_start><jupyter_text>Linear Model to get the lower bound<jupyter_code>from algorithms.google import googleORTools
from tools.graphGeneration import csvToGraph
from tools.graphChecker import calcPathEfficiency
from stats.stats import GetStats, showStats
import numpy as np

g500 = csvToGraph("dataset/ran-500x500.csv")
x, dataset = GetStats(2, 500, 1, ["google"], "AUTOMATIC", False, g500)
showStats(x, dataset, 500, "worst")

print(dataset)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.100418410041841, 'fraction_numerical': 0.04184100418410042, 'mean_word_length': 7.87037037037037, 'pattern_counts': {'":': 0, '<': 5, '<?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': '642072', 'license': 'MIT', 'n_tokens_mistral': 173, 'n_tokens_neox': 154, 'n_words': 43, 'path': 'linearModelLowerBound.ipynb', 'repo_name': 'BaptisteMiq/VRP-Project', 'size': '478'}