<jupyter_start><jupyter_text>MSC / Linear<jupyter_code>v1 = show_tree(tree, attr='fitness')
p1 = SidePanel(title='msc/linear')
with p1:
    display(v1.view, v1.filter)<jupyter_output><empty_output><jupyter_text>MSC /Quadratic<jupyter_code>v2 = show_tree(tree, attr='quadratic_fitness')
p2 = SidePanel(title='msc/quadratic')
with p2:
    display(v2.view, v2.filter)<jupyter_output><empty_output><jupyter_text>Details<jupyter_code>data = DataWidget(data=gauss)
details = DetailsView(data=data)
widgets.link((v2.view, 'details'), (details, 'show'))
p2 = SidePanel(title='MSC/details')
with p2:
    display(details)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def f(x):
    return 4.46933564*x -1.93693098*x*x
x = np.linspace(0, 1.67, 100)
y= f(x)
plt.plot(x*0.58826084, y, linewidth=2)
gauss.pts.scaler.inverse_transform(pd.DataFrame(x), copy=True)
gauss.pts.scaler.__dict__
x*0.58826084
1.67*0.58826084<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1536050156739812, 'fraction_numerical': 0.07210031347962383, 'mean_word_length': 7.258620689655173, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '592790', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 444, 'n_tokens_neox': 404, 'n_words': 62, 'path': 'notebooks/msc.ipynb', 'repo_name': 'yarden-livnat/ipyregulus', 'size': '957'}