
"""simple docstring"""




from math import sqrt




def  _lowerCAmelCase		(		_UpperCamelCase = 1_000_000						):






  """simple docstring"""



  _lowercase:			int       =     0
  _lowercase:			int       =     0
  _lowercase:			int

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2 ,				2 * max_cuboid_size + 1						):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2						).is_integer():
        num_cuboids += (
            min(_UpperCamelCase ,				sum_shortest_sides // 2						)
            - max(1 ,				sum_shortest_sides - max_cuboid_size						)
            + 1
        )

  return max_cuboid_size


if __name__ == "__main__":
  print(f"""{solution() = }""")


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
