




"""simple docstring"""





def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      str:


			"""simple docstring"""
			__UpperCAmelCase						:			list[list[str]]												=       [[] for _ in range(UpperCamelCase				)]
			__UpperCAmelCase						:			Union[str, Any]												=       key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"				)
			if key == 1 or len(UpperCamelCase				) <= key:
						return input_string

			for position, character in enumerate(UpperCamelCase				):
						__UpperCAmelCase						:			Dict												=       position % (lowest * 2)  # puts it in bounds
						__UpperCAmelCase						:			List[str]												=       min(UpperCamelCase					,							lowest * 2 - num				)  # creates zigzag pattern
						temp_grid[num].append(UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       ["".join(UpperCamelCase				) for row in temp_grid]
			__UpperCAmelCase						:			Any												=       "".join(UpperCamelCase				)

			return output_string

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      str:


			"""simple docstring"""
			__UpperCAmelCase						:			Tuple												=       []
			__UpperCAmelCase						:			Union[str, Any]												=       key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"				)
			if key == 1:
						return input_string

			__UpperCAmelCase						:			list[list[str]]												=       [[] for _ in range(UpperCamelCase				)]  # generates template
			for position in range(len(UpperCamelCase				)				):
						__UpperCAmelCase						:			Optional[int]												=       position % (lowest * 2)  # puts it in bounds
						__UpperCAmelCase						:			str												=       min(UpperCamelCase					,							lowest * 2 - num				)  # creates zigzag pattern
						temp_grid[num].append("*"				)

			__UpperCAmelCase						:			Union[str, Any]												=       0
			for row in temp_grid:  # fills in the characters
						__UpperCAmelCase						:			Tuple												=       input_string[counter : counter + len(UpperCamelCase				)]
						grid.append(list(UpperCamelCase				)				)
						counter += len(UpperCamelCase				)

			__UpperCAmelCase						:			List[str]												=       ""  # reads as zigzag
			for position in range(len(UpperCamelCase				)				):
						__UpperCAmelCase						:			Dict												=       position % (lowest * 2)  # puts it in bounds
						__UpperCAmelCase						:			Union[str, Any]												=       min(UpperCamelCase					,							lowest * 2 - num				)  # creates zigzag pattern
						output_string += grid[num][0]
						grid[num].pop(0				)
			return output_string

def        _UpperCamelCase     (      UpperCamelCase				)      ->      dict[int, str]:


			"""simple docstring"""
			__UpperCAmelCase						:			Tuple												=       {}
			for key_guess in range(1					,							len(UpperCamelCase				)				):  # tries every key
						__UpperCAmelCase						:			str												=       decrypt(UpperCamelCase					,							UpperCamelCase				)
			return results


if __name__ == "__main__":
					import doctest

					doctest.testmod()







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


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


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


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,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