


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


__UpperCAmelCase    :List[Any]				       =     train_test_split(X, y, test_size=0.2, random_state=0)


__UpperCAmelCase    :Any				       =     PolynomialFeatures(degree=4)
__UpperCAmelCase    :Union[str, Any]				       =     poly_reg.fit_transform(X)
__UpperCAmelCase    :Optional[int]				       =     LinearRegression()
pol_reg.fit(X_poly, y)




def 			_a			(		):





     '''simple docstring'''






     plt.scatter(_lowerCamelCase    ,      _lowerCamelCase    ,      color='''red'''						)
     plt.plot(_lowerCamelCase    ,      pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase						)						)    ,      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








'''simple docstring'''



def 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()