





from collections import defaultdict

class 						SCREAMING_SNAKE_CASE__							:


      def __init__(self			: Tuple							,     a__			: Union[str, Any]							,     a__			: Dict ):





         """simple docstring"""



         __snake_case							=  total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         __snake_case							=  [
             [-1 for i in range(total + 1 )] for j in range(2 ** len(__UpperCAmelCase ) )
         ]

         __snake_case							=  defaultdict(__UpperCAmelCase )  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         __snake_case							=  (1 << len(__UpperCAmelCase )) - 1


      def   a					(self			: List[Any]							,     a__			: List[Any]							,     a__			: List[str] ):





         """simple docstring"""



         if mask == self.final_mask:
            return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
            return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
            return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         __snake_case							=  self.count_ways_until(__UpperCAmelCase							,     task_no + 1 )

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
            for p in self.task[task_no]:
               # if p is already given a task
               if mask & (1 << p):
                  continue

               # assign this task to p and change the mask value. And recursively
               # assign tasks with the new mask value.
               total_ways_util += self.count_ways_until(mask | (1 << p)							,     task_no + 1 )

        # save the value.
         __snake_case							=  total_ways_util

         return self.dp[mask][task_no]


      def   a					(self			: Any							,     a__			: Tuple ):





         """simple docstring"""



         for i in range(len(__UpperCAmelCase ) ):
            for j in task_performed[i]:
               self.task[j].append(__UpperCAmelCase )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0							,     1 )


if __name__ == "__main__":
    snake_case_           =     5  # total no of tasks (the value of N)

    # the list of tasks that can be done by M persons.
    snake_case_           =     [[1, 3, 4], [1, 2, 5], [3, 4]]
    print(
        AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
            task_performed
        )
    )




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


A_       ,						A_       ,						A_       ,						A_				:      str           =			train_test_split(X, y, test_size=0.2, random_state=0)


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

def 						A							(  ):




			'''simple docstring'''


			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


