

"""simple docstring"""




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A_				:      List[str]           =			0

A_				:      int           =			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

A_				:      Any           =			[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A_				:      Union[str, Any]           =			tuple[int, int]





class  lowerCamelCase    :

			def __init__(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Node | None     ,					) ->     None:
						SCREAMING_SNAKE_CASE__										=   pos_x
						SCREAMING_SNAKE_CASE__										=   pos_y
						SCREAMING_SNAKE_CASE__										=   (pos_y, pos_x)
						SCREAMING_SNAKE_CASE__										=   goal_x
						SCREAMING_SNAKE_CASE__										=   goal_y
						SCREAMING_SNAKE_CASE__										=   g_cost
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   self.calculate_heuristic()
						SCREAMING_SNAKE_CASE__										=   self.g_cost + self.h_cost

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     float:
						SCREAMING_SNAKE_CASE__										=   self.pos_x - self.goal_x
						SCREAMING_SNAKE_CASE__										=   self.pos_y - self.goal_y
						if HEURISTIC == 1:
									return abs(__UpperCAmelCase							) + abs(__UpperCAmelCase							)
						else:
									return sqrt(dy**2 + dx**2							)





			def __lt__(       self					:    List[str]     ,					__UpperCAmelCase					:    Node							) ->     bool:
						return self.f_cost < other.f_cost





class  lowerCamelCase    :

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    TPosition     ,					__UpperCAmelCase					:    TPosition							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   Node(start[1]     ,					start[0]     ,					goal[1]     ,					goal[0]     ,					0     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   Node(goal[1]     ,					goal[0]     ,					goal[1]     ,					goal[0]     ,					9_9_9_9_9     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   [self.start]
						SCREAMING_SNAKE_CASE__										=   []

						SCREAMING_SNAKE_CASE__										=   False

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     list[TPosition]:
						while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									SCREAMING_SNAKE_CASE__										=   self.open_nodes.pop(0							)

									if current_node.pos == self.target.pos:
												return self.retrace_path(__UpperCAmelCase							)

									self.closed_nodes.append(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   self.get_successors(__UpperCAmelCase							)

									for child_node in successors:
												if child_node in self.closed_nodes:
															continue

												if child_node not in self.open_nodes:
															self.open_nodes.append(__UpperCAmelCase							)
												else:
															# retrieve the best current path
															SCREAMING_SNAKE_CASE__										=   self.open_nodes.pop(self.open_nodes.index(__UpperCAmelCase							)							)

															if child_node.g_cost < better_node.g_cost:
																		self.open_nodes.append(__UpperCAmelCase							)
															else:
																		self.open_nodes.append(__UpperCAmelCase							)

						return [self.start.pos]

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Node							) ->     list[Node]:
						SCREAMING_SNAKE_CASE__										=   []
						for action in delta:
									SCREAMING_SNAKE_CASE__										=   parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE__										=   parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]							) - 1 and 0 <= pos_y <= len(__UpperCAmelCase							) - 1):
												continue

									if grid[pos_y][pos_x] != 0:
												continue

									successors.append(
									    Node(
									        __UpperCAmelCase     ,					__UpperCAmelCase     ,					self.target.pos_y     ,					self.target.pos_x     ,					parent.g_cost + 1     ,					__UpperCAmelCase     ,					)							)
						return successors





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Node | None							) ->     list[TPosition]:
						SCREAMING_SNAKE_CASE__										=   node
						SCREAMING_SNAKE_CASE__										=   []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x)							)
									SCREAMING_SNAKE_CASE__										=   current_node.parent
						path.reverse()
						return path





class  lowerCamelCase    :

			def __init__(       self					:    Any     ,					__UpperCAmelCase					:    TPosition     ,					__UpperCAmelCase					:    TPosition							) ->     None:
						SCREAMING_SNAKE_CASE__										=   AStar(__UpperCAmelCase     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   AStar(__UpperCAmelCase     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   False

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     list[TPosition]:
						while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
									self.fwd_astar.open_nodes.sort()
									self.bwd_astar.open_nodes.sort()
									SCREAMING_SNAKE_CASE__										=   self.fwd_astar.open_nodes.pop(0							)
									SCREAMING_SNAKE_CASE__										=   self.bwd_astar.open_nodes.pop(0							)

									if current_bwd_node.pos == current_fwd_node.pos:
												return self.retrace_bidirectional_path(
												    __UpperCAmelCase     ,					__UpperCAmelCase							)

									self.fwd_astar.closed_nodes.append(__UpperCAmelCase							)
									self.bwd_astar.closed_nodes.append(__UpperCAmelCase							)

									SCREAMING_SNAKE_CASE__										=   current_bwd_node
									SCREAMING_SNAKE_CASE__										=   current_fwd_node

									SCREAMING_SNAKE_CASE__										=   {
									    self.fwd_astar: self.fwd_astar.get_successors(__UpperCAmelCase							),
									    self.bwd_astar: self.bwd_astar.get_successors(__UpperCAmelCase							),
									}

									for astar in [self.fwd_astar, self.bwd_astar]:
												for child_node in successors[astar]:
															if child_node in astar.closed_nodes:
																		continue

															if child_node not in astar.open_nodes:
																		astar.open_nodes.append(__UpperCAmelCase							)
															else:
																		# retrieve the best current path
																		SCREAMING_SNAKE_CASE__										=   astar.open_nodes.pop(
																		    astar.open_nodes.index(__UpperCAmelCase							)							)

																		if child_node.g_cost < better_node.g_cost:
																					astar.open_nodes.append(__UpperCAmelCase							)
																		else:
																					astar.open_nodes.append(__UpperCAmelCase							)

						return [self.fwd_astar.start.pos]





			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Node     ,					__UpperCAmelCase					:    Node							) ->     list[TPosition]:
						SCREAMING_SNAKE_CASE__										=   self.fwd_astar.retrace_path(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.bwd_astar.retrace_path(__UpperCAmelCase							)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE__										=   fwd_path + bwd_path
						return path


if __name__ == "__main__":
						# all coordinates are given in format [y,x]
						A_				:      Union[str, Any]           =			(0, 0)
						A_				:      Any           =			(len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						A_				:      Dict           =			time.time()
						A_				:      Optional[int]           =			AStar(init, goal)
						A_				:      List[str]           =			a_star.search()
						A_				:      List[Any]           =			time.time() - start_time
						print(F'AStar execution time = {end_time:f} seconds')

						A_				:      Dict           =			time.time()
						A_				:      Optional[int]           =			BidirectionalAStar(init, goal)
						A_				:      Any           =			time.time() - bd_start_time
						print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')





"""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


