'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX


def    lowerCamelCase       (			UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list		)			->	float:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase__		)]		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()				,						UpperCAmelCase__		)		)				,						x.transpose()		)				,						UpperCAmelCase__		)
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]		)


def    lowerCamelCase       (			UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list		)			->	float:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = (1, 2, 1)
							SCREAMING_SNAKE_CASE__     :str						    = (1, 1, 0, 7)
							SCREAMING_SNAKE_CASE__     :int						    = SARIMAX(
							    UpperCAmelCase__				,						exog=UpperCAmelCase__				,						order=UpperCAmelCase__				,						seasonal_order=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = model.fit(disp=UpperCAmelCase__				,						maxiter=6_0_0				,						method='nm'		)
							SCREAMING_SNAKE_CASE__     :str						    = model_fit.predict(1				,						len(UpperCAmelCase__		)				,						exog=[test_match]		)
							return result[0]


def    lowerCamelCase       (			UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  list		)			->	float:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = SVR(kernel='rbf'				,						C=1				,						gamma=0.1				,						epsilon=0.1		)
							regressor.fit(UpperCAmelCase__				,						UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = regressor.predict(UpperCAmelCase__		)
							return y_pred[0]


def    lowerCamelCase       (			UpperCAmelCase__				:  list		)			->	float:


							'''simple docstring'''






							train_user.sort()
							SCREAMING_SNAKE_CASE__     :Any						    = np.percentile(UpperCAmelCase__				,						2_5		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = np.percentile(UpperCAmelCase__				,						7_5		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = qa - qa
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = qa - (iqr * 0.1)
							return low_lim


def    lowerCamelCase       (			UpperCAmelCase__				:  list				,						UpperCAmelCase__				:  float		)			->	bool:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = 0
							SCREAMING_SNAKE_CASE__     :List[str]						    = 0
							for i in list_vote:
														if i > actual_result:
																					SCREAMING_SNAKE_CASE__     :Any						    = not_safe + 1
														else:
																					if abs(abs(UpperCAmelCase__		) - abs(UpperCAmelCase__		)		) <= 0.1:
																												safe += 1
																					else:
																												not_safe += 1
							return safe > not_safe


if __name__ == "__main__":
		# data_input_df = pd.read_csv("ex_data.csv", header=None)
		UpperCamelCase_      	=	[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
		UpperCamelCase_      	=	pd.DataFrame(
		    data_input, columns=['''total_user''', '''total_even''', '''days''']
		)
		UpperCamelCase_      	=	Normalizer().fit_transform(data_input_df.values)
		# split data
		UpperCamelCase_      	=	normalize_df[:, 2].tolist()
		UpperCamelCase_      	=	normalize_df[:, 0].tolist()
		UpperCamelCase_      	=	normalize_df[:, 1].tolist()

		# for svr (input variable = total date and total match)
		UpperCamelCase_      	=	normalize_df[:, [1, 2]].tolist()
		UpperCamelCase_      	=	x[: len(x) - 1]
		UpperCamelCase_      	=	x[len(x) - 1 :]

		# for linear regression & sarimax
		UpperCamelCase_      	=	total_date[: len(total_date) - 1]
		UpperCamelCase_      	=	total_user[: len(total_user) - 1]
		UpperCamelCase_      	=	total_match[: len(total_match) - 1]

		UpperCamelCase_      	=	total_date[len(total_date) - 1 :]
		UpperCamelCase_      	=	total_user[len(total_user) - 1 :]
		UpperCamelCase_      	=	total_match[len(total_match) - 1 :]

		# voting system with forecasting
		UpperCamelCase_      	=	[
		    linear_regression_prediction(
		        trn_date, trn_user, trn_match, tst_date, tst_match
		    ),
		    sarimax_predictor(trn_user, trn_match, tst_match),
		    support_vector_regressor(x_train, x_test, trn_user),
		]

		# check the safety of today's data
		UpperCamelCase_      	=	'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
		print('''Today\'s data is {not_str}safe.''')







'''simple docstring'''







from __future__ import annotations

UpperCamelCase_      	=	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def    lowerCamelCase       (			UpperCAmelCase__				:  list[list[int]]				,						UpperCAmelCase__				:  list[int]				,						UpperCAmelCase__				:  list[int]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  list[list[int]]				,						)			->	tuple[list[list[int]], list[list[int]]]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = [
							    [0 for col in range(len(grid[0]		)		)] for row in range(len(UpperCAmelCase__		)		)
							]  # the reference grid
							SCREAMING_SNAKE_CASE__     :Any						    = 1
							SCREAMING_SNAKE_CASE__     :Dict						    = [
							    [0 for col in range(len(grid[0]		)		)] for row in range(len(UpperCAmelCase__		)		)
							]  # the action grid

							SCREAMING_SNAKE_CASE__     :int						    = init[0]
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = init[1]
							SCREAMING_SNAKE_CASE__     :List[str]						    = 0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = g + heuristic[x][y]  # cost from starting cell to destination cell
							SCREAMING_SNAKE_CASE__     :List[Any]						    = [[f, g, x, y]]

							SCREAMING_SNAKE_CASE__     :Any						    = False  # flag that is set when search is complete
							SCREAMING_SNAKE_CASE__     :str						    = False  # flag set if we can't find expand

							while not found and not resign:
														if len(UpperCAmelCase__		) == 0:
																					raise ValueError('Algorithm is unable to find solution'		)
														else:  # to choose the least costliest action so as to move closer to the goal
																					cell.sort()
																					cell.reverse()
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = cell.pop()
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = next_cell[2]
																					SCREAMING_SNAKE_CASE__     :Any						    = next_cell[3]
																					SCREAMING_SNAKE_CASE__     :Dict						    = next_cell[1]

																					if x == goal[0] and y == goal[1]:
																												SCREAMING_SNAKE_CASE__     :Tuple						    = True
																					else:
																												for i in range(len(UpperCAmelCase__		)		):  # to try out different valid actions
																																			SCREAMING_SNAKE_CASE__     :Optional[int]						    = x + DIRECTIONS[i][0]
																																			SCREAMING_SNAKE_CASE__     :int						    = y + DIRECTIONS[i][1]
																																			if xa >= 0 and xa < len(UpperCAmelCase__		) and ya >= 0 and ya < len(grid[0]		):
																																										if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																																																	SCREAMING_SNAKE_CASE__     :str						    = g + cost
																																																	SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ga + heuristic[xa][ya]
																																																	cell.append([fa, ga, xa, ya]		)
																																																	SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1
																																																	SCREAMING_SNAKE_CASE__     :Any						    = i
							SCREAMING_SNAKE_CASE__     :int						    = []
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = goal[0]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = goal[1]
							invpath.append([x, y]		)  # we get the reverse path from here
							while x != init[0] or y != init[1]:
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = x - DIRECTIONS[action[x][y]][0]
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = y - DIRECTIONS[action[x][y]][1]
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = xa
														SCREAMING_SNAKE_CASE__     :int						    = ya
														invpath.append([x, y]		)

							SCREAMING_SNAKE_CASE__     :int						    = []
							for i in range(len(UpperCAmelCase__		)		):
														path.append(invpath[len(UpperCAmelCase__		) - 1 - i]		)
							return path, action


if __name__ == "__main__":
		UpperCamelCase_      	=	[
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 1, 0],
		    [0, 0, 0, 0, 1, 0],
		]

		UpperCamelCase_      	=	[0, 0]
		# all coordinates are given in format [y,x]
		UpperCamelCase_      	=	[len(grid) - 1, len(grid[0]) - 1]
		UpperCamelCase_      	=	1

		# the cost map which pushes the path closer to the goal
		UpperCamelCase_      	=	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
		for i in range(len(grid)):
				for j in range(len(grid[0])):
						UpperCamelCase_      	=	abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								UpperCamelCase_      	=	99

		UpperCamelCase_	,			UpperCamelCase_      	=	search(grid, init, goal, cost, heuristic)

		print('''ACTION MAP''')
		for i in range(len(action)):
				print(action[i])

		for i in range(len(path)):
				print(path[i])






