
from __future__ import annotations

lowercase        =  [
    [-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]]]:
							lowerCamelCase_							  =					[
							    [0 for col in range(len(grid[0]       )       )] for row in range(len(UpperCAmelCase__       )       )
							]  # the reference grid
							lowerCamelCase_							  =					1
							lowerCamelCase_							  =					[
							    [0 for col in range(len(grid[0]       )       )] for row in range(len(UpperCAmelCase__       )       )
							]  # the action grid

							lowerCamelCase_							  =					init[0]
							lowerCamelCase_							  =					init[1]
							lowerCamelCase_							  =					0
							lowerCamelCase_							  =					g + heuristic[x][y]  # cost from starting cell to destination cell
							lowerCamelCase_							  =					[[f, g, x, y]]

							lowerCamelCase_							  =					False  # flag that is set when search is complete
							lowerCamelCase_							  =					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()
																					lowerCamelCase_							  =					cell.pop()
																					lowerCamelCase_							  =					next_cell[2]
																					lowerCamelCase_							  =					next_cell[3]
																					lowerCamelCase_							  =					next_cell[1]

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

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


if __name__ == "__main__":
		lowercase        =  [
		    [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],
		]

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

		# the cost map which pushes the path closer to the goal
		lowercase        =  [[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])):
						lowercase        =  abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								lowercase        =  9_9

		lowercase   ,						lowercase        =  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])







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def      __lowerCAmelCase						(							UpperCAmelCase__				:      dict       )  ->    tuple:
							return (data["data"], data["target"])



def      __lowerCAmelCase						(							UpperCAmelCase__				:      np.ndarray			,				UpperCAmelCase__				:      np.ndarray       )  ->    XGBClassifier:
							lowerCamelCase_							  =					XGBClassifier()
							classifier.fit(UpperCAmelCase__			,				UpperCAmelCase__       )
							return classifier



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					load_iris()
							lowerCamelCase_  ,   lowerCamelCase_							  =					data_handling(UpperCAmelCase__       )
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					train_test_split(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				test_size=0.2_5       )

							lowerCamelCase_							  =					iris["""target_names"""]

							# Create an XGBoost Classifier from the training data
							lowerCamelCase_							  =					xgboost(UpperCAmelCase__			,				UpperCAmelCase__       )

							# Display the confusion matrix of the classifier with both training and test sets
							ConfusionMatrixDisplay.from_estimator(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				display_labels=UpperCAmelCase__			,				cmap="""Blues"""			,				normalize="""true"""			,				)
							plt.title("""Normalized Confusion Matrix - IRIS Dataset"""       )
							plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()





