




"""simple docstring"""




import numpy as np

class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_=5							,		UpperCamelCase_=5   )		->   Union[str, Any]:
					__lowercase	:       Tuple				=   depth
					__lowercase	:       Optional[int]				=   0
					__lowercase	:       List[str]				=   None
					__lowercase	:       Any				=   None
					__lowercase	:       Optional[Any]				=   min_leaf_size
					__lowercase	:       Union[str, Any]				=   None
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Optional[Any]:
					if labels.ndim != 1:
								print('''Error: Input labels must be one dimensional'''   )

					return np.mean((labels - prediction) ** 2   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					if x.ndim != 1:
								print('''Error: Input data set must be one dimensional'''   )
								return
					if len(UpperCamelCase_   ) != len(UpperCamelCase_   ):
								print('''Error: X and y have different lengths'''   )
								return
					if y.ndim != 1:
								print('''Error: Data set labels must be one dimensional'''   )
								return

					if len(UpperCamelCase_   ) < 2 * self.min_leaf_size:
								__lowercase	:       str				=   np.mean(UpperCamelCase_   )
								return

					if self.depth == 1:
								__lowercase	:       Tuple				=   np.mean(UpperCamelCase_   )
								return

					__lowercase	:       Optional[Any]				=   0
					__lowercase	:       str				=   self.mean_squared_error(UpperCamelCase_							,		np.mean(UpperCamelCase_   )   ) * 2
					for i in range(len(UpperCamelCase_   )   ):
								if len(x[:i]   ) < self.min_leaf_size:
											continue
								elif len(x[i:]   ) < self.min_leaf_size:
											continue
								else:
											__lowercase	:       Any				=   self.mean_squared_error(x[:i]							,		np.mean(y[:i]   )   )
											__lowercase	:       Optional[int]				=   self.mean_squared_error(x[i:]							,		np.mean(y[i:]   )   )
											__lowercase	:       List[Any]				=   error_left + error_right
											if error < min_error:
														__lowercase	:       str				=   i
														__lowercase	:       Union[str, Any]				=   error

					if best_split != 0:
								__lowercase	:       Optional[int]				=   x[:best_split]
								__lowercase	:       Optional[int]				=   y[:best_split]
								__lowercase	:       str				=   x[best_split:]
								__lowercase	:       Any				=   y[best_split:]

								__lowercase	:       int				=   x[best_split]
								__lowercase	:       str				=   DecisionTree(
								    depth=self.depth - 1							,		min_leaf_size=self.min_leaf_size   )
								__lowercase	:       int				=   DecisionTree(
								    depth=self.depth - 1							,		min_leaf_size=self.min_leaf_size   )
								self.left.train(UpperCamelCase_							,		UpperCamelCase_   )
								self.right.train(UpperCamelCase_							,		UpperCamelCase_   )
					else:
								__lowercase	:       List[Any]				=   np.mean(UpperCamelCase_   )

					return
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   int:
					if self.prediction is not None:
								return self.prediction
					elif self.left or self.right is not None:
								if x >= self.decision_boundary:
											return self.right.predict(UpperCamelCase_   )
								else:
											return self.left.predict(UpperCamelCase_   )
					else:
								print('''Error: Decision tree not yet trained'''   )
								return None


class        UpperCAmelCase_  :
		@staticmethod
		def 					_lowerCamelCase    (		UpperCamelCase_							,		UpperCamelCase_   )		->   Tuple:
					__lowercase	:       Optional[Any]				=   float(0   )
					for label in labels:
								squared_error_sum += (label - prediction) ** 2

					return float(squared_error_sum / labels.size   )







def       __UpperCAmelCase      (				):
			__lowercase	:       Dict				=   np.arange(-1.0					,							1.0					,							0.005					)
			__lowercase	:       Tuple				=   np.sin(__UpperCamelCase					)

			__lowercase	:       Tuple				=   DecisionTree(depth=10					,							min_leaf_size=10					)
			tree.train(__UpperCamelCase					,							__UpperCamelCase					)

			__lowercase	:       Dict				=   (np.random.rand(10					) * 2) - 1
			__lowercase	:       Any				=   np.array([tree.predict(__UpperCamelCase					) for x in test_cases]					)
			__lowercase	:       List[str]				=   np.mean((predictions - test_cases) ** 2					)

			print('''Test values: ''' + str(__UpperCamelCase					)					)
			print('''Predictions: ''' + str(__UpperCamelCase					)					)
			print('''Average error: ''' + str(__UpperCamelCase					)					)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod(name='mean_squarred_error', verbose=True)









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



