



'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[str]				):


			'''simple docstring'''







			return 1 / (1 + np.exp(-1 * x				))


class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	def __init__(		self					,   A					,   A=None					,   A=None					,   A=False			) ->							Dict:
				A:      str														=   units
				A:      str														=   None
				A:      Dict														=   None
				A:      int														=   activation
				if learning_rate is None:
							A:      List[Any]														=   0.3
				A:      Union[str, Any]														=   learning_rate
				A:      int														=   is_input_layer


	def      a__ (		self					,   A			) ->							Union[str, Any]:
				A:      Dict														=   np.asmatrix(np.random.normal(0					,   0.5					,   (self.units, back_units)			)			)
				A:      str														=   np.asmatrix(np.random.normal(0					,   0.5					,   self.units			)			).T
				if self.activation is None:
							A:      int														=   sigmoid


	def      a__ (		self			) ->							int:
				# activation function may be sigmoid or linear
				if self.activation == sigmoid:
							A:      Optional[int]														=   np.dot(self.output					,   (1 - self.output).T			)
							A:      Tuple														=   np.diag(np.diag(A			)			)
				else:
							A:      Optional[int]														=   1
				return gradient_activation


	def      a__ (		self					,   A			) ->							Optional[int]:
				A:      Optional[Any]														=   xdata
				if self.is_input_layer:
							# input layer
							A:      List[str]														=   xdata
							A:      int														=   xdata
							return xdata
				else:
							A:      List[Any]														=   np.dot(self.weight					,   self.xdata			) - self.bias
							A:      int														=   self.activation(self.wx_plus_b			)
							return self.output





	def      a__ (		self					,   A			) ->							List[str]:
				A:      Any														=   self.cal_gradient()  # i * i 维
				A:      int														=   np.asmatrix(np.dot(gradient.T					,   A			)			)

				A:      Tuple														=   np.asmatrix(self.xdata			)
				A:      List[str]														=   -1
				A:      Optional[int]														=   self.weight

				A:      Optional[Any]														=   np.dot(gradient.T					,   self._gradient_weight.T			)
				A:      str														=   gradient * self._gradient_bias
				A:      str														=   np.dot(A					,   self._gradient_x			).T
				# upgrade: the Negative gradient direction
				A:      List[str]														=   self.weight - self.learn_rate * self.gradient_weight
				A:      Any														=   self.bias - self.learn_rate * self.gradient_bias.T
				# updates the weights and bias according to learning rate (0.3 if undefined)
				return self.gradient







class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	def __init__(		self			) ->							Dict:
				A:      Union[str, Any]														=   []
				A:      Any														=   []
				A:      List[str]														=   plt.figure()
				A:      List[str]														=   self.fig_loss.add_subplot(1					,   1					,   1			)


	def      a__ (		self					,   A			) ->							List[str]:
				self.layers.append(A			)


	def      a__ (		self			) ->							Optional[Any]:
				for i, layer in enumerate(self.layers[:]			):
							if i < 1:
										A:      int														=   True
							else:
										layer.initializer(self.layers[i - 1].units			)


	def      a__ (		self			) ->							Union[str, Any]:
				for i, layer in enumerate(self.layers[:]			):
							print(f'------- layer {i} -------'			)
							print("""weight.shape """					,   np.shape(layer.weight			)			)
							print("""bias.shape """					,   np.shape(layer.bias			)			)


	def      a__ (		self					,   A					,   A					,   A					,   A			) ->							List[Any]:
				A:      Union[str, Any]														=   train_round
				A:      Optional[Any]														=   accuracy

				self.ax_loss.hlines(self.accuracy					,   0					,   self.train_round * 1.1			)

				A:      str														=   np.shape(A			)
				for _ in range(A			):
							A:      List[Any]														=   0
							for row in range(x_shape[0]			):
										A:      Any														=   np.asmatrix(xdata[row, :]			).T
										A:      str														=   np.asmatrix(ydata[row, :]			).T

										# forward propagation
										for layer in self.layers:
													A:      Any														=   layer.forward_propagation(_xdata			)

										A							,		A:      Union[str, Any]														=   self.cal_loss(_ydata					,   _xdata			)
										A:      Optional[Any]														=   all_loss + loss

										# back propagation: the input_layer does not upgrade
										for layer in self.layers[:0:-1]:
													A:      Tuple														=   layer.back_propagation(A			)

							A:      Tuple														=   all_loss / x_shape[0]
							self.train_mse.append(A			)

							self.plot_loss()

							if mse < self.accuracy:
										print("""----达到精度----"""			)
										return mse
				return None


	def      a__ (		self					,   A					,   A			) ->							Optional[int]:
				A:      Tuple														=   np.sum(np.power((ydata - ydata_)					,   2			)			)
				A:      Union[str, Any]														=   2 * (ydata_ - ydata)
				# vector (shape is the same as _ydata.shape)
				return self.loss, self.loss_gradient





	def      a__ (		self			) ->							Optional[Any]:
				if self.ax_loss.lines:
							self.ax_loss.lines.remove(self.ax_loss.lines[0]			)
				self.ax_loss.plot(self.train_mse					,   """r-"""			)
				plt.ion()
				plt.xlabel("""step"""			)
				plt.ylabel("""loss"""			)
				plt.show()
				plt.pause(0.1			)







def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      Union[str, Any]														=   np.random.randn(10							,							10				)
			A:      Optional[Any]														=   np.asarray(
			    [
			        [0.8, 0.4],
			        [0.4, 0.3],
			        [0.34, 0.45],
			        [0.67, 0.32],
			        [0.88, 0.67],
			        [0.78, 0.77],
			        [0.55, 0.66],
			        [0.55, 0.43],
			        [0.54, 0.1],
			        [0.1, 0.5],
			    ]				)
			A:      Any														=   BPNN()
			for i in (10, 20, 30, 2):
						model.add_layer(DenseLayer(lowerCamelCase__				)				)
			model.build()
			model.summary()
			model.train(xdata=lowerCamelCase__							,							ydata=lowerCamelCase__							,							train_round=100							,							accuracy=0.01				)


if __name__ == "__main__":
					example()










'''simple docstring'''
from __future__ import annotations



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			A:      int														=   [0] * len(lowerCamelCase__				)
			for i in range(1							,							len(lowerCamelCase__				)				):
						A:      Dict														=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]							,							lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(lowerCamelCase__				)
			]



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)


if __name__ == "__main__":
					# process id's
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[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
					__SCREAMING_SNAKE_CASE :      Dict             =[19, 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
					__SCREAMING_SNAKE_CASE :      Tuple             =calculate_waiting_times(duration_times)
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_turnaround_times(duration_times, waiting_times)

					# get the average times
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_average_waiting_time(waiting_times)
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =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}""")





