



from __future__ import annotations


def A						(       __UpperCamelCase					)						->      int:
			if not nums:
						return 0
			A__       =    nums[0]
			A__       =    0
			for num in nums[1:]:
						A__     , A__       =    (
						    max_excluding + num,
						    max(__UpperCamelCase ,	__UpperCamelCase					),
						)
			return max(__UpperCamelCase ,	__UpperCamelCase					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




