#### Simple Neural Network

p
```"""
Forward propagation explanation:
https://towardsdatascience.com/forward-propagation-in-neural-networks-simplified-math-and-code-version-bbcfef6f9250
"""

import math
import random

# Sigmoid
def sigmoid_function(value: float, deriv: bool = False) -> float:
"""Return the sigmoid function of a float.

>>> sigmoid_function(3.5)
0.9706877692486436
>>> sigmoid_function(3.5, True)
-8.75
"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value))

# Initial Value
INITIAL_VALUE = 0.02

def forward_propagation(expected: int, number_propagations: int) -> float:
"""Return the value found after the forward propagation training.

>>> res = forward_propagation(32, 450_000)  # Was 10_000_000
>>> res > 31 and res < 33
True

>>> res = forward_propagation(32, 1000)
>>> res > 31 and res < 33
False
"""

# Random weight
weight = float(2 * (random.randint(1, 100)) - 1)

for _ in range(number_propagations):
# Forward propagation
layer_1 = sigmoid_function(INITIAL_VALUE * weight)
# How much did we miss?
layer_1_error = (expected / 100) - layer_1
# Error delta
layer_1_delta = layer_1_error * sigmoid_function(layer_1, True)
# Update weight
weight += INITIAL_VALUE * layer_1_delta

return layer_1 * 100

if __name__ == "__main__":
import doctest

doctest.testmod()

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