from matplotlib import pyplot as plt
import math
import numpy as np
from icecream import ic


def activation_func(x: float) -> float:
    y = math.log1p(math.exp(-abs(x))) + max(x, 0)
    ic(x)
    ic(y)
    return y


w1 = -34.4
w2 = -2.52
w3 = -1.30
w4 = 2.28

b1 = 2.14
b2 = 1.29
b3 = -0.58


def my_neurale_net(x: float) -> float:

    h1 = x * w1 + b1
    h2 = x * w2 + b2

    h12 = activation_func(h1)
    h22 = activation_func(h2)
    ic(h12)
    ic(h22)

    y = h12 * w3 + h22 * w4 + b3

    return y


def f1(x):
    return np.sin(x)


def f2(x):
    return 4*x*x + 2*x + 4


def demo():

    # vfunc = np.vectorize(my_neurale_net)
    # vfunc = np.vectorize(f2)
    vfunc = np.vectorize(activation_func)

    x = np.linspace(-5, 5, 1000)
    y = vfunc(x)

    plt.plot(x, y, color='red')
    plt.show()


def debug():
    ic(my_neurale_net(0))
    ic(my_neurale_net(0.5))
    ic(my_neurale_net(1))


if __name__ == '__main__':
    debug()
    demo()