import numpy as np
import random
import json
import math
import os

ETA = 0.95

# Load training data from JSON files
base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

with open(os.path.join(base_path, 'training_input.json'), 'r') as f:
    training_input = np.array(json.load(f))

with open(os.path.join(base_path, 'desired_output.json'), 'r') as f:
    desired_output = np.array(json.load(f))

def sigmoid_func(z):
    return 1 / (1 + math.exp(-z)) 

def relu(z):
    return max(0, z)

def weight_loss_derivative(aL, aLm1, zL, y):
    sigmoid = 1 if zL > 0 else sigmoid = 0
    
    return aLm1 * sigmoid * 2 * (aL - y)

def bias_loss_derivative(a, y):
    return (a - y) * a * (1 - a)

sigmoid = relu

w = np.array(
    np.random.uniform(-1.0, 1.0, 8),
    np.random.uniform(-1.0, 1.0, 4)
)

b = np.array(
    np.random.uniform(-0.1, 0.1, 4),
    random.uniform(-0.1, 0.1)
)

a = np.array(
    [],
    []
)

c = np.array([])


for val_pair in training_input:
    for idx, i in enumerate(range(0, len(w[1]), 2)): # i steps by 2, idx steps by 1
        z = w[1][i] * val_pair[1] * w[1][i + 1] * val_pair[2] + b[1][idx]
        
        np.append(a[1], sigmoid(z))
        
    for j in range(len(w[2])):
        z = w[2][j] * a[1][j] + b[2]
        
        np.append(a[2], z)
    
    output = np.sum(a[2])
    print(output)
    
    # calc freakin cost function
    print(training_input.index[val_pair])
    np.append(c, (output - desired_output[training_input.index[val_pair]]) ** 2 / 2)

print(w)