import numpy as np

visible = np.ones((10,1))
hidden = np.ones((5,1))

visible_bias = np.ones((len(visible), 1)) * 0.1
hidden_bias = np.ones((len(hidden),1)) * 0.1

weights = np.random.rand(len(visible), len(hidden))
phases = 1

learnrate = 0.2

def sigmoid(x):
    return 1 / (1 + np.exp(-x))  # Sigmoidfunktion


for i in range(1):
    activation = sigmoid(np.matmul(visible.T, weights) + hidden_bias)

    # 2. Computer outer product vh
    positive_gradient = np.matmul(visible, hidden.T)

    t = sigmoid(np.matmul(weights, hidden) + visible_bias)
    reconstructed = sigmoid(np.matmul(weights, hidden) + visible_bias)

    # 4. Computer outer product v'h'
    negative_gradient = np.matmul(reconstructed.T, activation)
    # 5. Update weight matrix using gradients

    delta_weights = learnrate * (positive_gradient - negative_gradient)

    # 6. Update bias for visible and hidden layer
    
    delta_visible_bias = learnrate * (visible - reconstructed)
    delta_hidden_bias = learnrate * (hidden - activation)