lr-gd-loss.py

def fit(X, y, n_iter=100000, lr=0.01):

  W = np.zeros(X.shape[1])

  for i in range(n_iter):
      z = np.dot(X, W)
      h = sigmoid(z)
      gradient = np.dot(X.T, (h - y)) / y.size
      W -= lr * gradient
      
      if(i % 10000 == 0):
          e = loss(h, y)
          print(f'loss: {e} \t')
      
  return W