lr-intercept.py

def add_intercept(X):
  intercept = np.ones((X.shape[0], 1))
  return np.concatenate((intercept, X), axis=1)

def predict(X, W):
  X = add_intercept(X)
  return sigmoid(np.dot(X, W))
      
def fit(X, y, n_iter=100000, lr=0.01):
  
  X = add_intercept(X)
  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
  return W