lr-regressor.py

class LogisticRegressor:
  
  def _add_intercept(self, X):
    intercept = np.ones((X.shape[0], 1))
    return np.concatenate((intercept, X), axis=1)

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

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

    for i in range(n_iter):
        z = np.dot(X, self.W)
        h = sigmoid(z)
        gradient = np.dot(X.T, (h - y)) / y.size
        self.W -= lr * gradient
    return self