code: def fit(self, X, y, n_iter=None):
          
          self.n_iter = self.n_iter if n_iter is None else n_iter
          X = getattr(X, 'values', X).reshape(len(X), 1)
          X_1 = self.homogenize(X)
          for i in range(self.n_iter):
              for i in range(0, len(X), 10):  # minibatch learning for numerical stability
                  batch = slice(i, min(i + 10, len(X)))
                  Xbatch, ybatch = X[batch, :], y[batch]
                  X_1_batch = X_1[batch, :]
                  self.W += (self.alpha / len(X) ** 1.5) * (
                      self.delta(Xbatch, ybatch).reshape((len(Xbatch), 1)).T.dot(X_1_batch))
          return self
docs: w = w + α * δ * X