new_contents: from numpy.random import seed
  import numpy as np
  
  class AdalineSGD(object):
      """ADAptive LInear NEuron classifier.
  
      Parameters
      ------------
      eta : float
          Learning rate (between 0.0 and 1.0)
      n_iter : int
          Passes over the training dataset.
  
      Attributes
      -----------
      w_ : 1d-array
          Weights after fitting.
      errors_ : list
          Number of misclassifications in every epoch.
      shuffle : bool (default: True)
          Shuffles training data every epoch if True to prevent cycles.
      random_state : int (default: None)
          Set random state for shuffling and initializing the weights.
          
      """
      def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):
          self.eta = eta
          self.n_iter = n_iter
          self.w_initialized = False
          self.shuffle = shuffle
          if random_state:
              seed(random_state)
          
      def fit(self, X, y):
          """ Fit training data.
  
          Parameters
          ----------
          X : {array-like}, shape = [n_samples, n_features]
              Training vectors, where n_samples is the number of samples and
              n_features is the number of features.
          y : array-like, shape = [n_samples]
              Target values.
  
          Returns
          -------
          self : object
  
          """
          self._initialize_weights(X.shape[1])
          self.cost_ = []
          for i in range(self.n_iter):
              if self.shuffle:
                  X, y = self._shuffle(X, y)
              cost = []
              for xi, target in zip(X, y):
                  cost.append(self._update_weights(xi, target))
              avg_cost = sum(cost)/len(y)
              self.cost_.append(avg_cost)
          return self
  
      def partial_fit(self, X, y):
          """Fit training data without reinitializing the weights"""
          if not self.w_initialized:
              self._initialize_weights(X.shape[1])
          if y.ravel().shape[0] > 1:
              for xi, target in zip(X, y):
                  self._update_weights(xi, target)
          else:
              self._update_weights(X, y)
          return self
  
      def _shuffle(self, X, y):
          """Shuffle training data"""
          r = np.random.permutation(len(y))
          return X[r], y[r]
      
      def _initialize_weights(self, m):
          """Initialize weights to zeros"""
          self.w_ = np.zeros(1 + m)
          self.w_initialized = True
          
      def _update_weights(self, xi, target):
          """Apply Adaline learning rule to update the weights"""
          output = self.net_input(xi)
          error = (target - output)
          self.w_[1:] += self.eta * xi.dot(error)
          self.w_[0] += self.eta * error
          cost = 0.5 * error**2
          return cost
      
      def net_input(self, X):
          """Calculate net input"""
          return np.dot(X, self.w_[1:]) + self.w_[0]
  
      def activation(self, X):
          """Compute linear activation"""
          return self.net_input(X)
  
      def predict(self, X):
          """Return class label after unit step"""
          return np.where(self.activation(X) >= 0.0, 1, -1)
subject: add Adaptive linear neurons2