text: import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  from numpy.random import seed
  
  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)
  
  def plot_decision_regions(X, y, classifier, resolution=0.02):
  
      # setup marker generator and color map
      markers = ('s', 'x', 'o', '^', 'v')
      colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
      cmap = ListedColormap(colors[:len(np.unique(y))])
  
      # plot the decision surface
      x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                             np.arange(x2_min, x2_max, resolution))
      Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
      Z = Z.reshape(xx1.shape)
      plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
      plt.xlim(xx1.min(), xx1.max())
      plt.ylim(xx2.min(), xx2.max())
  
      # plot class samples
      for idx, cl in enumerate(np.unique(y)):
          plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                      alpha=0.8, c=cmap(idx),
                      marker=markers[idx], label=cl)
  
  def main():
      from sklearn.datasets import load_iris
      iris = load_iris()
      X, y = iris.data[:100, [0, 2]], iris.target[:100]
  
      # standardize features
      X_std = np.copy(X)
      X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
      X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
  
      
      ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
      ada.fit(X_std, y)
  
      plot_decision_regions(X_std, y, classifier=ada)
      plt.title('Adaline - Stochastic Gradient Descent')
      plt.xlabel('sepal length [standardized]')
      plt.ylabel('petal length [standardized]')
      plt.legend(loc='upper left')
  
      plt.tight_layout()
      #plt.savefig('./adaline_4.png', dpi=300)
      plt.show()
  
      plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
      plt.xlabel('Epochs')
      plt.ylabel('Average Cost')
  
      plt.tight_layout()
      # plt.savefig('./adaline_5.png', dpi=300)
      plt.show()
  
  if __name__ == '__main__':
      main()
  