content: import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  
  from Perceptron import Perceptron
  
  def plotRawData():
      plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
      plt.scatter(X[50:100, 0], X[50:100, 1], color='blue', marker='x', label='versicolor')
      plt.xlabel('petal length')
      plt.ylabel('sepal length')
      plt.legend(loc='upper left')
      plt.show()
      plt.cla()
  
  def plotErrors():
      plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
      plt.xlabel('Epochs')
      plt.ylabel('Number of misclassifications')
      plt.show()
      plt.cla()
  
  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)
      plt.xlabel('sepal length [cm]')
      plt.ylabel('petal length [cm]')
      plt.legend(loc='upper left')
  
  df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
  
  # print(df.tail())
  
  y = df.iloc[0:100, 4].values
  y = np.where(y == 'Iris-setosa', -1, 1)
  X = df.iloc[0:100, [0, 2]].values
  
  ppn = Perceptron(eta=0.1, n_iter=10)
  ppn.fit(X, y)
  
  plot_decision_regions(X, y, ppn)
  plt.cla()
  