Building Perceptron Model

Perceptron_Model.py

class Perceptron:
  
  #constructor
  def __init__ (self):
    self.w = None
    self.b = None
    
  #model  
  def model(self, x):
    return 1 if (np.dot(self.w, x) >= self.b) else 0
  
  #predictor to predict on the data based on w
  def predict(self, X):
    Y = []
    for x in X:
      result = self.model(x)
      Y.append(result)
    return np.array(Y)
    
  def fit(self, X, Y, epochs = 1, lr = 1):
    self.w = np.ones(X.shape[1])
    self.b = 0
    accuracy = {}
    max_accuracy = 0
    wt_matrix = []
    #for all epochs
    for i in range(epochs):
      for x, y in zip(X, Y):
        y_pred = self.model(x)
        if y == 1 and y_pred == 0:
          self.w = self.w + lr * x
          self.b = self.b - lr * 1
        elif y == 0 and y_pred == 1:
          self.w = self.w - lr * x
          self.b = self.b + lr * 1
          
      wt_matrix.append(self.w)    
      accuracy[i] = accuracy_score(self.predict(X), Y)
      if (accuracy[i] > max_accuracy):
        max_accuracy = accuracy[i]
        chkptw = self.w
        chkptb = self.b
    #checkpoint (Save the weights and b value)
    self.w = chkptw
    self.b = chkptb
        
    print(max_accuracy)
    #plot the accuracy values over epochs
    plt.plot(accuracy.values())
    plt.ylim([0, 1])
    plt.show()
    
    #return the weight matrix, that contains weights over all epochs
    return np.array(wt_matrix)