text: import numpy as np
  
  from . import PredictiveAlgorithm
  
  PACKAGE_PATH = r""
  
  class EegAlgorithm(PredictiveAlgorithm):
      def __init__(self, dataset, plot_idx=0, initial_learning_rate=0.02, delays=10):
          N, f, max_t = 3.33, 60, 0.5
          s = N * f
          self.ts = s * max_t + 1
          A1, A2, theta1, k = 1, 0.75, np.pi / 2, 0.00001
          # print(dataset)
          self.signal_ = k * (np.array(dataset).reshape(1,-1))[:, :int(self.ts) + 1]
          # print(self.signal_)
          i = np.arange(self.ts).reshape(1, -1)
          noise1, noise2 = 1.2 * np.sin(2 * np.pi * (i - 1) / N), 0.6 * np.sin(4 * np.pi * (i - 1) / N)
          noise = noise1 + noise2
          filtered_noise1 = A1 * 1.20 * np.sin(2 * np.pi * (i - 1) / N + theta1)
          filtered_noise2 = A2 * 0.6 * np.sin(4 * np.pi * (i - 1) / N + theta1)
          filtered_noise = filtered_noise1 + filtered_noise2
          noisy_signal = self.signal_ + filtered_noise
          self.time = np.arange(1, self.ts + 1) / self.ts * max_t
          self.P_ = np.zeros((21, 101))
          for i in range(21):
              self.P_[i, i + 1:] = noise[:, :101 - i - 1]
          self.P_ = np.array(self.P_)
          self.T = noisy_signal[:]
  
          self.R, self.P = None, None
          self.a, self.e = None, None        
  
          self.dataset_length = len(self.signal_[0])
          total_epoches = 1
          test_ratio = 0
          search_iteration_constant = len(self.time)
          most_correct_rate = False
          super().__init__(self.signal_[0], total_epoches, most_correct_rate, initial_learning_rate, 
              search_iteration_constant, test_ratio, need_best_neurons=False)
          self.plot_idx = plot_idx
          self.delays = delays
          self.lr = initial_learning_rate
  
      def _initialize_neurons(self):
          self.R = self.delays + 1
          self.P = self.P_[:self.R]
          self.w = np.zeros((1, self.R))
          self.a, self.e = np.zeros((1, 101)), np.zeros((1, 101))
      
      def _iterate(self):
          idx = self.current_iterations
          p = self.P[:, idx]
          self.a[0, idx] = np.dot(self.w, p)
          self.e[0, idx] = self.T[0, idx] - self.a[0, idx]
          self.w += self.lr * self.e[0, idx] * p.T   
          self.current_time = self.time[idx]     
          self.blue_line = self.signal_[0, idx]
          if(self.plot_idx == 0):
              self.red_line = self.e[0, idx]            
          else:
              self.red_line = (self.signal_ - self.e)[0, idx]
  
      def _correct_rate(self, dataset):
          pass