IndexError：Pythonでのディクショナリの範囲外のリストインデックスのリスト

Question

これはSVMを計算するために書いたコードです。 72.

opt_choice = opt_dict[norms[0]] 
IndexError: list index out of range 

The related parameter is passed almost at the end- line 120. svm.fit(data=data_dict)

私はビデオチュートリアル、次の書かれているこのコード、まだビデオの中でそれがうまく走ったけれども、それはエラーを与えている -

from matplotlib import style 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
style.use('ggplot') 

class SVM: 
    def __init__ (self, visualization=True): 
     self.color = { 1:'r', -1:'b'} 
     if visualization: 
      self.fig = plt.figure() 
      self.ax = self.fig.add_subplot(1,1,1) 

    #train 

    def fit(self, data): 
     self.data = data 
     opt_dict = {} 
     transforms = [ [1,1], 
        [-1, 1], 
        [-1, -1], 
        [1,-1]] 

     all_data=[] 
     for yi in self.data: 
      for featureset in self.data[yi]: 
       for feature in featureset: 
        all_data.append(feature) 

     self.max_feature_value= max(all_data) 
     self.min_feature_value= min(all_data) 
     all_data = None 

     step_sizes = [self.max_feature_value*0.1, 
        self.max_feature_value*0.01, 
        self.max_feature_value*0.001,] 

     b_range_multiple = 5 
     b_multiple = 5 
     latest_optimum = self.max_feature_value*10 

     for step in step_sizes: 
      w= np.array([latest_optimum, latest_optimum]) 
      #we can do this because it is convex 
      optimized = False 
      while not optimized: 
       for b in np.arange(-1*(self.max_feature_value*b_range_multiple), 
            self.max_feature_value*b_range_multiple, 
            step*b_multiple): 
        for transformation in transforms: 
         w_t = w*transformation 
         #weak link in the SVM fundamentally 
         # SMO tries to fix this a bit 
         # yi(xi.w+b) 
         for i in self.data: 
          for xi in self.data[i]: 
           yi=i 
           if not yi*(np.dot(w_t, xi) + b) >= 1: 
            found_option = False 

         if found_option: 
          opt_dict[np.linalg.norm(w_t)] = [w_t, b] 

       if w[0]<0: 
        optimized = True; 
        print ('Optimized a step') 
       else: 
        w=w-step 

       norms = sorted([n for n in opt_dict]) 

       opt_choice = opt_dict[norms[0]] 
       self.w = opt_choice[0] 
       self.w = opt_choice[1] 
       latest_optimum = opt_choice[0][0]+ step*2 

      def predict(self, features): 
       classification = np.sign(np.dot(np.array(features), self.w)+ self.b) 
       if classification !=0 and self.visualization: 
        self.ax.scatter(features[0], features[1], s=200, marker='*', c=self.colors[classification]) 
       return classification 

     def visualize(self): 
      [[self.ax.scatter(x[0], x[1], s=100, color=self.colors[i]) for x in data_dict[i]] for i in data_dict] 

      #hyperplane 
      # v = x.w + b 
      #psv = 1 
      #nsv = -1 
      # dec = 0 
      def hyperplane(x, w, b, v): 
       return (-w[0]*x + b -v)/w[1] 
      datarange = (self.min_feature_value*0.9, self.min_feature_value*1.1) 
      hyp_x_min = datarange[0] 
      hyp_x_max = datarange[1] 


      # (w.x + b) = 1 
      # positive support vector hyperplanes 
      psv1 = hyperplane(hyp_x_min, self.w, self.b, 1) 
      psv2 = hyperplane(hyp_x_max, self.w, self.b, 1) 
      self.ax.plot([hyp_x_min, hyp_x_max, [psv1, psv2]]) 

      # (w.x + b) = 0 
      # negative support vector hyperplanes 
      nsv1 = hyperplane(hyp_x_min, self.w, self.b, -1) 
      nsv2 = hyperplane(hyp_x_max, self.w, self.b, -1) 
      self.ax.plot([hyp_x_min, hyp_x_max, [nsv1, nsv2]]) 

      # (w.x + b) = 0 
      db1 = hyperplane(hyp_x_min, self.w, self.b, 0) 
      db2 = hyperplane(hyp_x_max, self.w, self.b, 0) 
      self.ax.plot([hyp_x_min, hyp_x_max, [db1, db2]]) 

      plt.show() 

data_dict = { -1: np.array([[1,7], [2,8], [5,8]]), 1: np.array([[5,1], [6, -1], [7,3]])} 

svm = SVM() 
svm.fit(data=data_dict) 
svm.visualize() 

それは行番号のため、このエラーを示しています。

Answer 1

わかりました。プロブレムは、がTrueの場合にのみopt_dictに新しい要素を追加することです。しかし、あなたはTrueにfound_optionを設定したことがないので、私はopt_dictが

:)オールウェイズ空で、found_optionがforループからローカル変数だと思います。