在Python中實現一個簡單的高斯樸素貝葉斯算法

Question

所以我是一個真正的業餘愛好者，試圖實現一些你可能稱之爲python中樸素貝葉斯算法的「簡化」版本，並且似乎有很多麻煩[原因可能是我不太確定我完全理解算法的工作方式。]。儘管我非常感謝任何幫助/建議。這是我的代碼：

class GaussianNB(object): 

    def __init__(self): 
     ''' 
     Constructor 
     ''' 
     # This variable will hold the gaussian distribution over your data 
     # In fact, you need a distribution per class for each feature variable. 
     # This can be done as a list of lists. 

     self.classmodels_count = {} 
     self.classmodels = {} 
     self.classmodelsMeanAndVariance = {} 
     self.featureTokenCount= 0; 
     self.featureTypeCount = 0; 

    def train(self, trainingdata): 
     for i in trainingdata: 
      current_class = i[0] 
      features = i[1] 

      if self.classmodels.has_key(current_class): 
       current_class_model = self.classmodels[current_class] 
       self.classmodels_count[current_class] = self.classmodels_count[current_class] + 1 
      else: 
       current_class_model = {} 
       self.classmodels_count[current_class] = 1 

      for f in features: 
       feature = f[0] 
       value = f[1] 
       if current_class_model.has_key(feature): 
        list_of_values = current_class_model[feature] 
        list_of_values.append(value) 
        current_class_model[feature] = list_of_values 
       else: 
        list_of_values = [] 
        list_of_values.append(value) 
        current_class_model[feature] = list_of_values 

      self.classmodels[current_class] = current_class_model 
     for a_class in self.classmodels.keys(): 
      a_class_model = self.classmodels[a_class] 
      a_class_model_mean_and_variance = {} 
      for feature in a_class_model.keys(): 
       a_class_model_mean_and_variance[feature] = findMeanSD(np.array(a_class_model[feature])) 
      self.classmodelsMeanAndVariance[a_class] = a_class_model_mean_and_variance 

    def classify(self, testing_vecs): 
     outputs = [] 
     for vec in testing_vecs: 
      features = vec[1] 
      class_model_output_prob = {} 
      for a_class in self.classmodelsMeanAndVariance.keys(): 
       a_class_output_prob = 0.0 
       a_class_model_mean_and_variance = self.classmodelsMeanAndVariance[a_class] 
       for feature_value in features: 
        feature = feature_value[0] 
        value = feature_value[1] 
        #simply ignore a feature if its not seen in training 
        if(a_class_model_mean_and_variance.has_key(feature)): 
         feature_mean = a_class_model_mean_and_variance[feature][0] 
         feature_std = a_class_model_mean_and_variance[feature][1] 
         a_class_output_prob = a_class_output_prob + math.log10(norm(value,feature_mean,feature_std)) 

       #ignoring P(class) prior.. assuming equal priors 
       class_model_output_prob[a_class_output_prob] = a_class 
      probs = class_model_output_prob.keys() 
      print probs 
      probs.sort() 
      max_prob = probs[len(probs)-1] 
      max_class =class_model_output_prob[max_prob] 
      outputs.append(max_class) 
     return outputs 

當一些數據運行，我得到的錯誤是

回溯（最近通話最後一個）： 文件「C：\用戶\東芝\工作區\ Assignment6 \ src \ gnb_test.py「，第34行，在 gaussian = Model.train（testData） 文件」C：\ Users \ Toshiba \ workspace \ Assignment6 \ src \ gnb.py「，第91行，在列車 中爲f in功能： TypeError：'numpy.float64'對象不可迭代

而我不重新y [at all]明白這是什麼意思

Answer 1

你的回溯表明問題是你試圖迭代通過features，但features是一個浮點數，而不是一個列表或元組 - 基本上，它不能被分解成個別元素。我認爲這是一個浮動，因爲線

for i in trainingdata: 
    current_class = i[0] 
    features = i[1] 

建議features狀態越來越改寫爲一系列連續的數字，當你似乎想要的是數字保存到一個可迭代的類型。嘗試

features = [] 
for i in trainingdata: 
    current_class = i[0] 
    features.append(i[1])