更新 function.py

function.py

@@ -41,64 +41,34 @@ name = "李劲"
 """
 
   
-python
+def classify(input_vector, dataSet, labels, k=3):  
-复制代码
-	def classify(input_vector, dataSet, labels, k=3):  
-
     cov_matrix = np.cov(dataSet, rowvar=False)  
-
     inv_cov_matrix = np.linalg.inv(cov_matrix)  
-
     diffMat = dataSet - input_vector  
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     left_term = np.dot(diffMat, inv_cov_matrix)  
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     mahalanobis_distances = np.sqrt(np.sum(left_term * diffMat, axis=1))  
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     k_indices = np.argsort(mahalanobis_distances)[:k]  
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     k_nearest_labels = [labels[i] for i in k_indices]  
-
     most_common = max(set(k_nearest_labels), key=k_nearest_labels.count)  
-
     return most_common
 
-python
+def load_dataset(directory):  
-复制代码
-	def load_dataset(directory):  
-
     features = []  
-
     labels = []  
-
     for filename in os.listdir(directory):  
-
         if filename.endswith(".txt"):  
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             filepath = os.path.join(directory, filename)  
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             label = int(filename.split('_')[0])  
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             image_vector = img_to_vector(filepath)  
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             features.append(image_vector)  
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             labels.append(label)  
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     return np.array(features), np.array(labels)
   
-python
+def img_to_vector(filename):  
-复制代码
-	def img_to_vector(filename):  
-
     with open(filename) as file:  
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         lines = file.readlines()  
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         image_vector = np.array([int(char) for line in lines for char in line.strip()])  
-
     return image_vector
-
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
     train_data, train_labels = load_dataset("trainingDigits")