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  1. import csv
  2. import matplotlib.pyplot as plt
  3.  
  4. class ANN:
  5.     def __init__(self, path): 
  6.         with open(path) as Data_file:
  7.             Datas = csv.reader(Data_file)
  8.             AData = [data for data in Datas]
  9.             self._RawData = self._InputData(path)
  10.             self._Input_array, self._Target_list = self._DivInOut()
  11.             self._DivTar()
  12.             self.fold = 0
  13.  
  14.     def _InputData(self, path):
  15.         with open(path) as Data_file:
  16.             Datas = csv.reader(Data_file)
  17.             AData = [data for data in Datas]
  18.             newMatrix = Matrix(len(AData), len(AData[0]))
  19.             count = 0
  20.             for i in range(newMatrix.numRows()):
  21.                 for j in range(newMatrix.numCols()):
  22.                     newMatrix[i, j] = AData[count][j]
  23.                 count += 1
  24.         return newMatrix
  25.  
  26.     def _DivInOut(self):
  27.         AInput = self._RawData.tolist()  # Convert Matrix to a list
  28.         AOutput = [row[-1] for row in AInput]  # Extract the last column as output
  29.  
  30.         self.NumData = len(AInput)
  31.         self.NumInput = len(AInput[0]) - 1  # Number of columns except the output column
  32.         return AInput, AOutput
  33.  
  34.  
  35.     def _DivTar(self):
  36.         Class = set(self._Target_list)
  37.         self.NumOutput = len(Class)
  38.         self._Target_array = [
  39.             [1 if self._Target_list[i] == val else 0 for val in Class] for i in range(self.NumData)
  40.         ]
  41.     def CleanData(self, data_range):
  42.         low, high = data_range
  43.         newList= Alist() #ใช้ลิสที่เขียนขึ้นมาเองในการเก็บค่า
  44.         case1,case2 = (0,0)
  45.         for i in range(self._Input_array.num_rows()):
  46.             for j in range(self._Input_array.num_cols()):
  47.                 try:
  48.                     self._Input_array[i,j]=float(self._Input_array[i,j])
  49.                 except ValueError:
  50.                     case1+=1
  51.                     newList.Append(i)
  52.                     continue
  53.                 if not ((low <= self._Input_array[i,j] <= high)):
  54.                     case2+=1
  55.                     newList.Append(i)        
  56.         newList.ClearRepeatVal() #เอาค่า row ที่ซ้ำกันออกไป
  57.         print(f"Inputs contain {case1} non-float values")
  58.         print(f"inputs contain {case2} out-of-range values")
  59.         print(f"Removing {len(newList)} rows......")
  60.         for i in range(len(newList)-1,-1,-1):
  61.             self._Input_array = self._Input_array.delete(newList[i],0)
  62.             self._Target_list.Pop(newList[i])
  63.         self.NumData,self.NumInput = self._Input_array.shape
  64.         print("Removing process done")
  65.         self._DivTar()
  66.  
  67.     def _SeparateFold(self,fold,reset = False): #แบ่งชุดข้อมูลการ Test
  68.         if reset:
  69.             self.fold = 0
  70.         assert self.fold != fold , "Out of range"
  71.         print(f"Divided into {fold} folds. This is round {self.fold + 1}")
  72.         Num=self.NumData//fold
  73.         self.Input_Train = Matrix.zeros((self.NumData-Num , self.NumInput) , 'f')
  74.         self.Input_Test = Matrix.zeros((Num , self.NumInput) , 'f')
  75.         self.Target_Train = Matrix.zeros((self.NumData-Num , self.NumOutput) , 'i')
  76.         self.Target_Test = Matrix.zeros((Num , self.NumOutput) , 'i')
  77.  
  78.         testing_indices = data_indices[:fold_size]
  79.         training_indices = data_indices[fold_size:]
  80.  
  81.         self.training_data = (
  82.             Matrix([self.inputs.data[i] for i in training_indices]),
  83.             Matrix([self.targets.data[i] for i in training_indices])
  84.         )
  85.  
  86.         self.testing_data = (
  87.             Matrix([self.inputs.data[i] for i in testing_indices]),
  88.             Matrix([self.targets.data[i] for i in testing_indices])
  89.         )
  90.  
  91.         self.fold += 1
  92.  
  93.     def _Training(self):  # การ Train ข้อมูล
  94.         for i in range(self.Input_Train.numRows()):
  95.             for j in range(self.Input_Train.numCols()):
  96.                 self.Input_Train[i, j] = float(self.Input_Train[i, j])
  97.         for i in range(self.Target_Train.numRows()):
  98.             for j in range(self.Target_Train.numCols()):
  99.                 self.Target_Train[i, j] = float(self.Target_Train[i, j])
  100.         self.Input_Train_T = self.Input_Train.transpose()
  101.         self.Target_Train_T = self.Target_Train.transpose()
  102.         self.weights_input_hidden = Matrix.uniform(-0.1, 0.1, (self.NumInput, self._hid_node))
  103.         self.weights_hidden_output = Matrix.uniform(-0.1, 0.1, (self._hid_node, self.NumOutput))
  104.  
  105.  
  106.     def _Plot(self): #Plot กราฟค่า losses เทียบกับ epochs
  107.         temp_epochs = Array(self._epochs)
  108.         temp_losses = Array(len(self.losses))
  109.         for i in range(1, self._epochs+1):
  110.             temp_epochs[i-1] = i
  111.         for i in range(len(self.losses)):
  112.             temp_losses[i] = self.losses[i]
  113.         plt.plot(temp_epochs, temp_losses)
  114.         plt.show()      
  115.  
  116.     def _Testing(self, curTrain_seq): #การ Test ข้อมูล
  117.         for i in range(self.Input_Test.numRows()):
  118.             for j in range(self.Input_Test.numCols()):
  119.                 self.Input_Test[i,j] = float(self.Input_Test[i,j])
  120.         for i in range(self.Target_Test.numRows()):
  121.             for j in range(self.Target_Test.numCols()):
  122.                 self.Target_Test[i,j] = float(self.Target_Test[i,j])
  123.         self.Input_Test_T = self.Input_Test.transpose()
  124.         self.Target_Test_T = self.Target_Test.transpose() 
  125.  
  126.     def Train_Model(self, fold): #4 #Train ข้อมูลไปตามจำนวน fold ที่ใส่ไว้ โดยเปลี่ยนชุดข้อมูลสำหรับการ Test ทุก ๆ รอบ
  127.         accuracy = Alist()
  128.         for i in range(fold):
  129.             if i == 0:
  130.                 reset = True
  131.             else:
  132.                 reset = False
  133.             self._SeparateFold(fold, reset)
  134.             acc = self._Training()
  135.             accuracy.Append(acc)
  136.         mean_acc = sum(accuracy)/len(accuracy)
  137.         print("-"*50)
  138.         print('Mean Accuracy = {0:.2f}%'.format(mean_acc*100))
  139.  
Success #stdin #stdout 2.18s 53880KB
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https://ideone.com/qQk324
language:
Python 3 (python  3.12)
created:
2 years ago
visibility:
public

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