init

StockTrainer.py

@@ -15,42 +15,10 @@ class StockTrainer:
         self.stock2Vector = Stock2Vector(RESOURCE_PATH)
         return
 
-    def getDataset(self, stock_code):
-        VECTOR_SIZE = 299
-        result = self.stock2Vector.getTrainData(stock_code)
-        df, minmax_df = self.stock2Vector.preprocessData(result)
-
-        TOTAL_X, TOTAL_Y = [], []
-        for key in minmax_df:
-            if key == "date":
-                continue
-            elif key == "label":
-                TOTAL_Y.append(minmax_df[key].tolist())
-            else:
-                TOTAL_X.append(minmax_df[key].tolist())
-
-        SIZE_WIDTH = len(TOTAL_X[0])
-        SIZE_HEIGHT = len(TOTAL_X)
-        X, Y = [], []
-        for i in range(VECTOR_SIZE, SIZE_WIDTH):
-            temp_X, temp_Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE)), np.zeros(0)
-            for j in range(SIZE_HEIGHT):
-                temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i-VECTOR_SIZE:i]
-            temp_X = np.stack([temp_X, temp_X, temp_X], axis=-1)
-            X.append(temp_X)
-            if int(TOTAL_Y[0][i]) == 0:
-                Y.append([1, 0, 0])
-            elif int(TOTAL_Y[0][i]) == 0.5:
-                Y.append([0, 1, 0])
-            else:
-                Y.append([0, 0, 1])
-
-        X = np.asarray(X)
-        Y = np.asarray(Y)
-        return X, Y
 
     def train(self, stock_code):
-        X, Y = self.getDataset(stock_code)
+        #X, Y = self.stock2Vector.getDataset3D(stock_code)
+        X, Y = self.stock2Vector.getDataset2D(stock_code)
 
         # build model
         n_classes = 3