diff --git a/StockTrainer.py b/StockTrainer.py
index de3abe1..8426d73 100644
--- a/StockTrainer.py
+++ b/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
diff --git a/stock/util/Stock2Vector.py b/stock/util/Stock2Vector.py
index 47f9058..57df8d6 100644
--- a/stock/util/Stock2Vector.py
+++ b/stock/util/Stock2Vector.py
@@ -158,14 +158,12 @@ class Stock2Vector(HTS):
         conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
         cursor = conn.cursor()
 
-        cursor.execute(
-            'SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? and (ymd >= ? and  ymd <= ?) order by ymd desc, hms ',
-            (stock_code, "20220721", "20220731"))
+        #cursor.execute('SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? and (ymd >= ? and  ymd <= ?) order by ymd desc, hms ', (stock_code, "20220721", "20220731"))
+        cursor.execute('SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? order by ymd desc, hms ', (stock_code,))
         db_result = cursor.fetchall()
         temp_result = []
         for rows in db_result:
-            temp_result.append(
-                [rows[0], rows[1], rows[2], rows[3], rows[4], rows[5], rows[6], 0 if rows[7] is None else rows[7]])
+            temp_result.append([rows[0], rows[1], rows[2], rows[3], rows[4], rows[5], rows[6], 0 if rows[7] is None else rows[7]])
         temp_result.sort(key=lambda x: (x[0], x[1]))
 
         result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
@@ -247,6 +245,71 @@ class Stock2Vector(HTS):
 
         return np.asarray(vector)
 
+    def getDataset2D(self, stock_code, VECTOR_SIZE = 224):
+        result = self.getTrainData(stock_code)
+        df, minmax_df = self.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]
+            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 getDataset3D(self, stock_code, VECTOR_SIZE = 299):
+        result = self.getTrainData(stock_code)
+        df, minmax_df = self.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
+
 if __name__ == "__main__":
     PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
     RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
@@ -262,6 +325,8 @@ if __name__ == "__main__":
         stock2Vector = Stock2Vector(RESOURCE_PATH)
 
         for given_day in stock_codes[stock_code]:
+            X, Y = stock2Vector.getDataset2D(stock_code)
+
             data, minmax_data = stock2Vector.makeData(given_day, stock_code)
             vector = stock2Vector.vectorize(data)
             minmax_vector = stock2Vector.vectorize(minmax_data)