init

stock/util/Stock2Vector.py

@@ -195,6 +195,7 @@ class Stock2Vector(HTS):
             result["label"].append(int(label))
 
         return result
+
     def preprocessData(self, result):
         # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
         df = self.buySellChecker.getVectorFeature(result)
@@ -285,12 +286,37 @@ class Stock2Vector(HTS):
         return X, Y
 
     def getVectorData(self, data, type="avg10", VECTOR_SIZE = 32):
-        X, Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE, 4)), np.zeros((32,1))
-        df, minmax_df = self.preprocessData(data)
+        df = self.buySellChecker.getVectorFeature(data)
 
-        X = np.asarray(X)
-        Y = np.asarray(Y, dtype='int64')
-        return X, Y
+        # channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
+        avg10 = df['avg10'].tolist()
+        diff_upper_lower = df['diff_upper_lower'].tolist()
+        rsi = df['rsi'].tolist()
+        abs_avg_3 = df['abs_avg_3'].tolist()
+
+        size = len(avg10)
+        batch_X, batch_Y = [], []
+        X = np.zeros((4, VECTOR_SIZE, VECTOR_SIZE))
+        label = df['label'].tolist()
+        for i in range(VECTOR_SIZE*VECTOR_SIZE-1, size):
+            w, h = 0, 0
+            for j in range(i-VECTOR_SIZE*VECTOR_SIZE+1, i+1):
+                X[0, h, w] = avg10[j]
+                X[1, h, w] = diff_upper_lower[j]
+                X[2, h, w] = abs_avg_3[j]
+                X[3, h, w] = rsi[j]
+                w += 1
+                if w >= VECTOR_SIZE:
+                    w = 0
+                    h += 1
+                    if h >= VECTOR_SIZE:
+                        h = 0
+                        batch_X.append(X)
+            batch_Y.append(label[i])
+
+        batch_X = np.asarray(batch_X)
+        batch_Y = np.asarray(batch_Y, dtype='int64')
+        return batch_X, batch_Y
 
 
     def getDataset3D(self, data, VECTOR_SIZE = 299):