Avoid having the same test sample twice

pywatts/main.py

@@ -8,17 +8,22 @@ from random import randint
 
 
 def train_split(data, size):
+    used_idxs = []
     X_values = {'dc': [], 'temp': [], 'wind': []}
     y_values = []
     for i in range(size):
         rnd_idx = randint(0, data.size / data.shape[1] - 337)
 
+        if rnd_idx in used_idxs:
+            continue
+        else:
+            used_idxs.append(rnd_idx)
+
         X_values['dc'].extend(data['dc'][rnd_idx:rnd_idx + 336].tolist())
         X_values['temp'].extend(data['temp'][rnd_idx:rnd_idx + 336].tolist())
         X_values['wind'].extend(data['wind'][rnd_idx:rnd_idx + 336].tolist())
         y_values.append(data['dc'][rnd_idx + 337].tolist())
 
-
     return pandas.DataFrame.from_dict(X_values), pandas.DataFrame.from_dict({'dc': y_values})
 
 
@@ -31,6 +36,7 @@ def input_query(json_str, idx=0):
          'wind': tmp_df['wind'][idx]}
     )
 
+
 def input_result(json_str, idx=0):
     tmp_df = pandas.read_json(json_str)
 
@@ -40,7 +46,7 @@ def input_result(json_str, idx=0):
 def train(nn, X_train, y_train, X_val, y_val, steps=100):
     evaluation = []
     for i in range(steps):
-        nn.train(X_train, y_train, steps=100)
+        nn.train(X_train, y_train, batch_size=int(len(X_train['dc'].tolist())/336), steps=100)
         evaluation.append(nn.evaluate(X_val, y_val))
         print("Training %s of %s" % ((i+1), steps))
     return evaluation