Added new test configuration

pywatts/kcross.py

@@ -18,7 +18,7 @@ def split(data, k):
     data_list = data['dc'].tolist()
 
     # Each sample has 337 elements
-    samples = [data_list[i:i+337] for i in range(0, len(data_list) - 337, 337)]
+    samples = [data_list[i:i+337] for i in range(0, len(data_list) - 337, 20)]
     # Randomly shuffle samples
     random.shuffle(samples)
 
@@ -49,7 +49,7 @@ def train(nn, X_train, y_train, X_eval, y_eval, steps=10):
     evaluation = []
     for count, train_data in enumerate(X_train):
         for i in range(steps):
-            nn.train(train_data, y_train[count], batch_size=int(len(train_data['dc'])/336), steps=1)
+            nn.train(train_data, y_train[count], batch_size=30, steps=100) #batch_size=int(len(train_data['dc'])/336), steps=1)
             evaluation.append(nn.evaluate(X_eval[count], y_eval[count], batch_size=int(len(X_eval[count]['dc'])/336)))
             print("Training %s: %s/%s" % (count, (i+1), steps))
 

pywatts/neural.py

@@ -22,7 +22,7 @@ def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
         dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
 
     if shuffle:
-        return dataset.shuffle(len(features['0']*batch_size*4)).repeat().batch(batch_size)
+        return dataset.shuffle(len(features['0']*len(features)*4)).repeat().batch(batch_size)
     else:
         return dataset.batch(batch_size)
 

pywatts/test_kcross_train.py

@@ -4,11 +4,11 @@ import pywatts.db
 from pywatts import kcross
 
 NUM_STATIONS_FROM_DB = 75
-K = 4
+K = 10
 NUM_EVAL_STATIONS = 40
 TRAIN = True
 PLOT = True
-TRAIN_STEPS = 4
+TRAIN_STEPS = 20
 
 
 df = pywatts.db.rows_to_df(list(range(1, NUM_STATIONS_FROM_DB)))