Capping to zero

pywatts/__init__.py

@@ -1,5 +1,5 @@
 from pywatts import db
 from pywatts import fetchdata
 from pywatts import neural
-from pywatts import main
+from pywatts import routines
 from pywatts import kcross

pywatts/routines.py

@@ -75,7 +75,8 @@ def predict24h(nn, X_pred):
 
     for i in range(24):
         pred = nn.predict1h(pandas.DataFrame.from_dict(input))
-        predictions.extend(list([p['predictions'][0] for p in pred]))
+        # Cap prediction to 0
+        predictions.extend(list([max(p['predictions'][0], 0) for p in pred]))
         # Remove first value and append predicted value
         del input['dc'][0]
         input['dc'].append(predictions[-1])

pywatts/test_predict.py

@@ -1,11 +1,11 @@
 import tensorflow as tf
 import pywatts.db
-from pywatts.main import *
+from pywatts.routines import *
 
 
 PREDICT_QUERY = "query-sample_1hour.json"
 PREDICT_RESULT = PREDICT_QUERY.replace("query", "result")
-QUERY_ID = 1
+QUERY_ID = 0
 
 
 pred_query = input_query("../sample_data/" + PREDICT_QUERY, QUERY_ID)
@@ -21,4 +21,4 @@ prediction = predict(n, pred_query)
 print(prediction)
 print(pred_result)
 
-pywatts.main.eval_prediction(prediction, pred_result)
+pywatts.routines.eval_prediction(prediction, pred_result)

pywatts/test_predict24.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 import pywatts.db
-from pywatts.main import *
+from pywatts.routines import *
 import matplotlib.pyplot as pp
 
 

pywatts/test_train.py

@@ -1,7 +1,7 @@
 import peewee
 import tensorflow as tf
 import pywatts.db
-from pywatts.main import *
+from pywatts.routines import *
 
 NUM_STATIONS_FROM_DB = 75
 NUM_TRAIN_STATIONS = 400
@@ -43,7 +43,7 @@ if TRAIN:
 
 if PLOT:
     # Plot training success rate (with 'average loss')
-    pywatts.main.plot_training(train_eval)
+    pywatts.routines.plot_training(train_eval)
 
 
 exit()