import tensorflow as tf import pywatts.db from pywatts.routines import * import matplotlib.pyplot as pp PREDICT_QUERY = "query-sample_24hour.json" PREDICT_RESULT = PREDICT_QUERY.replace("query", "result") QUERY_ID = 0 pred_query = input_query("../sample_data/" + PREDICT_QUERY, QUERY_ID) pred_result = input_result("../sample_data/" + PREDICT_RESULT, QUERY_ID) # Define feature columns and initialize Regressor feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)] n = pywatts.neural.Net(feature_cols=feature_col) prediction = predict24h(n, pred_query) print(prediction) print(pred_result) pp.plot(pred_result, 'black') pp.plot(prediction, 'red') pp.show() #pywatts.main.eval_prediction(prediction, pred_result)