From d305fd31f37b974d92d5c5c8d35e045edbe33a03 Mon Sep 17 00:00:00 2001 From: Newnius Date: Sat, 2 May 2020 17:31:33 +0800 Subject: [PATCH] update --- .idea/workspace.xml | 31 +++++++++++++++---------------- serve.py | 10 ++++++---- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index a015171..c4c42ce 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,7 +2,6 @@ - @@ -51,7 +50,7 @@ - + @@ -61,7 +60,7 @@ - + @@ -92,13 +91,13 @@ - - + + - - - + + + @@ -248,7 +247,7 @@ - + @@ -289,12 +288,12 @@ - @@ -439,13 +438,13 @@ - - + + - - - + + + diff --git a/serve.py b/serve.py index b4e9d4f..f9dfa7c 100644 --- a/serve.py +++ b/serve.py @@ -136,14 +136,15 @@ def predict(job, seq): 'value': 0, } - df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value']) - df = df.tail(int(models[job]['batch_size']) * 2 - 1) - df = df.append(data, ignore_index=True) - batch_size = int(models[job]['batch_size']) + df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value']) + df = df.tail(batch_size * 2 - 1) + df = df.append(data, ignore_index=True) + # transform data to be stationary raw_values = df.values + print(raw_values) diff_values = difference(raw_values, 1)[batch_size:] # transform data to be supervised learning lag = 4 @@ -151,6 +152,7 @@ def predict(job, seq): supervised_values = supervised.values test = supervised_values + print(test) test = test.reshape(test.shape[0], test.shape[1]) test_scaled = models[job]['scaler'].transform(test)