From 0f4ee702978a390f2961aced04860cfc50f303a3 Mon Sep 17 00:00:00 2001 From: Newnius Date: Sat, 2 May 2020 18:32:07 +0800 Subject: [PATCH] update --- .idea/workspace.xml | 30 +++++++++++++++--------------- serve.py | 17 +++++++++++++---- 2 files changed, 28 insertions(+), 19 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 1c4f837..98b3616 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -51,7 +51,7 @@ - + @@ -61,7 +61,7 @@ - + @@ -92,13 +92,13 @@ - - + + - - - + + + @@ -248,7 +248,7 @@ - + @@ -289,12 +289,12 @@ - @@ -440,13 +440,13 @@ - - + + - - - + + + diff --git a/serve.py b/serve.py index a821ce6..d8c19a3 100644 --- a/serve.py +++ b/serve.py @@ -18,6 +18,7 @@ from keras.layers import Dense from keras.layers import LSTM from math import sqrt import numpy +import random PORT_NUMBER = 8080 lock = Lock() @@ -134,13 +135,21 @@ def predict(job, seq): if job not in models or 'model' not in models[job]: return -1, False - # load dataset - batch_size = int(models[job]['batch_size']) - df = read_csv('./data/' + job + '.csv', header=0, index_col=0, squeeze=True) + data = { + 'seq': seq, + 'value': 0, + } + + file = './data/' + job + '.' + random.randint(1000, 9999) + '.csv' + df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value']) df = df.tail(batch_size * 2 - 1) - df.loc[df.shape[0]] = [seq, 0] + df = df.append(data, ignore_index=True) + df.to_csv(file, index=False) + + # load dataset + df = read_csv(file, header=0, index_col=0, squeeze=True) # transform data to be stationary raw_values = df.values