YAO/YAO-optimizer
1
0
Fork 0
mirror of https://github.com/newnius/YAO-optimizer.git synced 2025-12-15 09:06:43 +00:00
Code Issues Releases Wiki Activity

update

Browse Source
This commit is contained in:
Newnius
2020-05-02 17:29:33 +08:00
parent 0dc68c2ad5
commit c2a1e9156e
2 changed files with 10 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.idea/workspace.xml generated
View File

@@ -248,7 +248,7 @@
<component name="PropertiesComponent"> <component name="PropertiesComponent">
<property name="WebServerToolWindowFactoryState" value="false" /> <property name="WebServerToolWindowFactoryState" value="false" />
<property name="aspect.path.notification.shown" value="true" /> <property name="aspect.path.notification.shown" value="true" />
<property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588411500458" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588411617480" />
<property name="go.gopath.indexing.explicitly.defined" value="true" /> <property name="go.gopath.indexing.explicitly.defined" value="true" />
<property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" /> <property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
<property name="nodejs_npm_path_reset_for_default_project" value="true" /> <property name="nodejs_npm_path_reset_for_default_project" value="true" />

15
serve.py
View File

@@ -137,19 +137,19 @@ def predict(job, seq):
} }
df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value']) df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value'])
df = df.tail(models[job]['batch_size'] * 2 - 1) df = df.tail(int(models[job]['batch_size']) * 2 - 1)
df = df.append(data, ignore_index=True) df = df.append(data, ignore_index=True)
batch_size = int(models[job]['batch_size'])
# transform data to be stationary # transform data to be stationary
raw_values = df.values raw_values = df.values
diff_values = difference(raw_values, 1)[models[job]['batch_size']:] diff_values = difference(raw_values, 1)[batch_size:]
# transform data to be supervised learning # transform data to be supervised learning
lag = 4 lag = 4
supervised = timeseries_to_supervised(diff_values, lag) supervised = timeseries_to_supervised(diff_values, lag)
supervised_values = supervised.values supervised_values = supervised.values
batch_size = models[job]['batch_size']
test = supervised_values test = supervised_values
test = test.reshape(test.shape[0], test.shape[1]) test = test.reshape(test.shape[0], test.shape[1])
@@ -192,9 +192,12 @@ class MyHandler(BaseHTTPRequestHandler):
try: try:
job = query.get('job')[0] job = query.get('job')[0]
seq = query.get('seq')[0] seq = query.get('seq')[0]
predict(job, int(seq))
msg = {'code': 0, 'error': ""} msg = {'code': 0, 'error': ""}
pred, success = predict(job, int(seq))
if not success:
msg = {'code': 2, 'error': "Job " + job + " not exist"}
except Exception as e: except Exception as e:
msg = {'code': 1, 'error': str(e)} msg = {'code': 1, 'error': str(e)}