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update

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Newnius 2020-05-02 11:17:49 +08:00
parent d53554d3b5
commit f84743b291
2 changed files with 5 additions and 4 deletions
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2
.idea/workspace.xml
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@ -226,7 +226,7 @@
<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="1588389092834" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588389284728" />
<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" />

7
train.py
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@ -90,7 +90,8 @@ def experiment(repeats, series, seed):
supervised = timeseries_to_supervised(diff_values, lag2) supervised = timeseries_to_supervised(diff_values, lag2)
supervised_values = supervised.values supervised_values = supervised.values
# split data into train and test-sets # split data into train and test-sets
train, test = supervised_values[0:-12], supervised_values[-12:] test_data_num = 4
train, test = supervised_values[0:-test_data_num], supervised_values[-test_data_num:]
# transform the scale of the data # transform the scale of the data
print(test) print(test)
scaler, train_scaled, test_scaled = scale(train, test) scaler, train_scaled, test_scaled = scale(train, test)
@ -127,8 +128,8 @@ def experiment(repeats, series, seed):
# store forecast # store forecast
predictions.append(yhat) predictions.append(yhat)
# report performance # report performance
rmse = sqrt(mean_squared_error(raw_values[-12:], predictions)) rmse = sqrt(mean_squared_error(raw_values[-test_data_num:], predictions))
print(predictions, raw_values[-12:]) print(predictions, raw_values[-test_data_num:])
print('%d) Test RMSE: %.3f' % (r + 1, rmse)) print('%d) Test RMSE: %.3f' % (r + 1, rmse))
error_scores.append(rmse) error_scores.append(rmse)
return error_scores return error_scores