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Newnius 2020-05-01 18:18:18 +08:00
parent 1b99a877a1
commit 0339f52a39
2 changed files with 16 additions and 22 deletions
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2
.idea/workspace.xml
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@ -215,7 +215,7 @@
<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="1588327999670" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588328026943" />
<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" />

36
serve.py
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@ -137,41 +137,35 @@ class Data:
# add end # add end
def draw_yqy(config, origin_data, predict_norm_data, mean_yqy, std_yqy): def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
return
label_norm_data = (origin_data - mean_yqy) / std_yqy label_norm_data = (origin_data - mean_yqy) / std_yqy
assert label_norm_data.shape[0] == predict_norm_data.shape[ assert label_norm_data.shape[0] == predict_norm_data.shape[
0], "The element number in origin and predicted data is different" 0], "The element number in origin and predicted data is different"
print("dsa")
# label_norm_data=label_norm_data[:,1] # label_norm_data=label_norm_data[:,1]
label_names = ['pre', 'main', 'post'] label_name = 'high'
label_column_num = 1 label_column_num = 1
loss1 = \ loss = \
np.mean((label_norm_data[config.predict_day:][:, 5] - predict_norm_data[:-config.predict_day][0:]) ** 2, np.mean((label_norm_data[config.predict_day:][:, 1] - predict_norm_data[:-config.predict_day][0:]) ** 2,
axis=0)[5] axis=0)[1]
loss2 = \ print("The mean squared error of stock {} is ".format(label_name), loss)
np.mean((label_norm_data[config.predict_day:][:, 6] - predict_norm_data[:-config.predict_day][0:]) ** 2,
axis=0)[6]
loss3 = \
np.mean((label_norm_data[config.predict_day:][:, 7] - predict_norm_data[:-config.predict_day][0:]) ** 2,
axis=0)[7]
print("The mean squared error of stock {} is ".format(label_names[0]), loss1)
print("The mean squared error of stock {} is ".format(label_names[1]), loss2)
print("The mean squared error of stock {} is ".format(label_names[2]), loss3)
# label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test) # label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test)
# predict_X = [x + config.predict_day for x in label_X] # predict_X = [x + config.predict_day for x in label_X]
label_datas = label_norm_data * std_yqy + mean_yqy print("2")
predict_datas = predict_norm_data * std_yqy + mean_yqy label_data = label_norm_data[:, 1] * std_yqy[1] + mean_yqy[1]
print(label_datas) predict_data = predict_norm_data * std_yqy[1] + mean_yqy[1]
print(predict_datas)
print(label_datas[-1]) print(label_data)
print(predict_datas[-1]) print(predict_data)
print(label_data[-1])
print(predict_data[-1])
PORT_NUMBER = 8080 PORT_NUMBER = 8080