YAO/YAO-optimizer
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Newnius
2020-04-29 18:38:37 +08:00
parent 1515f4031b
commit e1c0c452ad
2 changed files with 3 additions and 2 deletions
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2
.idea/workspace.xml generated
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@@ -134,7 +134,7 @@
<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="1588156536427" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588156664537" />
<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" />

3
main.py
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@@ -1,7 +1,6 @@
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import os import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
frame = "pytorch" frame = "pytorch"
@@ -144,6 +143,7 @@ def draw(config, origin_data, predict_norm_data):
predict_data = predict_norm_data * origin_data.std[config.label_in_feature_columns] + \ predict_data = predict_norm_data * origin_data.std[config.label_in_feature_columns] + \
origin_data.mean[config.label_in_feature_columns] origin_data.mean[config.label_in_feature_columns]
'''
for i in range(label_column_num): for i in range(label_column_num):
plt.figure(i + 1) plt.figure(i + 1)
plt.plot(label_X, label_data[:, i], label='label') plt.plot(label_X, label_data[:, i], label='label')
@@ -159,6 +159,7 @@ def draw(config, origin_data, predict_norm_data):
config.used_frame)) config.used_frame))
plt.show() plt.show()
'''
def main(config): def main(config):