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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/test.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PCA_Train.py</url> |
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<line>271</line> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Test_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Test_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Train_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Test_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Train_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Train_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Train_offline.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PLS_Train.py</url> |
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<line>119</line> |
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<option name="timeStamp" value="473" /> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PCA_Train.py</url> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PCA_Train.py</url> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model.py</url> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model.py</url> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Train_offline.py</url> |
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<line>268</line> |
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|||
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/ANN_Test_offline.py</url> |
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<option name="timeStamp" value="498" /> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest_k.py</url> |
|||
<line>469</line> |
|||
<option name="timeStamp" value="508" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest_k.py</url> |
|||
<line>481</line> |
|||
<option name="timeStamp" value="510" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model_performance.py</url> |
|||
<line>149</line> |
|||
<option name="timeStamp" value="532" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model_performance.py</url> |
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<line>149</line> |
|||
<option name="timeStamp" value="539" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model_performance.py</url> |
|||
<line>170</line> |
|||
<option name="timeStamp" value="547" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model_performance.py</url> |
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<line>156</line> |
|||
<option name="timeStamp" value="548" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>461</line> |
|||
<option name="timeStamp" value="570" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PCA_Train.py</url> |
|||
<line>251</line> |
|||
<option name="timeStamp" value="585" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/update_points.py</url> |
|||
<line>21</line> |
|||
<option name="timeStamp" value="645" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/PCA_Train.py</url> |
|||
<line>259</line> |
|||
<option name="timeStamp" value="657" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>511</line> |
|||
<option name="timeStamp" value="713" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>516</line> |
|||
<option name="timeStamp" value="721" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>508</line> |
|||
<option name="timeStamp" value="722" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/model_performance.py</url> |
|||
<line>325</line> |
|||
<option name="timeStamp" value="740" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/clean.py</url> |
|||
<line>92</line> |
|||
<option name="timeStamp" value="744" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>190</line> |
|||
<option name="timeStamp" value="746" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>328</line> |
|||
<option name="timeStamp" value="750" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/get_data.py</url> |
|||
<line>242</line> |
|||
<option name="timeStamp" value="768" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/pcamtcltest.py</url> |
|||
<line>525</line> |
|||
<option name="timeStamp" value="776" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/update_points.py</url> |
|||
<line>49</line> |
|||
<option name="timeStamp" value="793" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file:$PROJECT_DIR$/../../..//D:/FlaskWebApi/get_data.py</url> |
|||
<line>149</line> |
|||
<option name="timeStamp" value="794" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$USER_HOME$/Desktop/test.py</url> |
|||
<line>9</line> |
|||
<option name="timeStamp" value="1092" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/cjl勿删/sae_diagnosis.py</url> |
|||
<line>60</line> |
|||
<option name="timeStamp" value="1099" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://C:/D盘/Flaskwebapi/recon.py</url> |
|||
<line>35</line> |
|||
<option name="timeStamp" value="1108" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/cjl勿删/pca_diagnosis.py</url> |
|||
<line>124</line> |
|||
<option name="timeStamp" value="1300" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/pca.py</url> |
|||
<line>307</line> |
|||
<option name="timeStamp" value="1304" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/pca.py</url> |
|||
<line>168</line> |
|||
<option name="timeStamp" value="1305" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/pca.py</url> |
|||
<line>114</line> |
|||
<option name="timeStamp" value="1306" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/base.py</url> |
|||
<line>94</line> |
|||
<option name="timeStamp" value="1307" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/test.py</url> |
|||
<line>6</line> |
|||
<option name="timeStamp" value="1316" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/pca.py</url> |
|||
<line>260</line> |
|||
<option name="timeStamp" value="1338" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/base.py</url> |
|||
<line>144</line> |
|||
<option name="timeStamp" value="1349" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/PCA_Train.py</url> |
|||
<line>276</line> |
|||
<option name="timeStamp" value="1361" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>172</line> |
|||
<option name="timeStamp" value="1392" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>115</line> |
|||
<option name="timeStamp" value="1394" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>59</line> |
|||
<option name="timeStamp" value="1396" /> |
|||
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|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>191</line> |
|||
<option name="timeStamp" value="1397" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>260</line> |
|||
<option name="timeStamp" value="1398" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/PCA_Train.py</url> |
|||
<line>259</line> |
|||
<option name="timeStamp" value="1405" /> |
|||
</line-breakpoint> |
|||
<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
|||
<url>file://$PROJECT_DIR$/AANN_Train.py</url> |
|||
<line>88</line> |
|||
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|||
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<url>file://$PROJECT_DIR$/PCA_Train.py</url> |
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<url>file://$PROJECT_DIR$/PCA_Train.py</url> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/AANN_Fit.py</url> |
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<option name="timeStamp" value="1418" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/pcamtcltest_recon.py</url> |
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<option name="timeStamp" value="1432" /> |
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<url>file://$PROJECT_DIR$/recon.py</url> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$PROJECT_DIR$/pcamtcltest_recon.py</url> |
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<option name="timeStamp" value="1436" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/pcamtcltest_recon.py</url> |
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<url>file://$PROJECT_DIR$/aannmtcltest_recon.py</url> |
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<url>file://$PROJECT_DIR$/AANN_SF_SBSRB.py</url> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$PROJECT_DIR$/AANN_Derivative.py</url> |
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<url>file://$PROJECT_DIR$/AANN_RB.py</url> |
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</line-breakpoint> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/PCA_Test.py</url> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$USER_HOME$/Desktop/HealthyScoringSystem/HealthyScoringSystem/main.py</url> |
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<option name="timeStamp" value="1520" /> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$USER_HOME$/Desktop/HealthyScoringSystem/HealthyScoringSystem/main.py</url> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/PCA_Test.py</url> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/PCA_Test.py</url> |
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<url>file://$PROJECT_DIR$/ASSESS.py</url> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$USER_HOME$/Desktop/HealthyScoringSystem/HealthyScoringSystem/main.py</url> |
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<url>file://$PROJECT_DIR$/ASSESS.py</url> |
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<option name="timeStamp" value="1562" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/pcamtcltest_recon.py</url> |
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<option name="timeStamp" value="1564" /> |
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</line-breakpoint> |
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</line-breakpoint> |
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<option name="timeStamp" value="1568" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/pca_test_by_rb_plot.py</url> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/ANN_Train_offline.py</url> |
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<option name="timeStamp" value="1578" /> |
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</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$PROJECT_DIR$/ANN_Train_offline.py</url> |
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</line-breakpoint> |
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<option name="timeStamp" value="1582" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/ANN_Train_offline.py</url> |
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<option name="timeStamp" value="1583" /> |
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</line-breakpoint> |
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<url>file://$PROJECT_DIR$/ANN_Train_offline.py</url> |
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<option name="timeStamp" value="1584" /> |
|||
</line-breakpoint> |
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line"> |
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<url>file://$PROJECT_DIR$/web.py</url> |
|||
<line>151</line> |
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<option name="timeStamp" value="1585" /> |
|||
</line-breakpoint> |
|||
</breakpoints> |
|||
<default-breakpoints> |
|||
<breakpoint type="python-exception"> |
|||
<properties notifyOnTerminate="true" exception="BaseException"> |
|||
<option name="notifyOnTerminate" value="true" /> |
|||
</properties> |
|||
</breakpoint> |
|||
</default-breakpoints> |
|||
</breakpoint-manager> |
|||
</component> |
|||
</project> |
|||
File diff suppressed because it is too large
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@ -0,0 +1,66 @@ |
|||
import numpy as np |
|||
import torch |
|||
from numba import jit |
|||
|
|||
@jit(nopython=True, fastmath=True, cache=True) |
|||
def sigmoid(x): |
|||
return 1.0/(1 + np.exp(-x)) |
|||
|
|||
|
|||
@jit(nopython=True, fastmath=True, cache=True) |
|||
def AANN_Derivative(v1, v2, w1, w2, testsample, faulty_directions): |
|||
""" |
|||
|
|||
:param v1: |
|||
:param v2: |
|||
:param w1: |
|||
:param w2: |
|||
:param testsample: |
|||
:param faulty_directions: |
|||
:return: |
|||
""" |
|||
[mmv1, nnv1] = v1.shape |
|||
[mmw1, nnw1] = w1.shape |
|||
[mmv2, nnv2] = v2.shape |
|||
[mmw2, nnw2] = w2.shape |
|||
z = np.dot(w1, v2) |
|||
faulty_number = faulty_directions.shape[0] |
|||
max_count = 3000 |
|||
spe = np.zeros(max_count+1) |
|||
count = 0 |
|||
derivative, ff, delta = np.zeros((max_count+1, mmv1)), np.zeros((max_count+1, mmv1)), np.zeros((max_count+1, mmv1)) |
|||
y_ = testsample |
|||
out, e = np.zeros((max_count, mmv1)),np.zeros((max_count, mmv1)) |
|||
ahfa1 = 0.0018 |
|||
ahfa2 = 0.9 |
|||
|
|||
while 1: |
|||
count = count + 1 |
|||
testsample = np.copy(y_) |
|||
delta[count, :] = (-ahfa1*derivative[count-1, :]+ahfa2*delta[count-1, :]) |
|||
ff[count, :] = (delta[count]) |
|||
y_ = y_-ff[count] |
|||
g = sigmoid(np.dot(y_, v1)) |
|||
t = np.dot(g, w1) |
|||
h = sigmoid(np.dot(t, v2)) |
|||
out[count-1, :] = np.dot(h, w2).reshape((1,mmv1)) |
|||
e[count-1, :] = y_-out[count-1, :] |
|||
spe[count] = np.sum(e[count-1, :]*e[count-1, :]) |
|||
|
|||
if count >= max_count or np.abs(spe[count-1]-spe[count]) < 0.000001: |
|||
iteration_number = count |
|||
break |
|||
for i in range(faulty_number): |
|||
deltyf = np.zeros((mmv1, mmv1)) |
|||
yitao, yita = np.zeros(nnv2), np.zeros(nnv2) |
|||
yitao = yitao + np.dot(g[0] * (1 - g[0]) * (-v1[int(faulty_directions[i])]), z) |
|||
yita = np.dot(h[0] * (1 - h[0]) * yitao, w2) |
|||
deltyf[:, int(faulty_directions[i])] = yita.T |
|||
ee = testsample - out[count-1, :] |
|||
derivative[count, int(faulty_directions[i])] = (-2*np.sum(ee*deltyf[:, int(faulty_directions[i])])-2*(ee[int(faulty_directions[i])]-2*deltyf[int(faulty_directions[i]), int(faulty_directions[i])]*ff[count, int(faulty_directions[i])])+2*ff[count, int(faulty_directions[i])]) |
|||
rbc_spe = spe[count] |
|||
delt_f = np.sum(ff, axis=0) |
|||
|
|||
return rbc_spe, delt_f.reshape(1,mmv1), iteration_number, spe[:count] |
|||
|
|||
|
|||
@ -0,0 +1,203 @@ |
|||
import numpy as np |
|||
from numba import jit |
|||
import config |
|||
import json |
|||
import sys |
|||
import requests |
|||
import datetime |
|||
import jenkspy |
|||
import xlrd |
|||
import AANN_Fit |
|||
import traceback |
|||
|
|||
@jit(nopython=True, cache=True) |
|||
def AANN_Fit(testdata, v1, v2, w1, w2): |
|||
""" |
|||
|
|||
:param testdata: |
|||
:param model: |
|||
:return: |
|||
""" |
|||
y1 = 1/(1+np.exp(-np.dot(testdata, v1))) |
|||
out1 = np.dot(y1, w1) |
|||
y2 = 1/(1+np.exp(-np.dot(out1, v2))) |
|||
output = np.dot(y2, w2) |
|||
return output |
|||
|
|||
def get_history_value(points,time,interval): |
|||
#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys" |
|||
url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token() |
|||
point_array = points.split(",") |
|||
time_span = time.split(";") |
|||
value_array = [] |
|||
for item in point_array: |
|||
value_group = [] |
|||
for time_piece in time_span: |
|||
st = time_piece.split(",")[0] |
|||
et = time_piece.split(",")[1] |
|||
para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval} |
|||
response = requests.get(url, headers=headers, params=para) |
|||
content = response.text.replace('"[','[').replace(']"',']') |
|||
value = json.loads(content) |
|||
if not isinstance(value, list): |
|||
print("aaa") |
|||
for row in value: |
|||
value_group.append(row[1]) |
|||
value_array.append(value_group) |
|||
return np.transpose(np.array(value_array)) |
|||
#return values |
|||
|
|||
def isnumber(limits): |
|||
flag=True |
|||
for item in limits: |
|||
item=item.replace("-","") |
|||
if(item.isdigit()==False): |
|||
flag=False |
|||
break |
|||
return flag |
|||
|
|||
def AANN_Test(model,Data): |
|||
v1 = np.array(model["v1"]) |
|||
v2 = np.array(model["v2"]) |
|||
w1 = np.array(model["w1"]) |
|||
w2 = np.array(model["w2"]) |
|||
maxdata = np.array(model["maxdata"]) |
|||
mindata = np.array(model["mindata"]) |
|||
mm, nn = Data.shape |
|||
# 预处理 数据归一化 |
|||
mdata = (2 * Data - (maxdata + mindata + np.zeros((mm, nn)))) / (maxdata - mindata + np.zeros((mm, nn))) |
|||
reconData = AANN_Fit(mdata, v1, v2, w1, w2) |
|||
# 预处理 数据反归一化 |
|||
a = maxdata - mindata + np.zeros((mm, nn)) |
|||
b = maxdata + mindata + np.zeros((mm, nn)) |
|||
reconData = (np.multiply(reconData, a) + b) / 2 |
|||
|
|||
res = {} |
|||
paraState = np.zeros([np.array(Data).shape[0], np.array(Data).shape[1]]) |
|||
SPE_list = [] |
|||
R = 0 |
|||
errorData = Data - reconData # 偏差值 |
|||
|
|||
res["sampleData"]=np.transpose(np.array(Data)).tolist() |
|||
res["reconData"]=np.around(np.transpose(np.array(reconData)), decimals=3).tolist() |
|||
res["errorData"]=np.around(np.transpose(np.array(errorData)), decimals=3).tolist() |
|||
res["R"]=np.around(R, decimals=3).tolist() |
|||
res["SPE"]=np.around(np.transpose(np.array(SPE_list)), decimals=3).tolist() |
|||
res["paraState"]=np.transpose(np.array(paraState)).tolist() |
|||
|
|||
return res |
|||
|
|||
|
|||
def clean_main(info): |
|||
try: |
|||
# datatype = info['type'] |
|||
condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<") |
|||
times = info["time"].split(';') |
|||
points = info["point"].split(',') |
|||
interval = 300000 |
|||
dead = info["dead"].split(',') |
|||
limit = info["limit"].split(',') |
|||
uplower = info["uplow"].split(';') |
|||
res = json.loads(info["model"]) |
|||
filename = res["Model_type"] |
|||
count = 0 |
|||
ItemsInfo, SamplingTimePeriods = [], [] |
|||
Constraint = "" |
|||
for i in range(len(points)): |
|||
iteminfo = {} |
|||
iteminfo["ItemName"] = points[i] # 加点 |
|||
if (dead[i] == "1"): # 判断是否参与死区清洗 |
|||
iteminfo["ClearDeadZone"] = "true" |
|||
else: |
|||
iteminfo["ClearDeadZone"] = "false" |
|||
if (limit[i] == "1"): # 参与上下限清洗 |
|||
limits = uplower[i].split(',') |
|||
if (isnumber(limits) == True): # 输入上下限正确 |
|||
count += 1 |
|||
Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[ |
|||
1] + " and " |
|||
ItemsInfo.append(iteminfo) |
|||
if (count != 0): |
|||
Constraint = Constraint[:len(Constraint) - 4:] |
|||
else: |
|||
Constraint = "1==1" # 没有上下限清洗 |
|||
Constraint += " and (" + condition + ")" |
|||
for i in range(len(times)): |
|||
Eachsampletime = {} |
|||
timess = times[i].split(',') |
|||
Eachsampletime["StartingTime"] = timess[0] |
|||
Eachsampletime["TerminalTime"] = timess[1] |
|||
SamplingTimePeriods.append(Eachsampletime) |
|||
Constraint = Constraint.replace("\n", " ") |
|||
url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % ( |
|||
ItemsInfo, SamplingTimePeriods, Constraint, interval) |
|||
response = requests.get(url) |
|||
content = json.loads(response.text) |
|||
origndata = np.array([item for item in content["ClearData"]]) |
|||
result = AANN_Test(res["Model_info"],origndata.T) |
|||
result["sampleData"] = origndata.tolist() |
|||
result["CleanOrNot"] = True |
|||
except Exception as e: |
|||
points = info['point'] |
|||
time1 = info["time"] |
|||
interval = info['interval'] |
|||
data_x, origndata = get_history_value(points, time1, interval) |
|||
res = json.loads(info["model"]) |
|||
filename = res["Model_type"] |
|||
origndata = origndata.tolist() |
|||
result = AANN_Test(res["Model_info"],origndata.T) |
|||
result["sampleData"] = origndata |
|||
result["CleanOrNot"] = False |
|||
return result |
|||
|
|||
|
|||
if __name__ == '__main__': |
|||
# info = {"point":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","dead":"1,1,1,1,1,1","condition":"[DH4_40MAG20CE102]>20","limit":"0,0,0,0,0,0","uplow":"null,null;null,null;null,null;null,null;null,null;null,null","time":"2021-06-02 17:37:17,2021-06-03 17:37:17","model":"{\"Model_info\":{\"Train_X_min\":[22.848,14.865,45.127,24.787,24.875,24.699],\"Train_X_max\":[49.215,54.841,185.917,80.635,81.084,80.097],\"Train_X_std\":[7.014,12.071,37.054,15.602,15.881,15.695],\"Train_X_mean\":[37.569,36.848,113.843,53.193,53.611,53.304],\"Train_X_bais_max\":[[0,0,0,0,0,0]],\"Train_X_bais_min\":[[0,0,0,0,0,0]],\"Train_X_bais_mean\":[[0,0,0,0,0,0]],\"QCUL_95_line\":[0,0,0,0,0,0],\"QCUL_99_line\":[0,0,0,0,0,0],\"count\":1957,\"maxdata\":[49.21523,54.841,185.917023,80.63535,81.08379,80.0973053],\"mindata\":[22.84761,14.864501,45.1272545,24.7868519,24.8750286,24.698679],\"sigma\":0.017395612757076084,\"v1\":[[0.05396457607128241,-1.1037970788364901,-0.9023458013688983,0.8166076266011788,-0.17679687427234053],[0.5200933810436045,0.007280858992126217,-0.5349408644184176,0.2845470690775108,-0.6855420074109138],[0.36869788692168753,0.022827023461346962,0.5064698648194299,-0.4703408850462729,-0.36541089496833906],[0.12892337610198143,0.8200230541433281,0.0447757887183383,0.1670333002651392,0.8993457054279524],[-0.2971657506583043,0.485522778322426,0.7944907999619857,-0.7713468714572788,-0.3803782724966992],[-0.24463129252472893,-0.8040155541706245,-0.4787131792272823,-0.6808617963702621,0.8823560072658708]],\"v2\":[[-0.624183704662755,-1.0451052181462246,-0.2814222311475065,-0.3354562640454462,2.000215994798745],[1.304013807858937,-1.2949290118426027,-1.274288771981608,1.3783276766624326,0.5918960339827718]],\"w1\":[[-1.804815936738639,-1.177820457498696],[-0.26858615380810363,0.942696914999828],[0.045393854822685514,1.0175755864672742],[0.9657639036369005,0.8851033322191303],[0.9882029320129792,-1.731780185362415]],\"w2\":[[-1.2783993770868123,0.12079935279430898,0.5404098532155389,-0.7614378857555226,-0.8806415399208807,-1.1425373462620356],[1.1519307867425934,1.7851341509876484,1.4232979025735768,1.9335508578138167,1.2388319720461638,1.2688802766586058],[1.0836842481718094,0.24511103285668004,0.19049263794782964,0.26731984115471025,1.0669397542018493,1.067090986764354],[-1.1907723532803773,0.19092261395624277,-0.17818889703150334,-1.2393336475691328,-0.9319272349811759,-0.7091295825768392],[0.2667338682884674,-2.3674951959521846,-2.19524658584935,-0.3717940912566747,-0.6570853336446724,-0.6287452290848574]]},\"Model_type\":\"AANN\",\"BeforeCleanSamNum\":2143,\"AfterCleanSamNum\":2067,\"CleanOrNot\":true}","interval":300000} |
|||
# result = clean_main(info) |
|||
# print('aaa') |
|||
model={ |
|||
"Train_X_min": [ 20.821, 14.869, 43.163, 21.262, 21.35, 21.262 ], |
|||
"Train_X_max": [ 49.126, 49.925, 169.502, 77.588, 78.394, 77.856 ], |
|||
"Train_X_std": [ 7.125, 10.472, 31.807, 14.634, 14.847, 14.718 ], |
|||
"Train_X_mean": [ 36.205, 36.429, 111.547, 50.583, 50.955, 50.672 ], |
|||
"Train_X_bais_max": [ [ 0, 0, 0, 0, 0, 0 ] ], |
|||
"Train_X_bais_min": [ [ 0, 0, 0, 0, 0, 0 ] ], |
|||
"Train_X_bais_mean": [ [ 0, 0, 0, 0, 0, 0 ] ], |
|||
"QCUL_95_line": [ 0, 0, 0, 0, 0, 0 ], |
|||
"QCUL_99_line": [ 0, 0, 0, 0, 0, 0 ], |
|||
"r2": [ 0.9868677166895696, 0.9972168348807384, 0.994155641760367, 0.997452401678795, 0.9972480237895449, 0.9973549357761101 ], |
|||
"count": 1837, |
|||
"maxdata": [ 49.1263924, 49.92462, 169.501648, 77.58756, 78.39406, 77.85637 ], |
|||
"mindata": [ 20.8214855, 14.86908, 43.1634521, 21.261837, 21.3499146, 21.261837 ], |
|||
"sigma": 0.01806189580260563, |
|||
"v1": [ |
|||
[ -0.23316133948572393, -0.9217914025920456, -0.07554897165584737, 0.5760860199806274, -1.0764354048741178 ], |
|||
[ 0.6269288587688561, 0.2873108121988404, -0.4522541335965181, 0.1976888146807624, -0.06952149372511203 ], |
|||
[ 0.1787240198568913, 0.19306067329316998, -0.6544931571469833, -0.7615224968711883, 0.34011752017824165 ], |
|||
[ 0.018938620699273264, 0.730637377526106, 0.4933076792373918, 0.6451509410317868, 0.053819869986792335 ], |
|||
[ -0.19609562960367105, 0.11172696177830604, -0.1584273023561399, 0.331300527227796, 0.5888365193680527 ], |
|||
[ 0.06189724560442225, -0.8971329436091366, 0.5277390828634656, -0.5983529844040149, 0.7979948968923328 ] |
|||
], |
|||
"v2": [ |
|||
[ 1.2232098033189849, -1.6743334472355316, 0.801596559892827, 1.1379819603344574, 0.6490732883102263 ], |
|||
[ 1.0900875026570802, -0.9667042438318552, -0.6416000023658467, -0.506978913609041, 1.6559592831593986 ] |
|||
], |
|||
"w1": [ |
|||
[ -1.330531486686979, 0.2236374553075324 ], |
|||
[ 0.7317719867861042, 2.0240829271774823 ], |
|||
[ 1.9002236694443064, -1.1102667989668475 ], |
|||
[ -0.18577976434243573, -0.5562627341260915 ], |
|||
[ -1.0437074026078084, -0.7558997859191849 ] |
|||
], |
|||
"w2": [ |
|||
[ -1.3748396962059533, -0.4858035766258788, -0.8013045354908804, -0.6155000036590996, -0.48891412875006274, -1.0627377754183602 ], |
|||
[ 1.4595863114619005, 2.68853721134863, 2.238122625694176, 1.9127830289008108, 1.9551336244607311, 1.8698425061744566 ], |
|||
[ 0.5758092226574661, -0.4237580862942265, -1.4401060479517787, 0.28527357176477214, 0.1858972429240083, 0.054877840090252594 ], |
|||
[ 0.7197290283109676, -1.73044863841078, -0.4906062165572168, -0.19012584014090636, -0.18883049934775636, 0.22915594950041754 ], |
|||
[ -1.4023393826788018, 0.16500469061488093, 0.4718410186558856, -1.5843596374153093, -1.6622678974964407, -1.2898135647821567 ] |
|||
] |
|||
} |
|||
Data=np.array([[24.25785,22.9852276,71.4623947,26.1979523,26.2861671,26.2861671]]) |
|||
result = AANN_Test(model, Data) |
|||
print('aaa') |
|||
File diff suppressed because it is too large
Binary file not shown.
@ -0,0 +1,77 @@ |
|||
import numpy as np |
|||
import time |
|||
from AANN_Fit import AANN_Fit |
|||
import itertools |
|||
from AANN_Derivative import AANN_Derivative |
|||
|
|||
|
|||
def AANN_RB(fault_sample, fault_magnitude,model): |
|||
""" |
|||
:param fault_sample: |
|||
:param fault_magnitude: |
|||
:param model: |
|||
:return: |
|||
""" |
|||
[m, n] = fault_sample.shape |
|||
v1 = model.v1 |
|||
v2 = model.v2 |
|||
w1 = model.w1 |
|||
w2 = model.w2 |
|||
sigma = model.sigma |
|||
isolated = np.zeros((m, n)) |
|||
detected = 0 |
|||
rod_n = 0 |
|||
mdata = (2*fault_sample-np.tile(model.maxdata, (m, 1))-np.tile(model.mindata, (m, 1)))/(np.tile(model.maxdata, (m, 1))-np.tile(model.mindata, (m, 1))) |
|||
# 数据正则化 |
|||
mdata1 = (2 * (fault_sample-fault_magnitude) - np.tile(model.maxdata, (m, 1)) - np.tile(model.mindata, (m, 1))) / ( |
|||
np.tile(model.maxdata, (m, 1)) - np.tile(model.mindata, (m, 1))) |
|||
# mdata = mdata/np.tile(pow(np.sum(pow(mdata1, 2), axis=1), 1/2), (n, 1)).T |
|||
time1 = time.time()*1000 |
|||
index = np.zeros(m) |
|||
index2 = np.zeros(m) |
|||
index_v = np.zeros((m, n)) |
|||
nodenum_record = np.zeros(m) |
|||
for sample_id in range(m): |
|||
output = AANN_Fit(mdata[sample_id, :], v1, v2, w1, w2) |
|||
output2 = AANN_Fit(mdata1[sample_id, :], v1, v2, w1, w2) |
|||
index[sample_id] = np.sum((mdata[sample_id, :]-output)*(mdata[sample_id, :]-output)) |
|||
index2[sample_id] = np.sum((mdata1[sample_id, :] - output2) * (mdata1[sample_id, :] - output2)) |
|||
index_v[sample_id, :] = (mdata[sample_id, :]-output)*(mdata[sample_id, :]-output) |
|||
if index[sample_id] > sigma: |
|||
vnum = 1 |
|||
detected += 1 |
|||
nodenum = 0 |
|||
while vnum <= n: |
|||
variablecombination = itertools.combinations([i for i in range(n)], vnum) |
|||
combi_list =np.array(list(variablecombination)) |
|||
cur_rbindex = np.zeros(combi_list.shape[0]) |
|||
vc_id = 0 |
|||
for v_combination in combi_list: |
|||
cur_fault_varibles =v_combination |
|||
cur_fault_variables = np.array(cur_fault_variables).astype(np.float64) |
|||
cur_rbindex[vc_id], B, C, spe = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :], cur_fault_varibles) |
|||
nodenum = nodenum+1 |
|||
vc_id = vc_id+1 |
|||
min_rbindex, op_vc_id = np.min(cur_rbindex), np.argmin(cur_rbindex) |
|||
cur_vc = combi_list[op_vc_id, :] |
|||
if min_rbindex < sigma*2: |
|||
isolated[sample_id, combi_list[op_vc_id, :]] = 1 |
|||
break |
|||
else: |
|||
vnum = vnum+1 |
|||
nodenum_record[sample_id] = nodenum |
|||
maxindex1 = np.argmax(isolated[sample_id, :]) |
|||
maxindex2 = np.argmax(np.abs(fault_magnitude[sample_id, :])) |
|||
if maxindex1 == maxindex2: |
|||
rod_n = rod_n+1 |
|||
time2 = time.time() * 1000 |
|||
tc = round(time2 - time1) |
|||
real_fault = (fault_magnitude > 0).astype('int') |
|||
fdr = np.sum(((real_fault - (isolated == 0).astype('int')) == 1).astype('int')) / np.sum(real_fault) * 100 |
|||
far = np.sum(((real_fault - isolated) == -1).astype('int')) / np.sum((real_fault == 0).astype('int')) * 100 |
|||
dr = detected / np.sum((np.sum(real_fault, axis=1) > 0).astype('int')) * 100 |
|||
rod = rod_n / detected * 100 |
|||
nodes = np.mean(nodenum_record) |
|||
|
|||
return fdr, far, tc, dr, rod |
|||
|
|||
@ -0,0 +1 @@ |
|||
#error Do not use this file, it is the result of a failed Cython compilation. |
|||
@ -0,0 +1,143 @@ |
|||
import numpy as np |
|||
import time |
|||
import copy |
|||
from AANN_Derivative import AANN_Derivative |
|||
from AANN_Fit import AANN_Fit |
|||
|
|||
|
|||
|
|||
def AANN_SF_SBSRB(fault_sample, fault_magnitude, v1, v2, w1, w2, sigma, mindata, maxdata): |
|||
""" |
|||
前向后向序列 |
|||
:param fault_sample: |
|||
:param fault_magnitude: |
|||
:param model: |
|||
:return: |
|||
""" |
|||
# print(type(fault_sample)) |
|||
a = 0 |
|||
[m, n] = fault_sample.shape |
|||
isolated = np.zeros((m, n)) |
|||
delt_ff=np.zeros((m, n)) |
|||
detected = 0 # 检测出故障样本数量 |
|||
rod_n = 0 |
|||
mdata = (2*fault_sample-np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1)))/(np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1))) |
|||
time1 = int(round(time.time()*1000)) |
|||
confidence = 2*sigma |
|||
index = [] |
|||
nodenum_record = np.zeros(m) |
|||
spe = np.zeros(m) |
|||
for sample_id in range(m): |
|||
# output = model(mdata[sample_id,:]) |
|||
if sample_id==225: |
|||
print(1) |
|||
output = AANN_Fit(mdata[sample_id, :], v1, v2, w1, w2) |
|||
index.append(np.sum((mdata[sample_id, :]-output)*(mdata[sample_id, :]-output))) |
|||
spe[sample_id] = index[sample_id] |
|||
if index[sample_id] > sigma: |
|||
vnum = 1 |
|||
detected += 1 |
|||
node_num = 0 |
|||
# 前向序列 |
|||
variables = [i for i in range(n)] |
|||
selected = np.zeros((n, 1)) |
|||
selected_v_num = -1 |
|||
while selected_v_num <= n: |
|||
selected_variables = [variables[i] for i in (np.where(selected == 1)[0])] |
|||
unselected_variables = [variables[i] for i in (np.where(selected == 0)[0])] |
|||
cur_rbindex = np.zeros(len(unselected_variables)) |
|||
for v_id in range(len(unselected_variables)): |
|||
cur_fault_variables = copy.deepcopy(selected_variables) |
|||
cur_fault_variables.append(unselected_variables[v_id]) |
|||
cur_fault_variables = np.array(cur_fault_variables).astype(np.float64) |
|||
cur_rbindex[v_id], B, C, SPE = AANN_Derivative(v1.astype(np.float64), v2.astype(np.float64), w1.astype(np.float64), w2.astype(np.float64), mdata[sample_id, :], cur_fault_variables) |
|||
node_num += node_num |
|||
min_rbindex, op_vc_id = min(cur_rbindex), np.argmin(cur_rbindex) |
|||
selected[unselected_variables[op_vc_id]] = 1 |
|||
if min_rbindex < confidence: |
|||
isolated_variable = [variables[i] for i in (np.where(selected == 1)[0])]#有问题 |
|||
isolated[sample_id, isolated_variable] = 1 |
|||
break |
|||
else: |
|||
selected_v_num += 1 |
|||
# 后向序列 |
|||
remained_v_num = len(np.where(selected == 1)[0]) |
|||
while remained_v_num > 1: |
|||
remained_variables = [variables[i] for i in (np.where(selected == 1)[0])] |
|||
cur_rbindex = np.zeros(len(remained_variables)) |
|||
for v_id in range(len(remained_variables)): |
|||
cur_fault_variables = copy.deepcopy(remained_variables) |
|||
cur_fault_variables = np.delete(cur_fault_variables, v_id) |
|||
cur_fault_variables = np.array(cur_fault_variables).astype(np.float64) |
|||
cur_rbindex[v_id], B, C, SPE = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :], cur_fault_variables) |
|||
node_num += node_num |
|||
min_rbindex, op_vc_id = min(cur_rbindex), np.argmin(cur_rbindex) |
|||
if min_rbindex > confidence: |
|||
isolated_variable = [variables[i] for i in (np.where(selected == 1)[0])] # 有问题 |
|||
isolated[sample_id, :] = 0 |
|||
isolated[sample_id, isolated_variable] = 1 |
|||
isolated_variable = np.array(isolated_variable).astype(np.float64) |
|||
A, delt_ff[sample_id,:], C, D = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :], |
|||
isolated_variable) |
|||
# spe[sample_id] = min_rbindex |
|||
break |
|||
else: |
|||
selected[remained_variables[op_vc_id], :] = 0 |
|||
remained_v_num -= 1 |
|||
# 后向序列结束 |
|||
nodenum_record[sample_id] = node_num |
|||
false_alarm = np.where((((fault_magnitude[sample_id, :]>0).astype('int')-isolated[sample_id, :]) == -1) == True) |
|||
print('sample_id-->{};false alarm:{}'.format(sample_id, false_alarm)) |
|||
maxvalue1, maxindex1 = max(isolated[sample_id, :]), np.argmax(isolated[sample_id, :]) |
|||
maxvalue2, maxindex2 = max(abs(fault_magnitude[sample_id, :])), np.argmax(abs(fault_magnitude[sample_id, :])) |
|||
if maxindex1 == maxindex2: |
|||
rod_n +=1 |
|||
time2 = int(round(time.time()*1000)) |
|||
tc = time2-time1 |
|||
real_fault = (abs(fault_magnitude) > 0).astype('int') |
|||
fdr = np.sum(((real_fault-(isolated == 0).astype('int')) == 1).astype('int'))/np.sum(real_fault) |
|||
far = np.sum(((real_fault-isolated) == -1).astype('int'))/np.sum((real_fault == 0).astype('int')) |
|||
|
|||
#fdr_variable, far_variable |
|||
# fdr_variable = [] |
|||
# far_variable = [] |
|||
# for i in range(real_fault.shape[1]): |
|||
# if np.sum(real_fault[:,i]) != 0: |
|||
# fdr_variable.append(np.sum(((real_fault[:,i]-(isolated[:,i] == 0).astype('int')) == 1).astype('int'))/np.sum(real_fault[:,i]))*100 |
|||
# else: |
|||
# fdr_variable.append(0) |
|||
# if np.sum((real_fault[:,i] == 0).astype('int')) != 0: |
|||
# far_variable.append(np.sum(((real_fault[:,i]-isolated[:,i]) == -1).astype('int'))/np.sum((real_fault[:,i] == 0).astype('int')))*100 |
|||
# else: |
|||
# far_variable.append(0) |
|||
fdr_variable = fdr |
|||
far_variable = far |
|||
|
|||
delt_ff=delt_ff/2*(np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1))) #对偏差值进行反归一化 |
|||
Reconstruction_precision=np.sum(np.average(abs(fault_magnitude-delt_ff), axis=0)) |
|||
|
|||
#为AANN_test_offline准备,2021-6-16 rsj&lf |
|||
Data_origin=fault_sample |
|||
errorData = delt_ff #测量值-重构值 |
|||
reconData = Data_origin-errorData |
|||
SPE_list = np.sum(errorData ** 2) |
|||
R = 0 |
|||
for index in range(0, reconData.shape[1]): |
|||
vector1 = Data_origin[:, index] |
|||
vector2 = np.array(reconData)[:, index] |
|||
R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2))) |
|||
R /= reconData.shape[1] |
|||
items = [('reconData', reconData.tolist()) |
|||
, ('errorData', errorData.tolist()), ('R', R.tolist()), ('SPE', SPE_list), ('FAI', SPE_list), |
|||
('paraState', paraState.tolist())] |
|||
ReconRes=json.dumps(dict(items)) # json.dumps(result) |
|||
# 为AANN_test_offline准备 |
|||
|
|||
|
|||
dr = detected/np.sum((np.sum(real_fault, axis=1) > 0).astype('int'))*100 |
|||
rod = rod_n/detected*100 |
|||
nodes = np.mean(nodenum_record) |
|||
|
|||
|
|||
|
|||
return delt_ff,fdr,far,fdr_variable,far_variable,Reconstruction_precision,spe,ReconRes |
|||
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|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Created on 2021-5-3 |
|||
PCA source code |
|||
@author: rsj zjl |
|||
""" |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
import matplotlib.pyplot as plt |
|||
from numba import jit |
|||
import time |
|||
import numpy as np |
|||
import traceback |
|||
from smote import smote |
|||
import config |
|||
import json |
|||
import sys |
|||
import requests |
|||
import datetime |
|||
import jenkspy |
|||
import xlrd |
|||
import AANN_Fit |
|||
|
|||
class Model(object): |
|||
def __init__(self): |
|||
self.v1 = [] |
|||
self.v2 = [] |
|||
self.w1 = [] |
|||
self.w2 = [] |
|||
self.sigma = 0 |
|||
self.maxdata = 0 |
|||
self.mindata = 0 |
|||
|
|||
def AANN(training_Sample, Nodes, num_epochs): |
|||
""" |
|||
AANN建模 |
|||
:param training_Sample: numpy的ndarray格式 |
|||
:param Nodes: [6,3,6] 三个隐层 |
|||
:param num_epochs: 迭代次数iteration |
|||
:return:应返回model(内含v1,w1,v2,w2,sigma,maxdata,mindata) |
|||
""" |
|||
time1 = int(round(time.time() * 1000)) |
|||
mm, nn = training_Sample.shape |
|||
# 预处理 数据归一化 |
|||
mx = np.mean(training_Sample, 0) |
|||
maxdata = np.max(training_Sample, 0) |
|||
mindata = np.min(training_Sample, 0) |
|||
mdata = (2 * training_Sample - (maxdata + mindata + np.zeros((mm, nn)))) / (maxdata - mindata + np.zeros((mm, nn))) |
|||
Nodes = np.array(Nodes).astype(np.int64) |
|||
model = Model() |
|||
# np.save('mdata.npy',mdata) |
|||
print(type(mm)) |
|||
print(type(nn)) |
|||
print(type(num_epochs)) |
|||
print(type(Nodes)) |
|||
print(type(Nodes[0])) |
|||
count, spe, o, v1, v2, w1, w2, sigma = cur_aann(mm, nn, num_epochs, Nodes, mdata) |
|||
reconData = AANN_Fit.AANN_Fit(mdata, v1, v2, w1, w2) |
|||
r2 = 1-np.sum(np.power((mdata-reconData),2),axis=0)/np.sum(np.power((np.tile(np.average(mdata,axis=0), (mm,1))-reconData),2),axis=0) |
|||
# 预处理 数据反归一化 |
|||
a = maxdata - mindata + np.zeros((mm, nn)) |
|||
b = maxdata + mindata + np.zeros((mm, nn)) |
|||
reconData = np.matrix((np.multiply(mdata,a) + b)/2) |
|||
Train_X_min = np.min(training_Sample, axis=0) # 训练值最小值 |
|||
Train_X_max = np.max(training_Sample, axis=0) # 训练值最大值 |
|||
Train_X_mean = np.mean(training_Sample, axis=0) # 训练值平均值 |
|||
Train_X_std = np.std(training_Sample, axis=0) # 训练值方差 |
|||
Train_X_bais = training_Sample - reconData # 训练值偏差 |
|||
Train_X_bais_max = np.max(np.abs(Train_X_bais), axis=0) # 训练值偏差最大值 axis=0 对各列求 |
|||
Train_X_bais_min = np.min(np.abs(Train_X_bais), axis=0) # 训练值偏差最小值 |
|||
Train_X_bais_mean = np.mean(np.abs(Train_X_bais), axis=0) # 训练值偏差平均值 |
|||
Train_X_bais_std_upperB95 = np.array(np.abs(1.96 * np.std(Train_X_bais, axis=0) + Train_X_bais_mean))[ |
|||
0] # 训练值偏差标准差 |
|||
Train_X_bais_std_upperB99 = np.array(np.abs(2.58 * np.std(Train_X_bais, axis=0) + Train_X_bais_mean))[0] |
|||
Train_X_bais_std_lowerB95 = np.array(np.abs(1.96 * np.std(Train_X_bais, axis=0) - Train_X_bais_mean))[ |
|||
0] # 训练值偏差标准差 |
|||
Train_X_bais_std_lowerB99 = np.array(np.abs(2.58 * np.std(Train_X_bais, axis=0) - Train_X_bais_mean))[0] |
|||
QCUL_95_line = [] # 限值 |
|||
QCUL_99_line = [] |
|||
for index1 in range(len(Train_X_bais_std_upperB95)): |
|||
QCUL_95_line.append(max(Train_X_bais_std_upperB95[index1], Train_X_bais_std_lowerB95[index1])) |
|||
QCUL_99_line.append(max(Train_X_bais_std_upperB99[index1], Train_X_bais_std_lowerB99[index1])) |
|||
QCUL_95_line = np.array(QCUL_95_line) |
|||
QCUL_99_line = np.array(QCUL_99_line) |
|||
##################################################################################################################### |
|||
items = [('Train_X_min', np.around(Train_X_min, decimals=3).tolist()), |
|||
('Train_X_max', np.around(Train_X_max, decimals=3).tolist()), |
|||
('Train_X_std', np.around(Train_X_std, decimals=3).tolist()), |
|||
('Train_X_mean',np.around(Train_X_mean, decimals=3).tolist()), |
|||
(('Train_X_bais_max',np.around(Train_X_bais_max, decimals=3).tolist())), |
|||
(('Train_X_bais_min', np.around(Train_X_bais_min, decimals=3).tolist())), |
|||
(('Train_X_bais_mean', np.around(Train_X_bais_mean, decimals=3).tolist())), |
|||
('QCUL_95_line', np.around(QCUL_95_line, decimals=3).tolist()), |
|||
('QCUL_99_line', np.around(QCUL_99_line, decimals=3).tolist()), |
|||
('r2', r2.tolist()), |
|||
('count', count), |
|||
('maxdata', maxdata.tolist()), |
|||
('mindata', mindata.tolist()), |
|||
('sigma', sigma), |
|||
('v1', v1.tolist()), |
|||
('v2', v2.tolist()), |
|||
('w1', w1.tolist()), |
|||
('w2', w2.tolist()) |
|||
] |
|||
time2 = int(round(time.time() * 1000)) |
|||
tc = time2 - time1 |
|||
res_items = [('Model_info', dict(items)), ('Model_type', 'AANN')] |
|||
result = dict(res_items) # json.dumps(result) |
|||
return json.dumps(result) |
|||
|
|||
@jit(nopython=True, cache=True) |
|||
def cur_aann(mm, nn, num_epochs, Nodes, mdata): |
|||
alpha0 = 0.001 |
|||
alfa = 0.5 |
|||
samplenum = mm |
|||
spe = np.zeros(num_epochs + 1) |
|||
errorp = np.zeros(samplenum) |
|||
v1 = 2 * np.random.rand(nn, Nodes[0]) - 1 |
|||
dv1 = np.zeros((nn, Nodes[0])) |
|||
w1 = 2 * np.random.rand(Nodes[0], Nodes[1]) - 1 |
|||
dw1 = np.zeros((Nodes[0], Nodes[1])) |
|||
v2 = 2 * np.random.rand(Nodes[1], Nodes[2]) - 1 |
|||
dv2 = np.zeros((Nodes[1], Nodes[2])) |
|||
w2 = 2 * np.random.rand(Nodes[2], nn) - 1 |
|||
dw2 = np.zeros((Nodes[2], nn)) |
|||
|
|||
xlist = mdata |
|||
expectlist = mdata |
|||
count = 0 |
|||
aa = alfa |
|||
alpha = alpha0 |
|||
y1, y2, y3, o, yitao = np.zeros((samplenum, Nodes[0])), np.zeros((samplenum, Nodes[1])), np.zeros((samplenum, |
|||
Nodes[ |
|||
2])), np.zeros( |
|||
(samplenum, nn)), np.zeros((samplenum, nn)) |
|||
yitay1, yitay2, yitay3 = np.zeros((samplenum, Nodes[0])), np.zeros((samplenum, Nodes[1])), np.zeros((samplenum, |
|||
Nodes[2])) |
|||
while count < num_epochs: |
|||
c = 0 |
|||
while c < samplenum: |
|||
d = expectlist[c, :] |
|||
x = xlist[c, :] |
|||
y1[c, :] = (1 / (1 + np.exp(-np.dot(x, v1)))) |
|||
y2[c, :] = np.dot(y1[c, :], w1) |
|||
y3[c, :] = (1 / (1 + np.exp(-np.dot(y2[c, :], v2)))) |
|||
o[c, :] = np.dot(y3[c, :], w2) |
|||
yitao[c, :] = (d - o[c, :]) |
|||
errorp[c] = 0.5 * np.sum(yitao[c, :] * yitao[c, :]) |
|||
yitay3[c, :] = (np.dot(yitao[c, :], w2.T) * y3[c, :] * (1 - y3[c, :])) |
|||
yitay2[c, :] = np.dot(yitay3[c, :], v2.T) |
|||
yitay1[c, :] = np.dot(yitay2[c, :], w1.T) * y1[c, :] * (1 - y1[c, :]) |
|||
# 调整各层权值 |
|||
deltw2 = np.dot(alpha * y3[c, :].reshape(Nodes[2], 1), yitao[c, :].reshape(1, nn)) |
|||
w2 = w2 + deltw2 + aa * dw2 |
|||
dw2 = deltw2 |
|||
deltv2 = np.dot(alpha * y2[c, :].reshape(Nodes[1], 1), yitay3[c, :].reshape(1, Nodes[2])) |
|||
v2 = v2 + deltv2 + aa * dv2 |
|||
dv2 = deltv2 |
|||
deltw1 = np.dot(alpha * y1[c, :].reshape(Nodes[0], 1), yitay2[c, :].reshape(1, Nodes[1])) |
|||
w1 = w1 + deltw1 + aa * dw1 |
|||
dw1 = deltw1 |
|||
deltv1 = np.dot(alpha * x.reshape(nn, 1), yitay1[c, :].reshape(1, Nodes[0])) |
|||
v1 = v1 + deltv1 + aa * dv1 |
|||
dv1 = deltv1 |
|||
c = c + 1 |
|||
spe[count] = np.sum(errorp) / samplenum |
|||
if count > 1 and abs(spe[count] - spe[count - 1]) < 0.0000001: |
|||
break |
|||
count += 1 |
|||
|
|||
sigma = 3 * np.sum((expectlist - o) * (expectlist - o)) / mm |
|||
|
|||
|
|||
return count, spe, o, v1, v2, w1, w2, sigma |
|||
|
|||
def isnumber(limits): |
|||
flag=True |
|||
for item in limits: |
|||
item=item.replace("-","") |
|||
if(item.isdigit()==False): |
|||
flag=False |
|||
break |
|||
return flag |
|||
|
|||
|
|||
def clearmain(info): |
|||
try: |
|||
Train_Data = info["Train_Data"] |
|||
times = Train_Data["time"].split(';') |
|||
points = Train_Data["points"].split(',') |
|||
interval = Train_Data["interval"] |
|||
if interval == 10000: |
|||
DCount = 60 |
|||
elif interval == 100000: |
|||
DCount = 6 |
|||
elif interval == 300000: |
|||
DCount = 5 |
|||
else: |
|||
DCount = 4 |
|||
dead = Train_Data["dead"].split(',') |
|||
limit = Train_Data["limit"].split(',') |
|||
uplower = Train_Data["uplow"].split(';') |
|||
|
|||
condition=info["conditon"].replace("=","==").replace(">=",">").replace("<=","<") |
|||
#percent = info["Hyper_para"]["percent"] |
|||
|
|||
count=0 |
|||
ItemsInfo, SamplingTimePeriods = [], [] |
|||
Constraint = "" |
|||
for i in range(len(points)): |
|||
iteminfo = {} |
|||
iteminfo["ItemName"] = points[i] # 加点 |
|||
if (dead[i] == "1"): # 判断是否参与死区清洗 |
|||
iteminfo["ClearDeadZone"] = "true" |
|||
else: |
|||
iteminfo["ClearDeadZone"] = "false" |
|||
if (limit[i] == "1"): # 参与上下限清洗 |
|||
limits = uplower[i].split(',') |
|||
if (isnumber(limits) == True): # 输入上下限正确 |
|||
count += 1 |
|||
Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[1] + " and " |
|||
ItemsInfo.append(iteminfo) |
|||
if(count!=0): |
|||
Constraint = Constraint[:len(Constraint) - 4:] |
|||
else: |
|||
Constraint="1==1"#没有上下限清洗 |
|||
Constraint+=" and ("+condition+")" |
|||
Constraint = Constraint.replace("\n", " ") |
|||
for i in range(len(times)): |
|||
Eachsampletime = {} |
|||
timess = times[i].split(',') |
|||
Eachsampletime["StartingTime"] = timess[0] |
|||
Eachsampletime["TerminalTime"] = timess[1] |
|||
SamplingTimePeriods.append(Eachsampletime) |
|||
url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=%d" % ( |
|||
ItemsInfo, SamplingTimePeriods, Constraint, interval, DCount) |
|||
response = requests.get(url) |
|||
content = json.loads(response.text) |
|||
data = np.array([item for item in content["ClearData"]]).T |
|||
try: |
|||
smote_data = info["smote"] |
|||
# smote_data = False |
|||
except KeyError: |
|||
smote_data = False |
|||
if smote_data: |
|||
try: |
|||
smote_index = [points.index(item["pointId"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
smote_num = [int(item["number"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
max_value = [float(item["max"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
min_value = [float(item["min"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
except KeyError: |
|||
pass |
|||
else: |
|||
if len(smote_num) != 0: |
|||
data, *_ = smote(data, smote_index, smote_num, max_value, min_value) |
|||
Nodes=info["layer"] |
|||
num_epochs=Train_Data["interval"] |
|||
result =AANN(data, Nodes, num_epochs)#看看nodes和num_epochs怎么传进来 |
|||
#result = pca(data, percent) |
|||
result = result.replace("NaN", "-1") |
|||
result=json.loads(result) |
|||
result["BeforeCleanSamNum"]=content["BeforeCleanSamNum"] |
|||
result["AfterCleanSamNum"]=content["AfterCleanSamNum"] |
|||
result["CleanOrNot"] = True |
|||
return json.dumps(result) |
|||
except Exception as e: |
|||
result = [{"CleanOrNot": False, "msg": traceback.format_exc()}] |
|||
return json.dumps(result, ensure_ascii=False) |
|||
|
|||
if __name__ == "__main__": |
|||
info_str='{"layer":[5,2,5],"Train_Data":{"time":"2020-10-14 04:10:28,2020-10-17 14:53:00;2021-04-07 07:32:47,2021-04-16 08:39:01;2021-06-01 18:48:17,2021-06-03 14:29:40","points":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","interval":300000,"dead":"1,1,1,1,1,1","limit":"0,0,0,0,0,0","uplow":"null,null;null,null;null,null;null,null;null,null;null,null"},"type":"AANN","conditon":"[DH4_40MAG20CE102]>20","epoch":"10000"}' |
|||
info = json.loads(info_str) |
|||
print(clearmain(info)) |
|||
|
|||
|
|||
|
|||
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@ -0,0 +1,301 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
@Time : 2019/9/12 15:30 |
|||
@Author : 杰森·家乐森 |
|||
@File : ANN_train.py |
|||
@Software: PyCharm |
|||
""" |
|||
import os |
|||
import json |
|||
import time |
|||
import datetime |
|||
import requests |
|||
import numpy as np |
|||
import pandas as pd |
|||
import tensorflow as tf |
|||
from tensorflow.keras import backend |
|||
import matplotlib.pyplot as plt |
|||
from tensorflow.keras import layers |
|||
from sklearn.preprocessing import MinMaxScaler |
|||
from tensorflow.keras.models import load_model |
|||
from tensorflow.keras.models import model_from_json |
|||
import config |
|||
|
|||
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" |
|||
|
|||
def get_history_value(points, time, interval,typedata): |
|||
# url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys" |
|||
url = f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"} # ,"token":get_token() |
|||
point_array = points.split(",") |
|||
time_span = time.split(";") |
|||
value_array = [] |
|||
for item in point_array: |
|||
value_group = [] |
|||
for time_piece in time_span: |
|||
st = time_piece.split(",")[0] |
|||
et = time_piece.split(",")[1] |
|||
para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval} |
|||
response = requests.get(url, headers=headers, params=para) |
|||
value = eval(str(response.text).replace("\"", "").replace("null", str(np.nan))) |
|||
for row in value: |
|||
value_group.append(row[1]) |
|||
value_array.append(value_group) |
|||
valuetrs=np.array(value_array) |
|||
typeArr = list(enumerate(typedata.split(","))) |
|||
data_x = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "0"] |
|||
data_y = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "1"] |
|||
x_data = np.array(data_x).T |
|||
y_data = np.array(data_y).T |
|||
return x_data,y_data,valuetrs |
|||
|
|||
def rmse(y_true, y_pred): |
|||
return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1)) |
|||
|
|||
|
|||
def TrainOffline(x_data,y_data,hidden_layers,epochsdata): |
|||
#计算原来每一列数据的最大值 |
|||
valuetrs = np.hstack((x_data, y_data)) |
|||
# valuetrs = valuetrs |
|||
train_max, train_min, train_ave = [], [], [] |
|||
for row in valuetrs.T: |
|||
train_max.append(np.max(row)) |
|||
train_min.append(np.min(row)) |
|||
train_ave.append(np.mean(row)) |
|||
mms1 = MinMaxScaler() |
|||
mms2 = MinMaxScaler() |
|||
mms3 = MinMaxScaler() |
|||
x_normal = mms1.fit_transform(x_data) |
|||
y_normal = mms2.fit_transform(y_data) |
|||
x_train = x_normal |
|||
y_train = y_normal |
|||
# 构建网络结构 |
|||
model = tf.keras.Sequential() |
|||
model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid")) |
|||
for i in range(len(hidden_layers) - 1): |
|||
model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid")) |
|||
model.add(layers.Dense(units=y_data.shape[1])) |
|||
# 选择损失函数，优化方法 |
|||
model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) |
|||
# 训练模型 |
|||
history = model.fit(x_train, y_train, epochs=epochsdata, batch_size=80) |
|||
path = os.getcwd() + "\\ModelOline"; |
|||
if not os.path.exists(path): |
|||
os.makedirs(path) |
|||
now = datetime.datetime.now() |
|||
# filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5" |
|||
# model.save(filepath) |
|||
filepath = model.to_json() |
|||
model_weight = model.get_weights() |
|||
test_data = model.predict(x_normal, batch_size=400) |
|||
|
|||
# 反归一化 |
|||
predict_data = mms2.inverse_transform(test_data) |
|||
with tf.compat.v1.Session(): |
|||
spe = rmse(test_data, y_normal).eval() |
|||
limit = 3 * spe |
|||
mimusdata = predict_data - y_data |
|||
mimusdatares = mimusdata.T |
|||
pre_min, pre_max, pre_ave, pre_s = [], [], [], [] |
|||
for row in mimusdatares: |
|||
pre_max.append(np.max(row)) |
|||
pre_min.append(np.min(row)) |
|||
pre_ave.append(np.mean(row)) |
|||
pre_s.append(np.std(row) * 3) |
|||
result, mms1new, mms2new = {}, {}, {} |
|||
mms1new["data_max_"] = mms1.data_max_.tolist() |
|||
mms1new["data_min_"] = mms1.data_min_.tolist() |
|||
mms1new["data_range_"] = mms1.data_range_.tolist() |
|||
mms1new["min_"] = mms1.min_.tolist() |
|||
mms1new["scale_"] = mms1.scale_.tolist() |
|||
mms2new["data_max_"] = mms2.data_max_.tolist() |
|||
mms2new["data_min_"] = mms2.data_min_.tolist() |
|||
mms2new["data_range_"] = mms2.data_range_.tolist() |
|||
mms2new["min_"] = mms2.min_.tolist() |
|||
mms2new["scale_"] = mms2.scale_.tolist() |
|||
result["filename"] = filepath |
|||
result["mms1"] = mms1new |
|||
result["mms2"] = mms2new |
|||
result["train_max"] = np.array(train_max).tolist() |
|||
result["train_min"] = np.array(train_min).tolist() |
|||
result["train_ave"] = np.array(train_ave).tolist() |
|||
result["pre_max"] = np.array(pre_max).tolist() |
|||
result["pre_min"] = np.array(pre_min).tolist() |
|||
result["pre_ave"] = np.array(pre_ave).tolist() |
|||
result["pre_s"] = np.array(pre_s).tolist() |
|||
result["pre_s_ave"] = np.mean(np.array(pre_s)).tolist() |
|||
result["limit"] = limit |
|||
result["weight"] = [model_weight[index].tolist() for index in range(len(model_weight))] |
|||
return json.dumps(result) |
|||
|
|||
def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata): |
|||
# 计算原来每一列数据的最大值 |
|||
#x_data = np.array(x_data) |
|||
#y_data = np.array(y_data) |
|||
valuetrs=valuetrs |
|||
train_max, train_min, train_ave = [], [], [] |
|||
for row in valuetrs.T: |
|||
train_max.append(np.max(row)) |
|||
train_min.append(np.min(row)) |
|||
train_ave.append(np.mean(row)) |
|||
mms1 = MinMaxScaler() |
|||
mms2 = MinMaxScaler() |
|||
mms3=MinMaxScaler() |
|||
x_normal = mms1.fit_transform(x_data) |
|||
y_normal = mms2.fit_transform(y_data) |
|||
x_train = x_normal |
|||
y_train = y_normal |
|||
# 构建网络结构 |
|||
model = tf.keras.Sequential() |
|||
model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid")) |
|||
for i in range(len(hidden_layers) - 1): |
|||
model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid")) |
|||
model.add(layers.Dense(units=y_data.shape[1])) |
|||
model.summary() |
|||
# 选择损失函数，优化方法 |
|||
model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) |
|||
# 训练模型 |
|||
history = model.fit(x_train, y_train, epochs=epochsdata, batch_size=80) |
|||
path=os.getcwd()+"\\ModelOline"; |
|||
if not os.path.exists(path): |
|||
os.makedirs(path) |
|||
now=datetime.datetime.now() |
|||
#filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5" |
|||
#model.save(filepath) |
|||
filepath=model.to_json() |
|||
model_weight=model.get_weights() |
|||
test_data = model.predict(x_normal, batch_size=400) |
|||
|
|||
|
|||
# 反归一化 |
|||
predict_data = mms2.inverse_transform(test_data) |
|||
with tf.compat.v1.Session(): |
|||
spe = rmse(y_normal, test_data).eval() |
|||
limit = 3 * spe |
|||
|
|||
mimusdata = predict_data - y_data |
|||
mimusdatares = mimusdata.T |
|||
pre_min, pre_max, pre_ave, pre_s = [], [], [], [] |
|||
for row in mimusdatares: |
|||
pre_max.append(np.max(row)) |
|||
pre_min.append(np.min(row)) |
|||
pre_ave.append(np.mean(row)) |
|||
pre_s.append(np.std(row) * 3) |
|||
result,mms1new,mms2new={},{},{} |
|||
mms1new["data_max_"]=mms1.data_max_.tolist() |
|||
mms1new["data_min_"]=mms1.data_min_.tolist() |
|||
mms1new["data_range_"]=mms1.data_range_.tolist() |
|||
mms1new["min_"]=mms1.min_.tolist() |
|||
mms1new["scale_"]=mms1.scale_.tolist() |
|||
mms2new["data_max_"] = mms2.data_max_.tolist() |
|||
mms2new["data_min_"] = mms2.data_min_.tolist() |
|||
mms2new["data_range_"] = mms2.data_range_.tolist() |
|||
mms2new["min_"] = mms2.min_.tolist() |
|||
mms2new["scale_"] = mms2.scale_.tolist() |
|||
result["filename"]=filepath |
|||
result["mms1"]=mms1new |
|||
result["mms2"]=mms2new |
|||
result["train_max"] = np.array(train_max).tolist() |
|||
result["train_min"] = np.array(train_min).tolist() |
|||
result["train_ave"] = np.array(train_ave).tolist() |
|||
result["pre_max"] = np.array(pre_max).tolist() |
|||
result["pre_min"] = np.array(pre_min).tolist() |
|||
result["pre_ave"] = np.array(pre_ave).tolist() |
|||
result["pre_s"] = np.array(pre_s).tolist() |
|||
result["pre_s_ave"] = np.mean(np.array(pre_s)).tolist() |
|||
result["limit"] = limit |
|||
result["weight"] = [model_weight[index].tolist() for index in range(len(model_weight))] |
|||
return result |
|||
|
|||
|
|||
def isnumber(limits): |
|||
flag=True |
|||
for item in limits: |
|||
item=item.replace("-","") |
|||
if(item.isdigit()==False): |
|||
flag=False |
|||
break |
|||
return flag |
|||
|
|||
def clearmain(info): |
|||
try: |
|||
points = info["point"].split(',') |
|||
times = info["time"].split(';') |
|||
epochs = info["iter"] |
|||
layer = info["layer"] |
|||
typedata = info["type"] |
|||
condition=info["condition"].replace("=","==").replace(">=",">").replace("<=","<") |
|||
interval = 300000 |
|||
dead = info["dead"].split(',') |
|||
limit = info["limit"].split(',') |
|||
uplower = info["uplow"].split(';') |
|||
count=0 |
|||
ItemsInfo, SamplingTimePeriods = [], [] |
|||
Constraint = "" |
|||
for i in range(len(points)): |
|||
iteminfo = {} |
|||
iteminfo["ItemName"] = points[i] # 加点 |
|||
if dead[i] == "1": # 判断是否参与死区清洗 |
|||
iteminfo["ClearDeadZone"] = "true" |
|||
else: |
|||
iteminfo["ClearDeadZone"] = "false" |
|||
if limit[i] == "1": # 参与上下限清洗 |
|||
limits = uplower[i].split(',') |
|||
if (isnumber(limits) == True): # 输入上下限正确 |
|||
count += 1 |
|||
Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[1] + " and " |
|||
ItemsInfo.append(iteminfo) |
|||
if count!=0: |
|||
Constraint = Constraint[:len(Constraint) - 4:] |
|||
else: |
|||
Constraint="1==1"#没有上下限清洗 |
|||
Constraint+=" and ("+condition+")" |
|||
Constraint = Constraint.replace("\n", " ") |
|||
for i in range(len(times)): |
|||
Eachsampletime = {} |
|||
timess = times[i].split(',') |
|||
Eachsampletime["StartingTime"] = timess[0] |
|||
Eachsampletime["TerminalTime"] = timess[1] |
|||
SamplingTimePeriods.append(Eachsampletime) |
|||
url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % ( |
|||
ItemsInfo, SamplingTimePeriods, Constraint, interval) |
|||
response = requests.get(url) |
|||
content = json.loads(response.text) |
|||
# data =np.array([item for item in content["ClearData"] if item ]).T |
|||
valuetrs = np.array([item for item in content["ClearData"]]).T |
|||
typeArr = list(enumerate(typedata.split(","))) |
|||
data_x = [valuetrs[:,item[0]].tolist() for item in typeArr if item[1]=="0"] |
|||
data_y = [valuetrs[:,item[0]].tolist() for item in typeArr if item[1]=="1"] |
|||
data_x = np.array(data_x).T |
|||
data_y = np.array(data_y).T |
|||
result = Train(data_x, data_y, layer, valuetrs, eval(epochs)) |
|||
result["BeforeCleanSamNum"]=content["BeforeCleanSamNum"] |
|||
result["AfterCleanSamNum"]=content["AfterCleanSamNum"] |
|||
result["CleanOrNot"] = True |
|||
return result |
|||
except Exception as e: |
|||
result = {"CleanOrNot": False} |
|||
return result |
|||
|
|||
|
|||
|
|||
def main(info): |
|||
points = info["point"] |
|||
time1 = info["time"] |
|||
interval = info["interval"] |
|||
epochs = info["iter"] |
|||
layer = info["layer"] |
|||
datatype = info["type"] |
|||
data_x, data_y, origndata = get_history_value(points, time1, interval, datatype) |
|||
result = Train(data_x, data_y, layer, origndata, eval(epochs)) |
|||
return result |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
# info_str = '{"time":"2020-01-19 23:26:04,2020-01-25 20:42:16;2020-01-26 16:23:49,2020-02-03 11:36:42;2020-02-05 20:02:49,2020-02-06 05:51:40","condition":"1=1","interval":300000,"dead":"1,1,1","layer":["6"],"point":"JL_D1_10MILLA:SEP_TEMP.PNT,JL_D1_10FSSS20A:HFC10CT301.PNT,JL_D1_10FSSS20A:HFC10CT302.PNT","type":"1,0,0","limit":"0,0,0","uplow":",;,;,","iter":"100"}' |
|||
info_str = '{"iter":"800","dead":"1,1,1,1","point":"DH4_40HLF10CE101,DH4_40HLF10CT351,DH4_40HLF10CT352,DH4_40HLF10CY101","limit":"0,0,0,0","layer":["5"],"type":"0,0,0,1","time":"2020-10-06 19:17:46,2020-10-12 14:58:19","condition":"1=1","interval":300000,"uplow":"null,null;null,null;null,null;null,null"}' |
|||
info = json.loads(info_str) |
|||
result = clearmain(info) |
|||
print(result) |
|||
# info = {"iter":"800","Train_Data_X":[[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0],[3.0,3.0,3.0,3.0,3.0,3.0,3.0],[4.0,4.0,4.0,4.0,4.0,4.0,4.0],[5.0,5.0,5.0,5.0,5.0,5.0,5.0],[6.0,6.0,6.0,6.0,6.0,6.0,6.0],[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0]],"hide":["7","5","1"],"Train_Data_Y":[[7.0],[8.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0],[1.0],[2.0]]} |
|||
# result = TrainOffline(np.array(info["Train_Data_X"]), np.array(info["Train_Data_Y"]),info["hide"], eval(info["iter"])) |
|||
File diff suppressed because it is too large
Binary file not shown.
@ -0,0 +1,330 @@ |
|||
import execjs |
|||
import csv |
|||
import numpy as np |
|||
import pandas as pd |
|||
import time |
|||
import json |
|||
import jenkspy |
|||
import requests |
|||
import pymssql |
|||
import config |
|||
|
|||
class HealthyScoringSystem: |
|||
def __init__(self): |
|||
# 读取配置 |
|||
ms = MSSQL(host="172.28.137.230", user="sa", pwd="powerSIS#123", database="ASSESS") |
|||
|
|||
conditionlist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[conditionlist]") |
|||
pointconfigs = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[pointconfigs]") |
|||
scorelist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[scorelist]") |
|||
subsystemweightlist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[subsystemweightlist]") |
|||
systemweightlist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[systemweightlist]") |
|||
unitweightlist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[unitweightlist]") |
|||
|
|||
condi = list(range(len(conditionlist))) |
|||
for i in range(len(condi)): |
|||
condi[i] = str(condi[i]) |
|||
|
|||
point = list(range(len(pointconfigs))) |
|||
for i in range(len(point)): |
|||
point[i] = str(point[i]) |
|||
|
|||
score = list(range(len(scorelist))) |
|||
for i in range(len(score)): |
|||
score[i] = str(score[i]) |
|||
|
|||
subsy = list(range(len(subsystemweightlist))) |
|||
for i in range(len(subsy)): |
|||
subsy[i] = str(subsy[i]) |
|||
|
|||
syste = list(range(len(systemweightlist))) |
|||
for i in range(len(syste)): |
|||
syste[i] = str(syste[i]) |
|||
|
|||
unitw = list(range(len(unitweightlist))) |
|||
for i in range(len(unitw)): |
|||
unitw[i] = str(unitw[i]) |
|||
|
|||
self.conditionlist = dict(zip(list(dict(zip(condi,condi)).values()),conditionlist)) |
|||
self.pointconfigs = dict(zip(list(dict(zip(point,point)).values()),pointconfigs)) |
|||
self.scorelist = dict(zip(list(dict(zip(score,score)).values()),scorelist)) |
|||
self.subsystemweightlist = dict(zip(list(dict(zip(subsy,subsy)).values()),subsystemweightlist)) |
|||
self.systemweightlist = dict(zip(list(dict(zip(syste,syste)).values()),systemweightlist)) |
|||
self.unitweightlist = dict(zip(list(dict(zip(unitw,unitw)).values()),unitweightlist)) |
|||
|
|||
# self.treeconfigs = self.indexMap("treeconfigs.csv") |
|||
|
|||
self.inputData = {} |
|||
self.conditionMap = {} |
|||
self.outputData = {} |
|||
|
|||
self.time = time.time() |
|||
|
|||
def indexMap(self, csvFileName): |
|||
map = {} |
|||
csvFile = open(csvFileName, "r") |
|||
reader = csv.reader(csvFile) |
|||
colnames = [] |
|||
for item in reader: |
|||
|
|||
if reader.line_num == 1: |
|||
for colname in item: |
|||
colnames.append(colname) |
|||
else: |
|||
details = {} |
|||
for i in range(len(colnames) - 1): |
|||
details[colnames[i + 1]] = item[i + 1] |
|||
map[item[0]] = details |
|||
csvFile.close() |
|||
return map |
|||
|
|||
def getInputDate(self): |
|||
# 取实时值 |
|||
for value in self.pointconfigs.values(): |
|||
self.inputData[value['pointname']] = get_now_data(value['pointname']) # 上线之后把这个换成取实时值 |
|||
self.time = time.time() |
|||
|
|||
def Scoring(self): |
|||
# 测点级打分 |
|||
for key, value in self.scorelist.items(): |
|||
x1 = self.inputData[self.pointconfigs[value['firstpoint']]['pointname']] |
|||
if value['formula'] == '0': |
|||
# 分数等于 np.clip(x1-x2,0,1) |
|||
x2 = self.inputData[self.pointconfigs[value['secondpoint']]['pointname']] |
|||
y = np.clip(x1 - x2, 0, 1) |
|||
elif value['formula'] == '1': |
|||
# 分数等于np.clip(abs(x1-x2),0,1) |
|||
x2 = self.inputData[self.pointconfigs[value['secondpoint']]['pointname']] |
|||
y = np.clip(abs(x1 - x2), 0, 1) |
|||
elif value['formula'] == '2': |
|||
# 分数等于np.tanh(x1) |
|||
y = np.tanh(x1) |
|||
else: |
|||
# 分数根据js字符串解析 |
|||
x2 = self.inputData[self.pointconfigs[value['secondpoint']]['pointname']] |
|||
y = execjs.eval(value['formula'].replace("x1", str(x1)).replace("x2", str(x2))) |
|||
self.outputData[value['scoreoutput']] = y |
|||
|
|||
def Conditioning(self): |
|||
# 求解计算条件 |
|||
for key, value in self.conditionlist.items(): |
|||
inputlist = value['inputlist'].split(',') |
|||
formula = value['code'] |
|||
for i in range(len(inputlist)): |
|||
formula = formula.replace("x" + str(len(inputlist) - i), |
|||
str(self.inputData[ |
|||
self.pointconfigs[inputlist[len(inputlist) - i - 1]]['pointname']])) |
|||
y = execjs.eval(formula) |
|||
self.conditionMap[key] = y |
|||
|
|||
def weightedSumScoring(self, level): |
|||
# 系统级打分 |
|||
switch = { |
|||
"subsystem": { |
|||
'currentlevellist': self.subsystemweightlist, |
|||
'nextlevellist': self.scorelist, |
|||
'nextlevelliststr': 'scorelist', |
|||
}, |
|||
"system": { |
|||
'currentlevellist': self.systemweightlist, |
|||
'nextlevellist': self.subsystemweightlist, |
|||
'nextlevelliststr': 'subsystemlist', |
|||
}, |
|||
"unit": { |
|||
'currentlevellist': self.unitweightlist, |
|||
'nextlevellist': self.systemweightlist, |
|||
'nextlevelliststr': 'systemlist', |
|||
}, |
|||
} |
|||
|
|||
currentlevellist = switch[level]['currentlevellist'] |
|||
nextlevellist = switch[level]['nextlevellist'] |
|||
nextlevelliststr = switch[level]['nextlevelliststr'] |
|||
|
|||
for key, value in currentlevellist.items(): |
|||
readyFlag = True |
|||
conditionlist = value['conditionlist'].split(',') |
|||
for condition in conditionlist: |
|||
readyFlag = readyFlag and self.conditionMap[condition] |
|||
readyFlag = True # 后面要删掉 |
|||
if readyFlag: |
|||
scoreindexlist = value[nextlevelliststr].split(',') |
|||
scorelist = [] |
|||
for scoreindex in scoreindexlist: |
|||
scorelist.append(self.outputData[nextlevellist[scoreindex]['scoreoutput']]) |
|||
weightlist = list(map(float, (value['weightlist'].split(',')))) |
|||
score = \ |
|||
np.dot(np.array(scorelist).reshape(1, len(scorelist)), |
|||
np.array(weightlist).reshape(len(scorelist), 1))[ |
|||
0, 0] / np.sum(weightlist) |
|||
self.outputData[value['scoreoutput']] = score |
|||
|
|||
def getScore(self, unit=None, system=None, subsystem=None, type=None): |
|||
# 获取指定级别分数的接口 |
|||
if subsystem != None: |
|||
for key, value in self.subsystemweightlist.items(): |
|||
if value['unit'] == unit and value['system'] == system and value['subsystem'] == subsystem and value[ |
|||
'type'] == type: |
|||
return self.outputData[value['scoreoutput']] |
|||
elif system != None and subsystem == None: |
|||
for key, value in self.systemweightlist.items(): |
|||
if value['unit'] == unit and value['system'] == system and value['type'] == type: |
|||
return self.outputData[value['scoreoutput']] |
|||
elif unit != None and system == None: |
|||
for key, value in self.unitweightlist.items(): |
|||
if value['unit'] == unit and value['type'] == type: |
|||
return self.outputData[value['scoreoutput']] |
|||
|
|||
def outputScore(self): |
|||
|
|||
nowtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(self.time)) |
|||
ms = MSSQL(host="172.28.137.230", user="sa", pwd="powerSIS#123", database="ASSESS") |
|||
|
|||
score = len(self.scorelist) |
|||
subsy = len(self.subsystemweightlist) + score |
|||
syste = len(self.systemweightlist) + subsy |
|||
|
|||
|
|||
for table in ['conditionlist', 'scorelist', 'subsystemweightlist', 'systemweightlist', 'unitweightlist']: |
|||
|
|||
if table == 'conditionlist': |
|||
for i in range(len(self.conditionlist)): |
|||
setitem = f"result = '{self.conditionMap[f'{i}']}' , time = '{nowtime}'" |
|||
whereitem = f"indice = {i}" |
|||
Queryitem = f"UPDATE [ASSESS].[dbo].[{table}] set {setitem} where {whereitem}" |
|||
ms.ExecNonQuery(Queryitem) |
|||
|
|||
elif table == 'scorelist': |
|||
j = 0 |
|||
for i in list(self.outputData.values())[:score]: |
|||
setitem = f"result = '{i}' , time = '{nowtime}'" |
|||
whereitem = f"indice = {j}" |
|||
Queryitem = f"UPDATE [ASSESS].[dbo].[{table}] set {setitem} where {whereitem}" |
|||
ms.ExecNonQuery(Queryitem) |
|||
j = j + 1 |
|||
|
|||
elif table == 'subsystemweightlist': |
|||
j = 0 |
|||
for i in list(self.outputData.values())[score:subsy]: |
|||
setitem = f"result = '{i}' , time = '{nowtime}'" |
|||
whereitem = f"indice = {j}" |
|||
Queryitem = f"UPDATE [ASSESS].[dbo].[{table}] set {setitem} where {whereitem}" |
|||
ms.ExecNonQuery(Queryitem) |
|||
j = j + 1 |
|||
|
|||
elif table == 'systemweightlist': |
|||
j = 0 |
|||
for i in list(self.outputData.values())[subsy:syste]: |
|||
setitem = f"result = '{i}' , time = '{nowtime}'" |
|||
whereitem = f"indice = {j}" |
|||
Queryitem = f"UPDATE [ASSESS].[dbo].[{table}] set {setitem} where {whereitem}" |
|||
ms.ExecNonQuery(Queryitem) |
|||
j = j + 1 |
|||
|
|||
elif table == 'unitweightlist': |
|||
j = 0 |
|||
for i in list(self.outputData.values())[syste:]: |
|||
setitem = f"result = '{i}' , time = '{nowtime}'" |
|||
whereitem = f"indice = {j}" |
|||
Queryitem = f"UPDATE [ASSESS].[dbo].[{table}] set {setitem} where {whereitem}" |
|||
ms.ExecNonQuery(Queryitem) |
|||
j = j + 1 |
|||
|
|||
|
|||
|
|||
class MSSQL: |
|||
def __init__(self,host,user,pwd,database): |
|||
self.host = host |
|||
self.user = user |
|||
self.pwd = pwd |
|||
self.db = database |
|||
|
|||
def __GetConnect(self): |
|||
""" |
|||
得到连接信息 |
|||
返回: conn.cursor() |
|||
""" |
|||
if not self.db: |
|||
raise(NameError,"没有设置数据库信息") |
|||
self.conn = pymssql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,port=config._PORT, charset="utf8") |
|||
cur = self.conn.cursor() |
|||
if not cur: |
|||
raise(NameError,"连接数据库失败") |
|||
else: |
|||
return cur |
|||
|
|||
def ExecQuery(self,sql): |
|||
""" |
|||
执行查询语句 |
|||
返回的是一个包含tuple的list，list的元素是记录行，tuple的元素是每行记录的字段 |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
resList = cur.fetchall() |
|||
for i in range(len(resList)): |
|||
resList[i] = resList[i][1:] |
|||
#读取 sql 得到dict by lifan |
|||
# 只用于select语句，返回一行的列名 |
|||
desc = cur.description |
|||
desc = desc[:0] + desc[1:] |
|||
object_dict = [ |
|||
dict(zip([col[0] for col in desc], row)) |
|||
for row in resList |
|||
] |
|||
|
|||
#查询完毕后必须关闭连接 |
|||
self.conn.close() |
|||
return object_dict |
|||
|
|||
def ExecNonQuery(self,sql): |
|||
""" |
|||
执行非查询语句 |
|||
|
|||
调用示例： |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
|
|||
|
|||
def get_now_data(items): |
|||
url = f"http://{config._EXA_IP}:9000/exawebapi/exanow/getfloatvaluebatch" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"} # ,"token":get_token() |
|||
#point_array = items.split(',') |
|||
data = {} |
|||
data['ItemNames'] = items |
|||
res = requests.get(url, headers=headers, params=data) |
|||
response = res.text.replace("[","").replace("]","") |
|||
return float(response) |
|||
|
|||
if __name__ == '__main__': |
|||
a = "JL_D2_20DAS05A:LAV10CE101.PNT" |
|||
#get_now_data(a) |
|||
|
|||
HSS = HealthyScoringSystem() |
|||
|
|||
HSS.getInputDate() |
|||
HSS.Scoring() |
|||
HSS.Conditioning() |
|||
HSS.weightedSumScoring('subsystem') |
|||
HSS.weightedSumScoring('system') |
|||
HSS.weightedSumScoring('unit') |
|||
HSS.outputScore() |
|||
|
|||
print(HSS.getScore(unit='1', type='1')) |
|||
print(HSS.getScore(unit='1', system='1', type='1')) |
|||
print(HSS.getScore(unit='1', system='1', subsystem='1', type='1')) |
|||
|
|||
|
|||
|
|||
|
|||
# url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=%d" % (ItemsInfo, SamplingTimePeriods, Constraint, interval, DCount) |
|||
# response = requests.get(url) |
|||
# content = json.loads(response.text) |
|||
|
|||
b = 1 |
|||
Binary file not shown.
@ -0,0 +1,294 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Created on Sun Feb 28 10:04:26 2016 |
|||
PCA source code |
|||
@author: liudiwei |
|||
""" |
|||
|
|||
import xlsxwriter as xw |
|||
import numpy as np |
|||
import pandas as pd |
|||
import matplotlib.pyplot as plt |
|||
from scipy.stats import norm |
|||
from scipy.stats.distributions import chi2 |
|||
import json |
|||
import sys |
|||
import pymssql |
|||
import requests |
|||
import datetime |
|||
from scipy.stats import norm |
|||
from scipy.stats import f |
|||
from scipy.stats import chi2 |
|||
import jenkspy |
|||
import xlrd |
|||
import time |
|||
# import PCA_Test_offline |
|||
import config |
|||
|
|||
|
|||
""" |
|||
参数： |
|||
- XMat：传入的是一个numpy的矩阵格式，行表示样本数，列表示特征 |
|||
- k：表示取前k个特征值对应的特征向量 |
|||
返回值： |
|||
- finalData：参数一指的是返回的低维矩阵，对应于输入参数二 |
|||
- reconData：参数二对应的是移动坐标轴后的矩阵 |
|||
""" |
|||
|
|||
|
|||
def min_pos(X): |
|||
X[X <= 0] = np.max(X) |
|||
m = np.min(X) |
|||
re = np.where(X == m) |
|||
min_i = re[0] |
|||
min_j = re[1] |
|||
if m < 0: |
|||
m = 0 |
|||
return m, min_i, min_j |
|||
|
|||
|
|||
def Lars(X, Y, D, DIAG, t, limit_line, lamuta): |
|||
n, m = X.shape |
|||
beta = np.zeros((1, m)) |
|||
A = [] |
|||
N_Co_added = 0 |
|||
i = 0 |
|||
mse = [] |
|||
for k in range(m): |
|||
i += 1 |
|||
ero = np.array(Y - beta[-1, :].T) |
|||
# c=np.dot(P,DIAG,P.T,ero) |
|||
c = np.dot(D, DIAG).dot(D.T).dot(ero) # 计算相关性 |
|||
C = np.max(np.abs(c)) |
|||
mse.append(np.dot(ero.T, D).dot(DIAG).dot(D.T).dot(ero)) |
|||
if mse[k] < limit_line: |
|||
break |
|||
elif k == 0: |
|||
addTndex = np.where(abs(c) == C)[-1][0] |
|||
A.append(addTndex) # 活动集 |
|||
# 更新正在添加的相应协方差索引的数量 |
|||
N_Co_added = N_Co_added + 1 |
|||
A_c = list(set(range(0, m)).difference(set(A))) # 非活动集 |
|||
s_A = np.diag(np.sign(c[A])) |
|||
|
|||
num_Zero_Coeff = len(A_c) |
|||
## 计算 X_A， A_A ， u_A ，the inner product vecto |
|||
X_A = np.dot(X[:, A], s_A).reshape(n, -1) |
|||
G_A = np.dot(X_A.T, X_A) |
|||
One_A = np.ones((len(A), 1)) |
|||
s = One_A.copy() |
|||
if G_A.shape == (): |
|||
inv_GA = 1 / G_A |
|||
else: |
|||
inv_GA = np.linalg.pinv(G_A) |
|||
# G_a_inv_red_cols = np.sum(inv_GA, 1) |
|||
A_A = 1 / np.sqrt(np.dot(s.T, inv_GA).dot(s)) |
|||
w_A = (A_A * inv_GA).dot(s) # w_A: (less then 90%)构成等角的单位向量 |
|||
u_A = np.dot(X_A, w_A) # .reshape(n) |
|||
a = X.T.dot(u_A) # inner product vector |
|||
gamma_Test = np.zeros((num_Zero_Coeff, 2)) |
|||
# gamma=[] |
|||
if N_Co_added == m - 1: |
|||
gamma = C / A_A |
|||
else: |
|||
for j in range(num_Zero_Coeff): |
|||
j_p = A_c[j] |
|||
first_term = (C - c[j_p]) / (A_A - a[j_p]) |
|||
second_term = (C + c[j_p]) / (A_A + a[j_p]) |
|||
gamma_Test[j, :] = np.array([first_term, second_term]).reshape(1, -1) |
|||
gamma, min_i, min_j = min_pos(gamma_Test) |
|||
# gamma.append(m_s) |
|||
addTndex = A_c[np.min(min_i)] |
|||
beta_temp = np.zeros((m, 1)) |
|||
beta_temp[A] = beta[k, A].reshape(-1, 1) + np.dot(s_A, gamma * w_A) |
|||
beta = np.vstack((beta, beta_temp.transpose())) # 更新的系数即故障f |
|||
return beta, mse |
|||
|
|||
|
|||
# import sklearn |
|||
# q=sklearn.linear_model.Lars |
|||
|
|||
|
|||
class MSSQL: |
|||
def __init__(self,host,user,pwd,database): |
|||
self.host = host |
|||
self.user = user |
|||
self.pwd = pwd |
|||
self.db = database |
|||
|
|||
def __GetConnect(self): |
|||
""" |
|||
得到连接信息 |
|||
返回: conn.cursor() |
|||
""" |
|||
if not self.db: |
|||
raise(NameError,"没有设置数据库信息") |
|||
self.conn = pymssql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,port=config._PORT, charset="utf8") |
|||
cur = self.conn.cursor() |
|||
if not cur: |
|||
raise(NameError,"连接数据库失败") |
|||
else: |
|||
return cur |
|||
|
|||
def ExecQuery(self,sql): |
|||
""" |
|||
执行查询语句 |
|||
返回的是一个包含tuple的list，list的元素是记录行，tuple的元素是每行记录的字段 |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
resList = cur.fetchall() |
|||
|
|||
#查询完毕后必须关闭连接 |
|||
self.conn.close() |
|||
return resList |
|||
|
|||
def ExecNonQuery(self,sql): |
|||
""" |
|||
执行非查询语句 |
|||
|
|||
调用示例： |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
|
|||
def get_model_by_ID(model_id, version="v-test"): |
|||
ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert") |
|||
resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[Model_CFG] where model_id={model_id}") |
|||
return json.loads(resList[0][0]) |
|||
|
|||
|
|||
def get_model_by_id_and_version(model_id, version): |
|||
ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert") |
|||
resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[model_version] where model_id={model_id} and version='{version}'") |
|||
return json.loads(resList[0][0]) |
|||
|
|||
def pca(model, Data_origin): |
|||
Data = (Data_origin - model["Train_X_mean"]) / model["Train_X_std"] |
|||
featValue = np.array(model["featValue"]) # 训练数据的特征值 |
|||
k=(model["K"]) # 主元个数 |
|||
featVec = np.array(model["featVec"]) # 训练数据的特征向量 |
|||
selectVec1 = np.array(model["selectVec"]) |
|||
selectVec=featVec[:, 0:k] |
|||
index = np.argsort(-np.array(featValue)) # 按照featValue进行从大到小排序 |
|||
featValue_sort = featValue[index] # 排序后的特征值 |
|||
|
|||
############----------*********-SPE-**************----------######################## |
|||
numbel_variable = featValue.shape[0] |
|||
C_ = np.eye(numbel_variable) - np.dot(selectVec, selectVec.T) |
|||
|
|||
SPE_list = [] |
|||
for i in range(Data.shape[0]): |
|||
Y = Data[i, :] # 测试数据的每一行 |
|||
#########*********************计算SPE****************************** |
|||
SPE_line =np.dot(Y, C_).dot(Y.T)###SPE根号 |
|||
SPE_list.append(SPE_line) |
|||
|
|||
paraState = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]]) |
|||
finalData = np.dot(Data, selectVec).dot(selectVec.T) |
|||
reconData = np.add(np.multiply(finalData, model["Train_X_std"]), model["Train_X_mean"]) # 重构值 |
|||
errorData =Data_origin - reconData # 偏差值 |
|||
# cos检验值 |
|||
R = 0 |
|||
res={} |
|||
for index in range(0, reconData.shape[1]): |
|||
vector1 = Data_origin[:, index] |
|||
vector2 = np.array(reconData)[:, index] |
|||
R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2))) |
|||
R /= reconData.shape[1] |
|||
#items = [('reconData', np.around(reconData, decimals=3).tolist()) |
|||
# , ('errorData', np.around(errorData, decimals=3).tolist()), ('R', R.tolist()), ('SPE', np.array(SPE_list).tolist()), |
|||
# ('paraState', paraState.tolist())] |
|||
#res["sampleData"]=np.transpose(Data_origin.tolist()) |
|||
res["sampleData"]=np.transpose(np.array(Data_origin)).tolist() |
|||
res["reconData"]=np.around(np.transpose(np.array(reconData)), decimals=3).tolist() |
|||
res["errorData"]=np.around(np.transpose(np.array(errorData)), decimals=3).tolist() |
|||
res["R"]=np.around(R, decimals=3).tolist() |
|||
res["SPE"]=np.around(np.transpose(np.array(SPE_list)), decimals=3).tolist() |
|||
res["paraState"]=np.transpose(np.array(paraState)).tolist() |
|||
|
|||
#result = json.dumps(dict(items)) # json.dumps(result) |
|||
#return result |
|||
return res |
|||
|
|||
|
|||
def get_history_value(points,time,interval): |
|||
#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys" |
|||
url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token() |
|||
point_array = points.split(",") |
|||
time_span = time.split(";") |
|||
value_array = [] |
|||
for item in point_array: |
|||
value_group = [] |
|||
for time_piece in time_span: |
|||
st = time_piece.split(",")[0] |
|||
et = time_piece.split(",")[1] |
|||
para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval} |
|||
response = requests.get(url, headers=headers, params=para) |
|||
content = response.text.replace('"[','[').replace(']"',']') |
|||
value = json.loads(content) |
|||
if not isinstance(value, list): |
|||
print("aaa") |
|||
for row in value: |
|||
value_group.append(row[1]) |
|||
value_array.append(value_group) |
|||
return np.transpose(np.array(value_array)) |
|||
#return valres |
|||
|
|||
|
|||
def main(info): |
|||
model_id = info["Model_id"] |
|||
try: |
|||
version = info["version"] |
|||
except KeyError: |
|||
version = "v-test" |
|||
if version == "v-test": |
|||
res = get_model_by_ID(model_id) |
|||
else: |
|||
res = get_model_by_id_and_version(model_id, version) |
|||
Test_Data = info["Test_Data"] |
|||
points = Test_Data["points"] |
|||
time1 = Test_Data["time"] |
|||
interval = Test_Data["interval"] |
|||
model = res["para"]["Model_info"] |
|||
Data = get_history_value(points, time1, interval) |
|||
result = pca(model, Data) |
|||
index = time1.index(",") |
|||
result["time"] = time1[:index:] |
|||
return result |
|||
|
|||
# 根据数据集data.txt |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
info_str='{"Test_Data":{"time":"2021-01-13 12:52:40,2021-01-14 12:52:40","points":"JL_D2_20SCS02A:MAG10CT311.PNT,JL_D2_20DAS01B:MAG10CE101.PNT,JL_D2_20MCS01A:MAG10AN001ZT.PNT,JL_D2_20SCS02A:MAG10CT312.PNT,JL_D2_20DAS01B:MAG10CE102.PNT,JL_D2_20MCS01A:MAG10AN002ZT.PNT,JL_D2_20SCS02A:MAG10CT313.PNT,JL_D2_20DAS01B:MAG10CE103.PNT,JL_D2_20MCS01A:MAG10AN003ZT.PNT,JL_D2_20SCS02A:MAG10CT314.PNT,JL_D2_20DAS01B:MAG10CE104.PNT,JL_D2_20MCS01A:MAG10AN004ZT.PNT,JL_D2_20SCS02A:MAG10CT315.PNT,JL_D2_20DAS01B:MAG10CE105.PNT,JL_D2_20MCS01A:MAG10AN005ZT.PNT,JL_D2_20SCS02A:MAG10CT316.PNT,JL_D2_20DAS01B:MAG10CE106.PNT,JL_D2_20MCS01A:MAG10AN006ZT.PNT,JL_D2_20SCS02A:MAG10CT317.PNT,JL_D2_20DAS01B:MAG10CE107.PNT,JL_D2_20MCS01A:MAG10AN007ZT.PNT,JL_D2_20DAS01B:MAJ10CT101.PNT,JL_D2_20DAS01B:MAJ10CT102.PNT,JL_D2_20SCS02A:MAG10CT101.PNT,JL_D2_20SCS02A:MAG10CT102.PNT,JL_D2_20DAS01B:MAG03CG101.PNT,JL_D2_20DAS01B:MAG03CS101.PNT","interval":300000},"Model_id":528,"version":"v-test"}' |
|||
info = json.loads(info_str) |
|||
print(main(info)) |
|||
# model_id=info["Model_id"] |
|||
# Test_Data = info["Test_Data"] |
|||
# points = Test_Data["points"] |
|||
# time = Test_Data["time"] |
|||
# interval = Test_Data["interval"] |
|||
# Data = get_history_value(points, time, interval) |
|||
# # workbook = xw.Workbook("pca_test.xlsx") |
|||
# # worksheet = workbook.add_worksheet() |
|||
# # for row, item in enumerate(Data.tolist()): |
|||
# # for col, cell in enumerate(item): |
|||
# # worksheet.write(row, col, cell) |
|||
# # workbook.close() |
|||
# model = PCA_Test_offline.get_model_by_ID(model_id)["para"]["Model_info"] |
|||
# result = pca(model,Data)#模型参数，训练数据 |
|||
# aaa=json.dumps(result) |
|||
# print (result) |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
@ -0,0 +1,317 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Created on Sun Feb 28 10:04:26 2016 |
|||
PCA source code————最新更新———————————————————————————— |
|||
@author: liudiwei |
|||
""" |
|||
|
|||
|
|||
import numpy as np |
|||
import pandas as pd |
|||
from scipy.stats import norm |
|||
from scipy.stats.distributions import chi2 |
|||
import json |
|||
import sys |
|||
import pymssql |
|||
import requests |
|||
import datetime |
|||
from scipy.stats import norm |
|||
from scipy.stats import f |
|||
from scipy.stats import chi2 |
|||
import jenkspy |
|||
import xlrd |
|||
import gc |
|||
import time |
|||
import pyodbc |
|||
from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c |
|||
import config |
|||
|
|||
|
|||
""" |
|||
参数： |
|||
- XMat：传入的是一个numpy的矩阵格式，行表示样本数，列表示特征 |
|||
- k：表示取前k个特征值对应的特征向量 |
|||
返回值： |
|||
- finalData：参数一指的是返回的低维矩阵，对应于输入参数二 |
|||
- reconData：参数二对应的是移动坐标轴后的矩阵 |
|||
""" |
|||
|
|||
|
|||
def min_pos(X): |
|||
X[X <= 0] = np.max(X) |
|||
m = np.min(X) |
|||
re = np.where(X == m) |
|||
min_i = re[0] |
|||
min_j = re[1] |
|||
if m < 0: |
|||
m = 0 |
|||
return m, min_i, min_j |
|||
|
|||
class MSSQL: |
|||
def __init__(self,host,user,pwd,database): |
|||
self.host = host |
|||
self.user = user |
|||
self.pwd = pwd |
|||
self.db = database |
|||
|
|||
def __GetConnect(self): |
|||
""" |
|||
得到连接信息 |
|||
返回: conn.cursor() |
|||
""" |
|||
if not self.db: |
|||
raise(NameError,"没有设置数据库信息") |
|||
self.conn = pymssql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,charset="utf8") |
|||
cur = self.conn.cursor() |
|||
if not cur: |
|||
raise(NameError,"连接数据库失败") |
|||
else: |
|||
return cur |
|||
|
|||
def ExecQuery(self,sql): |
|||
""" |
|||
执行查询语句 |
|||
返回的是一个包含tuple的list，list的元素是记录行，tuple的元素是每行记录的字段 |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
resList = cur.fetchall() |
|||
|
|||
#查询完毕后必须关闭连接 |
|||
self.conn.close() |
|||
return resList |
|||
|
|||
def ExecNonQuery(self,sql): |
|||
""" |
|||
执行非查询语句 |
|||
|
|||
调用示例： |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
""" |
|||
cur = self.__GetConnect() |
|||
cur.execute(sql) |
|||
self.conn.commit() |
|||
self.conn.close() |
|||
|
|||
def get_model_by_ID(model_id): |
|||
ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert") |
|||
resList = ms.ExecQuery("SELECT Model_info FROM [alert].[dbo].[Model_CFG] where \"model_id\"="+str(model_id)) |
|||
#return json.loads(resList[0][0])["para"] |
|||
return json.loads(resList[0][0]) |
|||
|
|||
|
|||
def get_model_by_id(model_id): |
|||
try: |
|||
conn = pyodbc.connect( |
|||
r"DRIVER={ODBC Driver 17 for SQL Server};SERVER=%s;DATABASE=alert;UID=sa;PWD=powerSIS#123" % config._SQL_IP) # 连接数据库 |
|||
except pyodbc.Error: |
|||
conn = pyodbc.connect( |
|||
r"DRIVER={SQL SERVER NATIVE CLIENT 10.0};SERVER=%s;DATABASE=alert;UID=sa;PWD=powerSIS#123" % config._SQL_IP) # 连接数据库 |
|||
cursor = conn.cursor() # 获得操作的游标 |
|||
cursor.execute(f"SELECT Model_info FROM [alert].[dbo].[Model_CFG] where model_id={model_id}") |
|||
res_list = cursor.fetchall() # 获取查询的结果 |
|||
conn.commit() # 提交执行 |
|||
cursor.close() # 关闭游标 |
|||
conn.close() # 关闭数据库连接 |
|||
return json.loads(res_list[0][0]) |
|||
|
|||
|
|||
def get_model_by_id_and_version(model_id, version): |
|||
try: |
|||
conn = pyodbc.connect( |
|||
r"DRIVER={ODBC Driver 17 for SQL Server};SERVER=%s;DATABASE=alert;UID=sa;PWD=powerSIS#123" % config._SQL_IP) # 连接数据库 |
|||
except pyodbc.Error: |
|||
conn = pyodbc.connect( |
|||
r"DRIVER={SQL SERVER NATIVE CLIENT 10.0};SERVER=%s;DATABASE=alert;UID=sa;PWD=powerSIS#123" % config._SQL_IP) # 连接数据库 |
|||
cursor = conn.cursor() # 获得操作的游标 |
|||
cursor.execute(f"SELECT Model_info FROM [alert].[dbo].[Model_Version] where model_id={model_id} and version='{version}'") |
|||
res_list = cursor.fetchall() # 获取查询的结果 |
|||
conn.commit() # 提交执行 |
|||
cursor.close() # 关闭游标 |
|||
conn.close() # 关闭数据库连接 |
|||
return json.loads(res_list[0][0]) |
|||
|
|||
|
|||
def pca(model,LockVariable, Data_origin): |
|||
Data = (Data_origin - model["Train_X_mean"]) / model["Train_X_std"] |
|||
featValue = np.array(model["featValue"]) # 训练数据的特征值 |
|||
featVec = np.array(model["featVec"]) # 训练数据的特征向量 |
|||
k = (model["K"]) # 主元个数 |
|||
# selectVec = np.array(model["selectVec"]) |
|||
selectVec = featVec[:, 0:k]####自己选择的,取前k个特征向量 |
|||
# index = np.argsort(-np.array(featValue)) # 按照featValue进行从大到小排序 |
|||
featValue_sort = featValue#[index] # 排序后的特征值 |
|||
''' |
|||
featValue, featVec = np.linalg.eig(model["COV"]) # 求解协方差矩阵的特征值和特征向量 |
|||
|
|||
index = np.argsort(-featValue) # 按照featValue进行从大到小排序 |
|||
featValue_sort =featValue[index]#排序后的特征值 |
|||
selectVec = np.matrix(featVec[:,index[:int(model["K"])]]) # 所以这里需要进行转置P |
|||
''' |
|||
############----------*********-SPE-**************----------######################## |
|||
numbel_variable = featValue.shape[0]#取特征值的个数 |
|||
# LockVariable="3,5" |
|||
|
|||
C_ = np.eye(numbel_variable) - np.dot(selectVec, selectVec.T)#生成numbel_variable阶单位矩阵 |
|||
X_SPE = C_.T |
|||
D_SPE = C_ |
|||
DIAG_SPE = np.eye(numbel_variable)#生成numbel_variable阶单位矩阵 |
|||
|
|||
''' |
|||
# ************************调用LARS******************************* |
|||
t = 50000 |
|||
lamuta = 1 |
|||
limit_line = model["QCUL_99"] |
|||
beta_path=[] |
|||
for i in range(Data.shape[0]): |
|||
Y=Data[i,:] |
|||
beta, mse=Lars(X_SPE, Y, D_SPE, DIAG_SPE, t, limit_line, lamuta) |
|||
beta_end=abs(beta[-1,:]) |
|||
jenk=jenkspy.jenks_breaks(beta_end,5) |
|||
limt=(jenk[1]+jenk[2])/2 |
|||
index=np.where(beta_end>limt)[0] |
|||
beta_path.append(beta[-1,:]) |
|||
''' |
|||
############----------*********-T2-**************----------######################## |
|||
DIAG_T2 = np.linalg.pinv(np.diag(featValue_sort[:int(model["K"])])) |
|||
D_T2 = selectVec.copy() |
|||
X_T2 = np.dot(D_T2, np.linalg.cholesky(DIAG_T2)).T |
|||
|
|||
############----------*********-综合指标-**************----------######################## |
|||
II = featValue_sort.copy() |
|||
II[:int(model["K"])] = II[:int(model["K"])] * model["T2CUL_99"] |
|||
II[int(model["K"]):] = model["QCUL_99"] |
|||
DIAG_Fai = np.linalg.pinv(np.diag(II)) |
|||
D_Fai = featVec.copy() |
|||
X_Fai = np.dot(D_Fai, np.linalg.cholesky(DIAG_Fai)).T |
|||
# ************************调用LARS******************************* |
|||
t = 50000 |
|||
lamuta = 1 |
|||
#limit_line = model["Kesi_99"]/np.sqrt(numbel_variable)#修改 |
|||
limit_line = model["Kesi_99"] |
|||
beta_path = [] |
|||
SPE_list = [] |
|||
FAI_list=[] |
|||
paraState = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]]) |
|||
if Data.shape[1] >= 12: |
|||
para_length = 3 |
|||
elif 12 > Data.shape[1] >= 7: |
|||
para_length = 2 |
|||
else: |
|||
para_length = 1 |
|||
Y = None |
|||
plots_matrix = [] # 贡献图法的矩阵 |
|||
plots_index = [] # 贡献图法的index |
|||
for i in range(Data.shape[0]): |
|||
Y = Data[i, :] # 测试数据的每一行 |
|||
#########*********************计算SPE****************************** |
|||
SPE_line = np.dot(Y, C_).dot(Y.T) |
|||
SPE_list.append(SPE_line) |
|||
#########################计算综合指标########## |
|||
FAI_list.append(np.dot(Y.T, D_Fai).dot(DIAG_Fai).dot(D_Fai.T).dot(Y)) |
|||
# **************计算LARS*************** |
|||
beta, mse = Lars(X_Fai, Y, D_Fai, DIAG_Fai, t, limit_line,LockVariable) |
|||
beta_end = abs(beta[-1, :]) |
|||
pi=len(beta_end) |
|||
if pi>7: |
|||
jenk = jenkspy.jenks_breaks(beta_end, 5) |
|||
else: |
|||
jenk = jenkspy.jenks_breaks(beta_end, 2) |
|||
limt = (jenk[1] + jenk[2]) / 2 |
|||
index = np.where(beta_end > 0)[0] |
|||
if len(index) > para_length: |
|||
# res = recon_fault_diagnosis_r_c(Y, D_Fai @ DIAG_Fai @ D_Fai.T, limit_line, list(zip(index, beta_end[index])), model, |
|||
# True, X_SPE @ X_SPE.T, rbc=None) |
|||
res = recon_fault_diagnosis_r_c(Y, D_Fai @ DIAG_Fai @ D_Fai.T, limit_line, |
|||
list(zip(index, beta_end[index])), model, |
|||
True, X_SPE @ X_SPE.T, LockVariable, selectVec, rbc=None) |
|||
if not isinstance(res[0], list): |
|||
if res[1] == "plot": |
|||
# beta[-1, :] = res[0] |
|||
plots_matrix.append(res[0]) |
|||
plots_index.append(i) |
|||
else: |
|||
beta[-1, :], index = res[0].T, res[1] |
|||
# beta[-1, :], index = res[0].T, res[1] |
|||
elif len(index) <= para_length and len(index) != 0: |
|||
res = recon_fault_diagnosis_r_l(Y, D_Fai @ DIAG_Fai @ D_Fai.T, index) |
|||
beta[-1, :], index = res[0].T, res[1] |
|||
paraState[i, index] = 1 |
|||
beta_new=beta[-1, :]*paraState[i,:] |
|||
beta_path.append(beta_new) |
|||
del Y |
|||
gc.collect() |
|||
beta_path = np.array(beta_path) |
|||
################-------------------------------------------------------------############### |
|||
#finalData = np.dot(Data - beta_path, selectVec).dot(selectVec.T) |
|||
finalData=Data - beta_path |
|||
reconData = np.add(np.multiply(finalData, model["Train_X_std"]), model["Train_X_mean"]) # 重构值 |
|||
if len(plots_matrix) != 0: |
|||
reconData[plots_index] = plots_matrix |
|||
errorData = Data_origin - reconData # 偏差值 |
|||
# cos检验值 |
|||
R = 0 |
|||
for index in range(0, reconData.shape[1]): |
|||
vector1 = Data_origin[:, index] |
|||
vector2 = np.array(reconData)[:, index] |
|||
R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2))) |
|||
R /= reconData.shape[1] |
|||
items = [('reconData', reconData.tolist()) |
|||
, ('errorData', errorData.tolist()), ('R', R.tolist()), ('SPE', SPE_list),('FAI', FAI_list), |
|||
('paraState', paraState.tolist())] |
|||
result = json.dumps(dict(items)) # json.dumps(result) |
|||
return result |
|||
|
|||
|
|||
def get_history_value(points,time,interval): |
|||
#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys" |
|||
url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token() |
|||
point_array = points.split(",") |
|||
time_span = time.split(";") |
|||
value_array = [] |
|||
for item in point_array: |
|||
for time_piece in time_span: |
|||
st = time_piece.split(",")[0] |
|||
et = time_piece.split(",")[1] |
|||
para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval} |
|||
response = requests.get(url, headers=headers, params=para) |
|||
value = eval(str(response.text).replace("\"","").replace("null","0")) |
|||
value_group = [] |
|||
for row in value: |
|||
value_group.append(row[1]) |
|||
value_array.append(value_group) |
|||
return np.transpose(np.array(value_array)) |
|||
|
|||
# 根据数据集data.txt |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
#lifan 调试模型计算引擎 |
|||
jsonstr = '{"Model_id":764,"version":"v-2021-04-02 09:43:50","Test_Data":[[129.3936,0.8944824,152.4081,119.2822,0.4589844]],"Target_Data":[[]]}' |
|||
jsonstr = json.loads(jsonstr) |
|||
model_id = jsonstr["Model_id"] |
|||
version = jsonstr["version"] |
|||
res = get_model_by_id_and_version(model_id, version) |
|||
filename = res["algorithm"] |
|||
Data = jsonstr["Test_Data"] |
|||
if filename == "PCA": |
|||
model = res["para"]["Model_info"] |
|||
lock = [] |
|||
point_info = res["pointInfo"] |
|||
for i in range(len(point_info)): |
|||
try: |
|||
if point_info[i]["lock"]: |
|||
lock.append(i) |
|||
except: |
|||
continue |
|||
result = pca(model, lock, np.array(Data)) |
|||
print('aaa') |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
@ -0,0 +1,304 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Created on Sun Feb 28 10:04:26 2016 |
|||
PCA source code |
|||
@author: liudiwe |
|||
""" |
|||
|
|||
import numpy as np |
|||
import traceback |
|||
import pandas as pd |
|||
from json import JSONDecodeError |
|||
#import matplotlib.pyplot as plt |
|||
from scipy.stats import norm |
|||
from scipy.stats import f |
|||
from scipy.stats.distributions import chi2 |
|||
import json |
|||
import sys |
|||
import requests |
|||
import datetime |
|||
import jenkspy |
|||
import xlrd |
|||
from smote import smote |
|||
import config |
|||
""" |
|||
参数： |
|||
- XMat：传入的是一个numpy的矩阵格式，行表示样本数，列表示特征 |
|||
- k：表示取前k个特征值对应的特征向量 |
|||
返回值： |
|||
""" |
|||
|
|||
|
|||
def get_history_value(points,time,interval): |
|||
#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys" |
|||
url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat" |
|||
headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token() |
|||
point_array = points.split(",") |
|||
time_span = time.split(";") |
|||
value_array = [] |
|||
for item in point_array: |
|||
value_group = [] |
|||
for time_piece in time_span: |
|||
st = time_piece.split(",")[0] |
|||
et = time_piece.split(",")[1] |
|||
para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval} |
|||
response = requests.get(url, headers=headers, params=para) |
|||
content = response.text.replace('"[','[').replace(']"',']') |
|||
value = json.loads(content) |
|||
for row in value: |
|||
value_group.append(row[1]) |
|||
value_array.append(value_group) |
|||
return np.transpose(np.array(value_array)) |
|||
|
|||
|
|||
def pca(XMat, p): |
|||
m = np.array(XMat).shape[1]#取参数个数，即矩阵列数 |
|||
average = np.mean(XMat, axis=0)#axis=0，分别对各列求均值 |
|||
std = np.std(XMat, axis=0)#axis=0，计算各列的标准差 |
|||
m, n = np.shape(XMat)#取矩阵的行数m和列数n |
|||
avgs = np.tile(average, (m, 1))#将average的数乘1倍，再以此为基，复制到每一行，变成m行的矩阵 |
|||
stds = np.tile(std, (m, 1))#将std的数乘1倍，再以此为基，复制到每一行，变成m行的矩阵 |
|||
data_adjust = np.divide(XMat - avgs, stds)#计算XMat-avgs，即每个元素与列平均值之间的差，再用此差除以每列数据的标准差 |
|||
covX = np.cov(data_adjust.T) # 计算协方差矩阵（对称阵） |
|||
# corr=np.corrcoef(data_adjust.T) |
|||
featValue, featVec = np.linalg.eig(covX) # 求解协方差矩阵的特征值和特征向量 |
|||
# ############可能协方差为0（一列数据相同，在训练的时候不要选择一样的数据，就不能除。############################ |
|||
featValue=np.real(featValue)#返回复杂参数的实部 |
|||
featVec=np.real(featVec)#返回复杂参数的实部 |
|||
index = np.argsort(-featValue) # 按照featValue进行从大到小排序 -featValue转置后把元素变成其相反数 |
|||
featValue=featValue[index]#按照排序进行重建 |
|||
featVec = featVec[:,index] |
|||
featValue_sum = np.divide(featValue, np.sum(featValue))#特征值分别除以特征值之和 |
|||
per = 0 # 特征值百分比 |
|||
k = 0 # 主元个数 |
|||
for precent in featValue_sum: |
|||
per += precent |
|||
k = k + 1 |
|||
if per > p: |
|||
break |
|||
#记录主元数小与设定值p的总值和个数 |
|||
finalData = [] |
|||
if k > n:#如果k比总数大，就返回 k必须小于特征数 |
|||
print |
|||
"k must lower than feature number" |
|||
return |
|||
else: |
|||
# 注意特征向量是列向量，而numpy的二维矩阵(数组)a[m][n]中，a[1]表示第1行值 |
|||
selectVec = np.matrix(featVec[:, :k]) # 所以这里需要进行转置 取第k列的特征向量，将ndarray对象转为matrix矩阵 |
|||
finalData = np.dot(data_adjust, selectVec).dot(selectVec.T)#将data_adjust，selectVec，selectVec.T三者相乘 |
|||
reconData = np.add(np.multiply(finalData, stds), avgs) # 重构值 将finalData和stds相乘，然后与avgs相加 |
|||
Train_X_min = np.min(XMat, axis=0) # 训练值最小值 |
|||
Train_X_max = np.max(XMat, axis=0) # 训练值最大值 |
|||
Train_X_mean = np.mean(XMat, axis=0) # 训练值平均值 |
|||
Train_X_std = np.std(XMat, axis=0) # 训练值方差 |
|||
Train_X_bais = XMat - reconData # 训练值偏差 |
|||
Train_X_bais_max = np.max(np.abs(Train_X_bais), axis=0) # 训练值偏差最大值 axis=0 对各列求 |
|||
Train_X_bais_min = np.min(np.abs(Train_X_bais), axis=0) # 训练值偏差最小值 |
|||
Train_X_bais_mean = np.mean(np.abs(Train_X_bais), axis=0) # 训练值偏差平均值 |
|||
Train_X_bais_std_upperB95 = np.array(np.abs(1.96 * np.std(Train_X_bais, axis=0) + Train_X_bais_mean))[ |
|||
0] # 训练值偏差标准差 |
|||
Train_X_bais_std_upperB99 = np.array(np.abs(2.58* np.std(Train_X_bais, axis=0) + Train_X_bais_mean))[0] |
|||
Train_X_bais_std_lowerB95 = np.array(np.abs(1.96 * np.std(Train_X_bais, axis=0) - Train_X_bais_mean))[ |
|||
0] # 训练值偏差标准差 |
|||
Train_X_bais_std_lowerB99 = np.array(np.abs(2.58 * np.std(Train_X_bais, axis=0) - Train_X_bais_mean))[0] |
|||
QCUL_95_line = []#限值 |
|||
QCUL_99_line = [] |
|||
for index1 in range(len(Train_X_bais_std_upperB95)): |
|||
QCUL_95_line.append(max(Train_X_bais_std_upperB95[index1], Train_X_bais_std_lowerB95[index1])) |
|||
QCUL_99_line.append(max(Train_X_bais_std_upperB99[index1], Train_X_bais_std_lowerB99[index1])) |
|||
QCUL_95_line = np.array(QCUL_95_line) |
|||
QCUL_99_line = np.array(QCUL_99_line) |
|||
################################################################################# |
|||
# 计算阈值----------------QUCL--------------------################################################ |
|||
theta1 = np.sum(featValue[k:]) |
|||
theta2 = np.sum(np.power(featValue[k:], 2)) |
|||
theta3 = np.sum(np.power(featValue[k:], 3)) |
|||
h0 = 1 - 2 * theta1 * theta3 / (3 * np.power(theta2, 2)) |
|||
ca_95 = norm.ppf(0.95, loc=0, scale=1) |
|||
QCUL_95 = theta1 * np.power( |
|||
h0 * ca_95 * np.sqrt(2 * theta2) / theta1 + 1 + theta2 * h0 * (h0 - 1) / np.power(theta1, 2), |
|||
1 / h0) # 置信域为百分之95 |
|||
# QCUL_95_line = Train_X_bais_std*2.58 # +Train_X_mean#反归一化阈值 |
|||
ca_99 = norm.ppf(0.99, loc=0, scale=1) |
|||
QCUL_99 = theta1 * np.power( |
|||
(h0 * ca_99 * np.sqrt(2 * theta2) / theta1 + 1 + theta2 * h0 * (h0 - 1) / np.power(theta1, 2)), |
|||
1 / h0) # 置信域为百分之99 |
|||
# QCUL_99_line = Train_X_bais_std*1.96 # + Train_X_mean # 反归一化阈值 |
|||
|
|||
# 计算阈值----------------T2UCL--------------------########################################### |
|||
f_95 = f.ppf(0.95, k, m - k) |
|||
T2CUL_95 = k * (m - 1) * (m + 1) * f_95 / (m * (m - k)) # 置信域为百分之95 |
|||
T2CUL_95_line = np.sqrt(T2CUL_95) * Train_X_std / np.sqrt(m) # +Train_X_mean#反归一化阈值 |
|||
f_99 = f.ppf(0.99, k, m - k) |
|||
T2CUL_99 = k * (m - 1) * (m + 1) * f_99 / (m * (m - k)) # 置信域为百分之99 |
|||
T2CUL_99_line = np.sqrt(T2CUL_99) * Train_X_std / np.sqrt(m) # +Train_X_mean#反归一化阈值 |
|||
|
|||
# 计算阈值----------------综合--------------------################################################# |
|||
gfi_95 = (k / pow(T2CUL_95, 2) + theta2 / pow(QCUL_95, 2)) / (k / T2CUL_95 + theta1 / QCUL_95) |
|||
hfi_95 = pow((k / T2CUL_95 + theta1 / QCUL_95), 2) / (k / pow(T2CUL_95, 2) + theta2 / pow(QCUL_95, 2)) |
|||
Kesi_95 = gfi_95 * chi2.ppf(0.95, hfi_95) # 卡方分布 |
|||
Kesi_95_line = np.sqrt(Kesi_95) * Train_X_std / np.sqrt(m) # 反归一化阈值 |
|||
|
|||
gfi_99 = (k / pow(T2CUL_99, 2) + theta2 / pow(QCUL_99, 2)) / (k / T2CUL_99 + theta1 / QCUL_99) |
|||
hfi_99 = pow((k / T2CUL_99 + theta1 / QCUL_99), 2) / (k / pow(T2CUL_99, 2) + theta2 / pow(QCUL_99, 2)) |
|||
Kesi_99 = gfi_99 * chi2.ppf(0.99, hfi_99) # 卡方分布 |
|||
Kesi_99_line = np.sqrt(Kesi_99) * Train_X_std / np.sqrt(m) # 反归一化阈值 |
|||
|
|||
# cos检验值 |
|||
R = per#相关性 |
|||
#for index in range(0, reconData.shape[1]): |
|||
#vector1 = XMat[:, index] |
|||
#vector2 = np.array(reconData)[:, index] |
|||
#R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2))) |
|||
#sR /= reconData.shape[1] |
|||
|
|||
##################################################################################################################### |
|||
items = [('Train_X_min', np.around(Train_X_min, decimals=3).tolist()), |
|||
('Train_X_max', np.around(Train_X_max, decimals=3).tolist()), |
|||
('Train_X_std', np.around(Train_X_std, decimals=3).tolist()), |
|||
('Train_X_mean',np.around(Train_X_mean, decimals=3).tolist()), |
|||
('Train_X_bais_max',np.around(Train_X_bais_max, decimals=3).tolist()), |
|||
('Train_X_bais_min', np.around(Train_X_bais_min, decimals=3).tolist()), |
|||
('Train_X_bais_mean',np.around(Train_X_bais_mean, decimals=3).tolist()), |
|||
('QCUL_95',np.around(QCUL_95, decimals=10).tolist()), |
|||
('QCUL_99', np.around(QCUL_99, decimals=10).tolist()), |
|||
('QCUL_95_line',np.around(QCUL_95_line, decimals=3).tolist()), |
|||
('QCUL_99_line',np.around(QCUL_99_line, decimals=3).tolist()), |
|||
('T2CUL_95', np.around(T2CUL_95, decimals=3).tolist()), |
|||
('T2CUL_99', np.around(T2CUL_99, decimals=3).tolist()), |
|||
('T2CUL_95_line', np.around(T2CUL_95_line, decimals=3).tolist()), |
|||
('T2CUL_99_line', np.around(T2CUL_99_line, decimals=3).tolist()), |
|||
('Kesi_95', np.around(Kesi_95, decimals=3).tolist()), |
|||
('Kesi_99', np.around(Kesi_99, decimals=3).tolist()), |
|||
('Kesi_95_line', np.around(Kesi_95_line, decimals=3).tolist()), |
|||
('Kesi_99_line', np.around(Kesi_99_line, decimals=3).tolist()), |
|||
('COV', np.around(covX, decimals=3).tolist()), |
|||
('K', k), |
|||
('R', np.around(R,decimals=3).tolist()), |
|||
("featValue",np.around(featValue, decimals=3).tolist()), |
|||
("featVec", np.around(featVec, decimals=3).tolist()), |
|||
("selectVec", np.around(selectVec, decimals=3).tolist())] |
|||
# model_info=json.dumps(dict(items)) |
|||
res_items = [('Model_info', dict(items)), ('Model_type', 'PCA')] |
|||
result = dict(res_items) # json.dumps(result) |
|||
return json.dumps(result) |
|||
|
|||
|
|||
def main(info): |
|||
Train_Data = info["Train_Data"] |
|||
points = Train_Data["points"] |
|||
time = Train_Data["time"] |
|||
interval = Train_Data["interval"] |
|||
Hyper_para = info["Hyper_para"] |
|||
percent = Hyper_para["percent"] |
|||
XMat = get_history_value(points, time, interval) |
|||
result = pca(XMat, percent) # 训练数据，主元百分比 |
|||
result = result.replace("NaN", "-1") # 防止出现非数 解析不出来 |
|||
return result |
|||
|
|||
def isnumber(limits): |
|||
flag=True |
|||
for item in limits: |
|||
item=item.replace("-","") |
|||
if(item.isdigit()==False): |
|||
flag=False |
|||
break |
|||
return flag |
|||
|
|||
|
|||
def clearmain(info): |
|||
try: |
|||
Train_Data = info["Train_Data"] |
|||
condition=info["conditon"].replace("=","==").replace(">=",">").replace("<=","<") |
|||
times = Train_Data["time"].split(';') |
|||
points = Train_Data["points"].split(',') |
|||
interval = Train_Data["interval"] |
|||
if interval == 10000: |
|||
DCount = 60 |
|||
elif interval == 100000: |
|||
DCount = 6 |
|||
elif interval == 300000: |
|||
DCount = 5 |
|||
else: |
|||
DCount = 4 |
|||
dead = Train_Data["dead"].split(',') |
|||
limit = Train_Data["limit"].split(',') |
|||
uplower = Train_Data["uplow"].split(';') |
|||
percent = info["Hyper_para"]["percent"] |
|||
count=0 |
|||
ItemsInfo, SamplingTimePeriods = [], [] |
|||
Constraint = "" |
|||
for i in range(len(points)): |
|||
iteminfo = {} |
|||
iteminfo["ItemName"] = points[i] # 加点 |
|||
if (dead[i] == "1"): # 判断是否参与死区清洗 |
|||
iteminfo["ClearDeadZone"] = "true" |
|||
else: |
|||
iteminfo["ClearDeadZone"] = "false" |
|||
if (limit[i] == "1"): # 参与上下限清洗 |
|||
limits = uplower[i].split(',') |
|||
if (isnumber(limits) == True): # 输入上下限正确 isnumber 是否为数字 |
|||
count += 1 |
|||
Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[1] + " and " |
|||
ItemsInfo.append(iteminfo) |
|||
if(count!=0): |
|||
Constraint = Constraint[:len(Constraint) - 4:] |
|||
else: |
|||
Constraint="1==1"#没有上下限清洗 |
|||
Constraint+=" and ("+condition+")" |
|||
for i in range(len(times)): |
|||
Eachsampletime = {} |
|||
timess = times[i].split(',') |
|||
Eachsampletime["StartingTime"] = timess[0] |
|||
Eachsampletime["TerminalTime"] = timess[1] |
|||
SamplingTimePeriods.append(Eachsampletime) |
|||
Constraint = Constraint.replace("\n", " ") |
|||
url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=%d" % ( |
|||
ItemsInfo, SamplingTimePeriods, Constraint, interval, DCount) |
|||
response = requests.get(url) |
|||
content = json.loads(response.text) |
|||
data = np.array([item for item in content["ClearData"]]).T |
|||
try: |
|||
smote_data = info["smote"] |
|||
# smote_data = False |
|||
except KeyError: |
|||
smote_data = False |
|||
if smote_data: |
|||
try: |
|||
smote_index = [points.index(item["pointId"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
smote_num = [int(item["number"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
max_value = [float(item["max"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
min_value = [float(item["min"]) for item in info["smote_config"] if item["LAY_CHECKED"]] |
|||
except KeyError: |
|||
pass |
|||
else: |
|||
if len(smote_num) != 0: |
|||
data, *_ = smote(data, smote_index, smote_num, max_value, min_value) |
|||
result = pca(data, percent) |
|||
result = result.replace("NaN", "-1") |
|||
result=json.loads(result) |
|||
result["BeforeCleanSamNum"]=content["BeforeCleanSamNum"] |
|||
result["AfterCleanSamNum"]=content["AfterCleanSamNum"] |
|||
result["CleanOrNot"] = True |
|||
return json.dumps(result) |
|||
except Exception as e: |
|||
result = [{"CleanOrNot": False, "msg": traceback.format_exc()}] |
|||
return json.dumps(result, ensure_ascii=False) |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
info_str = r'{"Train_Data":{"time":"2020-08-02 00:40:00,2020-08-02 07:36:03;2020-08-05 15:20:43,2020-08-05 18:45:46","points":"JL_D2_20DAS05A:LAV10CE101.PNT,JL_D2_20DAS05A:LAC10CE101.PNT,JL_D2_20DAS11A:HAG41CE101.PNT","interval":300000,"dead":"1,1,1","limit":"0,0,0","uplow":"null,null;null,null;null,null"},"Hyper_para":{"percent":0.94375},"type":"PCA","conditon":"1=1","smote_config":[],"smote":true,"target_point":null}' |
|||
info = json.loads(info_str) |
|||
res = json.loads(clearmain(info)) |
|||
print("aaa") |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
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Reference in new issue