ready to train

gatherInfo.py

@@ -1,11 +1,12 @@
 import pandas as pd
 import os
-import numpy
+import json
 import datetime
 import warnings
 
 warnings.filterwarnings ('ignore')
-
+InterDis = 155
+TH_N = 10
 f_stat = "./exata.stat"
 f_dir = '.\\jammerDetect'
 
@@ -48,6 +49,8 @@ def readall(path):
             if f.endswith('00_0.stat'):
                 df = read_stat(statfile)
                 out_file = f[:-7]+'-r.csv'
+                lr_file = f[:-7]+'-model.csv'
+                res_file = f[:-10]
                 continue
 
             if df is None:
@@ -56,7 +59,12 @@ def readall(path):
                 df = read_stat(statfile, df)
 
     df.to_csv(out_file)
-    return df
+    df.to_csv(out_file)
+
+    ndf = divide(out_file)
+    tagging(ndf, res_file)
+    ndf.to_csv(lr_file)
+    return ndf
 
 
 def divide(csvf):
@@ -80,8 +88,47 @@ def divide(csvf):
     return ndf
 
 
+def tagging(df, rf):
+    lend = len(df)
+    assert lend % 2 == 0
+    #print(len(df))
+    with open(rf, 'r', encoding='utf-8') as f:
+        result = json.load(f)
+        lenr = len(result)
+        assert 4*(lenr-2) == lend
+        df['flag'] = 0
+        df['isDetect'] = 0
+        for i in range(lenr-2):
+            res = result[i+2][1:]
+            print(res)
+            if res[0]['d25'] <= InterDis:
+                df['flag'][4*i+2] = 1
+            if res[0]['d45'] <= InterDis:
+                df['flag'][4*i+3] = 1
+
+            if res[1][0] > TH_N:
+                if res[1][1] >= 2*res[1][0]/3:  # node4 探测到
+                    df['isDetect'][4*i+1] = 1
+                elif res[1][1] <= res[1][0]/3:  # node2 探测到
+                    df['isDetect'][4 * i] = 1
+                else:   # 都探测到
+                    df['isDetect'][4 * i] = 1
+                    df['isDetect'][4 * i + 1] = 1
+
+            if res[2][0] > TH_N:
+                if res[1][1] >= 2*res[1][0]/3:  # node4 探测到
+                    df['isDetect'][4*i+3] = 1
+                elif res[1][1] <= res[1][0]/3:  # node2 探测到
+                    df['isDetect'][4 * i + 2] = 1
+                else:   # 都探测到
+                    df['isDetect'][4 * i + 2] = 1
+                    df['isDetect'][4 * i + 3] = 1
+        print(df)
+    return(df)
+
+
 if __name__ == '__main__':
-    #df = readall(f_dir)
-    #print(df)
-    divide('20220930-09_11-00-r.csv')
+    df = readall(f_dir)
+    print(df)
+
 

trainLR.py

@@ -64,38 +64,8 @@ def run():
     plt.show()
 
 
-def readcsv():
-    '''
-    data = {
-        "calories": [420, 380, 390],
-        "duration": [50, 40, 45]
-    }
-
-    df = pd.DataFrame(data, index=["day1", "day2", "day3"])
-
-    print(df)
-    df.rename(columns={0: 'n0', 1: 'n1'})
-    print(df)
-    print(df.)
-    '''
-    df = pd.read_csv("./exata.stat", sep=',|=', names=['c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', ])
-    print(df)
-    df2 = df.iloc[:, [3, 5, 6]]
-    print(df2)
-    s = df2.iloc[4, 0]
-    '''
-    print(s)
-    print(type(s))
-    print(s.strip() == 'Physical')'''
-    df3 = df2[df2['c4'].str.strip() == 'Physical']
-    print(df3)
-    df3.to_csv("j1.csv", index=False)
-
-
-
 # Press the green button in the gutter to run the script.
 if __name__ == '__main__':
-    #run()
-    readcsv()
+    run()
 
 # See PyCharm help at https://www.jetbrains.com/help/pycharm/