add supplememtal material

Author: dongtsi

AfterImageExtractor/AfterImage.pyx

@@ -804,6 +804,8 @@ cdef class extrapolator:
 
 # =======================================================================
 # =======================================================================
+# ================= Optimizated AfterImag ===============================
+# =======================================================================
 # =======================================================================
 cdef class incStatDB2(incStatDB):
 
@@ -812,21 +814,21 @@ cdef class incStatDB2(incStatDB):
     # cdef dict HT
     cdef public dict HT2
 
-    ## 2020.04修改-支持回退 ----
+    ## 2020.04 Update Roll back ----
     cdef public dict backup1
     cdef public dict backup2
     cdef public int roll_back
-    ## ---- 2020.04修改-支持回退 
+    ## ---- 2020.04 Update Roll back 
 
     def __init__(self,double limit=np.Inf,double default_lambda=np.nan,roll_back=False):
         super().__init__(limit,default_lambda)
         self.HT2 = dict()
 
-        ## 2020.04修改-支持回退 ----
+        ## 2020.04 Update Roll back ----
         self.backup1 = dict()
         self.backup2 = dict()
         self.roll_back = roll_back
-        ## ---- 2020.04修改-支持回退 
+        ## ---- 2020.04 Update Roll back 
 
     def register(self, str ID, double Lambda=1,double init_time=0,
                 int isTypeDiff=False, str dir='o'):
@@ -888,9 +890,6 @@ cdef class incStatDB2(incStatDB):
         else:
             raise RuntimeError("Invalid Params: dir")
 
-        ## 2020.04修改-支持回退 ---- 
-        ## ---- 2020.04修改-支持回退 
-        
         return incS
 
     def update_get_1D2D_Stats(self, str ID1, str ID2, double t1,double v1,double Lambda=1):  # weight, mean, std

KitNET/executeKitNET/KitNET.py

@@ -40,11 +40,11 @@ def __init__(self,model_save_path,n,max_autoencoder_size=10,learning_rate=0.1,hi
         self.n = n
 
         # Variables
-        ''' 修改标注 '''
+        ''' My Update '''
         # self.n_trained = 55000+10 # the number of training instances so far
         self.n_executed = 0 # the number of executed instances so far
         # self.v = feature_map
-        ''' 修改标注 '''
+        ''' My Update '''
         # if self.v is None:
         self.v = pickle.load(f)
         print("## mapping(v) has been loaded from file...")