edit ppt

@@ -218,8 +218,6 @@ def inference_pb(file_path, threshold):
                 predictForSegment[j*2] = normalize_tag(str(voca_dict[str(predictForSegment[j*2])]))
             #클래스 확률 클래스 확률... 일케 저장되어 있음
             inputVideoTagResults.append(predictForSegment)
-            
-        #이 다음문단은 이전거 원래 이게 recomment videos의 첫 파라미터임
         
         tag_result = []
         for itemIndex in range(TAG_TOP_K):

@@ -85,7 +85,6 @@ def recommend_videos(tags, segments, tag_model_path, video_model_path, video_id_
                     maxSimilarSegment =  len(segments) - int(len(kernel)/2) - 1
             #세그먼트 인덱스 증가
             currentIndex = currentIndex + 1
-        #######################################print('maxSimilarSegment',maxSimilarSegment,'len',len(segments))
         #커널 생성    
         for k in range (0,len(kernel)):
             segment = segments[maxSimilarSegment - math.floor(len(kernel)/2) + k]
@@ -124,10 +123,8 @@ def recommend_videos(tags, segments, tag_model_path, video_model_path, video_id_
             
             if video_id[0:4] != prevVideoId[0:4]:
                 
-                #여기서 모다진걸로 컨볼루션 연산
-                #convmaxima, convidx = convolution(videoVectorList,kernel,prevVideoId)
+                #여기서 모여진 segment로 컨볼루션 연산
                 maxima, idx = differenceMax(segmentTagList,tagKernel,tag_vectors,videoTagList)
-                #maxima = maxima + convmaxima
                 
                 localMinima = 100
                 localMinimaIndex = -1
@@ -135,9 +132,7 @@ def recommend_videos(tags, segments, tag_model_path, video_model_path, video_id_
                     if float(minimunVideoIds[seg][1]) < localMinima:
                         localMinima = float(minimunVideoIds[seg][1])
                         localMinimaIndex = seg  
-                #print(maxima)
                 if localMinima < maxima:
-                    #print(prevVideoId[0:4] + "_" + str(idx),localMinimaIndex,maxima)
                     minimunVideoIds[localMinimaIndex] = [prevVideoId[0:4] + "_" + str(idx),maxima]
 
                 
@@ -207,13 +202,13 @@ def differenceMax(arrs, _kernel, w2v, videoTaglist):
         arrs.insert(0,arrs[0])
         arrs.append(arrs[len(arrs)-1])
 
-    prevIndex = 0
+    maximaIndex = 0
     prevMax = -100
     for j in range(midpos,len(arrs) - midpos):
         convResult = 0 
         processed_vocabNum = 1
         for i in range(0, s):
-            if(_kernel[i][0] not in arrs[j - midpos + i][0]):# and ((videoTaglist[0] not in arrs[j - midpos + i][0:2])) and ((videoTaglist[1] not in arrs[j - midpos + i][0:5])):
+            if(_kernel[i][0] not in arrs[j - midpos + i][0]):
                 continue
             for ind in range(0,5):
                 if(arrs[j - midpos + i][ind][0] in w2v.vocab) and (_kernel[i][ind] in w2v.vocab):
@@ -225,11 +220,10 @@ def differenceMax(arrs, _kernel, w2v, videoTaglist):
 
         if prevMax < convResult:
             prevMax = convResult
-            prevIndex = j - midpos
+            maximaIndex = j - midpos
         result.append(convResult)
-    #maxVal = max(result)    
-    #index = result.index(maxVal)
-    return prevMax,prevIndex
+
+    return prevMax,maximaIndex
 
 def normalize(arrs):    
     maximum = max(arrs)