Fix Topic Model return bug

Bug fix: Return NoneType

hub_module/modules/text/semantic_model/lda_news/README.md

@@ -137,3 +137,7 @@ paddlehub >= 1.8.0
 * 1.0.0
 
   初始发布
+
+* 1.0.1
+
+  修复因为return的bug导致的NoneType错误

hub_module/modules/text/semantic_model/lda_news/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/lda_news/module.py

@@ -14,7 +14,7 @@ from lda_news.vocab import Vocab, WordCount
 
 @moduleinfo(
     name="lda_news",
-    version="1.0.0",
+    version="1.0.1",
     summary=
     "This is a PaddleHub Module for LDA topic model in news dataset, where we can calculate doc distance, calculate the similarity between query and document, etc",
     author="DesmonDay",

hub_module/modules/text/semantic_model/lda_novel/README.md

@@ -137,3 +137,7 @@ paddlehub >= 1.8.0
 * 1.0.0
 
   初始发布
+
+* 1.0.1
+
+  修复因为return的bug导致的NoneType错误

hub_module/modules/text/semantic_model/lda_novel/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/lda_novel/module.py

@@ -14,7 +14,7 @@ from lda_novel.vocab import Vocab, WordCount
 
 @moduleinfo(
     name="lda_novel",
-    version="1.0.0",
+    version="1.0.1",
     summary=
     "This is a PaddleHub Module for LDA topic model in novel dataset, where we can calculate doc distance, calculate the similarity between query and document, etc.",
     author="DesmonDay",

hub_module/modules/text/semantic_model/lda_webpage/README.md

@@ -133,3 +133,7 @@ paddlehub >= 1.8.0
 * 1.0.0
 
   初始发布
+
+* 1.0.1
+
+  修复因为return的bug导致的NoneType错误

hub_module/modules/text/semantic_model/lda_webpage/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/lda_webpage/module.py

@@ -14,7 +14,7 @@ from lda_webpage.vocab import Vocab, WordCount
 
 @moduleinfo(
     name="lda_webpage",
-    version="1.0.0",
+    version="1.0.1",
     summary=
     "This is a PaddleHub Module for LDA topic model in webpage dataset, where we can calculate doc distance, calculate the similarity between query and document, etc.",
     author="DesmonDay",

hub_module/modules/text/semantic_model/slda_news/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/slda_novel/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/slda_webpage/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue

hub_module/modules/text/semantic_model/slda_weibo/document.py

@@ -97,7 +97,7 @@ class LDADoc(object):
         topic_dist = []
         sum_ = np.sum(self._accum_topic_sum)
         if sum_ == 0:
-            return
+            return topic_dist
         for i in range(0, self._num_topics):
             if self._accum_topic_sum[i] == 0:
                 continue