1
首先，這是獲得執行LDA的語料庫的主題分佈的正確方法嗎？（Gensim）ValueError：形狀無效，使用alpha參數
1- lda = LdaModel(corpus, num_topics=500, update_every=0, passes=2,alpha=0.5)
2- result=lda[corpus]
3- gensim.matutils.corpus2csc(result).T
在從gensim語料稀疏矩陣轉換：
lda = LdaModel(corpus, num_topics=500, update_every=0, passes=2)
#get the topics distribution of the corpus
result=lda[corpus]
現在，當我的阿爾法參數添加到LDA並嘗試語料庫轉化爲稀疏矩陣如下出現問題如第3行，我得到錯誤ValueError: invalid shape
我只有當我添加ALPHA參數時出現此問題！
完整回溯：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-788-7fb54d5da9fb> in <module>()
----> 1 xp,xc=issam.lda(c)
C:\Anaconda\lib\issamKit.py in lda(X)
1745 corpus=gensim.matutils.Sparse2Corpus(X.T)
1746 lda = LdaModel(corpus, num_topics=500, update_every=0, passes=2,alpha=1)
-> 1747 return lda,gensim.matutils.corpus2csc(lda[corpus]).T
1748 def lsi(X):
1749 import gensim
C:\Anaconda\lib\site-packages\gensim-0.8.6-py2.7.egg\gensim\matutils.pyc in corpus2csc(corpus, num_terms, dtype, num_docs, num_nnz, printprogress)
97 data = numpy.asarray(data, dtype=dtype)
98 indices = numpy.asarray(indices)
---> 99 result = scipy.sparse.csc_matrix((data, indices, indptr), shape=(num_terms, num_docs), dtype=dtype)
100 return result
101
C:\Anaconda\lib\site-packages\scipy\sparse\compressed.pyc in __init__(self, arg1, shape, dtype, copy)
66 # Read matrix dimensions given, if any
67 if shape is not None:
---> 68 self.shape = shape # spmatrix will check for errors
69 else:
70 if self.shape is None:
C:\Anaconda\lib\site-packages\scipy\sparse\base.pyc in set_shape(self, shape)
69
70 if not (shape[0] >= 1 and shape[1] >= 1):
---> 71 raise ValueError('invalid shape')
72
73 if (self._shape != shape) and (self._shape is not None):
ValueError: invalid shape