Fix default -> auto prior parameter in documentation for lda-related models

gensim/models/ldamodel.py

@@ -369,7 +369,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None,
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
-                * 'default': Learns an asymmetric prior from the corpus.
+                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 
@@ -531,7 +531,7 @@ def init_dir_prior(self, prior, name):
 
                 * an 1D array of length equal to the number of expected topics,
                 * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'auto': Learns an assymetric prior from the corpus.
         name : {'alpha', 'eta'}
             Whether the `prior` is parameterized by the alpha vector (1 parameter per topic)
             or by the eta (1 parameter per unique term in the vocabulary).

gensim/models/ldamulticore.py

@@ -129,7 +129,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'auto': Learns an assymetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 

gensim/sklearn_api/atmodel.py

@@ -83,7 +83,7 @@ def __init__(self, num_topics=100, id2word=None, author2doc=None, doc2author=Non
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'auto': Learns an assymetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 

gensim/sklearn_api/ldamodel.py

@@ -61,7 +61,7 @@ def __init__(self, num_topics=100, id2word=None, chunksize=2000, passes=1, updat
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'auto': Learns an assymetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be: