Update Wikipedia URLs to https

README.md

@@ -169,8 +169,8 @@ BibTeX entry:
  [citing gensim in academic papers and theses]: https://scholar.google.cz/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:u-x6o8ySG0sC
 
  [documentation and Jupyter Notebook tutorials]: https://github.com/RaRe-Technologies/gensim/#documentation
- [Vector Space Model]: http:https://en.wikipedia.org/wiki/Vector_space_model
- [unsupervised document analysis]: http:https://en.wikipedia.org/wiki/Latent_semantic_indexing
+ [Vector Space Model]: https:https://en.wikipedia.org/wiki/Vector_space_model
+ [unsupervised document analysis]: https:https://en.wikipedia.org/wiki/Latent_semantic_indexing
  [NumPy and Scipy]: https://scipy.org/install/
  [ATLAS]: http:https://math-atlas.sourceforge.net/
  [OpenBLAS]: http:https://xianyi.github.io/OpenBLAS/

docs/notebooks/distributed.md

@@ -50,11 +50,11 @@ Available distributed algorithms
 * [Distributed Latent Dirichlet Allocation][8]
 
 
-[1]: http:https://en.wikipedia.org/wiki/Distributed_computing
-[2]: http:https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
+[1]: https:https://en.wikipedia.org/wiki/Distributed_computing
+[2]: https:https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
 [3]: https://pypi.org/project/Pyro4/
 [4]: https://radimrehurek.com/gensim/intro.html#design
 [5]: https://radimrehurek.com/gensim/distributed.html#term-worker
-[6]: http:https://en.wikipedia.org/wiki/Broadcast_domain
+[6]: https:https://en.wikipedia.org/wiki/Broadcast_domain
 [7]: https://radimrehurek.com/gensim/dist_lsi.html
 [8]: https://radimrehurek.com/gensim/dist_lda.html

docs/src/_index.rst.unused

@@ -21,8 +21,8 @@ against other documents, words or phrases.
 
 .. note::
  If the previous paragraphs left you confused, you can read more about the `Vector
- Space Model <http:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
- document analysis <http:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
+ Space Model <https:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
+ document analysis <https:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
 
 
 .. _design:

docs/src/dist_lsi.rst

@@ -57,7 +57,7 @@ ____________
 
 So let's test our setup and run one computation of distributed LSA. Open a Python
 shell on one of the five machines (again, this can be done on any computer
-in the same `broadcast domain <http:https://en.wikipedia.org/wiki/Broadcast_domain>`_,
+in the same `broadcast domain <https:https://en.wikipedia.org/wiki/Broadcast_domain>`_,
 our choice is incidental) and try:
 
 .. sourcecode:: pycon

docs/src/distributed.rst

@@ -10,7 +10,7 @@ Why distributed computing?
 
 Need to build semantic representation of a corpus that is millions of documents large and it's
 taking forever? Have several idle machines at your disposal that you could use?
-`Distributed computing <http:https://en.wikipedia.org/wiki/Distributed_computing>`_ tries
+`Distributed computing <https:https://en.wikipedia.org/wiki/Distributed_computing>`_ tries
 to accelerate computations by splitting a given task into several smaller subtasks,
 passing them on to several computing nodes in parallel.
 
@@ -23,7 +23,7 @@ much communication going on), so the network is allowed to be of relatively high
  The primary reason for using distributed computing is making things run faster. In `gensim`,
  most of the time consuming stuff is done inside low-level routines for linear algebra, inside
  NumPy, independent of any `gensim` code.
- **Installing a fast** `BLAS (Basic Linear Algebra) <http:https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms>`_ **library
+ **Installing a fast** `BLAS (Basic Linear Algebra) <https:https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms>`_ **library
  for NumPy can improve performance up to 15 times!** So before you start buying those extra computers,
  consider installing a fast, threaded BLAS that is optimized for your particular machine
  (as opposed to a generic, binary-distributed library).
@@ -71,7 +71,7 @@ inside `gensim` will try to look for and enslave all available worker nodes.
  Cluster
  Several nodes which communicate over TCP/IP. Currently, network broadcasting
  is used to discover and connect all communicating nodes, so the nodes must lie
- within the same `broadcast domain <http:https://en.wikipedia.org/wiki/Broadcast_domain>`_.
+ within the same `broadcast domain <https:https://en.wikipedia.org/wiki/Broadcast_domain>`_.
 
  Worker
  A process which is created on each node. To remove a node from your cluster,

docs/src/gallery/core/run_corpora_and_vector_spaces.py

@@ -72,10 +72,10 @@
 # by the features extracted from it, not by its "surface" string form: how you get to
 # the features is up to you. Below I describe one common, general-purpose approach (called
 # :dfn:`bag-of-words`), but keep in mind that different application domains call for
-# different features, and, as always, it's `garbage in, garbage out <http:https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_...
+# different features, and, as always, it's `garbage in, garbage out <https:https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_...
 #
 # To convert documents to vectors, we'll use a document representation called
-# `bag-of-words <http:https://en.wikipedia.org/wiki/Bag_of_words>`_. In this representation,
+# `bag-of-words <https:https://en.wikipedia.org/wiki/Bag_of_words>`_. In this representation,
 # each document is represented by one vector where each vector element represents
 # a question-answer pair, in the style of:
 #

docs/src/gallery/core/run_similarity_queries.py

@@ -96,10 +96,10 @@
 print(vec_lsi)
 
 ###############################################################################
-# In addition, we will be considering `cosine similarity <http:https://en.wikipedia.org/wiki/Cosine_similarity>`_
+# In addition, we will be considering `cosine similarity <https:https://en.wikipedia.org/wiki/Cosine_similarity>`_
 # to determine the similarity of two vectors. Cosine similarity is a standard measure
 # in Vector Space Modeling, but wherever the vectors represent probability distributions,
-# `different similarity measures <http:https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence#Symmetrised_divergence>`_
+# `different similarity measures <https:https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence#Symmetrised_divergence>`_
 # may be more appropriate.
 #
 # Initializing query structures

docs/src/gallery/core/run_topics_and_transformations.py

@@ -130,7 +130,7 @@
 corpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi
 
 ###############################################################################
-# Here we transformed our Tf-Idf corpus via `Latent Semantic Indexing <http:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_
+# Here we transformed our Tf-Idf corpus via `Latent Semantic Indexing <https:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_
 # into a latent 2-D space (2-D because we set ``num_topics=2``). Now you're probably wondering: what do these two latent
 # dimensions stand for? Let's inspect with :func:`models.LsiModel.print_topics`:
 
@@ -175,7 +175,7 @@
 #
 # Gensim implements several popular Vector Space Model algorithms:
 #
-# * `Term Frequency * Inverse Document Frequency, Tf-Idf <http:https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_
+# * `Term Frequency * Inverse Document Frequency, Tf-Idf <https:https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_
 # expects a bag-of-words (integer values) training corpus during initialization.
 # During transformation, it will take a vector and return another vector of the
 # same dimensionality, except that features which were rare in the training corpus
@@ -202,7 +202,7 @@
 #
 # model = models.OkapiBM25Model(corpus)
 #
-# * `Latent Semantic Indexing, LSI (or sometimes LSA) <http:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_
+# * `Latent Semantic Indexing, LSI (or sometimes LSA) <https:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_
 # transforms documents from either bag-of-words or (preferrably) TfIdf-weighted space into
 # a latent space of a lower dimensionality. For the toy corpus above we used only
 # 2 latent dimensions, but on real corpora, target dimensionality of 200--500 is recommended
@@ -247,7 +247,7 @@
 #
 # model = models.RpModel(tfidf_corpus, num_topics=500)
 #
-# * `Latent Dirichlet Allocation, LDA <http:https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_
+# * `Latent Dirichlet Allocation, LDA <https:https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_
 # is yet another transformation from bag-of-words counts into a topic space of lower
 # dimensionality. LDA is a probabilistic extension of LSA (also called multinomial PCA),
 # so LDA's topics can be interpreted as probability distributions over words. These distributions are,

docs/src/intro.rst

@@ -24,8 +24,8 @@ Once these statistical patterns are found, any plain text documents (sentence, p
 
 .. note::
  If the previous paragraphs left you confused, you can read more about the `Vector
- Space Model <http:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
- document analysis <http:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
+ Space Model <https:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
+ document analysis <https:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
 
 .. _design:
 

docs/src/wiki.rst

@@ -221,7 +221,7 @@ into LDA topic distributions:
  in your list appear to be meta topics, concerning the administration and
  cleanup of Wikipedia. These show up because you didn't exclude templates
  such as these, some of which are included in most articles for quality
- control: http:https://en.wikipedia.org/wiki/Wikipedia:Template_messages/Cleanup
+ control: https:https://en.wikipedia.org/wiki/Wikipedia:Template_messages/Cleanup
 
  The fourth and fifth topics clearly shows the influence of bots that import
  massive databases of cities, countries, etc. and their statistics such as
@@ -232,7 +232,7 @@ into LDA topic distributions:
  So the top ten concepts are apparently dominated by Wikipedia robots and expanded
  templates; this is a good reminder that LSA is a powerful tool for data analysis,
  but no silver bullet. As always, it's `garbage in, garbage out
- <http:https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_...
+ <https:https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_...
  By the way, improvements to the Wiki markup parsing code are welcome :-)
 
 .. [3] Hoffman, Blei, Bach. 2010. Online learning for Latent Dirichlet Allocation

gensim/corpora/hashdictionary.py

@@ -4,7 +4,7 @@
 # Copyright (C) 2012 Homer Strong, Radim Rehurek
 # Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
 
-"""Implements the `"hashing trick" <http:https://en.wikipedia.org/wiki/Hashing-Trick>`_ -- a mapping between words
+"""Implements the `"hashing trick" <https:https://en.wikipedia.org/wiki/Hashing-Trick>`_ -- a mapping between words
 and their integer ids using a fixed, static mapping (hash function).
 
 Notes

setup.py

@@ -193,8 +193,8 @@ def run(self):
 
 
 If this feature list left you scratching your head, you can first read more about the `Vector
-Space Model <http:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
-document analysis <http:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
+Space Model <https:https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
+document analysis <https:https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.
 
 Installation
 ------------