docs: Fix a few typos (#3366)

timgates42 · mpenkov · web-flow · commit c4a7acdfd347 · 2023-08-23T16:33:12.000+02:00
* docs: Fix a few typos

There are small typos in:
- docs/src/auto_examples/howtos/run_doc2vec_imdb.py
- docs/src/auto_examples/howtos/run_doc2vec_imdb.rst
- docs/src/gallery/howtos/run_doc2vec_imdb.py
- gensim/test/test_corpora.py

Fixes:
- Should read `output` rather than `ouput`.
- Should read `count` rather than `counnt`.

Signed-off-by: Tim Gates &lt;tim.gates@iress.com&gt;

* make -C docs/src html

rebuilt docs that were updated by this PR with Sphinx 5.1.1

* updated documentation

---------

Signed-off-by: Tim Gates &lt;tim.gates@iress.com&gt;
Co-authored-by: Michael Penkov &lt;m@penkov.dev&gt;
diff --git a/docs/src/auto_examples/howtos/run_doc2vec_imdb.ipynb b/docs/src/auto_examples/howtos/run_doc2vec_imdb.ipynb
diff --git a/docs/src/auto_examples/howtos/run_doc2vec_imdb.py b/docs/src/auto_examples/howtos/run_doc2vec_imdb.py
@@ -229,7 +229,7 @@ def extract_documents():
 #
 # Given a document, our ``Doc2Vec`` models output a vector representation of the document.
 # How useful is a particular model?
-# In case of sentiment analysis, we want the ouput vector to reflect the sentiment in the input document.
+# In case of sentiment analysis, we want the output vector to reflect the sentiment in the input document.
 # So, in vector space, positive documents should be distant from negative documents.
 #
 # We train a logistic regression from the training set:
diff --git a/docs/src/auto_examples/howtos/run_doc2vec_imdb.py.md5 b/docs/src/auto_examples/howtos/run_doc2vec_imdb.py.md5
@@ -1 +1 @@
-507b6c07ce76db341761559a96daa17d
+ba0868fcd69185ffc435fc591667b67a
diff --git a/docs/src/auto_examples/howtos/run_doc2vec_imdb.rst b/docs/src/auto_examples/howtos/run_doc2vec_imdb.rst
diff --git a/docs/src/auto_examples/howtos/sg_execution_times.rst b/docs/src/auto_examples/howtos/sg_execution_times.rst
@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**00:00.171** total execution time for **auto_examples_howtos** files:
+**56:58.813** total execution time for **auto_examples_howtos** files:
 
-+----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_howtos_run_doc.py` (``run_doc.py``)                       | 00:00.171 | 6.1 MB |
-+----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_howtos_run_compare_lda.py` (``run_compare_lda.py``)       | 00:00.000 | 0.0 MB |
-+----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_howtos_run_doc2vec_imdb.py` (``run_doc2vec_imdb.py``)     | 00:00.000 | 0.0 MB |
-+----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_howtos_run_downloader_api.py` (``run_downloader_api.py``) | 00:00.000 | 0.0 MB |
-+----------------------------------------------------------------------------------------+-----------+--------+
++----------------------------------------------------------------------------------------+-----------+-----------+
+| :ref:`sphx_glr_auto_examples_howtos_run_doc2vec_imdb.py` (``run_doc2vec_imdb.py``)     | 56:58.813 | 3772.5 MB |
++----------------------------------------------------------------------------------------+-----------+-----------+
+| :ref:`sphx_glr_auto_examples_howtos_run_compare_lda.py` (``run_compare_lda.py``)       | 00:00.000 | 0.0 MB    |
++----------------------------------------------------------------------------------------+-----------+-----------+
+| :ref:`sphx_glr_auto_examples_howtos_run_doc.py` (``run_doc.py``)                       | 00:00.000 | 0.0 MB    |
++----------------------------------------------------------------------------------------+-----------+-----------+
+| :ref:`sphx_glr_auto_examples_howtos_run_downloader_api.py` (``run_downloader_api.py``) | 00:00.000 | 0.0 MB    |
++----------------------------------------------------------------------------------------+-----------+-----------+
diff --git a/docs/src/auto_examples/index.rst b/docs/src/auto_examples/index.rst
@@ -348,7 +348,7 @@ Blog posts, tutorial videos, hackathons and other useful Gensim resources, from
 - *Use FastText or Word2Vec?* Comparison of embedding quality and performance. `Jupyter Notebook <https://github.com/RaRe-Technologies/gensim/blob/ba1ce894a5192fc493a865c535202695bb3c0424/docs/notebooks/Word2Vec_FastText_Comparison.ipynb>`__
 - Multiword phrases extracted from *How I Met Your Mother*. `Blog post by Mark Needham <http://www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/>`__
 - *Using Gensim LDA for hierarchical document clustering*. `Jupyter notebook by Brandon Rose <http://brandonrose.org/clustering#Latent-Dirichlet-Allocation>`__
-- *Evolution of Voldemort topic through the 7 Harry Potter books*. `Blog post <http://rare-technologies.com/understanding-and-coding-dynamic-topic-models/>`__
+- *Evolution of Voldemort topic through the 7 Harry Potter books*. `Blog post <https://rare-technologies.com/understanding-and-coding-dynamic-topic-models/>`__
 - *Movie plots by genre*: Document classification using various techniques: TF-IDF, word2vec averaging, Deep IR, Word Movers Distance and doc2vec. `Github repo <https://github.com/RaRe-Technologies/movie-plots-by-genre>`__
 - *Word2vec: Faster than Google? Optimization lessons in Python*, talk by Radim Řehůřek at PyData Berlin 2014. `Youtube video <https://www.youtube.com/watch?v=vU4TlwZzTfU>`__
 - *Word2vec & friends*, talk by Radim Řehůřek at MLMU.cz 7.1.2015. `Youtube video <https://www.youtube.com/watch?v=wTp3P2UnTfQ>`__
diff --git a/docs/src/auto_examples/other/index.rst b/docs/src/auto_examples/other/index.rst
@@ -10,7 +10,7 @@ Blog posts, tutorial videos, hackathons and other useful Gensim resources, from
 - *Use FastText or Word2Vec?* Comparison of embedding quality and performance. `Jupyter Notebook <https://github.com/RaRe-Technologies/gensim/blob/ba1ce894a5192fc493a865c535202695bb3c0424/docs/notebooks/Word2Vec_FastText_Comparison.ipynb>`__
 - Multiword phrases extracted from *How I Met Your Mother*. `Blog post by Mark Needham <http://www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/>`__
 - *Using Gensim LDA for hierarchical document clustering*. `Jupyter notebook by Brandon Rose <http://brandonrose.org/clustering#Latent-Dirichlet-Allocation>`__
-- *Evolution of Voldemort topic through the 7 Harry Potter books*. `Blog post <http://rare-technologies.com/understanding-and-coding-dynamic-topic-models/>`__
+- *Evolution of Voldemort topic through the 7 Harry Potter books*. `Blog post <https://rare-technologies.com/understanding-and-coding-dynamic-topic-models/>`__
 - *Movie plots by genre*: Document classification using various techniques: TF-IDF, word2vec averaging, Deep IR, Word Movers Distance and doc2vec. `Github repo <https://github.com/RaRe-Technologies/movie-plots-by-genre>`__
 - *Word2vec: Faster than Google? Optimization lessons in Python*, talk by Radim Řehůřek at PyData Berlin 2014. `Youtube video <https://www.youtube.com/watch?v=vU4TlwZzTfU>`__
 - *Word2vec & friends*, talk by Radim Řehůřek at MLMU.cz 7.1.2015. `Youtube video <https://www.youtube.com/watch?v=wTp3P2UnTfQ>`__
diff --git a/docs/src/gallery/howtos/run_doc2vec_imdb.py b/docs/src/gallery/howtos/run_doc2vec_imdb.py
@@ -229,7 +229,7 @@ def extract_documents():
 #
 # Given a document, our ``Doc2Vec`` models output a vector representation of the document.
 # How useful is a particular model?
-# In case of sentiment analysis, we want the ouput vector to reflect the sentiment in the input document.
+# In case of sentiment analysis, we want the output vector to reflect the sentiment in the input document.
 # So, in vector space, positive documents should be distant from negative documents.
 #
 # We train a logistic regression from the training set:
diff --git a/gensim/test/test_corpora.py b/gensim/test/test_corpora.py
@@ -418,7 +418,7 @@ def test_save_format_for_dtm(self):
         self.corpus_class.save_corpus(test_file, corpus)
         with open(test_file) as f:
             for line in f:
-                # unique_word_count index1:count1 index2:count2 ... indexn:counnt
+                # unique_word_count index1:count1 index2:count2 ... indexn:count
                 tokens = line.split()
                 words_len = int(tokens[0])
                 if words_len > 0:

Original file line number	Diff line number	Diff line change
`@@ -229,7 +229,7 @@ def extract_documents():`
`229`	`229`	`#`
`230`	`230`	# Given a document, our ``Doc2Vec`` models output a vector representation of the document.
`231`	`231`	`# How useful is a particular model?`
`232`		`-# In case of sentiment analysis, we want the ouput vector to reflect the sentiment in the input document.`
	`232`	`+# In case of sentiment analysis, we want the output vector to reflect the sentiment in the input document.`
`233`	`233`	`# So, in vector space, positive documents should be distant from negative documents.`
`234`	`234`	`#`
`235`	`235`	`# We train a logistic regression from the training set:`
Original file line number	Diff line number	Diff line change
`@@ -1 +1 @@`
`1`		`-507b6c07ce76db341761559a96daa17d`
	`1`	`+ba0868fcd69185ffc435fc591667b67a`