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Merge branch 'release-1.0.0'
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‎CHANGELOG.md‎

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Unreleased:
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========
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1.0.0, 2017-02-24
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New features:
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* Add Author-topic modeling (@olavurmortensen,[#893](https://github.com/RaRe-Technologies/gensim/pull/893))
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* Add FastText word embedding wrapper (@Jayantj,[#847](https://github.com/RaRe-Technologies/gensim/pull/847))
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* Add WordRank word embedding wrapper (@parulsethi,[#1066](https://github.com/RaRe-Technologies/gensim/pull/1066), [#1125](https://github.com/RaRe-Technologies/gensim/pull/1125))
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* Add sklearn wrapper for LDAModel (@AadityaJ,[#932](https://github.com/RaRe-Technologies/gensim/pull/932))
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Deprecated features:
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* Move `load_word2vec_format` and `save_word2vec_format` out of Word2Vec class to KeyedVectors (@tmylk,[#1107](https://github.com/RaRe-Technologies/gensim/pull/1107))
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* Move properties `syn0norm`, `syn0`, `vocab`, `index2word` from Word2Vec class to KeyedVectors (@tmylk,[#1147](https://github.com/RaRe-Technologies/gensim/pull/1147))
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* Remove support for Python 2.6, 3.3 and 3.4 (@tmylk,[#1145](https://github.com/RaRe-Technologies/gensim/pull/1145))
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Improvements:
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* Python 3.6 support (@tmylk [#1077](https://github.com/RaRe-Technologies/gensim/pull/1077))
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* Phrases and Phraser allow a generator corpus (ELind77 [#1099](https://github.com/RaRe-Technologies/gensim/pull/1099))
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* Ignore DocvecsArray.doctag_syn0norm in save. Fix #789 (@accraze,[#1053](https://github.com/RaRe-Technologies/gensim/pull/1053))
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* Fix bug in LsiModel that occurs when id2word is a Python 3 dictionary. (@cvangysel,[#1103](https://github.com/RaRe-Technologies/gensim/pull/1103)
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* Fix broken link to paper in readme (@bhargavvader,[#1101](https://github.com/RaRe-Technologies/gensim/pull/1101))
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* Lazy formatting in evaluate_word_pairs (@akutuzov,[#1084](https://github.com/RaRe-Technologies/gensim/pull/1084))
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* Deacc option to keywords pre-processing (@bhargavvader,[#1076](https://github.com/RaRe-Technologies/gensim/pull/1076))
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* Generate Deprecated exception when using Word2Vec.load_word2vec_format (@tmylk, [#1165](https://github.com/RaRe-Technologies/gensim/pull/1165))
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* Fix hdpmodel constructor docstring for print_topics (#1152) (@toliwa, [#1152](https://github.com/RaRe-Technologies/gensim/pull/1152))
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* Default to per_word_topics=False in LDA get_item for performance (@menshikh-iv, [#1154](https://github.com/RaRe-Technologies/gensim/pull/1154))
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* Fix bound computation in Author Topic models. (@olavurmortensen, [#1156](https://github.com/RaRe-Technologies/gensim/pull/1156))
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* Write UTF-8 byte strings in tensorboard conversion (@tmylk,[#1144](https://github.com/RaRe-Technologies/gensim/pull/1144))
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* Make top_topics and sparse2full compatible with numpy 1.12 strictly int idexing (@tmylk,[#1146](https://github.com/RaRe-Technologies/gensim/pull/1146))
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Tutorial and doc improvements:
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* Clarifying comment in is_corpus func in utils.py (@greninja,[#1109](https://github.com/RaRe-Technologies/gensim/pull/1109))
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* Tutorial Topics_and_Transformations fix markdown and add references (@lgmoneda,[#1120](https://github.com/RaRe-Technologies/gensim/pull/1120))
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* Fix doc2vec-lee.ipynb results to match previous behavior (@bahbbc,[#1119](https://github.com/RaRe-Technologies/gensim/pull/1119))
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* Remove Pattern lib dependency in News Classification tutorial (@luizcavalcanti,[#1118](https://github.com/RaRe-Technologies/gensim/pull/1118))
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* Corpora_and_Vector_Spaces tutorial text clarification (@lgmoneda,[#1116](https://github.com/RaRe-Technologies/gensim/pull/1116))
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* Update Transformation and Topics link from quick start notebook (@mariana393,[#1115](https://github.com/RaRe-Technologies/gensim/pull/1115))
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* Quick Start Text clarification and typo correction (@luizcavalcanti,[#1114](https://github.com/RaRe-Technologies/gensim/pull/1114))
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* Fix typos in Author-topic tutorial (@Fil,[#1102](https://github.com/RaRe-Technologies/gensim/pull/1102))
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* Address benchmark inconsistencies in Annoy tutorial (@droudy,[#1113](https://github.com/RaRe-Technologies/gensim/pull/1113))
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* Add note about Annoy speed depending on numpy BLAS setup in annoytutorial.ipynb (@greninja,[#1137](https://github.com/RaRe-Technologies/gensim/pull/1137))
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* Add documentation for WikiCorpus metadata. (@kirit93, [#1163](https://github.com/RaRe-Technologies/gensim/pull/1163))
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1.0.0RC2, 2017-02-16
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* Add note about Annoy speed depending on numpy BLAS setup in annoytutorial.ipynb (@greninja,[#1137](https://github.com/RaRe-Technologies/gensim/pull/1137))

‎README.md‎

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development installation, optional install features), see the
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[documentation].
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This version has been tested under Python 2.6, 2.7, 3.3, 3.4, 3.5 and 3.6
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(support for Python 2.5 was dropped in gensim 0.10.0; install gensim
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0.9.1 if you *must* use Python 2.5). Gensim’s github repo is hooked
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This version has been tested under Python 2.7, 3.5 and 3.6. Gensim’s github repo is hooked
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against [Travis CI for automated testing] on every commit push and pull
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request.
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request. Support for Python 2.6, 3.3 and 3.4 was dropped in gensim 1.0.0. Install gensim 0.13.4 if you *must* use Python 2.6, 3.3 or 3.4. Support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you *must* use Python 2.5).
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How come gensim is so fast and memory efficient? Isn’t it pure Python, and isn’t Python slow and greedy?
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--------------------------------------------------------------------------------------------------------
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[Tutorials]: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials
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[Tutorial Videos]: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos
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[Official Documentation and Walkthrough]: http://radimrehurek.com/gensim/
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[Official API Documentation]: http://radimrehurek.com/gensim/apiref.html
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‎docs/notebooks/WMD_tutorial.ipynb‎

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‎docs/notebooks/Word2Vec_FastText_Comparison.ipynb‎

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‎docs/notebooks/Wordrank_comparisons.ipynb‎

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‎docs/notebooks/deepir.ipynb‎

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‎docs/notebooks/doc2vec-IMDB.ipynb‎

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{
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"cell_type": "code",
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"metadata": {},
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"metadata": {},
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" error_rate = float(errors) / len(test_predictions)\n",
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" return (error_rate, errors, len(test_predictions), predictor)\n"
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" return (error_rate, errors, len(test_predictions), predictor)"
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"for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n",
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" print(u'%s %s: «%s»\\n' % (label, sims[index], ' '.join(alldocs[sims[index][0]].words)))\n"
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"metadata": {},
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