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‎.travis.yml

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- "2.7"
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- "3.3"
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- "3.4"
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- "3.5"
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- "3.5"
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before_install:
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- wget 'http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh' -O miniconda.sh
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- chmod +x miniconda.sh
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- pip install pyemd
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- pip install annoy
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- pip install testfixtures
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- pip install unittest2
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- python setup.py install
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script: python setup.py test

‎CHANGELOG.md

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Changes
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=======
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===========
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Unreleased:
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None
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0.13.4, 2016-12-22
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* Evaluation of word2vec models against semantic similarity datasets like SimLex-999 (#1047) (@akutuzov, [#1047](https://github.com/RaRe-Technologies/gensim/pull/1047))
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* TensorBoard word embedding visualisation of Gensim Word2vec format (@loretoparisi, [#1051](https://github.com/RaRe-Technologies/gensim/pull/1051))
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* Throw exception if load() is called on instance rather than the class in word2vec and doc2vec (@dus0x,[(#889](https://github.com/RaRe-Technologies/gensim/pull/889))
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* Loading and Saving LDA Models across Python 2 and 3. Fix #853 (@anmolgulati, #913, [#1093](https://github.com/RaRe-Technologies/gensim/pull/1093))
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* Fix automatic learning of eta (prior over words) in LDA (@olavurmortensen, [#1024](https://github.com/RaRe-Technologies/gensim/pull/1024)).
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* eta should have dimensionality V (size of vocab) not K (number of topics). eta with shape K x V is still allowed, as the user may want to impose specific prior information to each topic.
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* eta is no longer allowed the "asymmetric" option. Asymmetric priors over words in general are fine (learned or user defined).
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* As a result, the eta update (`update_eta`) was simplified some. It also no longer logs eta when updated, because it is too large for that.
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* Unit tests were updated accordingly. The unit tests expect a different shape than before; some unit tests were redundant after the change; `eta='asymmetric'` now should raise an error.
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* Optimise show_topics to only call get_lambda once. Fix #1006. (@bhargavvader, [#1028](https://github.com/RaRe-Technologies/gensim/pull/1028))
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* HdpModel doc improvement. Inference and print_topics (@dsquareindia, [#1029](https://github.com/RaRe-Technologies/gensim/pull/1029))
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* Removing Doc2Vec defaults so that it won't override Word2Vec defaults. Fix #795 (@markroxor, [#929](https://github.com/RaRe-Technologies/gensim/pull/929))
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Remove warning on gensim import "pattern not installed". Fix #1009 (@shashankg7, #1018)
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* Add delete_temporary_training_data() function to word2vec and doc2vec models. (@deepmipt-VladZhukov, [#987](https://github.com/RaRe-Technologies/gensim/pull/987))
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* New class KeyedVectors to store embedding separate from training code (@anmol01gulati and @droudy, [#980](https://github.com/RaRe-Technologies/gensim/pull/980))
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* Documentation improvements (@IrinaGoloshchapova, [#1010](https://github.com/RaRe-Technologies/gensim/pull/1010), [#1011](https://github.com/RaRe-Technologies/gensim/pull/1011))
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* LDA tutorial by Olavur, tips and tricks (@olavurmortensen, [#779](https://github.com/RaRe-Technologies/gensim/pull/779))
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* Add double quote in commmand line to run on Windows (@akarazeev, [#1005](https://github.com/RaRe-Technologies/gensim/pull/1005))
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* Fix directory names in notebooks to be OS-independent (@mamamot, [#1004](https://github.com/RaRe-Technologies/gensim/pull/1004))
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* Respect clip_start, clip_end in most_similar. Fix #601. (@parulsethi, [#994](https://github.com/RaRe-Technologies/gensim/pull/994))
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* Replace Python sigmoid function with scipy in word2vec & doc2vec (@markroxor, [#989](https://github.com/RaRe-Technologies/gensim/pull/989))
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* WMD to return 0 instead of inf for sentences that contain a single word (@rbahumi, [#986](https://github.com/RaRe-Technologies/gensim/pull/986))
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* Pass all the params through the apply call in lda.get_document_topics(), test case to use the per_word_topics through the corpus in test_ldamodel (@parthoiiitm, [#978](https://github.com/RaRe-Technologies/gensim/pull/978))
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* Pyro annotations for lsi_worker (@markroxor, [#968](https://github.com/RaRe-Technologies/gensim/pull/968))
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0.13.3, 2016-10-20
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@@ -20,6 +52,7 @@ Changes
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* Remove ShardedCorpus from init because of Theano dependency (@tmylk, [#919](https://github.com/RaRe-Technologies/gensim/pull/919))
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* Documentation improvements ( @dsquareindia & @tmylk, [#914](https://github.com/RaRe-Technologies/gensim/pull/914), [#906](https://github.com/RaRe-Technologies/gensim/pull/906) )
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* Add Annoy memory-mapping example (@harshul1610, [#899](https://github.com/RaRe-Technologies/gensim/pull/899))
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* Fixed issue [#601](https://github.com/RaRe-Technologies/gensim/issues/601), correct docID in most_similar for clip range (@parulsethi, [#994](https://github.com/RaRe-Technologies/gensim/pull/994))
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0.13.2, 2016-08-19
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‎appveyor.yml

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WHEELHOUSE_UPLOADER_USERNAME: "Lev.Konstantinovskiy"
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WHEELHOUSE_UPLOADER_SECRET:
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secure: qXqY3dFmLOqvxa3Om2gQi/BjotTOK+EP2IPLolBNo0c61yDtNWxbmE4wH3up72Be
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matrix:
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- PYTHON: "C:\\Python27"
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PYTHON_VERSION: "2.7.8"
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# Install the build and runtime dependencies of the project.
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# Install the build and runtime dependencies of the project.
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- "%CMD_IN_ENV% pip install --timeout=60 --trusted-host 28daf2247a33ed269873-7b1aad3fab3cc330e1fd9d109892382a.r6.cf2.rackcdn.com -r continuous_integration/appveyor/requirements.txt"
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- "%CMD_IN_ENV% python setup.py bdist_wheel bdist_wininst "
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- "%CMD_IN_ENV% python setup.py bdist_wheel bdist_wininst"
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- ps: "ls dist"
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# Install the genreated wheel package to test it
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# installed library.
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- "mkdir empty_folder"
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- "cd empty_folder"
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- "pip install pyemd testfixtures"
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- "pip install pyemd testfixtures unittest2"
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- "python -c \"import nose; nose.main()\" -s -v gensim"
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# Move back to the project folder
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# container, speed up the appveyor jobs and reduce bandwidth
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# usage on our rackspace account.
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- '%APPDATA%\pip\Cache'
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‎docs/notebooks/Word2Vec_FastText_Comparison.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `accuracy` takes an optional parameter `restrict_vocab`, which limits the vocabulary of model considered for fast approximate evaluation (default is 30000).\n",
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"\n",
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"Word2Vec embeddings seem to be slightly better than fastText embeddings at the semantic tasks, while the fastText embeddings do significantly better on the syntactic analogies. Makes sense, since fastText embeddings are trained for understanding morphological nuances, and most of the syntactic analogies are morphology based. \n",
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"\n",
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"Let me explain that better.\n",

‎docs/notebooks/datasets/mycorpus.txt

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1+
Human machine interface for lab abc computer applications
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A survey of user opinion of computer system response time
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The EPS user interface management system
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System and human system engineering testing of EPS
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Relation of user perceived response time to error measurement
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The generation of random binary unordered trees
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The intersection graph of paths in trees
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Graph minors IV Widths of trees and well quasi ordering
9+
Graph minors A survey

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