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Note added to annoytutorial.ipynb #1137

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merged 5 commits into from
Feb 16, 2017
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Note added to annoytutorial.ipynb #1137

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2901206
Adding a note to annoytutorial.ipynb
greninja Feb 5, 2017
dbde461
Changed few words in annoytutorial.ipynb
greninja Feb 5, 2017
e6943fc
Changed the description
greninja Feb 6, 2017
af10e20
Removed the misleading initial note
greninja Feb 8, 2017
974341a
Adding link to the scipy docs for details
greninja Feb 13, 2017
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4 changes: 3 additions & 1 deletion docs/notebooks/annoytutorial.ipynb
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Expand Up @@ -177,7 +177,9 @@
"\n",
"**This speedup factor is by no means constant** and will vary greatly from run to run and is particular to this data set, BLAS setup, Annoy parameters(as tree size increases speedup factor decreases), machine specifications, among other factors.\n",
"\n",
">**Note**: Initialization time for the annoy indexer was not included in the times. The optimal knn algorithm for you to use will depend on how many queries you need to make and the size of the corpus. If you are making very few similarity queries, the time taken to initialize the annoy indexer will be longer than the time it would take the brute force method to retrieve results. If you are making many queries however, the time it takes to initialize the annoy indexer will be made up for by the incredibly fast retrieval times for queries once the indexer has been initialized"
">**Note**: Initialization time for the annoy indexer was not included in the times. The optimal knn algorithm for you to use will depend on how many queries you need to make and the size of the corpus. If you are making very few similarity queries, the time taken to initialize the annoy indexer will be longer than the time it would take the brute force method to retrieve results. If you are making many queries however, the time it takes to initialize the annoy indexer will be made up for by the incredibly fast retrieval times for queries once the indexer has been initialized\n",
"\n",
">**Note** : Gensim's 'most_similar' method is using numpy operations in the form of dot product whereas Annoy's method isnt. If 'numpy' on your machine is using one of the BLAS libraries like ATLAS or LAPACK, it'll run on multiple cores(only if your machine has multicore support ). Check [SciPy Cookbook](http://scipy-cookbook.readthedocs.io/items/ParallelProgramming.html) for more details."
]
},
{
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