Merge remote-tracking branch 'origin/master' into develop

README.md

@@ -5,7 +5,7 @@
 
 Non-Metric Space Library (NMSLIB) 
 =================
-The latest **pre**-release is [1.6](https://github.com/searchivarius/nmslib/releases/tag/v1.6). Note that the manual is not updated to reflect 1.6 changes. In particular, we changed the build procedure for Windows. Also note that the manual targets primiarily developers who will extend the library. For most other folks, [Python binding docs should be sufficient](python_bindings).
+The latest **pre**-release is [1.7](https://github.com/searchivarius/nmslib/releases/tag/v1.7). Note that the manual is not updated to reflect some of the changes. In particular, we changed the build procedure for Windows. Also note that the manual targets primiarily developers who will extend the library. For most other folks, [Python binding docs should be sufficient](python_bindings).
 -----------------
 Non-Metric Space Library (NMSLIB) is an **efficient** cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The core-library does **not** have any third-party dependencies.
 

python_bindings/README.md

@@ -45,6 +45,15 @@ ids, distances = index.knnQuery(data[0], k=10)
 neighbours = index.knnQueryBatch(data, k=10, num_threads=4)
 ```
 
+#### Logging
+
+NMSLIB produces quite a few informational messages. By default, they are not shown in Python. To enable debugging, one should use the following commands before importing the library:
+
+```
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
 #### Installing with Extras
 
 To enable extra methods like those provided by FALCONN and LSHKIT you need to follow an extra couple steps.

python_bindings/notebooks/README.md

@@ -0,0 +1,5 @@
+We have three Notebooks: two are for dense spaces and one is for the sparse space. For the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real onese. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data.
+
+HNSW, can save real indices, but only for the dense spaces: Euclidean and the cosine. When you use these optimized indices, the search does not require reloading all the data. However, reloading the data is **required** if you want to use the function **getDistance**. Furthermore, creation of the optimized index can always be disabled specifying the index-time parameter **skip_optimized_index** (value 1).
+
+This separation into optimized and non-optimized indices is not very convenient. In the future, we will fix this issue.