Skip to content

Fixes of some typos in readme #253

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Nov 9, 2020
Merged

Fixes of some typos in readme #253

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@ Header-only C++ HNSW implementation with python bindings. Paper's code for the H

**NEWS:**

* **Thanks to Apoorv Sharma [@apoorv-sharma](https://github.com/apoorv-sharma), hnswlib now supports true element updates (the interface remained the same, but when you the perfromance/memory should not degrade as you update the element embeddinds).**
* **Thanks to Apoorv Sharma [@apoorv-sharma](https://github.com/apoorv-sharma), hnswlib now supports true element updates (the interface remained the same, but when you the perfromance/memory should not degrade as you update the element embeddings).**

* **Thanks to Dmitry [@2ooom](https://github.com/2ooom), hnswlib got a boost in performance for vector dimensions that are not mutiple of 4**
* **Thanks to Dmitry [@2ooom](https://github.com/2ooom), hnswlib got a boost in performance for vector dimensions that are not multiple of 4**

* **Thanks to Louis Abraham ([@louisabraham](https://github.com/louisabraham)) hnswlib can now be installed via pip!**

Expand Down Expand Up @@ -49,14 +49,14 @@ Index methods:
* `data_labels` specifies the labels for the data. If index already has the elements with the same labels, their features will be updated. Note that update procedure is slower than insertion of a new element, but more memory- and query-efficient.
* Thread-safe with other `add_items` calls, but not with `knn_query`.

* `mark_deleted(data_label)` - marks the element as deleted, so it will be ommited from search results.
* `mark_deleted(data_label)` - marks the element as deleted, so it will be omitted from search results.

* `resize_index(new_size)` - changes the maximum capacity of the index. Not thread safe with `add_items` and `knn_query`.

* `set_ef(ef)` - sets the query time accuracy/speed trade-off, defined by the `ef` parameter (
[ALGO_PARAMS.md](ALGO_PARAMS.md)). Note that the parameter is currently not saved along with the index, so you need to set it manually after loading.

* `knn_query(data, k = 1, num_threads = -1)` make a batch query for `k` closests elements for each element of the
* `knn_query(data, k = 1, num_threads = -1)` make a batch query for `k` closest elements for each element of the
* `data` (shape:`N*dim`). Returns a numpy array of (shape:`N*k`).
* `num_threads` sets the number of cpu threads to use (-1 means use default).
* Thread-safe with other `knn_query` calls, but not with `add_items`.
Expand Down Expand Up @@ -191,7 +191,7 @@ or you can install via pip:

### Other implementations
* Non-metric space library (nmslib) - main library(python, C++), supports exotic distances: https://github.com/nmslib/nmslib
* Faiss libary by facebook, uses own HNSW implementation for coarse quantization (python, C++):
* Faiss library by facebook, uses own HNSW implementation for coarse quantization (python, C++):
https://github.com/facebookresearch/faiss
* Code for the paper
["Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors"](https://arxiv.org/abs/1802.02422)
Expand Down