FALCONN-LIB.ORG

FALCONN: Similarity Search Over High-Dimensional Data

FAst Lookups of Cosine and Other Nearest Neighbors) is a C library with a Python wrapper for similarity search over high-dimensional data. It supports cosine similarity. And the Euclidean distance. The main ingredient of FALCONN is a Locality-Sensitive Hashing. Family for cosine similarity that is:. Optimal in theory,. See the github repo. For the source code and documentation. Released under the MIT license. Version 1.2.2. To install the Pypi. For more detailed results, see ann-benchmarks.

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FALCONN: Similarity Search Over High-Dimensional Data | falconn-lib.org Reviews

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FAst Lookups of Cosine and Other Nearest Neighbors) is a C library with a Python wrapper for similarity search over high-dimensional data. It supports cosine similarity. And the Euclidean distance. The main ingredient of FALCONN is a Locality-Sensitive Hashing. Family for cosine similarity that is:. Optimal in theory,. See the github repo. For the source code and documentation. Released under the MIT license. Version 1.2.2. To install the Pypi. For more detailed results, see ann-benchmarks.

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FALCONN: Similarity Search Over High-Dimensional Data | falconn-lib.org Reviews

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FAst Lookups of Cosine and Other Nearest Neighbors) is a C library with a Python wrapper for similarity search over high-dimensional data. It supports cosine similarity. And the Euclidean distance. The main ingredient of FALCONN is a Locality-Sensitive Hashing. Family for cosine similarity that is:. Optimal in theory,. See the github repo. For the source code and documentation. Released under the MIT license. Version 1.2.2. To install the Pypi. For more detailed results, see ann-benchmarks.

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Locality Sensitive Hashing (LSH) Home Page

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LSH Algorithm and Implementation (E2LSH). Is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. This webpage links to the newest LSH algorithms in Euclidean and Hamming spaces, as well as the E2LSH. An implementation of an early practical LSH algorithm. These algorithms achieve performance better than the classic LSH algorithms by using data-dependent. Hashing and hence allow updates to the pointset. Practical and Optimal LSH for Angular Distance. Near-Opti...

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falconn-lib.org

FALCONN: Similarity Search Over High-Dimensional Data

FAst Lookups of Cosine and Other Nearest Neighbors) is a C library with a Python wrapper for similarity search over high-dimensional data. It supports cosine similarity. And the Euclidean distance. The main ingredient of FALCONN is a Locality-Sensitive Hashing. Family for cosine similarity that is:. Optimal in theory,. See the github repo. For the source code and documentation. Released under the MIT license. Version 1.2.2. To install the Pypi. For more detailed results, see ann-benchmarks.

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