SimHash Document Encoder

SimHash Document Encoder is now live in HTM.core as C++ with Python bindings. It provides the simple and immediate encoding of text for use with Hierarchical Temporal Memory (HTM), a machine intelligence framework. This may be of interest to Natural Language Processing (NLP), Search, or HTM engineers.

SimHash Document Encoder converts text-based documents into Sparse Distributed Representations (SDR), “the brain’s data structure,” ready for use with HTM. Similar documents will result in similar encodings, while dissimilar documents will have differing encodings. “Similarity” here refers to bitwise similarity (small hamming distance, high overlap), not semantic similarity (encodings for “apple” and “computer” will have no relation here).


A wide selection of helpful parameters can be passed to the encoder, including options for setting token case sensitivity, vocabulary, weightings, exclusions, frequency ceiling/flooring, orphan handling, and character similarity sensitivity. The documentation in the header file has more details.


The following is a usage example in C++:

#include <htm/encoders/types/Sdr.hpp>
#include <htm/encoders/SimHashDocumentEncoder.hpp>

SimHashDocumentEncoderParameters params;
params.size = 400u;
params.activeBits = 21u;

SDR output({ params.size });
SimHashDocumentEncoder encoder(params);

encoder.encode({ "bravo", "delta", "echo" }, output);
encoder.encode("bravo delta echo", output);  // same

The C++ Unit Tests provide more usage examples.


The following is a usage example in Python:

from htm.bindings.encoders import \

params = SimHashDocumentEncoderParameters()
params.size = 400
params.activeBits = 21

encoder = SimHashDocumentEncoder(params)

other = encoder.encode([ "bravo", "delta", "echo" ])
other = encoder.encode("bravo delta echo")  # same

The Python Unit Tests provide more usage information.

Python Example Runner

An example of the encoder in action is provided in Python. It will generate many random documents, and find the most/least similar. It will also generate a visual chart of encoding space usage.

For help getting started:

python \
  -m htm.examples.encoders.simhash_document_encoder \

To run a simple example:

python \
  -m htm.examples.encoders.simhash_document_encoder \
  --size 400 \
  --activeBits 150

Python Module Help

Helpful documentation on encoder parameters and usage is available in Python module form:

>>> import htm.bindings.encoders
>>> help(htm.bindings.encoders.SimHashDocumentEncoder)

Learn More


HTM.core is the active HTM Community fork of Numenta’s hibernating NuPIC HTM codebase. Thanks again to the team for their help and support, they’ve got a beautiful codebase, and are wonderful to work with.


SimHash is a Locality-Sensitive Hashing (LSH) algorithm from the world of nearest-neighbor document similarity search. It is used by the GoogleBot Web Crawler to find near-duplicate web pages.

We provide an encoder-specific README file for an in-depth tour of the SimHash algorithm.

Semantic Similarity

For encodings that do support semantic similarity (encodings for “apple” and “computer” will relate), offers their highly-recommended Semantic Folding technology.