HighP

C/C++ implementations of various clustering algorithms, trajectory processing, and binary similarity metrics with Python SWIG/WebAssembly bindings to select algorithms.

Usage

C++

C++ has broader access to more algorithmd including implementations for:

• Dozens of Binary attribute similarity metrics
• Trajectories
• Convoy identification
• Trajectory Stop detection
• Maximum Subarray
• KDTree
• etc.

The examples of C++ usage can be found in src/cpp/test.cpp

Test

To build the C++ implementations and execute the unit tests, use the make test command:

make test

Python

The Python bindings open a subset of the algorithms implemented in C++. The best examples for using the python binding are through the test.py module.

Dependencies

Install swig to build the Python bindings locally:

sudo apt install swig
make python_build  # build in-place
make python_install # install library and bindings

WebAssembly

The Python bindings open a subset of the algorithms implemented in C++. The best examples for using the WebAssembly are through the webassembly.html file. To build and install the webAssembly bindings, use the make wasm_prod command:

make wasm_prod

Then open the webassembly.html file to see the output from the wasm module.