Machine learning: Using embedPy to apply LASSO regression

23 Oct 2018 | , , ,
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By Samantha Gallagher


The use of kdb+ for machine learning in financial technology and other industries is expanding following the release by Kx of the powerful embedPy interface, which allows the kdb+ interpreter to manipulate Python objects, call Python functions, and load Python libraries. Now Python and kdb+ developers can fuse both technologies together, allowing for a seamless application of q’s high-speed analytics and Python’s expansive collection of libraries.

In our latest technical white paper, Kx engineer Samantha Gallagher introduces embedPy, covering both a range of basic tutorials as well as a comprehensive solution to a machine-learning project. EmbedPy is available on GitHub to use with kdb+ V3.5+ and Python 3.5 or higher, for macOS or Linux operating systems and Python 3.6 or higher on the Windows operating system. The installation directory also contains a README.txt about embedPy, and an example directory containing thorough examples.

You can read Samantha’s paper on the Kx Developer’s site, here.


Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

Detection of Exoplanets at NASA FDL with kdb+

13 Dec 2018 | , , , ,

Kx data scientist Espe Aguilera explains a NASA FDL mission to improve the accuracy of finding new exoplanets using machine learning models. The data for the project will come from the Transiting Exoplanet Survey Satellite (TESS), which was launched in April 2018, with the objective of discovering new exoplanets in orbit around the brightest stars in the solar neighborhood.

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

The Exploration of Space Weather at NASA FDL with kdb+

4 Dec 2018 | , , , ,

Our society is dependent on GNSS services for navigation in everyday life, so it is critically important to know when signal disruptions might occur. Physical models have struggled to predict astronomic scintillation events. One method for making predictions is to use machine learning (ML) techniques. This article describes how kdb+ and embedPy were used in the ML application.