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Dense Armor
Dense Armor is a Python machine learning library that processes streaming data and controls robots one sample at a time.
Dense Armor provides algorithms and utilities for online machine learning, anomaly detection, time-series forecasting, and robot control. Developers use it to process data streams sample by sample or handle fixed datasets with the densearmor.ml classes. The library includes linear models, trees, ensembles, deep learning components, and bridges to PyTorch, TensorFlow, and scikit-learn. It handles data cleaning, drift detection, and command bounding for applications like robot control loops, sensor pipelines, and financial data. Computation runs on CPU or GPU using JAX as the backend.
Dense Armor at a glance
Key features
- Online learning estimators for single-sample updates
- Drift and anomaly detection on data streams
- Robot command bounding with control barrier functions
- Deep learning blocks with continual learning and memory
- Time-series models including Kalman filters and GARCH
- Bridges to scikit-learn, PyTorch, and TensorFlow
- Shielding for data cleanup and runtime protection
Use it for
- Controlling robot joints with rate limits and safety filters
- Detecting fraud and anomalies in event streams
- Monitoring streaming metrics with drift detection
- Processing deployed machine learning models with input shielding
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