This repository provides a simple framework to highlight the benefits of SCAN (see paper) over Multiple Least-Squares (MLS) when applied to multi-hop calibration. In particular, it shows that SCAN minimizes error accumulation over multiple hops in constrast to MLS that suffers from the bias-towards-zero (also known as regression dilution (wiki link) problem and, thus, also error accumulation.

Following files are provided:

• MultihopCalibration.py: Main experiment loop. Run python MultihopCalibration.py --config_file=config.json to start a new experiment
• DataCeator.py: Generates artificial data used to test the calibration methods. The data resembles measurements from cross-sensitive and noisy low-cost sensor-arrays that measure typical air pollution concentrations.
• CalibrationStatistics.py: Calculates different statistics/metrics to benchmark the performance of the calibration
• ResultPlotter.py: Plots the results of the calibration, in particular shows an errorbar plot of different metrics over the different hops within the calibration path.
• MLS.py: Calculates calibration parameters according to Multiple Least-Squares
• SCAN.py: Calculates calibration parameters according to SCAN
• config.json: Different configuration parameters used to perform the experiment

Implemented with:

• Python 2.7.12
• numpy 1.14.5
• scipy 0.19.1
• matplotlib 2.1.1