Initial commit of rda.py experimental implementation v0.1. With images of output trial.

Author: pmdscully

.gitignore

@@ -1,5 +1,6 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
+*~
 *.py[cod]
 *$py.class
 

README.md

@@ -1,2 +1,36 @@
-# RDA_in_Python
-This is the Receptor Density Algorithm (RDA) implemented in Python 2.7. RDA is an Artificial Immune System (AIS) anomaly detection algorithm modelled upon how T-cell receptors respond to antigen, originally modelled by Owens et al in 2009. This was part of a sub-project to investigate its applicability as an AIS anomaly classifier for our self-healing architecture.
+# RDA in Python
+
+This is an implementation of the 'Receptor Density Algorithm' (RDA) Python 2.7. RDA is an Artificial Immune System (AIS) anomaly detection algorithm modelled upon how T-cell receptors respond to antigen, originally modelled by Owens et al in 2009 at University of York. This was part of a sub-project to investigate its applicability as an AIS anomaly classifier for our self-healing architecture.
+
+
+Details of the experimentation and implementation to be added. See /images/ directory for outputs of our trial run with k=10 cross fold validation on some sampled two-stage normal data.
+
+
+### Versions:
+
+Version 0.1 implementation of RDA in python 2.7.
+
+- Uses one numerical sensor input from a generated model.
+- Includes signature classification of 1 class.
+- Suitable for post-processing style classification. The anomaly matching approach is not suitable for real-time responding.
+- Requires python 2.7 and libraries: scipy, numpy, matplotlib
+
+### Tested on: Python 2.7.5+ (default, Feb 27 2014, 19:37:08)
+
+In [1]: import matplotlib
+In [2]: matplotlib.__version__
+Out[2]: '1.2.1'
+In [3]: import numpy
+In [4]: numpy.__version__
+Out[4]: '1.7.1'
+In [5]: import scipy
+In [6]: scipy.__version__
+Out[6]: '0.12.0'
+
+
+### References:
+
+Hilder, J. A., Owens, N. D., Hickey, P. J., Cairns, S. N., Kilgour, D. P., Timmis, J., & Tyrrell, A. (2011). Parameter optimisation in the receptor density algorithm. In Artificial Immune Systems (pp. 226-239). Springer Berlin Heidelberg.
+
+Owens, N. D., Greensted, A., Timmis, J., & Tyrrell, A. (2009). T cell receptor signalling inspired kernel density estimation and anomaly detection. In Artificial Immune Systems (pp. 122-135). Springer Berlin Heidelberg.
+