OeSNN anomaly detector implementation for Python.
Project description
Online evolutionary Spiking Neural Network anomaly detector
Description
Python implementation of OeSNN anomaly detector model. Model finds anomalies in one dimensional data streams. Theoretical basics about this model could be find here: https://arxiv.org/pdf/1912.08785.pdf.
Dependencies
- numpy
- PyEMD (pip install EMD-signal)
Package instalation
To install package for python you should type in terminal:
pip install OeSNN-AD
Usage
Our model require from data stream to be numpy array. Additional model parameters are passed as arguments in object constructor.
The following code snippet shows package basic usage.
from oesnn_ad import OeSNNAD
import numpy as np
data_stream = np.array([1, 2, 3, 4, 5])
model = oesnn_ad(data_stream)
results = model.predict()
Parameters
The following table shows model parameters and their values range.
Parameter | Default value | Minimal value | Maximum value |
---|---|---|---|
window_size | 100 | 1 | - |
num_in_neurons | 10 | 1 | - |
num_out_neurons | 50 | 1 | - |
ts_factor | 1000 | 0 | - |
mod | 0.6 | 0 | 1 |
c_factor | 0.6 | 0 | 1 |
epsilon | 2 | 2 | - |
ksi | 0.9 | 0 | 1 |
sim | 0.15 | 0 | - |
beta | 1.6 | 0 | - |
Project details
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