cetrulin / GrowingNeuralGas

Simple implementation of the "growing neural gas" artificial neural network

GrowingNeuralGas

About

A simple implementation of the Growing Neural Gas algorithm, based on:

A Growing Neural Gas Network Learns Topologies, B. Fritzke
Advances in Neural Information Processing Systems 7 (1995)`

Usage

Instantiate a GNG object with some data, then fit the neural network:

gng = GrowingNeuralGas(data)
gng.fit_network(e_b=0.1, e_n=0.006, a_max=10, l=200, a=0.5, d=0.995, passes=8, plot_evolution=True)

Example

This example shows (i) how to generate toy data (two interleaving half circles) using the scikit-learn lib and (ii) how to perform cluster analysis through the growing neural gas network:

from gng import GrowingNeuralGas
from sklearn import datasets
from sklearn.preprocessing import StandardScaler

print('Generating data...')
data = datasets.make_moons(n_samples=2000, noise=.05) 
data = StandardScaler().fit_transform(data[0])
print('Done.')
print('Fitting neural network...')
gng = GrowingNeuralGas(data)
gng.fit_network(e_b=0.1, e_n=0.006, a_max=10, l=200, a=0.5, d=0.995, passes=8, plot_evolution=True)
print('Found %d clusters.' % gng.number_of_clusters())
gng.plot_clusters(gng.cluster_data())

Running example.py will produce the following output:

Preparing data...
Done.
Fitting neural network...
   Pass #1
   Pass #2
   Pass #3
   Pass #4
   Pass #5
   Pass #6
   Pass #7
   Pass #8
Found 2 clusters.

Clusters

Network properties

Global error vs. number of passes

Accumulated local error vs. total number of iterations