MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.
Updates about MiniSom are posted on Twitter.
Just use pip:
pip install minisom
or download MiniSom to a directory of your choice and use the setup script:
git clone https://github.com/JustGlowing/minisom.git
python setup.py install
In order to use MiniSom you need your data organized as a Numpy matrix where each row corresponds to an observation or as list of lists like the following:
data = [[ 0.80, 0.55, 0.22, 0.03],
[ 0.82, 0.50, 0.23, 0.03],
[ 0.80, 0.54, 0.22, 0.03],
[ 0.80, 0.53, 0.26, 0.03],
[ 0.79, 0.56, 0.22, 0.03],
[ 0.75, 0.60, 0.25, 0.03],
[ 0.77, 0.59, 0.22, 0.03]] Then you can run MiniSom just as follows:
from minisom import MiniSom
som = MiniSom(6, 6, 4, sigma=0.3, learning_rate=0.5) # initialization of 6x6 SOM
som.train_random(data, 100) # trains the SOM with 100 iterationsMiniSom implements two types of training. The random training (implemented by the method train_random), where the model is trained picking random samples from your data, and the batch training (implemented by the method train_batch), where the samples are picked in the order they are stored.
The weights of the network are randomly initialized by default. Two additional methods are provided to initialize the weights in a data driven fashion: random_weights_init and pca_weights_init.
For an overview of all the features implemented in minisom you can browse the following examples: https://github.com/JustGlowing/minisom/tree/master/examples
A model can be saved using pickle as follows
import pickle
som = MiniSom(7, 7, 4)
# ...train the som here
# saving the som in the file som.p
with open('som.p', 'wb') as outfile:
pickle.dump(som, outfile)and can be loaded as follows
with open('som.p', 'rb') as infile:
som = pickle.load(infile)Note that if a lambda function is used to define the decay factor MiniSom will not be pickable anymore.
Here are some of the charts you'll see how to generate in the examples:
Iris map  |
Class assignment  |
Handwritteng digits mapping  |
Images mapping  |
Color quantization  |
Outliers detection  |
- Self Organizing Maps on the Glowing Python by me ;-)
- Lecture notes from the Machine Learning course at the University of Lisbon
- Introduction to Self-Organizing by Derrick Mwiti
- Discovering SOM, an Unsupervised Neural Network by Gisely Alves
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Minisom has been tested under Python 3.6.2.
MiniSom by Giuseppe Vettigli is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/.
