MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Networks able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.
Download MiniSom to the directory of your choice, enter it and run (may require root privileges):
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 an as list of lists like the following:
data = [[ 5.1 3.5 1.4 0.2],
[ 4.9 3. 1.4 0.2],
[ 4.7 3.2 1.3 0.2], # <-- single observation
[ 4.6 3.1 1.5 0.2],
[ 5. 3.6 1.4 0.2],
[ 4.1 3.3 1.4 0.2],
[ 4.2 3.2 1.2 0.2]]
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
print "Training..."
som.train_random(data, 100) # trains the SOM with 100 iterations
print "...ready!"
MiniSom 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.
A data driven initialization of the weights is also provided by the method random_weights_init which initializes the weights picking random samples from the data.
After the training you will be able to
- Compute the coordinate assigned to an observation
xon the map with the methodwinner(x). - Compute the average distance map of the weights on the map with the method
distance_map(). - Compute the number of times that each neuron have been considered winner for the observations of a new data set with the method
activation_response(data). - Compute the quantization error with the method
quantization_error(data).
The data can be quantized by assigning a code book (weights vector of the winning neuron) to each sample in data. This kind of vector quantization is implemented by the method quantization that can be called as follows:
qnt = som.quantization(data)
In this example we have that qnt[i] is the quantized version of data[i].
In examples/example_iris.py you can find an example that shows how to train MiniSom and visualize the result using the Iris flower dataset. Here is the result of the script:
For each observation we have a marker placed on the position of the winning neuron on the map. Each type of marker represents a class of the iris data. The average distance map of the weights is used as background (see the color bar on the right to associate the value).
In examples/example_digits.py there is an example of how to use MiniSom for images clustering. The example uses the scitkits-learn wrapper to the handwritten digits dataset. Here is one of the result that MiniSom can achieve in digits recognition:
The graph above represent each image with the handwritten digit it contains. The position corresponds to the position of the winning neuron for the image. Here we also have a version of this graphs that shows the original images:
In examples/example_color.py you can find an example of how to use MiniSom for color quantization. Here is one of the possible results:
(the examples require matplotlib for the visualization of the results).
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- Remi Domingues. Machine Learning for Unsupervised Fraud Detection. Royal Institute of Technology School of Computer Science and Communication KTH CSC. 2015.
- Ivana Kajić, Guido Schillaci, Saša Bodiroža, Verena V. Hafner, Learning hand-eye coordination for a humanoid robot using SOMs. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction Pages 192-193.
Minisom has been tested under python 2.7.3 and 3.2.3.
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/.
