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.
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 used 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 are 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).
Minisom have 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/.
