We can now implement an SOM using the Olivetti faces dataset. As the process can be very long, in this example we limit the number of input patterns to 100 (with a 5 × 5 matrix). The reader can try with the whole dataset and a larger map.
The first step is loading the data, normalizing it so that all values are bounded between 0.0 and 1.0, and setting the constants:
import numpy as npfrom sklearn.datasets import fetch_olivetti_facesfaces = fetch_olivetti_faces(shuffle=True)Xcomplete = faces['data'].astype(np.float64) / np.max(faces['data'])np.random.shuffle(Xcomplete)nb_iterations = 5000nb_startup_iterations = 500pattern_length = 64 * 64pattern_width = pattern_height = 64eta0 = 1.0sigma0 = 3.0tau = 100.0X = Xcomplete[0:100] ...
