def __init__(self,
nNeurons,
mNeurons,
learningrate=0.01,
neighbourdecay=0.9999):
# KohonenMap of IMG_SIZE dimensions and a 2x2 grid. (try 4x1 later)
self.som = KohonenMap(IMG_SIZE, nNeurons, mNeurons)
self.som.learningrate = learningrate
self.som.neighbourdecay = neighbourdecay
self.emo_clusters = [[-1] * mNeurons for x in range(nNeurons)]
from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot
from scipy import diag, arange, meshgrid, where
from numpy.random import multivariate_normal
from numpy import zeros
cluster_data = []
#means = [(-1,0),(2,4),(3,1)]
means = [(-10, 10), (10, 10), (0, 0), (10, -10)]
cov = [diag([1, 1]), diag([0.5, 1.2]), diag([1.5, 0.7]), diag([1.5, 0.7])]
for n in xrange(400):
for klass in range(len(means)):
cluster_data.append(multivariate_normal(means[klass], cov[klass]))
som = KohonenMap(2, 9, 9)
pylab.ion()
p = pylab.plot(som.neurons[:, :, 0].flatten(), som.neurons[:, :, 1].flatten(),
's')
pylab.axis([-15, 15, -15, 15])
i = 0
for j in range(40):
print j
for data in cluster_data:
i += 1
# one forward and one backward (training) pass
som.activate(data)
som.backward()
