def Main(plan, names, sigma, sigmaDecay, alphaDecay):
#samples, names = TestSamples()
samples = SOMUtils.ExtractSOMSamples(plan, names)
print "%d samples extracted" % len(samples), names
scales, mins = ComputeScales(samples)
dims = len(samples[0][1])
# Neighborhood distance function.
distFunc = lambda dsq: m.exp(-0.5 * dsq / (sigma * sigma))
#som = SOM(CartesianGrid((20, 20)), dims, scales, distFunc)
som = SOM(HexagonalGrid((20, 20)), dims, scales, distFunc)
# Train the SOM.
alpha = 1.0
for i in range(100):
if (i + 1) % 10 == 0:
print "Iteration ", i + 1, "alpha = ", alpha, "sigma = ", sigma
for id, sample in samples:
som.Train(sample, alpha)
alpha = alpha * alphaDecay
sigma = sigma * sigmaDecay
som.DistFunc = lambda dsq: m.exp(-0.5 * dsq / (sigma * sigma))
SOMUtils.ExportSOM(open("OUT.som", "w"), som, names, samples)
