merlin
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coarse coding features
Three coarse coding features vectors are exactly the same. `def compute_coarse_coding_features(self, num_states): assert num_states == 3
npoints = 600
cc_features = numpy.zeros((num_states, npoints))
x1 = numpy.linspace(-1.5, 1.5, npoints)
x2 = numpy.linspace(-1.0, 2.0, npoints)
x3 = numpy.linspace(-0.5, 2.5, npoints)
mu1 = 0.0
mu2 = 0.5
mu3 = 1.0
sigma = 0.4
cc_features[0, :] = mlab.normpdf(x1, mu1, sigma)
cc_features[1, :] = mlab.normpdf(x2, mu2, sigma)
cc_features[2, :] = mlab.normpdf(x3, mu3, sigma)
` Samples and mean are shifted in same manner. Variance remains the same thus three probability vectors are exactly the same.