def test(alpha=0.382, beta=0.77):
"""Test of entropy rate function for two-state Markov chain."""
d = {(0,0): 1.-alpha, (0,1): alpha, (1,0):beta, (1,1):1.-beta}
s = [beta / (alpha + beta), alpha / (alpha + beta)]
e = approximate_entropy_rate(d, s)
print d
print s
print e
print ( beta * binary_entropy(alpha) + alpha * binary_entropy(beta) ) / (alpha + beta)
def test(alpha=0.382, beta=0.77):
"""Test of entropy rate function for two-state Markov chain."""
d = {(0, 0): 1. - alpha, (0, 1): alpha, (1, 0): beta, (1, 1): 1. - beta}
s = [beta / (alpha + beta), alpha / (alpha + beta)]
e = approximate_entropy_rate(d, s)
print d
print s
print e
print(beta * binary_entropy(alpha) +
alpha * binary_entropy(beta)) / (alpha + beta)
def static_plot(Ns, m, incentive_func=None, eta=None, w=None, mutation_func=None, mu_ab=0.001, mu_ba=0.001, test_func=None):
"""Plot entropy rate over a range of population sizes."""
params = [Ns]
for p in [m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
#k = 0.5 + 0.5* math.log(math.pi / 2)
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
#pyplot.plot(Ns, [(es[i][-1] -k)/(0.5 * math.log(Ns[i])) for i in range(len(es))])
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
print mu_ab, es[-1][-1] - binary_entropy(mu_ab)
pyplot.plot(Ns, [es[i][-1] - binary_entropy(mu_ab) for i in range(len(es))])
return es
def static_plot(Ns,
m,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
mu_ab=0.001,
mu_ba=0.001,
test_func=None):
"""Plot entropy rate over a range of population sizes."""
params = [Ns]
for p in [
m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func
]:
params.append(constant_generator(p))
es = run_batches(params)
#k = 0.5 + 0.5* math.log(math.pi / 2)
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
#pyplot.plot(Ns, [(es[i][-1] -k)/(0.5 * math.log(Ns[i])) for i in range(len(es))])
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
print mu_ab, es[-1][-1] - binary_entropy(mu_ab)
pyplot.plot(Ns,
[es[i][-1] - binary_entropy(mu_ab) for i in range(len(es))])
return es
