#samples = range(500,50500,500)
#samples = range(50,5050,50)
#samples = np.logspace(2,15,num=14,base=2)
#samples = samples.astype(int)
samples = range(3, 20)
a = parameters[p, 1]
b = parameters[p, 2]
c = parameters[p, 3]
x00 = parameters[p, 13]
x10 = parameters[p, 14]
T2P = parameters[p, 12]
print "b = ", -b
for si in samples:
ls = linear_simulator(a, b, c, x00, x10, DT, T2P, noise, False)
ls.run()
D = ls.get_dynamics()
S = sampler(si, D)
S.sample()
calc = timme_calculator(S, NUMBER_OF_BINS)
results, err = calc.calculate()
results = results[0:6]
print(results[2])
#print(S.sampled_dynamics)
## a0 | J00 | J01 | a1 | J10 | J11 | err1 | err2
## relative error in all of the above, excepting J11, err1, err2
sa_id = 0
for pi in params:
for r in range(REPEATS_PER_PARAMETER_SET):
a = parameters[pi,1]
b = parameters[pi,2]
c = parameters[pi,3]
x00 = parameters[pi,13]
x10 = parameters[pi,14]
T2P = parameters[pi,12]
ls = linear_simulator(a, b, c, x00, x10, DT, T2P, ni, plot_dynamics)
ls.run()
D = ls.get_dynamics()
E_prey = np.asarray(ls.ext_prey)
E_pred = np.asarray(ls.ext_pred)
## now do inference:
S = sampler(NUMBER_OF_SAMPLES, D)
S.sample()
calc = timme_calculator(S, NUMBER_OF_BINS)
results, err = calc.calculate()
results = results[0:6]
results = np.append(results, err[0])
results = np.append(results, err[1])
#samples = samples.astype(int)
samples = range(3,20)
a = parameters[p,1]
b = parameters[p,2]
c = parameters[p,3]
x00 = parameters[p,13]
x10 = parameters[p,14]
T2P = parameters[p,12]
print "b = ", -b
for si in samples:
ls = linear_simulator(a, b, c, x00, x10, DT, T2P, noise, False)
ls.run()
D = ls.get_dynamics()
S = sampler(si, D)
S.sample()
calc = timme_calculator(S, NUMBER_OF_BINS)
results, err = calc.calculate()
results = results[0:6]
print(results[2])
#print(S.sampled_dynamics)
