#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)