inference.run_evaluation()
# Do statistics for the current inference
Ra_stat[i, 0], Ra_stat[i, 1], Ra_stat[i, 2], Ra_stat[
i, 3], Ra_stat[4], Ra_stat[5] = stat(Ra)
gpas_stat[i, 0], gpas_stat[i, 1], gpas_stat[i, 2], gpas_stat[
i, 3], gpas_stat[4], gpas_stat[5] = stat(gpas)
cm_stat[i, 0], cm_stat[i, 1], cm_stat[i, 2], cm_stat[
i, 3], cm_stat[4], cm_stat[5] = stat(cm)
# Plot some single joint distribution
if i == num_of_iter - 1:
print(inference)
fullplot(inference)
plot_joint(inference, Ra, gpas)
plot_joint(inference, Ra, cm)
plot_joint(inference, cm, gpas)
print("\n\n")
runningTime = (time.time() - startTime) / 60
lasted = "The Ra-gpas-cm ball-and-stick simulation was running for %f minutes\n" % runningTime
configuration = "--\n"
setup1 = 'Multi compartment simulation; White noise sigma=7; ramp stimulus; Ra parameter; dt=0.1\n'
setup2 = 'Multi compartment simulation; White noise sigma=7; ramp stimulus; gpas parameter; dt=0.1\n'
setup3 = 'Multi compartment simulation; White noise sigma=7; ramp stimulus; cm parameter; dt=0.1\n'
header1 = "Number of simulations: " + str(
num_of_iter) + '\n' + setup1 + configuration + lasted
header2 = "Number of simulations: " + str(
num_of_iter) + '\n' + setup2 + configuration + lasted
delimiter='\t')
if __name__ == "__main__":
# load_statistics(50, ["Ra", "cm", "gpas"], "/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp/loglikelihood",
# "/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp")
cm = np.loadtxt(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/100/loglikelihood/cm(0).txt",
dtype=str)
gpas = np.loadtxt(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/100/loglikelihood/gpas(0).txt",
dtype=str)
Ra = np.loadtxt(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/100/loglikelihood/Ra(0).txt",
dtype=str)
ll = np.loadtxt(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/100/loglikelihood/loglikelihood(0).txt"
)
inf = load_inference(
ll, "/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/100", Ra, cm,
gpas)
inf.run_evaluation()
print(inf)
from module.plot import fullplot, plot_joint
plot_joint(inf, inf.p.params[0], inf.p.params[1])
plot_joint(inf, inf.p.params[0], inf.p.params[2])
plot_joint(inf, inf.p.params[1], inf.p.params[2])
fullplot(inf)
# 2.) Set up parameter set
cm_gpas = ParameterSet(cm, gpas)
# 3.) Sythetic data
t, v = stick_and_ball()
exp_v = white(noise, v)
# 4.) Set up inference
inf = IndependentInference(
model=stick_and_ball,
noise_std=noise,
target_trace=exp_v,
parameter_set=cm_gpas,
working_path=
"/home/terbed/PROJECTS/SPE/parameter-inference/module/examples/output",
speed='max',
save=False)
# 5.) Run inference
if __name__ == "__main__":
inf.run_sim()
# 6.) Run evaluation
inf.run_evaluation()
print("KL divergence test: %f" % inf.KL)
# 7.) Plot solution
print(inf)
plot_joint(inf, cm, gpas)
fullplot(inf)
def joint_plot(self):
# Plot joints
for item in self.p.margin_ax:
plot.plot_joint(self, self.p.params[item[0]],
self.p.params[item[1]])
# load list of matrices with stat
for j, stat in enumerate(params_stat):
stat_list[j][i - m, 0], stat_list[j][i - m, 1], stat_list[j][i - m, 2], stat_list[j][i - m, 3], \
stat_list[j][i - m, 4], stat_list[j][i - m, 5] = stat
# Plot last signle result at the end
if i == n - 1:
# Marginal plots
print(inf)
# Fullplot
fullplot(inf)
# Joint plots (BRUTEFORCE for 3 parameter solution!)
plot_joint(inf, current_params[0], current_params[1])
plot_joint(inf, current_params[0], current_params[2])
plot_joint(inf, current_params[1], current_params[2])
# Plot results
for q, item in enumerate(stat_list):
print("Result saved to: " + working_path + "/" + p_names[q] +
"_stat.txt")
np.savetxt(
working_path + "/" + p_names[q] + "_stat.txt",
item,
header='\nsigma\tfit_err\trdiff\taccuracy\tsharper\tbroadness',
delimiter='\t')
# Save plots
for idx, item in enumerate(stat_list):