plt.savefig("sig_traces.pdf")
plt.clf()
def logprobs():
plt.plot(lprobs[:500])
plt.savefig("logprobs.pdf")
plt.clf()
def kdes():
mu_kernel = gaussian_kde(draws[50:,0])
x_min = min(draws[50:,0]) - 1
x_max = max(draws[50:,0]) + 1
x = np.arange(x_min, x_max, step=0.1)
plt.plot(x,mu_kernel(x))
plt.savefig("mu_kernel.pdf")
plt.clf()
sig_kernel = gaussian_kde(draws[50:,1])
x_min = 20
x_max = 200
x = np.arange(x_min, x_max, step=0.1)
plt.plot(x,sig_kernel(x))
plt.savefig("sig_kernel.pdf")
plt.clf()
if __name__ == "__main__":
draws, lprobs, r = metropolis(np.array([40, 10], dtype=float), 20., 10000)
traces()
logprobs()
kdes()
def logprobs():
plt.plot(lprobs[:500])
plt.savefig("logprobs.pdf")
plt.clf()
def kdes():
mu_kernel = gaussian_kde(draws[50:, 0])
x_min = min(draws[50:, 0]) - 1
x_max = max(draws[50:, 0]) + 1
x = np.arange(x_min, x_max, step=0.1)
plt.plot(x, mu_kernel(x))
plt.savefig("mu_kernel.pdf")
plt.clf()
sig_kernel = gaussian_kde(draws[50:, 1])
x_min = 20
x_max = 200
x = np.arange(x_min, x_max, step=0.1)
plt.plot(x, sig_kernel(x))
plt.savefig("sig_kernel.pdf")
plt.clf()
if __name__ == "__main__":
draws, lprobs, r = metropolis(np.array([40, 10], dtype=float), 20., 10000)
traces()
logprobs()
kdes()
i = np.where(x == 0)[0]
if i.size > 0:
x = np.delete(x, i)
# Creation of noise free synthetic data
pred = calcPred(mtarget, x) # Unnormalized noise free data
# Noisy data
data = pred + sigdata * np.random.randn(pred.size)
# Noisy data
data_dict = {'x': x, 'data': data, 'sigma': sigdata}
## Run Metropolis
start_time = time.time()
M, LLK, accepted = metropolis(n_samples, calcLLK, verify, data_dict, m_ini,
prior_bounds, prop_cov)
run_time = time.time() - start_time
# Mean/STD
M_mean = M[n_burn:, :].mean(axis=0)
M_std = M[n_burn:, :].std(axis=0)
## Output display & figures
# Print information
print("--- %s seconds ---" % (run_time))
print("Acceptance rate : %f" % (float(accepted) / n_samples))
print("Posterior mean : %f , %f , %f" % (M_mean[0], M_mean[1], M_mean[2]))
print("2-sigma error : %f , %f , %f" %
(2 * M_std[0], 2 * M_std[1], 2 * M_std[2]))
i = np.where(x == 0)[0]
if i.size > 0:
x = np.delete(x, i)
# Creation of noise free synthetic data
pred = calcPred(mtarget, x) # Unnormalized noise free data
# Noisy data
data = pred + sigdata * np.random.randn(pred.size)
# Noisy data
data_dict = {"x": x, "data": data, "sigma": sigdata}
## Run Metropolis
start_time = time.time()
M, LLK, accepted = metropolis(n_samples, calcLLK, verify, data_dict, m_ini, prior_bounds, prop_cov)
run_time = time.time() - start_time
# Mean/STD
M_mean = M[n_burn:, :].mean(axis=0)
M_std = M[n_burn:, :].std(axis=0)
## Output display & figures
# Print information
print("--- %s seconds ---" % (run_time))
print("Acceptance rate : %f" % (float(accepted) / n_samples))
print("Posterior mean : %f , %f , %f" % (M_mean[0], M_mean[1], M_mean[2]))
print("2-sigma error : %f , %f , %f" % (2 * M_std[0], 2 * M_std[1], 2 * M_std[2]))
def find_g_from_A(A, N, L, g_min, g_max):
f = lambda x: norm(A, x)
g = [g_min] * (N * (L - 1) + L * (N - 1))
g = metropolis(f, g, g_min, g_max, shake_simple)
return g
def test_main():
xl = -1.0
xh = 1.0
x = [1.0, 1.0, 1.0]
T = 1.0
print metropolis(pythagoras, x, xl, xh, shake_simple)
