def test_gaussian_prior(self):
mean = 10
std = 2
p = pints.GaussianLogPrior(mean, std)
n = 10000
r = 6 * np.sqrt(std)
# Test left half of distribution
x = np.linspace(mean - r, mean, n)
px = [p([i]) for i in x]
self.assertTrue(np.all(px[1:] >= px[:-1]))
# Test right half of distribution
y = np.linspace(mean, mean + std, n)
py = [p([i]) for i in y]
self.assertTrue(np.all(py[1:] <= py[:-1]))
# Test means
self.assertAlmostEqual(p.mean(), mean)
# Test derivatives
x = [8]
y, dy = p.evaluateS1(x)
self.assertEqual(y, p(x))
self.assertEqual(dy.shape, (1, ))
self.assertEqual(dy[0], (mean - x[0]) / std**2)
p = pints.GaussianLogPrior(-1, 4.5)
x = [3.75]
self.assertAlmostEqual(p(x), -2.9801146954130457)
p = pints.GaussianLogPrior(10.4, 0.5)
x = [5.5]
y, dy = p.evaluateS1(x)
self.assertAlmostEqual(y, -48.245791352644737)
self.assertEqual(dy, 19.6)
# Test deprecated alias
p = pints.NormalLogPrior(mean, std)
self.assertIsInstance(p, pints.GaussianLogPrior)
# Test assertRaises with negative sd
self.assertRaises(ValueError, pints.GaussianLogPrior, 0, 0)
self.assertRaises(ValueError, pints.GaussianLogPrior, 0, -1)
rate_dict,
transform,
sine_wave=1)
n_params = model.n_params
#
# Define problem
#
problem = pints.SingleOutputProblem(model, time, current)
#
# Define log-posterior
#
log_likelihood = LogLikelihood.tiLogLikelihood(problem, sigma_noise, voltage)
log_prior_rates = prior.LogPrior(rate_dict, lower_conductance, n_params,
transform)
log_prior_discrp = pints.NormalLogPrior(0., 1.5)
log_prior = pints.ComposedLogPrior(log_prior_rates, log_prior_discrp)
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
rate_checker = Rates.ratesPrior(transform, lower_conductance)
#
# Run
#
nchains = 1
# Define starting point for mcmc routine
xs = []
x0 = np.loadtxt(cmaes_result_files + model_name + '-cell-' + str(cell) +
'-cmaes.txt')
ds = log_prior_discrp.sample().reshape((1, ))
self._offset = -0.5 * self._nt * np.log(2 * np.pi)
self._offset -= self._nt * np.log(sigma)
self._multip = -1 / (2.0 * sigma**2)
def __call__(self, x):
error = self._values - self._problem.evaluate(x)
return self.temperature * np.sum(self._offset + self._multip *
np.sum(error**2, axis=0))
# In[ ]:
sigma_noise = 1.
log_prior_a = pints.NormalLogPrior(2., 1.)
log_prior_b = pints.NormalLogPrior(3., 1.)
log_prior = pints.ComposedLogPrior(log_prior_a, log_prior_b)
# In[ ]:
import mcmcsampling
from joblib import Parallel, delayed
import multiprocessing
niter = 10000
def mcmc_runner(temps):
nchains = 1
#print('temperature', temps)