def test_scaled_log_likelihood(self):
import pints
import pints.toy as toy
import numpy as np
model = toy.LogisticModel()
real_parameters = [0.015, 500]
test_parameters = [0.014, 501]
sigma = 0.001
times = np.linspace(0, 1000, 100)
values = model.simulate(real_parameters, times)
# Create an object with links to the model and time series
problem = pints.SingleSeriesProblem(model, times, values)
# Create a scaled and not scaled log_likelihood
log_likelihood_not_scaled = pints.KnownNoiseLogLikelihood(
problem, sigma)
log_likelihood_scaled = pints.ScaledLogLikelihood(
log_likelihood_not_scaled)
eval_not_scaled = log_likelihood_not_scaled(test_parameters)
eval_scaled = log_likelihood_scaled(test_parameters)
self.assertEqual(int(eval_not_scaled), -20959169232)
self.assertAlmostEqual(eval_scaled * len(times), eval_not_scaled)
def __init__(self, name):
super(TestCMAES, self).__init__(name)
# Create toy model
self.model = toy.LogisticModel()
self.real_parameters = [0.015, 500]
self.times = np.linspace(0, 1000, 1000)
self.values = self.model.simulate(self.real_parameters, self.times)
# Create an object with links to the model and time series
self.problem = pints.SingleSeriesProblem(self.model, self.times,
self.values)
# Select a score function
self.score = pints.SumOfSquaresError(self.problem)
# Select some boundaries
self.boundaries = pints.Boundaries([0, 400], [0.03, 600])
# Set an initial position
self.x0 = 0.014, 499
# Set a guess for the standard deviation around the initial position
# (in both directions)
self.sigma0 = 0.01
# Minimum score function value to obtain
self.cutoff = 1e-9
# Maximum tries before it counts as failed
self.max_tries = 3
def __init__(self, name):
super(TestAdaptiveCovarianceMCMC, self).__init__(name)
# Create toy model
self.model = toy.LogisticModel()
self.real_parameters = [0.015, 500]
self.times = np.linspace(0, 1000, 1000)
self.values = self.model.simulate(self.real_parameters, self.times)
# Add noise
noise = 10
self.values += np.random.normal(0, noise, self.values.shape)
self.real_parameters.append(noise)
# Create an object with links to the model and time series
self.problem = pints.SingleSeriesProblem(
self.model, self.times, self.values)
# Create a uniform prior over both the parameters and the new noise
# variable
self.prior = pints.UniformPrior(
[0.01, 400, noise * 0.1],
[0.02, 600, noise * 100]
)
# Create an un-normalised log-posterior (prior * likelihood)
self.log_likelihood = pints.LogPosterior(
self.prior, pints.UnknownNoiseLogLikelihood(self.problem))
# Select initial point and covariance
self.x0 = np.array(self.real_parameters) * 1.1
self.sigma0 = [0.005, 100, 0.5 * noise]
def test_known_unknown_log_likelihood(self):
import pints
import pints.toy as toy
import numpy as np
model = toy.LogisticModel()
parameters = [0.015, 500]
sigma = 0.1
times = np.linspace(0, 1000, 100)
values = model.simulate(parameters, times)
problem = pints.SingleSeriesProblem(model, times, values)
# Test if known/unknown give same result
l1 = pints.KnownNoiseLogLikelihood(problem, sigma)
l2 = pints.UnknownNoiseLogLikelihood(problem)
self.assertAlmostEqual(l1(parameters), l2(parameters + [sigma]))
priors.append(pints.NormalPrior(E2, E2_diff**2))
if reversible:
priors.append(pints.UniformPrior(lower_bounds[6:], upper_bounds[6:]))
else:
priors.append(pints.UniformPrior(lower_bounds[6:], upper_bounds[6:]))
else:
if reversible:
priors.append(pints.UniformPrior(lower_bounds[0:], upper_bounds[0:]))
else:
priors.append(pints.UniformPrior(lower_bounds[0:], upper_bounds[0:]))
# Load a forward model
pints_model = electrochemistry.PintsModelAdaptor(poms_model, names)
# Create an object with links to the model and time series
problem = pints.SingleSeriesProblem(pints_model, data.time, data.current)
# Create a log-likelihood function scaled by n
log_likelihood = pints.UnknownNoiseLogLikelihood(problem)
#score = pints.RMSError(problem)
print 'log_like dim = ', log_likelihood.dimension()
# Create a uniform prior over both the parameters and the new noise variable
prior = pints.ComposedPrior(*priors)
print 'prior dim = ', prior.dimension()
class BayesianScore(pints.ErrorMeasure):
"""
Inverts a log-likelihood to use it as an error.
"""
# values = np.zeros((samples,len(times)))
log_posteriors = []
samplers = []
exp_parameters = np.zeros((len(mean), nexp))
for i in range(nexp):
# sample from parameter distribution
parameters = np.random.normal(mean, stddev)
print('exp', i, ': param =', parameters)
exp_parameters[:, i] = parameters
# generate from model + add noise
values = model.simulate(parameters, times) + \
np.random.normal(0, noise, len(times))
# Create a new log-likelihood function (adds an extra parameter!)
problem = pints.SingleSeriesProblem(model, times, values)
log_likelihood = pints.UnknownNoiseLogLikelihood(problem)
# Create a new prior
large = 1e9
param_prior = pints.MultivariateNormalLogPrior(mean,
large * np.eye(len(mean)))
noise_prior = pints.UniformLogPrior([noise / 10.0], [noise * 10.0])
log_prior = pints.ComposedLogPrior(param_prior, noise_prior)
# Create a posterior log-likelihood (log(likelihood * prior))
log_posterior = pints.LogPosterior(log_likelihood, log_prior)
log_posteriors.append(log_posterior)
sampler = pints.AdaptiveCovarianceMCMC(mean + [noise])
samplers.append(sampler)