def test_gibbs_chain_set_boundary(line_posterior):
chain = GibbsChain(posterior=line_posterior, start=[0.5, 0.1])
left, right = (0.45, 0.55)
chain.set_boundaries(0, [left, right])
chain.advance(100)
gradient = array(chain.get_parameter(0))
assert all(gradient >= left)
assert all(gradient <= right)
def test_gibbs_chain_remove_boundary(line_posterior):
chain = GibbsChain(posterior=line_posterior, start=[0.5, 0.1])
left, right = (0.45, 0.4500000000001)
chain.set_boundaries(0, [left, right])
chain.set_boundaries(0, None, remove=True)
chain.advance(100)
gradient = array(chain.get_parameter(0))
# Some values should be outside the original boundary
assert not all(gradient >= left) or not all(gradient <= right)
# plot the synthetic data and underlying line plt.plot(x, m*x + c) plt.plot(x, y, '.') plt.grid() plt.show() # create an instance of the posterior class posterior = LinePosterior(x = x, y = y, err = ones(N)*sigma) # pass the posterior to the MCMC sampler chain = GibbsChain(posterior = posterior, start = [0.5, 0.1]) # Now suppose we know the offset parameter must be non-negative. # This constraint can be imposed by passing the index of the # parameter to the set_non_negative method as follows: chain.set_non_negative(1) # For the purposes of this demo, let's assume we also know that # the gradient must exist in the range [0.45, 0.55]. # The gradient can be constrained to values between chosen boundaries # by passing the parameter index and the boundary values to the # set_boundaries method as follows: chain.set_boundaries(0, [0.45, 0.55]) # Advance the chain chain.advance(50000) chain.burn = 5000 # Use the matrix plot functionality to check the constraints are working chain.matrix_plot()