def __init__(
self, distribution, kernel, nu2=0.1, gamma=None, sample_discard=500, num_samples_Z=1000, stop_adapt=20000
):
Kameleon.__init__(self, distribution, kernel, Z=None, nu2=nu2, gamma=gamma)
assert stop_adapt > sample_discard
assert num_samples_Z > 0
self.sample_discard = sample_discard
self.num_samples_Z = num_samples_Z
self.stop_adapt = stop_adapt
def __init__(self, distribution, kernel, Z, nu2=0.1, gamma=0.1, num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0 : self.num_eigen]
self.eigvectors = u[:, 0 : self.num_eigen]
def __init__(self, distribution, kernel, nu2=0.1, gamma=None, \
sample_discard=500, num_samples_Z=1000, stop_adapt=20000):
Kameleon.__init__(self,
distribution,
kernel,
Z=None,
nu2=nu2,
gamma=gamma)
assert (stop_adapt > sample_discard)
assert (num_samples_Z > 0)
self.sample_discard = sample_discard
self.num_samples_Z = num_samples_Z
self.stop_adapt = stop_adapt
def __str__(self):
s = self.__class__.__name__ + "=["
s += "sample_discard=" + str(self.sample_discard)
s += ", num_samples_Z=" + str(self.num_samples_Z)
s += ", stop_adapt=" + str(self.stop_adapt)
s += ", " + Kameleon.__str__(self)
s += "]"
return s
def __str__(self):
s = self.__class__.__name__ + "=["
s += "sample_discard=" + str(self.sample_discard)
s += ", num_samples_Z=" + str(self.num_samples_Z)
s += ", stop_adapt=" + str(self.stop_adapt)
s += ", " + Kameleon.__str__(self)
s += "]"
return s
def plot_proposal(self, ys):
# evaluate density itself
Visualise.visualise_distribution(self.distribution, Z=self.Z, Xs=self.Xs, Ys=self.Ys)
# precompute constants of proposal
mcmc_hammer = Kameleon(self.distribution, self.kernel, self.Z, \
self.nu2, self.gamma)
# plot proposal around each y
for y in ys:
mu, L_R = mcmc_hammer.compute_constants(y)
gaussian = Gaussian(mu, L_R, is_cholesky=True)
hold(True)
Visualise.contour_plot_density(gaussian)
hold(False)
draw()
def __init__(self,
distribution,
kernel,
Z,
nu2=0.1,
gamma=0.1,
num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0:self.num_eigen]
self.eigvectors = u[:, 0:self.num_eigen]
def main():
distribution = Banana(dimension=8)
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = Kameleon(distribution, kernel,
distribution.sample(100).samples)
start = zeros(distribution.dimension)
mcmc_params = MCMCParams(start=start, num_iterations=20000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.run()
def init(self, start):
Kameleon.init(self, start)
self.Z = None
def init(self, start):
Kameleon.init(self, start)
self.Z = None