def __init__(self, distribution, num_eigen=2, \
mean_est=array([-2.0, -2.0]), cov_est=0.05 * eye(2), \
sample_discard=500, sample_lag=10, accstar=0.234):
AdaptiveMetropolis.__init__(self, distribution=distribution, \
mean_est=mean_est, cov_est=cov_est, \
sample_discard=sample_discard, sample_lag=sample_lag, accstar=accstar)
assert (num_eigen <= distribution.dimension)
self.num_eigen = num_eigen
self.dwscale = self.globalscale * ones([self.num_eigen])
u, s, _ = svd(self.cov_est)
self.eigvalues = s[0:self.num_eigen]
self.eigvectors = u[:, 0:self.num_eigen]
def __init__(self, distribution, \
mean_est=None, cov_est=None, \
sample_discard=500, sample_lag=20, accstar=0.234):
AdaptiveMetropolis.__init__(self, distribution, mean_est, cov_est, \
sample_discard, sample_lag, accstar)
def __init__(self, distribution, \
mean_est=None, cov_est=None, \
sample_discard=500, sample_lag=20, accstar=0.234):
AdaptiveMetropolis.__init__(self, distribution, mean_est, cov_est, \
sample_discard, sample_lag, accstar)
