class SingleBanana(object):
"""Creates the log_target as in [WKB09]
:param dim:
integer; the dimension
"""
def __init__(self, dim, twistdim):
assert dim >= 2
assert twistdim < dim - 1
self.dim = dim
self.twistdim = twistdim
# kilbinger_params:
self.sigma1_squared = 100.0
self.p = 10.0
self.b = 0.03
self.mean = np.zeros(dim)
self.cov = np.eye(dim)
self.cov[twistdim, twistdim] = self.sigma1_squared
self.underlying_gauss = Gauss(self.mean, self.cov)
def __call__(self, x):
x = np.array(x)
x[1 + self.twistdim] += self.b * (x[0 + self.twistdim] * x[0 + self.twistdim] - self.sigma1_squared)
return self.underlying_gauss.evaluate(x)
class LogTarget(object):
"""Creates the log_target as in [WKB09]
:param dim:
integer; the dimension
"""
def __init__(self, dim):
assert dim >= 2
self.dim = dim
# kilbinger_params:
self.sigma1_squared = 100.
self.p = 10.
self.b = .03
self.mean = np.zeros(dim)
self.cov = np.diag([self.sigma1_squared] + [1. for i in range(dim-1)])
self.underlying_gauss = Gauss(self.mean, self.cov)
def __call__(self, x):
x1 = np.array(x)
x1[1] += 5.
x1[2] -= 5.
x1[1] += self.b * (x1[0]*x1[0] - self.sigma1_squared)
x2 = np.array(x)
x2[1] -= 5.
x2[2] += 5.
x2[2] += self.b * (x2[0]*x2[0] - self.sigma1_squared)
return np.log( .5 * np.exp( self.underlying_gauss.evaluate(x1) )
+ .5 * np.exp( self.underlying_gauss.evaluate(x2) ) )
