class Q0(Q0_Base):
""" Define prior """
def __init__(self):
""" Define prior pdfs over stiffness, damping, and
noise std.
"""
self.p_k = Normal_PDF(mean=3, cov=0.5)
self.p_c = Gamma_PDF(a=1, scale=0.1)
self.p_sigma = Gamma_PDF(a=1, scale=0.1)
def logpdf(self, x):
# Convert to 2D array if currently 1D
if len(np.shape(x)) == 1:
x = np.array([x])
# Calculate logpdf
logpdf = (self.p_k.logpdf(x[:, 0]) + self.p_c.logpdf(x[:, 1]) +
self.p_sigma.logpdf(x[:, 2]))
return logpdf
def rvs(self, size):
k = np.vstack(self.p_k.rvs(size))
c = np.vstack(self.p_c.rvs(size))
sigma = np.vstack(self.p_sigma.rvs(size))
return np.hstack([k, c, sigma])
class Target(Target_Base):
""" Define target """
def __init__(self):
self.pdf = Normal_PDF(mean=np.array([3.0, 2.0]), cov=np.eye(2))
def logpdf(self, x):
return self.pdf.logpdf(x)
class Target(Target_Base):
""" Define target """
def __init__(self):
self.pdf = Normal_PDF(mean=np.repeat(2, D), cov=0.1*np.eye(D))
def logpdf(self, x):
return self.pdf.logpdf(x)
class Q0(Q0_Base):
""" Define initial proposal """
def __init__(self):
self.pdf = Normal_PDF(mean=np.zeros(2), cov=np.eye(2))
def logpdf(self, x):
return self.pdf.logpdf(x)
def rvs(self, size):
return self.pdf.rvs(size)