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HalfT prior is working

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Ricardo 2014-10-03 11:37:09 +01:00
parent 657c78ba34
commit 94b19242f0
1 changed files with 20 additions and 17 deletions
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37
GPy/core/parameterization/priors.py
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@ -254,7 +254,7 @@ class Gamma(Prior):
b = E / V b = E / V
return Gamma(a, b) return Gamma(a, b)
class inverse_gamma(Prior): class InverseGamma(Prior):
""" """
Implementation of the inverse-Gamma probability function, coupled with random variables. Implementation of the inverse-Gamma probability function, coupled with random variables.
@ -265,6 +265,7 @@ class inverse_gamma(Prior):
""" """
domain = _POSITIVE domain = _POSITIVE
_instances = []
def __new__(cls, a, b): # Singleton: def __new__(cls, a, b): # Singleton:
if cls._instances: if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()] cls._instances[:] = [instance for instance in cls._instances if instance()]
@ -291,7 +292,7 @@ class inverse_gamma(Prior):
def rvs(self, n): def rvs(self, n):
return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n) return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
class half_t(Prior): class HalfT(Prior):
""" """
Implementation of the half student t probability function, coupled with random variables. Implementation of the half student t probability function, coupled with random variables.
@ -313,25 +314,27 @@ class half_t(Prior):
def __init__(self, A, nu): def __init__(self, A, nu):
self.A = float(A) self.A = float(A)
self.nu = float(nu) self.nu = float(nu)
#self.constant = -gammaln(self.a) + a * np.log(b) self.constant = gammaln(.5*(self.nu+1.)) - gammaln(.5*self.nu) - .5*np.log(np.pi*self.A*self.nu)
def __str__(self): def __str__(self):
return "half_t(" + str(np.round(self.A)) + ', ' + str(np.round(self.nu)) + ')' return "hT(" + str(np.round(self.A)) + ', ' + str(np.round(self.nu)) + ')'
def lnpdf(self,theta): def lnpdf(self,theta):
theta = theta if isinstance(theta,np.ndarray) else np.array([theta]) return self.constant*(theta>0) -.5*(self.nu+1) * np.log( 1.+ (1./self.nu) * (theta/self.A)**2 )
lnpdfs = np.zeros_like(theta)
theta = np.array([theta]) #theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
above_zero = theta.flatten()>1e-6 #lnpdfs = np.zeros_like(theta)
v = self.nu #theta = np.array([theta])
sigma2=self.A #above_zero = theta.flatten()>1e-6
lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5) #v = self.nu
- gammaln(v * 0.5) #sigma2=self.A
- 0.5*np.log(sigma2 * v * np.pi) #stop
- 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2)) #lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
) # - gammaln(v * 0.5)
#return np.sum(objective) # - 0.5*np.log(sigma2 * v * np.pi)
return lnpdfs # - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
#)
#return lnpdfs
def lnpdf_grad(self,theta): def lnpdf_grad(self,theta):
theta = theta if isinstance(theta,np.ndarray) else np.array([theta]) theta = theta if isinstance(theta,np.ndarray) else np.array([theta])