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Merge pull request #191 from mellorjc/master

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adding an exponential prior
This commit is contained in:
James Hensman 2015-08-13 12:08:15 +01:00
commit 2bd22ff0f5
1 changed files with 83 additions and 28 deletions
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111
GPy/core/parameterization/priors.py
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@ -13,6 +13,7 @@ import weakref
class Prior(object):
domain = None
_instance = None
def __new__(cls, *args, **kwargs):
if not cls._instance or cls._instance.__class__ is not cls:
newfunc = super(Prior, cls).__new__
@ -91,6 +92,7 @@ class Gaussian(Prior):
# self.sigma2 = np.square(self.sigma)
# self.constant = -0.5 * np.log(2 * np.pi * self.sigma2)
class Uniform(Prior):
domain = _REAL
_instances = []
@ -129,6 +131,7 @@ class Uniform(Prior):
# self.lower = state[0]
# self.upper = state[1]
class LogGaussian(Gaussian):
"""
Implementation of the univariate *log*-Gaussian probability function, coupled with random variables.
@ -246,6 +249,7 @@ class MultivariateGaussian(Prior):
self.inv, self.hld = pdinv(self.var)
self.constant = -0.5 * self.input_dim * np.log(2 * np.pi) - self.hld
def gamma_from_EV(E, V):
warnings.warn("use Gamma.from_EV to create Gamma Prior", FutureWarning)
return Gamma.from_EV(E, V)
@ -327,6 +331,7 @@ class Gamma(Prior):
self.b = state[1]
self.constant = -gammaln(self.a) + self.a * np.log(self.b)
class InverseGamma(Gamma):
"""
Implementation of the inverse-Gamma probability function, coupled with random variables.
@ -339,7 +344,8 @@ class InverseGamma(Gamma):
"""
domain = _POSITIVE
_instances = []
def __new__(cls, a=1, b=.5): # Singleton:
def __new__(cls, a=1, b=.5): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
@ -366,6 +372,7 @@ class InverseGamma(Gamma):
def rvs(self, n):
return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
class DGPLVM_KFDA(Prior):
"""
Implementation of the Discriminative Gaussian Process Latent Variable function using
@ -512,6 +519,7 @@ class DGPLVM_KFDA(Prior):
self.A = self.compute_A(lst_ni)
self.x_shape = x_shape
class DGPLVM(Prior):
"""
Implementation of the Discriminative Gaussian Process Latent Variable model paper, by Raquel.
@ -1198,6 +1206,7 @@ class DGPLVM_T(Prior):
class HalfT(Prior):
"""
Implementation of the half student t probability function, coupled with random variables.
@ -1208,15 +1217,17 @@ class HalfT(Prior):
"""
domain = _POSITIVE
_instances = []
def __new__(cls, A, nu): # Singleton:
def __new__(cls, A, nu): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().A == A and instance().nu == nu:
return instance()
return instance()
o = super(Prior, cls).__new__(cls, A, nu)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, A, nu):
self.A = float(A)
self.nu = float(nu)
@ -1225,37 +1236,81 @@ class HalfT(Prior):
def __str__(self):
return "hT({:.2g}, {:.2g})".format(self.A, self.nu)
def lnpdf(self,theta):
return (theta>0) * ( self.constant -.5*(self.nu+1) * np.log( 1.+ (1./self.nu) * (theta/self.A)**2 ) )
def lnpdf(self, theta):
return (theta > 0) * (self.constant - .5*(self.nu + 1) * np.log(1. + (1./self.nu) * (theta/self.A)**2))
#theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
#lnpdfs = np.zeros_like(theta)
#theta = np.array([theta])
#above_zero = theta.flatten()>1e-6
#v = self.nu
#sigma2=self.A
#stop
#lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
# - gammaln(v * 0.5)
# - 0.5*np.log(sigma2 * v * np.pi)
# - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
#)
#return lnpdfs
# theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
# lnpdfs = np.zeros_like(theta)
# theta = np.array([theta])
# above_zero = theta.flatten()>1e-6
# v = self.nu
# sigma2=self.A
# stop
# lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
# - gammaln(v * 0.5)
# - 0.5*np.log(sigma2 * v * np.pi)
# - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
# )
# return lnpdfs
def lnpdf_grad(self,theta):
theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
def lnpdf_grad(self, theta):
theta = theta if isinstance(theta, np.ndarray) else np.array([theta])
grad = np.zeros_like(theta)
above_zero = theta>1e-6
above_zero = theta > 1e-6
v = self.nu
sigma2=self.A
sigma2 = self.A
grad[above_zero] = -0.5*(v+1)*(2*theta[above_zero])/(v*sigma2 + theta[above_zero][0]**2)
return grad
def rvs(self, n):
#return np.random.randn(n) * self.sigma + self.mu
from scipy.stats import t
#[np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
ret = t.rvs(self.nu,loc=0,scale=self.A, size=n)
ret[ret<0] = 0
return ret
# return np.random.randn(n) * self.sigma + self.mu
from scipy.stats import t
# [np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
ret = t.rvs(self.nu, loc=0, scale=self.A, size=n)
ret[ret < 0] = 0
return ret
class Exponential(Prior):
"""
Implementation of the Exponential probability function,
coupled with random variables.
:param l: shape parameter
"""
domain = _POSITIVE
_instances = []
def __new__(cls, l): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().l == l:
return instance()
o = super(Exponential, cls).__new__(cls, l)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, l):
self.l = l
def __str__(self):
return "Exp({:.2g})".format(self.l)
def summary(self):
ret = {"E[x]": 1. / self.l,
"E[ln x]": np.nan,
"var[x]": 1. / self.l**2,
"Entropy": 1. - np.log(self.l),
"Mode": 0.}
return ret
def lnpdf(self, x):
return np.log(self.l) - self.l * x
def lnpdf_grad(self, x):
return - self.l
def rvs(self, n):
return np.random.exponential(scale=self.l, size=n)