mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-27 14:25:16 +02:00
[merge] merge master into devel
This commit is contained in:
Max Zwiessele
commit
cf2673632b
12 changed files with 257 additions and 314 deletions
|
|
@ -366,6 +366,7 @@ 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 +513,7 @@ 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.
|
||||
|
|
@ -903,7 +905,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
|
|||
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
|
||||
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
|
||||
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
|
||||
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
|
||||
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
|
||||
return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw))
|
||||
|
||||
# This function calculates derivative of the log of prior function
|
||||
|
|
@ -927,7 +929,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
|
|||
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
|
||||
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
|
||||
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
|
||||
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
|
||||
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
|
||||
Sb_inv_N_trans = np.transpose(Sb_inv_N)
|
||||
Sb_inv_N_trans_minus = -1 * Sb_inv_N_trans
|
||||
Sw_trans = np.transpose(Sw)
|
||||
|
|
@ -1198,6 +1200,7 @@ class DGPLVM_T(Prior):
|
|||
|
||||
|
||||
|
||||
|
||||
class HalfT(Prior):
|
||||
"""
|
||||
Implementation of the half student t probability function, coupled with random variables.
|
||||
|
|
@ -1208,15 +1211,17 @@ 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 +1230,81 @@ 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)
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ class Transformation(object):
|
|||
import matplotlib.pyplot as plt
|
||||
from ...plotting.matplot_dep import base_plots
|
||||
x = np.linspace(-8,8)
|
||||
base_plots.meanplot(x, self.f(x),axes=axes*args,**kw)
|
||||
base_plots.meanplot(x, self.f(x), *args, ax=axes, **kw)
|
||||
axes = plt.gca()
|
||||
axes.set_xlabel(xlabel)
|
||||
axes.set_ylabel(ylabel)
|
||||
|
|
@ -488,7 +488,7 @@ class Logistic(Transformation):
|
|||
return instance()
|
||||
newfunc = super(Transformation, cls).__new__
|
||||
if newfunc is object.__new__:
|
||||
o = newfunc(cls)
|
||||
o = newfunc(cls)
|
||||
else:
|
||||
o = newfunc(cls, lower, upper, *args, **kwargs)
|
||||
cls._instances.append(weakref.ref(o))
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue