apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-26 15:49:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

merge the changes

Browse source
This commit is contained in:
Fariba 2015-07-27 17:42:01 +01:00
commit fc0e8f3e7e
66 changed files with 55761 additions and 702 deletions
Download patch file Download diff file Expand all files Collapse all files

49
GPy/core/parameterization/priors.py
View file

@ -522,16 +522,9 @@ class DGPLVM(Prior):
"""
domain = _REAL
# _instances = []
# def __new__(cls, mu, sigma): # Singleton:
# if cls._instances:
# cls._instances[:] = [instance for instance in cls._instances if instance()]
# for instance in cls._instances:
# if instance().mu == mu and instance().sigma == sigma:
# return instance()
# o = super(Prior, cls).__new__(cls, mu, sigma)
# cls._instances.append(weakref.ref(o))
# return cls._instances[-1]()
def __new__(cls, sigma2, lbl, x_shape):
return super(Prior, cls).__new__(cls, sigma2, lbl, x_shape)
def __init__(self, sigma2, lbl, x_shape):
self.sigma2 = sigma2
@ -730,7 +723,7 @@ class DGPLVM(Prior):
# ******************************************
from parameterized import Parameterized
from .. import Parameterized
from .. import Param
class DGPLVM_Lamda(Prior, Parameterized):
"""
@ -758,12 +751,12 @@ class DGPLVM_Lamda(Prior, Parameterized):
self.sigma2 = sigma2
# self.x = x
self.lbl = lbl
self.lamda = lamda
self.lamda = lamda
self.classnum = lbl.shape[1]
self.datanum = lbl.shape[0]
self.x_shape = x_shape
self.dim = x_shape[1]
self.lamda = Param('lamda', np.diag(lamda))
self.lamda = Param('lamda', np.diag(lamda))
self.link_parameter(self.lamda)
def get_class_label(self, y):
@ -789,7 +782,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
M_i = np.zeros((self.classnum, self.dim))
for i in cls:
# Mean of each class
class_i = cls[i]
class_i = cls[i]
M_i[i] = np.mean(class_i, axis=0)
return M_i
@ -899,8 +892,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
#!!!!!!!!!!!!!!!!!!!!!!!!!!!
#self.lamda.values[:] = self.lamda.values/self.lamda.values.sum()
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -916,8 +909,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
# This function calculates derivative of the log of prior function
def lnpdf_grad(self, x):
x = x.reshape(self.x_shape)
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -951,14 +944,14 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Because of the GPy we need to transpose our matrix so that it gets the same shape as out matrix (denominator layout!!!)
DPxprim_Dx = DPxprim_Dx.T
DPxprim_Dlamda = DPx_Dx.dot(x)
DPxprim_Dlamda = DPx_Dx.dot(x)
# Because of the GPy we need to transpose our matrix so that it gets the same shape as out matrix (denominator layout!!!)
DPxprim_Dlamda = DPxprim_Dlamda.T
DPxprim_Dlamda = DPxprim_Dlamda.T
self.lamda.gradient = np.diag(DPxprim_Dlamda)
self.lamda.gradient = np.diag(DPxprim_Dlamda)
# print DPxprim_Dx
return DPxprim_Dx
return DPxprim_Dx
# def frb(self, x):
@ -1139,8 +1132,8 @@ class DGPLVM_T(Prior):
# This function calculates log of our prior
def lnpdf(self, x):
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
xprim = x.dot(self.vec)
x = xprim
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -1156,11 +1149,11 @@ class DGPLVM_T(Prior):
# This function calculates derivative of the log of prior function
def lnpdf_grad(self, x):
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
# print x
cls = self.compute_cls(x)
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
M_i = self.compute_Mi(cls)
Sb = self.compute_Sb(cls, M_i, M_0)