mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-02 14:45:15 +02:00
maint: Remove tabs (and some trailing spaces)
This commit is contained in:
Julien Bect
parent
490c4c73f5
commit
0a9b1cc10d
3 changed files with 85 additions and 84 deletions
|
|
@ -17,7 +17,7 @@ class Prior(object):
|
|||
if not cls._instance or cls._instance.__class__ is not cls:
|
||||
newfunc = super(Prior, cls).__new__
|
||||
if newfunc is object.__new__:
|
||||
cls._instance = newfunc(cls)
|
||||
cls._instance = newfunc(cls)
|
||||
else:
|
||||
cls._instance = newfunc(cls, *args, **kwargs)
|
||||
return cls._instance
|
||||
|
|
@ -58,9 +58,9 @@ class Gaussian(Prior):
|
|||
return instance()
|
||||
newfunc = super(Prior, cls).__new__
|
||||
if newfunc is object.__new__:
|
||||
o = newfunc(cls)
|
||||
o = newfunc(cls)
|
||||
else:
|
||||
o = newfunc(cls, mu, sigma)
|
||||
o = newfunc(cls, mu, sigma)
|
||||
cls._instances.append(weakref.ref(o))
|
||||
return cls._instances[-1]()
|
||||
|
||||
|
|
@ -102,9 +102,9 @@ class Uniform(Prior):
|
|||
return instance()
|
||||
newfunc = super(Prior, cls).__new__
|
||||
if newfunc is object.__new__:
|
||||
o = newfunc(cls)
|
||||
o = newfunc(cls)
|
||||
else:
|
||||
o = newfunc(cls, lower, upper)
|
||||
o = newfunc(cls, lower, upper)
|
||||
cls._instances.append(weakref.ref(o))
|
||||
return cls._instances[-1]()
|
||||
|
||||
|
|
@ -282,7 +282,7 @@ class Gamma(Prior):
|
|||
return instance()
|
||||
newfunc = super(Prior, cls).__new__
|
||||
if newfunc is object.__new__:
|
||||
o = newfunc(cls)
|
||||
o = newfunc(cls)
|
||||
else:
|
||||
o = newfunc(cls, a, b)
|
||||
cls._instances.append(weakref.ref(o))
|
||||
|
|
@ -542,8 +542,8 @@ class DGPLVM(Prior):
|
|||
|
||||
"""
|
||||
domain = _REAL
|
||||
|
||||
def __new__(cls, sigma2, lbl, x_shape):
|
||||
|
||||
def __new__(cls, sigma2, lbl, x_shape):
|
||||
return super(Prior, cls).__new__(cls, sigma2, lbl, x_shape)
|
||||
|
||||
def __init__(self, sigma2, lbl, x_shape):
|
||||
|
|
@ -909,13 +909,13 @@ class DGPLVM_Lamda(Prior, Parameterized):
|
|||
# This function calculates log of our prior
|
||||
def lnpdf(self, x):
|
||||
x = x.reshape(self.x_shape)
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
#self.lamda.values[:] = self.lamda.values/self.lamda.values.sum()
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
#self.lamda.values[:] = self.lamda.values/self.lamda.values.sum()
|
||||
|
||||
xprime = x.dot(np.diagflat(self.lamda))
|
||||
x = xprime
|
||||
# print x
|
||||
# print x
|
||||
cls = self.compute_cls(x)
|
||||
M_0 = np.mean(x, axis=0)
|
||||
M_i = self.compute_Mi(cls)
|
||||
|
|
@ -932,7 +932,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
|
|||
x = x.reshape(self.x_shape)
|
||||
xprime = x.dot(np.diagflat(self.lamda))
|
||||
x = xprime
|
||||
# print x
|
||||
# print x
|
||||
cls = self.compute_cls(x)
|
||||
M_0 = np.mean(x, axis=0)
|
||||
M_i = self.compute_Mi(cls)
|
||||
|
|
@ -964,14 +964,14 @@ 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)
|
||||
|
||||
# Because of the GPy we need to transpose our matrix so that it gets the same shape as out matrix (denominator layout!!!)
|
||||
# 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
|
||||
|
||||
self.lamda.gradient = np.diag(DPxprim_Dlamda)
|
||||
# print DPxprim_Dx
|
||||
# print DPxprim_Dx
|
||||
return DPxprim_Dx
|
||||
|
||||
|
||||
|
|
@ -1046,7 +1046,7 @@ class DGPLVM_T(Prior):
|
|||
M_i = np.zeros((self.classnum, self.dim))
|
||||
for i in cls:
|
||||
# Mean of each class
|
||||
# class_i = np.multiply(cls[i],vec)
|
||||
# class_i = np.multiply(cls[i],vec)
|
||||
class_i = cls[i]
|
||||
M_i[i] = np.mean(class_i, axis=0)
|
||||
return M_i
|
||||
|
|
@ -1155,7 +1155,7 @@ class DGPLVM_T(Prior):
|
|||
x = x.reshape(self.x_shape)
|
||||
xprim = x.dot(self.vec)
|
||||
x = xprim
|
||||
# print x
|
||||
# print x
|
||||
cls = self.compute_cls(x)
|
||||
M_0 = np.mean(x, axis=0)
|
||||
M_i = self.compute_Mi(cls)
|
||||
|
|
@ -1163,7 +1163,7 @@ class DGPLVM_T(Prior):
|
|||
Sw = self.compute_Sw(cls, M_i)
|
||||
# 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)
|
||||
#print 'SB_inv: ', Sb_inv_N
|
||||
#print 'SB_inv: ', Sb_inv_N
|
||||
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
|
||||
Sb_inv_N = pdinv(Sb+np.eye(Sb.shape[0])*0.1)[0]
|
||||
return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw))
|
||||
|
|
@ -1172,8 +1172,8 @@ class DGPLVM_T(Prior):
|
|||
def lnpdf_grad(self, x):
|
||||
x = x.reshape(self.x_shape)
|
||||
xprim = x.dot(self.vec)
|
||||
x = xprim
|
||||
# print x
|
||||
x = xprim
|
||||
# print x
|
||||
cls = self.compute_cls(x)
|
||||
M_0 = np.mean(x, axis=0)
|
||||
M_i = self.compute_Mi(cls)
|
||||
|
|
@ -1188,7 +1188,7 @@ class DGPLVM_T(Prior):
|
|||
# Calculating inverse of Sb and its transpose and minus
|
||||
# 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)
|
||||
#print 'SB_inv: ',Sb_inv_N
|
||||
#print 'SB_inv: ',Sb_inv_N
|
||||
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
|
||||
Sb_inv_N = pdinv(Sb+np.eye(Sb.shape[0])*0.1)[0]
|
||||
Sb_inv_N_trans = np.transpose(Sb_inv_N)
|
||||
|
|
@ -1375,4 +1375,5 @@ class StudentT(Prior):
|
|||
def rvs(self, n):
|
||||
from scipy.stats import t
|
||||
ret = t.rvs(self.nu, loc=self.mu, scale=self.sigma, size=n)
|
||||
return ret
|
||||
return ret
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue