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maint: Remove tabs (and some trailing spaces)

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This commit is contained in:
Julien Bect 2020-06-20 08:08:21 +02:00
parent 490c4c73f5
commit 0a9b1cc10d
3 changed files with 85 additions and 84 deletions
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20
GPy/kern/src/ODE_t.py
View file

@ -20,7 +20,7 @@ class ODE_t(Kern):
self.link_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
def K(self, X, X2=None):
"""Compute the covariance matrix between X and X2."""
"""Compute the covariance matrix between X and X2."""
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
@ -31,9 +31,9 @@ class ODE_t(Kern):
tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
ttdist = (X[:,0][:,None] - X2[:,0][None,:])
vyt = self.variance_Yt
lyt=1/(2*self.lengthscale_Yt)
a = -self.a
@ -69,10 +69,10 @@ class ODE_t(Kern):
lyt = 1./(2*self.lengthscale_Yt)
a = -self.a
c = self.c
c = self.c
k1 = (2*lyt )*vyt
Kdiag = np.zeros(X.shape[0])
slices = index_to_slices(X[:,-1])
@ -106,7 +106,7 @@ class ODE_t(Kern):
tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
ttdist = (X[:,0][:,None] - X2[:,0][None,:])
#rdist = [tdist,xdist]
rd=tdist.shape[0]
dka = np.zeros([rd,rd])
@ -146,7 +146,7 @@ class ODE_t(Kern):
elif i==1 and j==1:
dkYdvart[ss1,ss2] = (k1(tdist[ss1,ss2]) + 1. )* kyy(tdist[ss1,ss2])
dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])*( k1(tdist[ss1,ss2]) + 1. ) +\
vyt*kyy(tdist[ss1,ss2])*dk1dlyt(tdist[ss1,ss2])
vyt*kyy(tdist[ss1,ss2])*dk1dlyt(tdist[ss1,ss2])
dkdubias[ss1,ss2] = 1
else:
dkYdvart[ss1,ss2] = (-k4(ttdist[ss1,ss2])+1)*kyy(tdist[ss1,ss2])
@ -156,10 +156,10 @@ class ODE_t(Kern):
dkdubias[ss1,ss2] = 0
#dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (-2*lyt*(ttdist[ss1,ss2])+1.)+\
#vyt*kyy(tdist[ss1,ss2])*(-2)*(ttdist[ss1,ss2])
self.variance_Yt.gradient = np.sum(dkYdvart * dL_dK)
self.lengthscale_Yt.gradient = np.sum(dkYdlent*(-0.5*self.lengthscale_Yt**(-2)) * dL_dK)
self.ubias.gradient = np.sum(dkdubias * dL_dK)
self.ubias.gradient = np.sum(dkdubias * dL_dK)

102
GPy/kern/src/grid_kerns.py
View file

@ -10,67 +10,67 @@ from paramz.caching import Cache_this
class GridKern(Stationary):
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name, originalDimensions, useGPU=False):
super(GridKern, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
self.originalDimensions = originalDimensions
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name, originalDimensions, useGPU=False):
super(GridKern, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
self.originalDimensions = originalDimensions
@Cache_this(limit=3, ignore_args=())
def dKd_dVar(self, X, X2=None):
"""
Derivative of Kernel function wrt variance applied on inputs X and X2.
In the stationary case there is an inner function depending on the
distances from X to X2, called r.
@Cache_this(limit=3, ignore_args=())
def dKd_dVar(self, X, X2=None):
"""
Derivative of Kernel function wrt variance applied on inputs X and X2.
In the stationary case there is an inner function depending on the
distances from X to X2, called r.
dKd_dVar(X, X2) = dKdVar_of_r((X-X2)**2)
"""
r = self._scaled_dist(X, X2)
return self.dKdVar_of_r(r)
dKd_dVar(X, X2) = dKdVar_of_r((X-X2)**2)
"""
r = self._scaled_dist(X, X2)
return self.dKdVar_of_r(r)
@Cache_this(limit=3, ignore_args=())
def dKd_dLen(self, X, dimension, lengthscale, X2=None):
"""
Derivate of Kernel function wrt lengthscale applied on inputs X and X2.
In the stationary case there is an inner function depending on the
distances from X to X2, called r.
@Cache_this(limit=3, ignore_args=())
def dKd_dLen(self, X, dimension, lengthscale, X2=None):
"""
Derivate of Kernel function wrt lengthscale applied on inputs X and X2.
In the stationary case there is an inner function depending on the
distances from X to X2, called r.
dKd_dLen(X, X2) = dKdLen_of_r((X-X2)**2)
"""
r = self._scaled_dist(X, X2)
return self.dKdLen_of_r(r, dimension, lengthscale)
dKd_dLen(X, X2) = dKdLen_of_r((X-X2)**2)
"""
r = self._scaled_dist(X, X2)
return self.dKdLen_of_r(r, dimension, lengthscale)
class GridRBF(GridKern):
"""
Similar to regular RBF but supplemented with methods required for Gaussian grid regression
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
"""
Similar to regular RBF but supplemented with methods required for Gaussian grid regression
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
.. math::
.. math::
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
"""
_support_GPU = True
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='gridRBF', originalDimensions=1, useGPU=False):
super(GridRBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, originalDimensions, useGPU=useGPU)
"""
_support_GPU = True
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='gridRBF', originalDimensions=1, useGPU=False):
super(GridRBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, originalDimensions, useGPU=useGPU)
def K_of_r(self, r):
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2)
def K_of_r(self, r):
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2)
def dKdVar_of_r(self, r):
"""
Compute derivative of kernel wrt variance
"""
return np.exp(-0.5 * r**2)
def dKdVar_of_r(self, r):
"""
Compute derivative of kernel wrt variance
"""
return np.exp(-0.5 * r**2)
def dKdLen_of_r(self, r, dimCheck, lengthscale):
"""
Compute derivative of kernel for dimension wrt lengthscale
Computation of derivative changes when lengthscale corresponds to
the dimension of the kernel whose derivate is being computed.
"""
if (dimCheck == True):
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2) * (r**2) / (lengthscale**(float(1)/self.originalDimensions))
else:
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2) / (lengthscale**(float(1)/self.originalDimensions))
def dKdLen_of_r(self, r, dimCheck, lengthscale):
"""
Compute derivative of kernel for dimension wrt lengthscale
Computation of derivative changes when lengthscale corresponds to
the dimension of the kernel whose derivate is being computed.
"""
if (dimCheck == True):
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2) * (r**2) / (lengthscale**(float(1)/self.originalDimensions))
else:
return (self.variance**(float(1)/self.originalDimensions)) * np.exp(-0.5 * r**2) / (lengthscale**(float(1)/self.originalDimensions))
def dK_dr(self, r):
return -r*self.K_of_r(r)
def dK_dr(self, r):
return -r*self.K_of_r(r)