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last ARD flag changes to kernels

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This commit is contained in:
Nicolas 2013-01-18 16:03:20 +00:00
parent 8571103530
commit 69743be33e
6 changed files with 81 additions and 49 deletions
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6
GPy/kern/Matern32.py
View file

@ -20,8 +20,10 @@ class Matern32(kernpart):
:type D: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (D,)
:param lengthscale: the vector of lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (1,) or (D,) depending on ARD
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean
:rtype: kernel object
"""

67
GPy/kern/Matern52.py
View file

@ -19,42 +19,53 @@ class Matern52(kernpart):
:type D: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscales :math:`\ell_i`
:type lengthscale: np.ndarray of size (D,)
:param lengthscale: the vector of lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (1,) or (D,) depending on ARD
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean
:rtype: kernel object
"""
def __init__(self,D,variance=1.,lengthscales=None):
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
if lengthscales is not None:
assert lengthscales.shape==(self.D,)
self.ARD = ARD
if ARD == False:
self.Nparam = 2
self.name = 'Mat32'
if lengthscale is not None:
assert lengthscale.shape == (1,)
else:
lengthscale = np.ones(1)
else:
lengthscales = np.ones(self.D)
self.Nparam = self.D + 1
self.name = 'Mat52'
self._set_params(np.hstack((variance,lengthscales)))
self.Nparam = self.D + 1
self.name = 'Mat32_ARD'
if lengthscale is not None:
assert lengthscale.shape == (self.D,)
else:
lengthscale = np.ones(self.D)
self._set_params(np.hstack((variance,lengthscale)))
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscales))
return np.hstack((self.variance,self.lengthscale))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==(self.D+1)
assert x.size == self.Nparam
self.variance = x[0]
self.lengthscales = x[1:]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.D==1:
if self.Nparam == 2:
return ['variance','lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscales.size)]
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
np.add(self.variance*(1+np.sqrt(5.)*dist+5./3*dist**2)*np.exp(-np.sqrt(5.)*dist), target,target)
def Kdiag(self,X,target):
@ -64,13 +75,19 @@ class Matern52(kernpart):
def dK_dtheta(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
invdist = 1./np.where(dist!=0.,dist,np.inf)
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscales**3
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscale**3
dvar = (1+np.sqrt(5.)*dist+5./3*dist**2)*np.exp(-np.sqrt(5.)*dist)
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[0] += np.sum(dvar*partial)
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
if self.ARD:
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
else:
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist)) * dist2M.sum(-1)*invdist
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist)) * dist2M.sum(-1)*invdist
target[1] += np.sum(dl*partial)
def dKdiag_dtheta(self,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
@ -79,8 +96,8 @@ class Matern52(kernpart):
def dK_dX(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscales**2/np.where(dist!=0.,dist,np.inf)
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*partial.T[:,:,None],0)
@ -104,18 +121,18 @@ class Matern52(kernpart):
"""
assert self.D == 1
def L(x,i):
return(5*np.sqrt(5)/self.lengthscales**3*F[i](x) + 15./self.lengthscales**2*F1[i](x)+ 3*np.sqrt(5)/self.lengthscales*F2[i](x) + F3[i](x))
return(5*np.sqrt(5)/self.lengthscale**3*F[i](x) + 15./self.lengthscale**2*F1[i](x)+ 3*np.sqrt(5)/self.lengthscale*F2[i](x) + F3[i](x))
n = F.shape[0]
G = np.zeros((n,n))
for i in range(n):
for j in range(i,n):
G[i,j] = G[j,i] = integrate.quad(lambda x : L(x,i)*L(x,j),lower,upper)[0]
G_coef = 3.*self.lengthscales**5/(400*np.sqrt(5))
G_coef = 3.*self.lengthscale**5/(400*np.sqrt(5))
Flower = np.array([f(lower) for f in F])[:,None]
F1lower = np.array([f(lower) for f in F1])[:,None]
F2lower = np.array([f(lower) for f in F2])[:,None]
orig = 9./8*np.dot(Flower,Flower.T) + 9.*self.lengthscales**4/200*np.dot(F2lower,F2lower.T)
orig2 = 3./5*self.lengthscales**2 * ( np.dot(F1lower,F1lower.T) + 1./8*np.dot(Flower,F2lower.T) + 1./8*np.dot(F2lower,Flower.T))
orig = 9./8*np.dot(Flower,Flower.T) + 9.*self.lengthscale**4/200*np.dot(F2lower,F2lower.T)
orig2 = 3./5*self.lengthscale**2 * ( np.dot(F1lower,F1lower.T) + 1./8*np.dot(Flower,F2lower.T) + 1./8*np.dot(F2lower,Flower.T))
return(1./self.variance* (G_coef*G + orig + orig2))

47
GPy/kern/constructors.py
View file

@ -32,6 +32,8 @@ def rbf(D,variance=1., lengthscale=None,ARD=False):
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param ARD: Auto Relevance Determination (one lengthscale per dimension)
:type ARD: Boolean
"""
part = rbfpart(D,variance,lengthscale,ARD)
return kern(D, [part])
@ -74,13 +76,16 @@ def white(D,variance=1.):
def exponential(D,variance=1., lengthscale=None, ARD=False):
"""
Construct a exponential kernel.
Construct an exponential kernel
Arguments
---------
D (int), obligatory
variance (float)
lengthscales (np.ndarray)
:param D: dimensionality of the kernel, obligatory
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param ARD: Auto Relevance Determination (one lengthscale per dimension)
:type ARD: Boolean
"""
part = exponentialpart(D,variance, lengthscale, ARD)
return kern(D, [part])
@ -89,26 +94,32 @@ def Matern32(D,variance=1., lengthscale=None, ARD=False):
"""
Construct a Matern 3/2 kernel.
Arguments
---------
D (int), obligatory
variance (float)
lengthscales (np.ndarray)
:param D: dimensionality of the kernel, obligatory
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param ARD: Auto Relevance Determination (one lengthscale per dimension)
:type ARD: Boolean
"""
part = Matern32part(D,variance, lengthscale, ARD)
return kern(D, [part])
def Matern52(D,variance=1., lengthscales=None):
def Matern52(D,variance=1., lengthscale=None, ARD=False):
"""
Construct a Matern 5/2 kernel.
Arguments
---------
D (int), obligatory
variance (float)
lengthscales (np.ndarray)
:param D: dimensionality of the kernel, obligatory
:type D: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
:type lengthscale: float
:param ARD: Auto Relevance Determination (one lengthscale per dimension)
:type ARD: Boolean
"""
part = Matern52part(D,variance, lengthscales)
part = Matern52part(D,variance, lengthscale, ARD)
return kern(D, [part])
def bias(D,variance=1.):

6
GPy/kern/exponential.py
View file

@ -19,8 +19,10 @@ class exponential(kernpart):
:type D: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscales :math:`\ell_i`
:type lengthscale: np.ndarray of size (D,)
:param lengthscale: the vector of lengthscale :math:`\ell_i`
:type lengthscale: np.ndarray of size (1,) or (D,) depending on ARD
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean
:rtype: kernel object
"""

2
GPy/kern/rbf.py
View file

@ -21,7 +21,7 @@ class rbf(kernpart):
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the vector of lengthscale of the kernel
:type lengthscale: np.ndarray
:type lengthscale: np.ndarray od size (1,) or (D,) depending on ARD
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean