mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-24 14:15:14 +02:00
Merge branch 'master' of github.com:SheffieldML/GPy
This commit is contained in:
Ricardo Andrade
commit
4b9064bb0e
9 changed files with 51 additions and 32 deletions
|
|
@ -199,3 +199,10 @@ def tuto_kernel_overview():
|
|||
WN[100] = 1.
|
||||
pb.subplot(3,4,i+1)
|
||||
pb.plot(X,WN,'b')
|
||||
|
||||
def model_interaction():
|
||||
X = np.random.randn(20,1)
|
||||
Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
|
||||
k = GPy.kern.rbf(1) + GPy.kern.bias(1)
|
||||
return GPy.models.GP_regression(X,Y,kernel=k)
|
||||
|
||||
|
|
|
|||
|
|
@ -389,6 +389,11 @@ class kern(parameterised):
|
|||
target += p1.variance*(p2._psi1[:,:,None]+p2._psi1[:,None,:])
|
||||
elif p2.name=='bias' and p1.name=='rbf':
|
||||
target += p2.variance*(p1._psi1[:,:,None]+p1._psi1[:,None,:])
|
||||
#linear X bias
|
||||
elif p1.name=='bias' and p2.name=='linear':
|
||||
raise NotImplementedError
|
||||
elif p2.name=='bias' and p1.name=='linear':
|
||||
raise NotImplementedError
|
||||
#rbf X linear
|
||||
elif p1.name=='linear' and p2.name=='rbf':
|
||||
raise NotImplementedError #TODO
|
||||
|
|
@ -396,7 +401,6 @@ class kern(parameterised):
|
|||
raise NotImplementedError #TODO
|
||||
else:
|
||||
raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
|
||||
return target
|
||||
|
||||
def dpsi2_dtheta(self,dL_dpsi2,Z,mu,S,slices1=None,slices2=None):
|
||||
|
|
@ -417,11 +421,11 @@ class kern(parameterised):
|
|||
pass
|
||||
#rbf X bias
|
||||
elif p1.name=='bias' and p2.name=='rbf':
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1.variance,Z,mu,S,target[ps2])
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2._psi1,Z,mu,S,target[ps1])
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1.variance*2.,Z,mu,S,target[ps2])
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2._psi1*2.,Z,mu,S,target[ps1])
|
||||
elif p2.name=='bias' and p1.name=='rbf':
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance,Z,mu,S,target[ps1])
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1._psi1,Z,mu,S,target[ps2])
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance*2.,Z,mu,S,target[ps1])
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1._psi1*2.,Z,mu,S,target[ps2])
|
||||
#rbf X linear
|
||||
elif p1.name=='linear' and p2.name=='rbf':
|
||||
raise NotImplementedError #TODO
|
||||
|
|
@ -444,9 +448,9 @@ class kern(parameterised):
|
|||
pass
|
||||
#rbf X bias
|
||||
elif p1.name=='bias' and p2.name=='rbf':
|
||||
p2.dpsi1_dX(dL_dpsi2.sum(1)*p1.variance,Z,mu,S,target)
|
||||
p2.dpsi1_dX(dL_dpsi2.sum(1).T*p1.variance,Z,mu,S,target)
|
||||
elif p2.name=='bias' and p1.name=='rbf':
|
||||
p1.dpsi1_dZ(dL_dpsi2.sum(2)*p2.variance,Z,mu,S,target)
|
||||
p1.dpsi1_dZ(dL_dpsi2.sum(1).T*p2.variance,Z,mu,S,target)
|
||||
#rbf X linear
|
||||
elif p1.name=='linear' and p2.name=='rbf':
|
||||
raise NotImplementedError #TODO
|
||||
|
|
@ -471,9 +475,9 @@ class kern(parameterised):
|
|||
pass
|
||||
#rbf X bias
|
||||
elif p1.name=='bias' and p2.name=='rbf':
|
||||
p2.dpsi1_dmuS(partial.sum(1)*p1.variance,Z,mu,S,target_mu,target_S)
|
||||
p2.dpsi1_dmuS(dL_dpsi2.sum(1).T*p1.variance*2.,Z,mu,S,target_mu,target_S)
|
||||
elif p2.name=='bias' and p1.name=='rbf':
|
||||
p1.dpsi1_dmuS(partial.sum(2)*p2.variance,Z,mu,S,target_mu,target_S)
|
||||
p1.dpsi1_dmuS(dL_dpsi2.sum(1).T*p2.variance*2.,Z,mu,S,target_mu,target_S)
|
||||
#rbf X linear
|
||||
elif p1.name=='linear' and p2.name=='rbf':
|
||||
raise NotImplementedError #TODO
|
||||
|
|
|
|||
|
|
@ -81,6 +81,13 @@ class linear(kernpart):
|
|||
self._K_computations(X, X2)
|
||||
target += np.sum(self._dot_product*dL_dK)
|
||||
|
||||
def dKdiag_dtheta(self,dL_dKdiag, X, target):
|
||||
tmp = dL_dKdiag[:,None]*X**2
|
||||
if self.ARD:
|
||||
target += tmp.sum(0)
|
||||
else:
|
||||
target += tmp.sum()
|
||||
|
||||
def dK_dX(self,dL_dK,X,X2,target):
|
||||
target += (((X2[:, None, :] * self.variances)) * dL_dK[:,:, None]).sum(0)
|
||||
|
||||
|
|
@ -92,13 +99,6 @@ class linear(kernpart):
|
|||
self._psi_computations(Z,mu,S)
|
||||
target += np.sum(self.variances*self.mu2_S,1)
|
||||
|
||||
def dKdiag_dtheta(self,dL_dKdiag, X, target):
|
||||
tmp = dL_dKdiag[:,None]*X**2
|
||||
if self.ARD:
|
||||
target += tmp.sum(0)
|
||||
else:
|
||||
target += tmp.sum()
|
||||
|
||||
def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):
|
||||
self._psi_computations(Z,mu,S)
|
||||
tmp = dL_dpsi0[:, None] * self.mu2_S
|
||||
|
|
@ -134,6 +134,7 @@ class linear(kernpart):
|
|||
self._psi_computations(Z,mu,S)
|
||||
psi2 = self.ZZ*np.square(self.variances)*self.mu2_S[:, None, None, :]
|
||||
target += psi2.sum(-1)
|
||||
#TODO: this could be faster using np.tensordot
|
||||
|
||||
def dpsi2_dtheta(self,dL_dpsi2,Z,mu,S,target):
|
||||
self._psi_computations(Z,mu,S)
|
||||
|
|
|
|||
|
|
@ -103,8 +103,12 @@ class sparse_GP(GP):
|
|||
|
||||
self.psi1V = np.dot(self.psi1, self.V)
|
||||
self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
|
||||
self.C = mdot(self.Lmi.T, self.Bi, self.Lmi)
|
||||
self.E = mdot(self.C, self.psi1VVpsi1/sf2, self.C.T)
|
||||
tmp = np.dot(self.Lmi.T, self.LBi.T)
|
||||
self.C = np.dot(tmp,tmp.T)
|
||||
#self.C = mdot(self.Lmi.T, self.Bi, self.Lmi)
|
||||
#self.E = mdot(self.C, self.psi1VVpsi1/sf2, self.C.T)
|
||||
tmp = np.dot(self.C,self.psi1V/sf)
|
||||
self.E = np.dot(tmp,tmp.T)
|
||||
|
||||
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case
|
||||
self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
|
||||
|
|
|
|||
|
|
@ -58,6 +58,7 @@ class BGPLVMTests(unittest.TestCase):
|
|||
m.randomize()
|
||||
self.assertTrue(m.checkgrad())
|
||||
|
||||
@unittest.skip('psi2 cross terms are NotImplemented for this combination')
|
||||
def test_linear_bias_kern(self):
|
||||
N, M, Q, D = 10, 3, 2, 4
|
||||
X = np.random.rand(N, Q)
|
||||
|
|
|
|||
|
|
@ -73,18 +73,10 @@ examples Package
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`tuto_GP_regression` Module
|
||||
--------------------------------
|
||||
:mod:`tutorials` Module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.examples.tuto_GP_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`tuto_kernel_overview` Module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.tuto_kernel_overview
|
||||
.. automodule:: GPy.examples.tutorials
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -9,6 +9,14 @@ GPy Package
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`test_coreg` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.test_coreg
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
|
|
@ -20,5 +28,6 @@ Subpackages
|
|||
GPy.kern
|
||||
GPy.likelihoods
|
||||
GPy.models
|
||||
GPy.testing
|
||||
GPy.util
|
||||
|
||||
|
|
|
|||
|
|
@ -8,9 +8,10 @@ Welcome to GPy's documentation!
|
|||
For a quick start, you can have a look at one of the tutorials:
|
||||
|
||||
* `Basic Gaussian process regression <tuto_GP_regression.html>`_
|
||||
* `Interacting with models <tuto_interacting_with_models.html>`_
|
||||
* `A kernel overview <tuto_kernel_overview.html>`_
|
||||
* Advanced GP regression (Forthcoming)
|
||||
* Writting kernels (Forthcoming)
|
||||
* Writing kernels (Forthcoming)
|
||||
|
||||
You may also be interested by some examples in the GPy/examples folder.
|
||||
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -25,7 +25,7 @@ setup(name = 'GPy',
|
|||
long_description=read('README.md'),
|
||||
#ext_modules = [Extension(name = 'GPy.kern.lfmUpsilonf2py',
|
||||
# sources = ['GPy/kern/src/lfmUpsilonf2py.f90'])],
|
||||
install_requires=['sympy', 'numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1'],
|
||||
install_requires=['sympy', 'numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'],
|
||||
extras_require = {
|
||||
'docs':['Sphinx', 'ipython'],
|
||||
},
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue