mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-12 05:22:38 +02:00
cross-terms
This commit is contained in:
Nicolo Fusi
parent
ddd3ece3ce
commit
19407293dc
5 changed files with 40 additions and 105 deletions
|
|
@ -21,7 +21,7 @@ def BGPLVM(seed=default_seed):
|
|||
K = k.K(X)
|
||||
Y = np.random.multivariate_normal(np.zeros(N), K, D).T
|
||||
|
||||
k = GPy.kern.linear(Q, ARD=True) + GPy.kern.white(Q)
|
||||
k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
|
||||
# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
|
||||
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
|
||||
# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
|
||||
|
|
|
|||
|
|
@ -55,7 +55,8 @@ class bias(kernpart):
|
|||
target += self.variance
|
||||
|
||||
def psi1(self, Z, mu, S, target):
|
||||
target += self.variance
|
||||
self._psi1 = self.variance
|
||||
target += self._psi1
|
||||
|
||||
def psi2(self, Z, mu, S, target):
|
||||
target += self.variance**2
|
||||
|
|
|
|||
130
GPy/kern/kern.py
130
GPy/kern/kern.py
|
|
@ -316,32 +316,19 @@ class kern(parameterised):
|
|||
# compute the "cross" terms
|
||||
# TODO: input_slices needed
|
||||
crossterms = 0
|
||||
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
prod = np.multiply
|
||||
# # white doesn;t combine with anything
|
||||
# if p1.name == 'white' or p2.name == 'white':
|
||||
# pass
|
||||
# # rbf X bias
|
||||
# elif p1.name == 'bias' and p2.name == 'rbf':
|
||||
# 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':
|
||||
# tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
# p2.psi1(Z, mu, S, tmp)
|
||||
# target += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
|
||||
# elif p2.name == 'bias' and p1.name == 'linear':
|
||||
# tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
# p1.psi1(Z, mu, S, tmp)
|
||||
# target += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
|
||||
# # rbf X linear
|
||||
# elif p1.name == 'linear' and p2.name == 'rbf':
|
||||
# raise NotImplementedError # TODO
|
||||
# elif p2.name == 'linear' and p1.name == 'rbf':
|
||||
# raise NotImplementedError # TODO
|
||||
# else:
|
||||
# raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
|
||||
# TODO psi1 this must be faster/better/precached/more nice
|
||||
tmp1 = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p1.psi1(Z, mu, S, tmp1)
|
||||
tmp2 = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p2.psi1(Z, mu, S, tmp2)
|
||||
|
||||
prod = np.multiply(tmp1, tmp2)
|
||||
crossterms += prod[:,:,None] + prod[:, None, :]
|
||||
|
||||
target += crossterms
|
||||
return target
|
||||
|
||||
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S):
|
||||
|
|
@ -350,71 +337,34 @@ class kern(parameterised):
|
|||
|
||||
# compute the "cross" terms
|
||||
# TODO: better looping, input_slices
|
||||
for i1, i2 in itertools.combinations(range(len(self.parts)), 2):
|
||||
for i1, i2 in itertools.permutations(range(len(self.parts)), 2):
|
||||
p1, p2 = self.parts[i1], self.parts[i2]
|
||||
# ipsl1, ipsl2 = self.input_slices[i1], self.input_slices[i2]
|
||||
ps1, ps2 = self.param_slices[i1], self.param_slices[i2]
|
||||
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
pass
|
||||
# rbf X bias
|
||||
elif p1.name == 'bias' and p2.name == 'rbf':
|
||||
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 * 2., Z, mu, S, target[ps1])
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1._psi1 * 2., Z, mu, S, target[ps2])
|
||||
# linear X bias
|
||||
elif p1.name == 'bias' and p2.name == 'linear':
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2]) # [ps1])
|
||||
psi1 = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p2.psi1(Z, mu, S, psi1)
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps1])
|
||||
elif p2.name == 'bias' and p1.name == 'linear':
|
||||
p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target[ps1])
|
||||
psi1 = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p1.psi1(Z, mu, S, psi1)
|
||||
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps2])
|
||||
# rbf X linear
|
||||
elif p1.name == 'linear' and p2.name == 'rbf':
|
||||
raise NotImplementedError # TODO
|
||||
elif p2.name == 'linear' and p1.name == 'rbf':
|
||||
raise NotImplementedError # TODO
|
||||
else:
|
||||
raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p1.psi1(Z, mu, S, tmp)
|
||||
p2.dpsi1_dtheta((tmp[:,None,:]*dL_dpsi2).sum(1)*2., Z, mu, S, target[ps2])
|
||||
|
||||
return self._transform_gradients(target)
|
||||
|
||||
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S):
|
||||
target = np.zeros_like(Z)
|
||||
[p.dpsi2_dZ(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
#target *= 2
|
||||
|
||||
# compute the "cross" terms
|
||||
# TODO: we need input_slices here.
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
pass
|
||||
# rbf X bias
|
||||
elif p1.name == 'bias' and p2.name == 'rbf':
|
||||
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(1).T * p2.variance, Z, mu, S, target)
|
||||
# linear X bias
|
||||
elif p1.name == 'bias' and p2.name == 'linear':
|
||||
p2.dpsi1_dZ(dL_dpsi2.sum(1).T * p1.variance, Z, mu, S, target)
|
||||
elif p2.name == 'bias' and p1.name == 'linear':
|
||||
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
|
||||
elif p2.name == 'linear' and p1.name == 'rbf':
|
||||
raise NotImplementedError # TODO
|
||||
else:
|
||||
raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
for p1, p2 in itertools.permutations(self.parts, 2):
|
||||
if p1.name == 'linear' and p2.name == 'linear':
|
||||
raise NotImplementedError("We don't handle linear/linear cross-terms")
|
||||
tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p1.psi1(Z, mu, S, tmp)
|
||||
tmp2 = np.zeros_like(target)
|
||||
p2.dpsi1_dZ((tmp[:,None,:]*dL_dpsi2).sum(1).T, Z, mu, S, tmp2)
|
||||
target += tmp2
|
||||
|
||||
return target * 2.
|
||||
return target * 2
|
||||
|
||||
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S):
|
||||
target_mu, target_S = np.zeros((2, mu.shape[0], mu.shape[1]))
|
||||
|
|
@ -422,27 +372,13 @@ class kern(parameterised):
|
|||
|
||||
# compute the "cross" terms
|
||||
# TODO: we need input_slices here.
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
pass
|
||||
# rbf X bias
|
||||
elif p1.name == 'bias' and p2.name == 'rbf':
|
||||
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(dL_dpsi2.sum(1).T * p2.variance * 2., Z, mu, S, target_mu, target_S)
|
||||
# linear X bias
|
||||
elif p1.name == 'bias' and p2.name == 'linear':
|
||||
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 == 'linear':
|
||||
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
|
||||
elif p2.name == 'linear' and p1.name == 'rbf':
|
||||
raise NotImplementedError # TODO
|
||||
else:
|
||||
raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
for p1, p2 in itertools.permutations(self.parts, 2):
|
||||
if p1.name == 'linear' and p2.name == 'linear':
|
||||
raise NotImplementedError("We don't handle linear/linear cross-terms")
|
||||
|
||||
tmp = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
p1.psi1(Z, mu, S, tmp)
|
||||
p2.dpsi1_dmuS((tmp[:,None,:]*dL_dpsi2).sum(1).T*2., Z, mu, S, target_mu, target_S)
|
||||
|
||||
return target_mu, target_S
|
||||
|
||||
|
|
|
|||
|
|
@ -54,5 +54,3 @@ class kernpart(object):
|
|||
raise NotImplementedError
|
||||
def dK_dX(self,X,X2,target):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ class white(kernpart):
|
|||
self.Nparam = 1
|
||||
self.name = 'white'
|
||||
self._set_params(np.array([variance]).flatten())
|
||||
self._psi1 = 0 # TODO: more elegance here
|
||||
|
||||
def _get_params(self):
|
||||
return self.variance
|
||||
|
|
@ -81,4 +82,3 @@ class white(kernpart):
|
|||
|
||||
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
|
||||
pass
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue