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reverted broken kern

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Max Zwiessele 2013-11-08 11:12:26 +00:00
parent 3d991fd127
commit 4f6dfba5be
1 changed files with 66 additions and 62 deletions
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128
GPy/kern/kern.py
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@ -18,37 +18,37 @@ class kern(Parameterized):
like which parameters live where.
The technical code for kernels is divided into _parts_ (see
e.g. rbf.py). This object contains a list of _parameters_, which are
computed additively. For multiplication, special _prod_ _parameters_
e.g. rbf.py). This object contains a list of parts, which are
computed additively. For multiplication, special _prod_ parts
are used.
:param input_dim: The dimensionality of the kernel's input space
:type input_dim: int
:param _parameters_: the '_parameters_' (PD functions) of the kernel
:type _parameters_: list of Kernpart objects
:param parts: the 'parts' (PD functions) of the kernel
:type parts: list of Kernpart objects
:param input_slices: the slices on the inputs which apply to each kernel
:type input_slices: list of slice objects, or list of bools
"""
self._parameters_ = parts
self.parts = parts
self.num_parts = len(parts)
self.num_params = sum([p.num_params for p in self._parameters_])
self.num_params = sum([p.num_params for p in self.parts])
self.input_dim = input_dim
part_names = [k.name for k in self._parameters_]
part_names = [k.name for k in self.parts]
self.name=''
for name in part_names:
self.name += name + '+'
self.name = self.name[:-1]
# deal with input_slices
if input_slices is None:
self.input_slices = [slice(None) for p in self._parameters_]
self.input_slices = [slice(None) for p in self.parts]
else:
assert len(input_slices) == len(self._parameters_)
assert len(input_slices) == len(self.parts)
self.input_slices = [sl if type(sl) is slice else slice(None) for sl in input_slices]
for p in self._parameters_:
for p in self.parts:
assert isinstance(p, Kernpart), "bad kernel part"
self.compute_param_slices()
@ -60,7 +60,7 @@ class kern(Parameterized):
Get the current state of the class,
here just all the indices, rest can get recomputed
"""
return Parameterized.getstate(self) + [self._parameters_,
return Parameterized.getstate(self) + [self.parts,
self.num_parts,
self.num_params,
self.input_dim,
@ -74,7 +74,7 @@ class kern(Parameterized):
self.input_dim = state.pop()
self.num_params = state.pop()
self.num_parts = state.pop()
self._parameters_ = state.pop()
self.parts = state.pop()
Parameterized.setstate(self, state)
@ -99,7 +99,7 @@ class kern(Parameterized):
xticklabels = []
bars = []
x0 = 0
for p in self._parameters_:
for p in self.parts:
c = Tango.nextMedium()
if hasattr(p, 'ARD') and p.ARD:
if title is None:
@ -173,7 +173,7 @@ class kern(Parameterized):
"""
self.param_slices = []
count = 0
for p in self._parameters_:
for p in self.parts:
self.param_slices.append(slice(count, count + p.num_params))
count += p.num_params
@ -202,7 +202,7 @@ class kern(Parameterized):
other_input_indices = [sl.indices(other.input_dim) for sl in other.input_slices]
other_input_slices = [slice(i[0] + self.input_dim, i[1] + self.input_dim, i[2]) for i in other_input_indices]
newkern = kern(D, self._parameters_ + other._parameters_, self_input_slices + other_input_slices)
newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [x + self.num_params for x in other.constrained_indices]
@ -213,7 +213,7 @@ class kern(Parameterized):
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
else:
assert self.input_dim == other.input_dim
newkern = kern(self.input_dim, self._parameters_ + other._parameters_, self.input_slices + other.input_slices)
newkern = kern(self.input_dim, self.parts + other.parts, self.input_slices + other.input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [i + self.num_params for i in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
@ -251,7 +251,7 @@ class kern(Parameterized):
s1[sl1], s2[sl2] = [True], [True]
slices += [s1 + s2]
newkernparts = [prod(k1, k2, tensor) for k1, k2 in itertools.product(K1._parameters_, K2._parameters_)]
newkernparts = [prod(k1, k2, tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
if tensor:
newkern = kern(K1.input_dim + K2.input_dim, newkernparts, slices)
@ -266,12 +266,12 @@ class kern(Parameterized):
# Build the array that allows to go from the initial indices of the param to the new ones
K1_param = []
n = 0
for k1 in K1._parameters_:
for k1 in K1.parts:
K1_param += [range(n, n + k1.num_params)]
n += k1.num_params
n = 0
K2_param = []
for k2 in K2._parameters_:
for k2 in K2.parts:
K2_param += [range(K1.num_params + n, K1.num_params + n + k2.num_params)]
n += k2.num_params
index_param = []
@ -303,19 +303,19 @@ class kern(Parameterized):
self.constrain(np.where(index_param == i)[0], t)
def _get_params(self):
return np.hstack([p._get_params() for p in self._parameters_])
return np.hstack([p._get_params() for p in self.parts])
def _set_params(self, x):
[p._set_params(x[s]) for p, s in zip(self._parameters_, self.param_slices)]
[p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)]
def _get_param_names(self):
# this is a bit nasty: we want to distinguish between _parameters_ with the same name by appending a count
part_names = np.array([k.name for k in self._parameters_], dtype=np.str)
# this is a bit nasty: we want to distinguish between parts with the same name by appending a count
part_names = np.array([k.name for k in self.parts], dtype=np.str)
counts = [np.sum(part_names == ni) for i, ni in enumerate(part_names)]
cum_counts = [np.sum(part_names[i:] == ni) for i, ni in enumerate(part_names)]
names = [name + '_' + str(cum_count) if count > 1 else name for name, count, cum_count in zip(part_names, counts, cum_counts)]
return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self._parameters_)], [])
return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
def K(self, X, X2=None, which_parts='all'):
"""
@ -334,10 +334,10 @@ class kern(Parameterized):
assert X.shape[1] == self.input_dim
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
[p.K(X[:, i_s], None, target=target) for p, i_s, part_i_used in zip(self._parameters_, self.input_slices, which_parts) if part_i_used]
[p.K(X[:, i_s], None, target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
else:
target = np.zeros((X.shape[0], X2.shape[0]))
[p.K(X[:, i_s], X2[:, i_s], target=target) for p, i_s, part_i_used in zip(self._parameters_, self.input_slices, which_parts) if part_i_used]
[p.K(X[:, i_s], X2[:, i_s], target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
return target
def dK_dtheta(self, dL_dK, X, X2=None):
@ -356,9 +356,9 @@ class kern(Parameterized):
assert X.shape[1] == self.input_dim
target = np.zeros(self.num_params)
if X2 is None:
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self.param_slices)]
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self.parts, self.input_slices, self.param_slices)]
else:
[p.dK_dtheta(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self.param_slices)]
[p.dK_dtheta(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self.parts, self.input_slices, self.param_slices)]
return self._transform_gradients(target)
@ -374,9 +374,9 @@ class kern(Parameterized):
target = np.zeros_like(X)
if X2 is None:
[p.dK_dX(dL_dK, X[:, i_s], None, target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dK_dX(dL_dK, X[:, i_s], None, target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
else:
[p.dK_dX(dL_dK, X[:, i_s], X2[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dK_dX(dL_dK, X[:, i_s], X2[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target
def Kdiag(self, X, which_parts='all'):
@ -385,7 +385,7 @@ class kern(Parameterized):
which_parts = [True] * self.num_parts
assert X.shape[1] == self.input_dim
target = np.zeros(X.shape[0])
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self._parameters_, self.input_slices, which_parts) if part_on]
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self.parts, self.input_slices, which_parts) if part_on]
return target
def dKdiag_dtheta(self, dL_dKdiag, X):
@ -393,49 +393,49 @@ class kern(Parameterized):
assert X.shape[1] == self.input_dim
assert dL_dKdiag.size == X.shape[0]
target = np.zeros(self.num_params)
[p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self._parameters_, self.input_slices, self.param_slices)]
[p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
return self._transform_gradients(target)
def dKdiag_dX(self, dL_dKdiag, X):
assert X.shape[1] == self.input_dim
target = np.zeros_like(X)
[p.dKdiag_dX(dL_dKdiag, X[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dKdiag_dX(dL_dKdiag, X[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target
def psi0(self, Z, mu, S):
target = np.zeros(mu.shape[0])
[p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
return target
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S):
target = np.zeros(self.num_params)
[p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self._parameters_, self.param_slices, self.input_slices)]
[p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S):
target_mu, target_S = np.zeros_like(mu), np.zeros_like(S)
[p.dpsi0_dmuS(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dpsi0_dmuS(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target_mu, target_S
def psi1(self, Z, mu, S):
target = np.zeros((mu.shape[0], Z.shape[0]))
[p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
return target
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S):
target = np.zeros((self.num_params))
[p.dpsi1_dtheta(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self._parameters_, self.param_slices, self.input_slices)]
[p.dpsi1_dtheta(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S):
target = np.zeros_like(Z)
[p.dpsi1_dZ(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dpsi1_dZ(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S):
"""return shapes are num_samples,num_inducing,input_dim"""
target_mu, target_S = np.zeros((2, mu.shape[0], mu.shape[1]))
[p.dpsi1_dmuS(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dpsi1_dmuS(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target_mu, target_S
def psi2(self, Z, mu, S):
@ -445,7 +445,7 @@ class kern(Parameterized):
:returns psi2: np.ndarray (N,M,M)
"""
target = np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
[p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
# compute the "cross" terms
# TODO: input_slices needed
@ -454,46 +454,49 @@ class kern(Parameterized):
from parts.rbf_inv import RBFInv
from parts.bias import Bias
from parts.linear import Linear
from parts.fixed import Fixed
for (p1, i1), (p2, i2) in itertools.combinations(itertools.izip(self._parameters_, self._param_slices_), 2):
for (p1, i1), (p2, i2) in itertools.combinations(itertools.izip(self.parts, self.param_slices), 2):
# white doesn;t combine with anything
if isinstance(p1, White) or isinstance(p2, White):
pass
# rbf X bias
elif isinstance(p1, Bias) and isinstance(p2, (RBF, RBFInv)):
elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
target += p1.variance * (p2._psi1[:, :, None] + p2._psi1[:, None, :])
elif isinstance(p2, Bias) and isinstance(p1, (RBF, RBFInv)):
elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
import ipdb;ipdb.set_trace()
tmp1 = p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
renorm = p1.variance*np.exp()
tmp2 = asd
target += p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
# linear X bias
elif isinstance(p1, Bias) and isinstance(p2, Linear):
elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
p2.psi1(Z, mu, S, tmp)
target += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
elif isinstance(p2, Bias) and isinstance(p1, Linear):
elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, 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 isinstance(p1, Linear) and isinstance(p2, (RBF, RBFInv)):
# rbf X any
elif isinstance(p1, (RBF, RBFInv)):
pass
elif isinstance(p2, Linear) and isinstance(p1, (RBF, RBFInv)):
raise NotImplementedError # TODO
elif isinstance(p1, (RBF, RBFInv)) and isinstance(p2, (RBF, RBFInv)):
raise NotImplementedError # TODO
elif isinstance(p2, (RBF, RBFInv)) and isinstance(p1, (RBF, RBFInv)):
elif isinstance(p2, (RBF, RBFInv)):
raise NotImplementedError # TODO
else:
raise NotImplementedError, "psi2 cannot be computed for this kernel"
return target
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S):
target = np.zeros(self.Nparam)
[p.dpsi2_dtheta(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, i_s, ps in zip(self._parameters_, self.input_slices, self.param_slices)]
target = np.zeros(self.num_params)
[p.dpsi2_dtheta(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
# compute the "cross" terms
# TODO: better looping, input_slices
for i1, i2 in itertools.combinations(range(len(self._parameters_)), 2):
p1, p2 = self._parameters_[i1], self._parameters_[i2]
for i1, i2 in itertools.combinations(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]
@ -518,7 +521,8 @@ class kern(Parameterized):
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
# rbf X any
elif p1.name == 'linear' and p2.name == 'rbf':
raise NotImplementedError # TODO
elif p2.name == 'linear' and p1.name == 'rbf':
@ -530,11 +534,11 @@ class kern(Parameterized):
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._parameters_, self.input_slices)]
[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)]
# compute the "cross" terms
# TODO: we need input_slices here.
for p1, p2 in itertools.combinations(self._parameters_, 2):
for p1, p2 in itertools.combinations(self.parts, 2):
# white doesn;t combine with anything
if p1.name == 'white' or p2.name == 'white':
pass
@ -560,11 +564,11 @@ class kern(Parameterized):
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S):
target_mu, target_S = np.zeros((2, mu.shape[0], mu.shape[1]))
[p.dpsi2_dmuS(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
[p.dpsi2_dmuS(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
# compute the "cross" terms
# TODO: we need input_slices here.
for p1, p2 in itertools.combinations(self._parameters_, 2):
for p1, p2 in itertools.combinations(self.parts, 2):
# white doesn;t combine with anything
if p1.name == 'white' or p2.name == 'white':
pass