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merged params here
This commit is contained in:
Max Zwiessele
commit
dab35dcbb0
13 changed files with 220 additions and 412 deletions
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@ -12,24 +12,19 @@ class Add(Kern):
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assert all([isinstance(k, Kern) for k in subkerns])
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if tensor:
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input_dim = sum([k.input_dim for k in subkerns])
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self.self.active_dims = []
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self.input_slices = []
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n = 0
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for k in subkerns:
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self.self.active_dims.append(slice(n, n+k.input_dim))
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self.input_slices.append(slice(n, n+k.input_dim))
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n += k.input_dim
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else:
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#assert all([k.input_dim == subkerns[0].input_dim for k in subkerns])
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#input_dim = subkerns[0].input_dim
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#self.input_slices = [slice(None) for k in subkerns]
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input_dim = reduce(np.union1d, map(lambda x: np.r_[x.active_dims], subkerns))
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assert all([k.input_dim == subkerns[0].input_dim for k in subkerns])
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input_dim = subkerns[0].input_dim
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self.input_slices = [slice(None) for k in subkerns]
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super(Add, self).__init__(input_dim, 'add')
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self.add_parameters(*subkerns)
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@property
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def parts(self):
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return self._parameters_
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@Cache_this(limit=1, force_kwargs=('which_parts',))
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def K(self, X, X2=None):
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"""
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Compute the kernel function.
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@ -78,19 +73,18 @@ class Add(Kern):
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def psi0(self, Z, variational_posterior):
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return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
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return np.sum([p.psi0(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
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def psi1(self, Z, variational_posterior):
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return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
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return np.sum([p.psi1(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
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def psi2(self, Z, variational_posterior):
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psi2 = np.sum([p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
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psi2 = np.sum([p.psi2(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
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# compute the "cross" terms
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from white import White
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from static import White, Bias
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from rbf import RBF
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#from rbf_inv import RBFInv
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from bias import Bias
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from linear import Linear
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#ffrom fixed import Fixed
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@ -101,24 +95,20 @@ class Add(Kern):
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# rbf X bias
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#elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
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elif isinstance(p1, Bias) and isinstance(p2, (RBF, Linear)):
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tmp = p2.psi1(Z[:,i2], mu[:,i2], S[:,i2])
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tmp = p2.psi1(Z[:,i2], variational_posterior[:, i_s])
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psi2 += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
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#elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
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elif isinstance(p2, Bias) and isinstance(p1, (RBF, Linear)):
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tmp = p1.psi1(Z[:,i1], mu[:,i1], S[:,i1])
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tmp = p1.psi1(Z[:,i1], variational_posterior[:, i_s])
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psi2 += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
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else:
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raise NotImplementedError, "psi2 cannot be computed for this kernel"
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return psi2
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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from white import White
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from rbf import RBF
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#from rbf_inv import RBFInv
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#from bias import Bias
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from linear import Linear
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#ffrom fixed import Fixed
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from static import White, Bias
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mu, S = variational_posterior.mean, variational_posterior.variance
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for p1, is1 in zip(self._parameters_, self.input_slices):
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#compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2!
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@ -131,20 +121,15 @@ class Add(Kern):
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elif isinstance(p2, Bias):
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
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else:
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is1]) * 2.
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p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
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p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
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def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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from white import White
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from rbf import RBF
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#from rbf_inv import rbfinv
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from bias import Bias
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from linear import Linear
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#ffrom fixed import fixed
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from static import White, Bias
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target = np.zeros(Z.shape)
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for p1, is1 in zip(self._parameters_, self.input_slices):
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@ -158,22 +143,17 @@ class Add(Kern):
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elif isinstance(p2, Bias):
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
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else:
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is2]) * 2.
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target += p1.gradients_z_variational(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
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target += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
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return target
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def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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from white import white
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from rbf import rbf
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#from rbf_inv import rbfinv
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#from bias import bias
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from linear import linear
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#ffrom fixed import fixed
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target_mu = np.zeros(mu.shape)
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target_S = np.zeros(S.shape)
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from static import White, Bias
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target_mu = np.zeros(variational_posterior.shape)
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target_S = np.zeros(variational_posterior.shape)
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for p1, is1 in zip(self._parameters_, self.input_slices):
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#compute the effective dL_dpsi1. extra terms appear becaue of the cross terms in psi2!
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@ -181,15 +161,15 @@ class Add(Kern):
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for p2, is2 in zip(self._parameters_, self.input_slices):
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if p2 is p1:
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continue
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if isinstance(p2, white):
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if isinstance(p2, White):
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continue
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elif isinstance(p2, bias):
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elif isinstance(p2, Bias):
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
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else:
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(z[:,is2], mu[:,is2], s[:,is2]) * 2.
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is2]) * 2.
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a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], s[:,is1], z[:,is1])
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a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
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target_mu += a
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target_S += b
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return target_mu, target_S
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