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automatic slicing
This commit is contained in:
Max Zwiessele
parent
e078bb47e1
commit
01f5d789c5
3 changed files with 72 additions and 144 deletions
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@ -23,7 +23,7 @@ class Add(CombinationKernel):
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elif not isinstance(which_parts, (list, tuple)):
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# if only one part is given
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which_parts = [which_parts]
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return sum([p.K(X, X2) for p in which_parts])
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return reduce(np.add, (p.K(X, X2) for p in which_parts))
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def gradients_X(self, dL_dK, X, X2=None):
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"""Compute the gradient of the objective function with respect to X.
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@ -49,14 +49,14 @@ class Add(CombinationKernel):
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def psi0(self, Z, variational_posterior):
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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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return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
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def psi1(self, Z, variational_posterior):
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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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return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))
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def psi2(self, Z, variational_posterior):
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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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psi2 = reduce(np.add, (p.psi2(Z, variational_posterior) for p in self.parts))
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return psi2
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# compute the "cross" terms
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from static import White, Bias
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from rbf import RBF
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@ -64,18 +64,23 @@ class Add(CombinationKernel):
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from linear import Linear
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#ffrom fixed import Fixed
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for (p1, i1), (p2, i2) in itertools.combinations(itertools.izip(self._parameters_, self.input_slices), 2):
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for p1, p2 in itertools.combinations(self.parts, 2):
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i1, i2 = p1.active_dims, p2.active_dims
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# white doesn;t combine with anything
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if isinstance(p1, White) or isinstance(p2, White):
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pass
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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], variational_posterior[:, i_s])
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# manual override for slicing:
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p2._sliced_X = p1._sliced_X = True
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tmp = p2.psi1(Z[:,i2], variational_posterior[:, i1])
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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], variational_posterior[:, i_s])
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# manual override for slicing:
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p2._sliced_X = p1._sliced_X = True
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tmp = p1.psi1(Z[:,i1], variational_posterior[:, i2])
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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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@ -83,11 +88,10 @@ class Add(CombinationKernel):
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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from static import White, Bias
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for p1, is1 in zip(self._parameters_, self.input_slices):
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for p1 in self.parts:
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#compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2!
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eff_dL_dpsi1 = dL_dpsi1.copy()
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for p2, is2 in zip(self._parameters_, self.input_slices):
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for p2 in self.parts:
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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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@ -95,42 +99,35 @@ class Add(CombinationKernel):
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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], variational_posterior[:, is1]) * 2.
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p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
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p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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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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for p1 in self.parts:
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#compute the effective dL_dpsi1. extra terms appear becaue of the cross terms in psi2!
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eff_dL_dpsi1 = dL_dpsi1.copy()
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for p2, is2 in zip(self._parameters_, self.input_slices):
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for p2 in self.parts:
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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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continue
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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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eff_dL_dpsi1 += 0#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], variational_posterior[:, is2]) * 2.
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target += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
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eff_dL_dpsi1 += 0#dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
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target[:, p1.active_dims] += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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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 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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for p1 in self._parameters_:
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#compute the effective dL_dpsi1. extra terms appear becaue of the cross terms in psi2!
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eff_dL_dpsi1 = dL_dpsi1.copy()
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for p2, is2 in zip(self._parameters_, self.input_slices):
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for p2 in self._parameters_:
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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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@ -138,35 +135,20 @@ class Add(CombinationKernel):
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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], variational_posterior[:, is2]) * 2.
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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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eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z, variational_posterior) * 2.
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a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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target_mu[:, p1.active_dims] += a
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target_S[:, p1.active_dims] += b
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return target_mu, target_S
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def input_sensitivity(self):
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in_sen = np.zeros((self.num_params, self.input_dim))
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for i, [p, i_s] in enumerate(zip(self._parameters_, self.input_slices)):
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in_sen[i, i_s] = p.input_sensitivity()
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return in_sen
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def _getstate(self):
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"""
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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"""
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return Parameterized._getstate(self) + [#self._parameters_,
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self.input_dim,
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self.input_slices,
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self._param_slices_
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]
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return super(Add, self)._getstate()
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def _setstate(self, state):
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self._param_slices_ = state.pop()
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self.input_slices = state.pop()
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self.input_dim = state.pop()
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Parameterized._setstate(self, state)
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super(Add, self)._setstate(state)
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