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stability enhancing clipping in logexp_clipped and reverse of stability clipping of parameters
This commit is contained in:
Max Zwiessele
parent
524c6e26bf
commit
ba4bd50924
2 changed files with 46 additions and 45 deletions
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@ -66,7 +66,7 @@ class kern(parameterised):
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def _transform_gradients(self, g):
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x = self._get_params()
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[np.put(x,i,x*t.gradfactor(x[i])) for i,t in zip(self.constrained_indices, self.constraints)]
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[np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
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[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
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if len(self.tied_indices) or len(self.fixed_indices):
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to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
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@ -88,7 +88,7 @@ class kern(parameterised):
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"""
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return self.add(other)
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def add(self, other,tensor=False):
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def add(self, other, tensor=False):
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"""
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Add another kernel to this one. Both kernels are defined on the same _space_
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:param other: the other kernel to be added
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@ -103,7 +103,7 @@ class kern(parameterised):
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newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [x+self.Nparam for x in other.constrained_indices]
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newkern.constrained_indices = self.constrained_indices + [x + self.Nparam for x in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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@ -113,7 +113,7 @@ class kern(parameterised):
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assert self.D == other.D
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newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [i+self.Nparam for i in other.constrained_indices]
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newkern.constrained_indices = self.constrained_indices + [i + self.Nparam for i in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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@ -126,7 +126,7 @@ class kern(parameterised):
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"""
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return self.prod(other)
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def prod(self, other,tensor=False):
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def prod(self, other, tensor=False):
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"""
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multiply two kernels (either on the same space, or on the tensor product of the input space)
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:param other: the other kernel to be added
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@ -136,12 +136,12 @@ class kern(parameterised):
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K2 = other.copy()
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slices = []
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for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
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s1, s2 = [False]*K1.D, [False]*K2.D
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for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
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s1, s2 = [False] * K1.D, [False] * K2.D
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s1[sl1], s2[sl2] = [True], [True]
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slices += [s1+s2]
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slices += [s1 + s2]
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newkernparts = [prod(k1, k2,tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
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newkernparts = [prod(k1, k2, tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
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if tensor:
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newkern = kern(K1.D + K2.D, newkernparts, slices)
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@ -189,14 +189,13 @@ class kern(parameterised):
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index = np.where(index_param == i)[0]
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if index.size > 1:
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self.tie_params(index)
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for i,t in zip(prev_constr_ind,prev_constr):
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self.constrain(np.where(index_param == i)[0],t)
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for i, t in zip(prev_constr_ind, prev_constr):
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self.constrain(np.where(index_param == i)[0], t)
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def _get_params(self):
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return np.hstack([p._get_params() for p in self.parts])
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def _set_params(self, x):
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x = np.clip(x, -1e300, 1e300)
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[p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)]
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def _get_param_names(self):
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@ -209,15 +208,15 @@ class kern(parameterised):
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return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
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def K(self, X, X2=None, which_parts='all'):
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if which_parts=='all':
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which_parts = [True]*self.Nparts
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if which_parts == 'all':
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which_parts = [True] * self.Nparts
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assert X.shape[1] == self.D
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if X2 is None:
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target = np.zeros((X.shape[0], X.shape[0]))
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[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]
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else:
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target = np.zeros((X.shape[0], X2.shape[0]))
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[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]
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[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]
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return target
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def dK_dtheta(self, dL_dK, X, X2=None):
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@ -249,8 +248,8 @@ class kern(parameterised):
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return target
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def Kdiag(self, X, which_parts='all'):
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if which_parts=='all':
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which_parts = [True]*self.Nparts
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if which_parts == 'all':
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which_parts = [True] * self.Nparts
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assert X.shape[1] == self.D
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target = np.zeros(X.shape[0])
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[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]
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@ -271,22 +270,22 @@ class kern(parameterised):
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def psi0(self, Z, mu, S):
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target = np.zeros(mu.shape[0])
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[p.psi0(Z[:,i_s], mu[:,i_s], S[:,i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
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[p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
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return target
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def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S):
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target = np.zeros(self.Nparam)
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[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)]
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[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)]
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return self._transform_gradients(target)
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def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S):
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target_mu, target_S = np.zeros_like(mu), np.zeros_like(S)
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[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)]
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[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)]
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return target_mu, target_S
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def psi1(self, Z, mu, S):
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target = np.zeros((mu.shape[0], Z.shape[0]))
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[p.psi1(Z[:,i_s], mu[:,i_s], S[:,i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
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[p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
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return target
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def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S):
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@ -315,7 +314,7 @@ class kern(parameterised):
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[p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
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# compute the "cross" terms
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#TODO: input_slices needed
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# TODO: input_slices needed
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for p1, p2 in itertools.combinations(self.parts, 2):
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# white doesn;t combine with anything
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if p1.name == 'white' or p2.name == 'white':
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@ -336,9 +335,9 @@ class kern(parameterised):
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target += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
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# rbf X linear
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elif p1.name == 'linear' and p2.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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elif p2.name == 'linear' and p1.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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else:
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raise NotImplementedError, "psi2 cannot be computed for this kernel"
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return target
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@ -366,7 +365,7 @@ class kern(parameterised):
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p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1._psi1 * 2., Z, mu, S, target[ps2])
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# linear X bias
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elif p1.name == 'bias' and p2.name == 'linear':
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p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2]) # [ps1])
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p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2]) # [ps1])
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psi1 = np.zeros((mu.shape[0], Z.shape[0]))
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p2.psi1(Z, mu, S, psi1)
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p1.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps1])
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@ -377,9 +376,9 @@ class kern(parameterised):
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p2.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps2])
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# rbf X linear
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elif p1.name == 'linear' and p2.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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elif p2.name == 'linear' and p1.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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else:
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raise NotImplementedError, "psi2 cannot be computed for this kernel"
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@ -390,7 +389,7 @@ class kern(parameterised):
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[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)]
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# compute the "cross" terms
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#TODO: we need input_slices here.
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# TODO: we need input_slices here.
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for p1, p2 in itertools.combinations(self.parts, 2):
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# white doesn;t combine with anything
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if p1.name == 'white' or p2.name == 'white':
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@ -407,9 +406,9 @@ class kern(parameterised):
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p1.dpsi1_dZ(dL_dpsi2.sum(1).T * p2.variance, Z, mu, S, target)
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# rbf X linear
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elif p1.name == 'linear' and p2.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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elif p2.name == 'linear' and p1.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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else:
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raise NotImplementedError, "psi2 cannot be computed for this kernel"
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@ -420,7 +419,7 @@ class kern(parameterised):
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[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)]
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# compute the "cross" terms
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#TODO: we need input_slices here.
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# TODO: we need input_slices here.
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for p1, p2 in itertools.combinations(self.parts, 2):
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# white doesn;t combine with anything
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if p1.name == 'white' or p2.name == 'white':
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@ -437,9 +436,9 @@ class kern(parameterised):
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p1.dpsi1_dmuS(dL_dpsi2.sum(1).T * p2.variance * 2., Z, mu, S, target_mu, target_S)
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# rbf X linear
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elif p1.name == 'linear' and p2.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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elif p2.name == 'linear' and p1.name == 'rbf':
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raise NotImplementedError # TODO
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raise NotImplementedError # TODO
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else:
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raise NotImplementedError, "psi2 cannot be computed for this kernel"
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