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https://github.com/SheffieldML/GPy.git
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Merge branch 'params' of github.com:SheffieldML/GPy into params
This commit is contained in:
James Hensman
commit
328e0124c7
23 changed files with 441 additions and 364 deletions
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@ -3,7 +3,6 @@
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import numpy as np
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import itertools
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from ...core.parameterization import Parameterized
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from ...util.caching import Cache_this
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from kern import CombinationKernel
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@ -156,7 +156,7 @@ class Kern(Parameterized):
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other.active_dims += self.input_dim
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return self.prod(other)
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def prod(self, other, name=None):
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def prod(self, other, name='mul'):
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"""
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Multiply two kernels (either on the same space, or on the tensor
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product of the input space).
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@ -169,12 +169,12 @@ class Kern(Parameterized):
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"""
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assert isinstance(other, Kern), "only kernels can be added to kernels..."
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from prod import Prod
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kernels = []
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if isinstance(self, Prod): kernels.extend(self._parameters_)
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else: kernels.append(self)
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if isinstance(other, Prod): kernels.extend(other._parameters_)
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else: kernels.append(other)
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return Prod(self, other, name)
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#kernels = []
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#if isinstance(self, Prod): kernels.extend(self._parameters_)
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#else: kernels.append(self)
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#if isinstance(other, Prod): kernels.extend(other._parameters_)
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#else: kernels.append(other)
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return Prod([self, other], name)
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def _getstate(self):
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"""
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@ -195,8 +195,10 @@ class Kern(Parameterized):
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class CombinationKernel(Kern):
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def __init__(self, kernels, name):
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assert all([isinstance(k, Kern) for k in kernels])
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# make sure the active dimensions of all underlying kernels are covered:
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ma = reduce(lambda a,b: max(a, max(b)), (x.active_dims for x in kernels), 0)
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input_dim = np.r_[0:ma+1]
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# initialize the kernel with the full input_dim
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super(CombinationKernel, self).__init__(input_dim, name)
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self.add_parameters(*kernels)
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@ -9,17 +9,17 @@ class KernCallsViaSlicerMeta(ParametersChangedMeta):
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def __call__(self, *args, **kw):
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instance = super(ParametersChangedMeta, self).__call__(*args, **kw)
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instance.K = _slice_wrapper(instance, instance.K)
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instance.Kdiag = _slice_wrapper(instance, instance.Kdiag, True)
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instance.update_gradients_full = _slice_wrapper(instance, instance.update_gradients_full, False, True)
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instance.update_gradients_diag = _slice_wrapper(instance, instance.update_gradients_diag, True, True)
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instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, False, True)
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instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, True, True)
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instance.psi0 = _slice_wrapper(instance, instance.psi0, False, False)
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instance.psi1 = _slice_wrapper(instance, instance.psi1, False, False)
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instance.psi2 = _slice_wrapper(instance, instance.psi2, False, False)
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instance.update_gradients_expectations = _slice_wrapper(instance, instance.update_gradients_expectations, psi_stat=True)
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instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, psi_stat_Z=True)
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instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, psi_stat=True)
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instance.Kdiag = _slice_wrapper(instance, instance.Kdiag, diag=True)
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instance.update_gradients_full = _slice_wrapper(instance, instance.update_gradients_full, diag=False, derivative=True)
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instance.update_gradients_diag = _slice_wrapper(instance, instance.update_gradients_diag, diag=True, derivative=True)
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instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, diag=False, derivative=True)
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instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, diag=True, derivative=True)
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instance.psi0 = _slice_wrapper(instance, instance.psi0, diag=False, derivative=False)
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instance.psi1 = _slice_wrapper(instance, instance.psi1, diag=False, derivative=False)
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instance.psi2 = _slice_wrapper(instance, instance.psi2, diag=False, derivative=False)
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instance.update_gradients_expectations = _slice_wrapper(instance, instance.update_gradients_expectations, derivative=True, psi_stat=True)
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instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, derivative=True, psi_stat_Z=True)
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instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, derivative=True, psi_stat=True)
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instance.parameters_changed()
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return instance
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@ -44,7 +44,29 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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elif psi_stat:
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def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
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kern._sliced_X += 1
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try:
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ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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elif psi_stat_Z:
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def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
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kern._sliced_X += 1
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try:
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ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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def x_slice_wrapper(dL_dK, X, X2=None):
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X, X2 = kern._slice_X(X) if not kern._sliced_X else X, kern._slice_X(X2) if X2 is not None and not kern._sliced_X else X2
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kern._sliced_X += 1
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@ -55,28 +77,6 @@ def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False
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finally:
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kern._sliced_X -= 1
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return ret
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elif psi_stat:
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def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
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kern._sliced_X += 1
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try:
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ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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elif psi_stat_Z:
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def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
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kern._sliced_X += 1
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try:
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ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
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except:
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raise
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finally:
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kern._sliced_X -= 1
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return ret
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else:
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if diag:
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def x_slice_wrapper(X, *args, **kw):
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@ -96,12 +96,12 @@ class MLP(Kern):
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vec = (X*X).sum(1)*self.weight_variance+self.bias_variance + 1.
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return 2*four_over_tau*self.weight_variance*self.variance*((X[None, :, :]/denom[:, :, None] - vec[None, :, None]*X[:, None, :]*(numer/denom3)[:, :, None])*(dL_dK/np.sqrt(1-arg*arg))[:, :, None]).sum(1)
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def dKdiag_dX(self, dL_dKdiag, X, target):
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def gradients_X_diag(self, dL_dKdiag, X):
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"""Gradient of diagonal of covariance with respect to X"""
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self._K_diag_computations(X)
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arg = self._K_diag_asin_arg
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denom = self._K_diag_denom
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numer = self._K_diag_numer
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#numer = self._K_diag_numer
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return four_over_tau*2.*self.weight_variance*self.variance*X*(1./denom*(1. - arg)*dL_dKdiag/(np.sqrt(1-arg*arg)))[:, None]
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@ -85,8 +85,9 @@ class PeriodicExponential(Periodic):
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self.b = [1]
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self.basis_alpha = np.ones((self.n_basis,))
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self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))[:,0]
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self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
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self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
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self.basis_phi = np.zeros(self.n_freq * 2)
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self.basis_phi[::2] = -np.pi/2
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self.G = self.Gram_matrix()
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self.Gi = np.linalg.inv(self.G)
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@ -100,7 +101,6 @@ class PeriodicExponential(Periodic):
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Flower = np.array(self._cos(self.basis_alpha,self.basis_omega,self.basis_phi)(self.lower))[:,None]
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return(self.lengthscale/(2*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T))
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#@silence_errors
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def update_gradients_full(self, dL_dK, X, X2=None):
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"""derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
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if X2 is None: X2 = X
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@ -194,8 +194,9 @@ class PeriodicMatern32(Periodic):
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self.b = [1,self.lengthscale**2/3]
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self.basis_alpha = np.ones((self.n_basis,))
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self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))
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self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
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self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
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self.basis_phi = np.zeros(self.n_freq * 2)
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self.basis_phi[::2] = -np.pi/2
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self.G = self.Gram_matrix()
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self.Gi = np.linalg.inv(self.G)
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@ -212,8 +213,8 @@ class PeriodicMatern32(Periodic):
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return(self.lengthscale**3/(12*np.sqrt(3)*self.variance) * Gint + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscale**2/(3.*self.variance)*np.dot(F1lower,F1lower.T))
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@silence_errors
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def update_gradients_full(self,dL_dK,X,X2,target):
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#@silence_errors
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def update_gradients_full(self,dL_dK,X,X2):
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"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
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if X2 is None: X2 = X
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FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
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@ -307,8 +308,9 @@ class PeriodicMatern52(Periodic):
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self.b = [9./8, 9*self.lengthscale**4/200., 3*self.lengthscale**2/5., 3*self.lengthscale**2/(5*8.), 3*self.lengthscale**2/(5*8.)]
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self.basis_alpha = np.ones((2*self.n_freq,))
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self.basis_omega = np.array(sum([[i*2*np.pi/self.period]*2 for i in range(1,self.n_freq+1)],[]))
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self.basis_phi = np.array(sum([[-np.pi/2, 0.] for i in range(1,self.n_freq+1)],[]))
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self.basis_omega = (2*np.pi*np.arange(1,self.n_freq+1)/self.period).repeat(2)
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self.basis_phi = np.zeros(self.n_freq * 2)
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self.basis_phi[::2] = -np.pi/2
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self.G = self.Gram_matrix()
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self.Gi = np.linalg.inv(self.G)
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@ -17,7 +17,8 @@ class Prod(CombinationKernel):
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:rtype: kernel object
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"""
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def __init__(self, kernels, name='prod'):
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def __init__(self, kernels, name='mul'):
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assert len(kernels) == 2, 'only implemented for two kernels as of yet'
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super(Prod, self).__init__(kernels, name)
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@Cache_this(limit=2, force_kwargs=['which_parts'])
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@ -37,26 +38,28 @@ class Prod(CombinationKernel):
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which_parts = self.parts
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return reduce(np.multiply, (p.Kdiag(X) for p in which_parts))
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def update_gradients_full(self, dL_dK, X):
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def update_gradients_full(self, dL_dK, X, X2=None):
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for k1,k2 in itertools.combinations(self.parts, 2):
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k1._sliced_X = k1._sliced_X2 = k2._sliced_X = k2._sliced_X2 = True
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k1.update_gradients_full(dL_dK*k2.K(X, X))
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self.k2.update_gradients_full(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2])
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k1.update_gradients_full(dL_dK*k2.K(X, X2), X, X2)
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k2.update_gradients_full(dL_dK*k1.K(X, X2), X, X2)
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def update_gradients_diag(self, dL_dKdiag, X):
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for k1,k2 in itertools.combinations(self.parts, 2):
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k1.update_gradients_diag(dL_dKdiag*k2.Kdiag(X), X)
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k2.update_gradients_diag(dL_dKdiag*k1.Kdiag(X), X)
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def gradients_X(self, dL_dK, X, X2=None):
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target = np.zeros(X.shape)
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if X2 is None:
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target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1], None)
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target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2], None)
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else:
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target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1])
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target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2])
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for k1,k2 in itertools.combinations(self.parts, 2):
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target[:,k1.active_dims] += k1.gradients_X(dL_dK*k2.K(X, X2), X, X2)
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target[:,k2.active_dims] += k2.gradients_X(dL_dK*k1.K(X, X2), X, X2)
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return target
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def gradients_X_diag(self, dL_dKdiag, X):
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target = np.zeros(X.shape)
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target[:,self.slice1] = self.k1.gradients_X(dL_dKdiag*self.k2.Kdiag(X[:,self.slice2]), X[:,self.slice1])
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target[:,self.slice2] += self.k2.gradients_X(dL_dKdiag*self.k1.Kdiag(X[:,self.slice1]), X[:,self.slice2])
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for k1,k2 in itertools.combinations(self.parts, 2):
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target[:,k1.active_dims] += k1.gradients_X(dL_dKdiag*k2.Kdiag(X), X)
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target[:,k2.active_dims] += k2.gradients_X(dL_dKdiag*k1.Kdiag(X), X)
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return target
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@ -116,6 +116,7 @@ class Sympykern(Kern):
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if self.output_dim > 1:
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self.arg_list += self._sp_theta_i + self._sp_theta_j
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self.diag_arg_list += self._sp_theta_i
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# psi_stats aren't yet implemented.
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if False:
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self.compute_psi_stats()
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