speed tuning for mlp kernel and gauss qudrature for psi-statistics

GPy/kern/_src/kern.py

@@ -88,6 +88,8 @@ class Kern(Parameterized):
         return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=True)[2]
     def gradients_X(self, dL_dK, X, X2):
         raise NotImplementedError
+    def gradients_X_X2(self, dL_dK, X, X2):
+        return self.gradients_X(dL_dK, X, X2), self.gradients_X(dL_dK.T, X2, X)
     def gradients_XX(self, dL_dK, X, X2):
         raise(NotImplementedError, "This is the second derivative of K wrt X and X2, and not implemented for this kernel")
     def gradients_XX_diag(self, dL_dKdiag, X):

GPy/kern/_src/kernel_slice_operations.py

@@ -19,6 +19,7 @@ class KernCallsViaSlicerMeta(ParametersChangedMeta):
         put_clean(dct, 'update_gradients_full', _slice_update_gradients_full)
         put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag)
         put_clean(dct, 'gradients_X', _slice_gradients_X)
+        put_clean(dct, 'gradients_X_X2', _slice_gradients_X)
         put_clean(dct, 'gradients_XX', _slice_gradients_XX)
         put_clean(dct, 'gradients_XX_diag', _slice_gradients_X_diag)
         put_clean(dct, 'gradients_X_diag', _slice_gradients_X_diag)

GPy/kern/_src/mlp.py

@@ -5,6 +5,7 @@ from .kern import Kern
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 import numpy as np
+from ...util.linalg import tdot
 from ...util.caching import Cache_this
 four_over_tau = 2./np.pi
 
@@ -40,6 +41,7 @@ class MLP(Kern):
         self.link_parameters(self.variance, self.weight_variance, self.bias_variance)
 
 
+    @Cache_this(limit=20, ignore_args=())
     def K(self, X, X2=None):
         if X2 is None:
             X_denom = np.sqrt(self._comp_prod(X)+1.)
@@ -51,6 +53,7 @@ class MLP(Kern):
         XTX = self._comp_prod(X,X2)/X_denom[:,None]/X2_denom[None,:]
         return self.variance*four_over_tau*np.arcsin(XTX)
 
+    @Cache_this(limit=20, ignore_args=())
     def Kdiag(self, X):
         """Compute the diagonal of the covariance matrix for X."""
         X_prod = self._comp_prod(X)
@@ -73,6 +76,10 @@ class MLP(Kern):
         """Derivative of the covariance matrix with respect to X"""
         return self._comp_grads(dL_dK, X, X2)[3]
 
+    def gradients_X_X2(self, dL_dK, X, X2):
+        """Derivative of the covariance matrix with respect to X"""
+        return self._comp_grads(dL_dK, X, X2)[3:]
+
     def gradients_X_diag(self, dL_dKdiag, X):
         """Gradient of diagonal of covariance with respect to X"""
         return self._comp_grads_diag(dL_dKdiag, X)[3]

GPy/kern/_src/psi_comp/gaussherm.py

@@ -80,8 +80,9 @@ class PSICOMP_GH(PSICOMP):
             dL_dkfu = (dL_dpsi1+ 2.*Kfu.dot(dL_dpsi2))*self.weights[i]
             kern.update_gradients_full(dL_dkfu, X, Z)
             dtheta += kern.gradient
-            dX += kern.gradients_X(dL_dkfu, X, Z)
-            dZ += kern.gradients_X(dL_dkfu.T, Z, X)
+            dX_i, dZ_i = kern.gradients_X_X2(dL_dkfu, X, Z)
+            dX += dX_i
+            dZ += dZ_i
             dmu += dX
             dS += dX*self.locs[i]/(2.*S_sq)
         kern.gradient[:] = dtheta_old