add missing file

GPy/kern/_src/psi_comp/gaussherm.py

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+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+"""
+An approximated psi-statistics implementation based on Gauss-Hermite Quadrature
+"""
+
+import numpy as np
+
+from GPy.util.caching import Cache_this
+from ....util.linalg import tdot
+from ....core.parameterization.parameter_core import Pickleable
+
+class PSICOMP_GH(Pickleable):
+    
+    def __init__(self, degree=5, cache_K=True):
+        self.degree = degree
+        self.cache_K = cache_K
+        self.locs, self.weights = np.polynomial.hermite.hermgauss(degree)
+        self.locs *= np.sqrt(2.)
+        self.weights*= 1./np.sqrt(np.pi)
+        self.Xs = None
+
+    def _setup_observers(self):
+        pass
+    
+    @Cache_this(limit=10, ignore_args=(0,))
+    def comp_K(self, Z, qX):
+        if self.Xs is None or self.Xs.shape != qX.mean.shape:
+            from ....core.parameterization import ObsAr
+            self.Xs = ObsAr(np.empty((self.degree,)+qX.mean.shape))
+        mu, S = qX.mean.values, qX.variance.values
+        S_sq = np.sqrt(S)
+        for i in xrange(self.degree):
+            self.Xs[i] = self.locs[i]*S_sq+mu
+        return self.Xs
+    
+    @Cache_this(limit=10, ignore_args=(0,))
+    def psicomputations(self, kern, Z, qX):
+        mu, S = qX.mean.values, qX.variance.values
+        N,M,Q = mu.shape[0],Z.shape[0],mu.shape[1]
+        if self.cache_K: Xs = self.comp_K(Z, qX)
+        else: S_sq = np.sqrt(S)
+        
+        psi0 = np.zeros((N,))
+        psi1 = np.zeros((N,M))
+        psi2 = np.zeros((M,M))
+        for i in xrange(self.degree):
+            if self.cache_K:
+                X = Xs[i]
+            else:
+                X = self.locs[i]*S_sq+mu
+            psi0 += self.weights[i]* kern.Kdiag(X)
+            Kfu = kern.K(X,Z)
+            psi1 += self.weights[i]* Kfu
+            psi2 += self.weights[i]* tdot(Kfu.T)
+        return psi0, psi1, psi2
+    
+    @Cache_this(limit=10, ignore_args=(0, 2,3,4))
+    def psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, qX):
+        mu, S = qX.mean.values, qX.variance.values
+        if self.cache_K: Xs = self.comp_K(Z, qX)
+        S_sq = np.sqrt(S)
+        
+        dtheta_old = kern.gradient.copy()
+        dtheta = np.zeros_like(kern.gradient)
+        dZ = np.zeros_like(Z.values)
+        dmu = np.zeros_like(mu)
+        dS = np.zeros_like(S)
+        for i in xrange(self.degree):
+            if self.cache_K:
+                X = Xs[i]
+            else:
+                X = self.locs[i]*S_sq+mu
+            dL_dpsi0_i = dL_dpsi0*self.weights[i]
+            kern.update_gradients_diag(dL_dpsi0_i, X)
+            dtheta += kern.gradient
+            dX = kern.gradients_X_diag(dL_dpsi0_i, X)
+            Kfu = kern.K(X,Z)
+            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)
+            dmu += dX
+            dS += dX*self.locs[i]/(2.*S_sq)
+        kern.gradient[:] = dtheta_old
+        return dtheta, dZ, dmu, dS
+        
+
+
+