Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/examples/dimensionality_reduction.py

@@ -24,7 +24,7 @@ def BGPLVM(seed=default_seed):
     Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
     lik = Gaussian(Y, normalize=True)
 
-    k = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
+    k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
@@ -144,7 +144,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
     data = GPy.util.datasets.oil()
 
     # create simple GP model
-    kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
+    kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
 
     Y = data['X'][:N]
     Yn = Y - Y.mean(0)
@@ -160,9 +160,9 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
 
     # optimize
     if optimize:
-        m.constrain_fixed('noise')
+#         m.constrain_fixed('noise')
-        m.optimize('scg', messages=1, max_iters=200, gtol=.05)
+#         m.optimize('scg', messages=1, max_iters=200, gtol=.05)
-        m.constrain_positive('noise')
+#         m.constrain_positive('noise')
         m.optimize('scg', messages=1, max_iters=max_iters, gtol=.05)
 
     if plot:
@@ -377,10 +377,10 @@ def stick_bgplvm(model=None):
     data = GPy.util.datasets.stick()
     Q = 6
     kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
-    m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20,kernel=kernel)
+    m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
     # optimize
     m.ensure_default_constraints()
-    m.optimize(messages=1, max_iters=3000,xtol=1e-300,ftol=1e-300)
+    m.optimize(messages=1, max_iters=3000, xtol=1e-300, ftol=1e-300)
     m._set_params(m._get_params())
     plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
     plt.sca(latent_axes)

GPy/kern/kern.py

@@ -104,6 +104,8 @@ class kern(Parameterized):
         x = np.arange(x0)
         transOffset = offset_copy(ax.transData, fig=fig,
                                   x=0., y= -2., units='points')
+        transOffsetUp = offset_copy(ax.transData, fig=fig,
+                                  x=0., y=2., units='points')
         for bar in bars:
             for patch, num in zip(bar.patches, np.arange(len(bar.patches))):
                 height = patch.get_height()
@@ -111,10 +113,12 @@ class kern(Parameterized):
                 va = 'top'
                 c = 'w'
                 t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, usetex=True, ha='center')
+                transform = transOffset
                 if patch.get_extents().height <= t.get_extents().height + 2:
                     va = 'bottom'
                     c = 'k'
-                ax.text(xi, height, "${xi}$".format(xi=int(num)), color=c, rotation=0, ha='center', va=va, transform=transOffset)
+                    transform = transOffsetUp
+                ax.text(xi, height, "${xi}$".format(xi=int(num)), color=c, rotation=0, ha='center', va=va, transform=transform)
         # for xi, t in zip(x, xticklabels):
         #    ax.text(xi, maxi / 2, t, rotation=90, ha='center', va='center')
         # ax.set_xticklabels(xticklabels, rotation=17)

GPy/kern/parts/linear.py

@@ -140,30 +140,26 @@ class Linear(Kernpart):
     def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
         self.dK_dX(dL_dpsi1.T, Z, mu, target)
 
-    def psi2(self, Z, mu, S, target):
+    def psi2_old(self, Z, mu, S, target):
-        """
-        returns N,num_inducing,num_inducing matrix
-        """
         self._psi_computations(Z, mu, S)
-#         psi2_old = self.ZZ * np.square(self.variances) * self.mu2_S[:, None, None, :]
-#         target += psi2.sum(-1)
-        # slow way of doing it, but right
-#         psi2_real = rm np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
-#         for n in range(mu.shape[0]):
-#             for m_prime in range(Z.shape[0]):
-#                 for m in range(Z.shape[0]):
-#                     tmp = self._Z[m:m + 1] * self.variances
-#                     tmp = np.dot(tmp, (tdot(self._mu[n:n + 1].T) + np.diag(S[n])))
-#                     psi2_real[n, m, m_prime] = np.dot(tmp, (
-#                             self._Z[m_prime:m_prime + 1] * self.variances).T)
-#         mu2_S = (self._mu[:, None, :] * self._mu[:, :, None])
-#         mu2_S[:, np.arange(self.input_dim), np.arange(self.input_dim)] += self._S
-#         psi2 = (self.ZA[None, :, None, :] * mu2_S[:, None]).sum(-1)
-#         psi2 = (psi2[:, :, None] * self.ZA[None, None]).sum(-1)
-#         psi2_tensor = np.tensordot(self.ZZ[None, :, :, :] * np.square(self.variances), self.mu2_S[:, None, None, :], ((3), (3))).squeeze().T
         target += self._psi2
 
+    def psi2(self,Z,mu,S,target):
+        tmp = np.zeros((mu.shape[0], Z.shape[0]))
+        self.K(mu,Z,tmp)
+        target += tmp[:,:,None]*tmp[:,None,:] + np.sum(S[:,None,None,:]*self.variances**2*Z[None,:,None,:]*Z[None,None,:,:],-1)
+
     def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
+        tmp = np.zeros((mu.shape[0], Z.shape[0]))
+        self.K(mu,Z,tmp)
+        self.dK_dtheta(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
+        result= 2.*(dL_dpsi2[:,:,:,None]*S[:,None,None,:]*self.variances*Z[None,:,None,:]*Z[None,None,:,:]).sum(0).sum(0).sum(0)
+        if self.ARD:
+            target += result.sum(0).sum(0).sum(0)
+        else:
+            target += result.sum()
+
+    def dpsi2_dtheta_old(self, dL_dpsi2, Z, mu, S, target):
         self._psi_computations(Z, mu, S)
         tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
         if self.ARD:
@@ -172,6 +168,15 @@ class Linear(Kernpart):
             target += tmp.sum()
 
     def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
+        tmp = np.zeros((mu.shape[0], Z.shape[0]))
+        self.K(mu,Z,tmp)
+        self.dK_dX(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
+
+        Zs = Z*self.variances
+        Zs_sq = Zs[:,None,:]*Zs[None,:,:]
+        target_S += (dL_dpsi2[:,:,:,None]*Zs_sq[None,:,:,:]).sum(1).sum(1)
+
+    def dpsi2_dmuS_old(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
         """Think N,num_inducing,num_inducing,input_dim """
         self._psi_computations(Z, mu, S)
         AZZA = self.ZA.T[:, None, :, None] * self.ZA[None, :, None, :]

GPy/kern/parts/rbf.py

@@ -221,9 +221,10 @@ class RBF(Kernpart):
     #---------------------------------------#
 
     def _K_computations(self, X, X2):
-        if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2) and fast_array_equal(self._params , self._get_params())):
+        params = self._get_params()
+        if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2) and fast_array_equal(self._params , params)):
             self._X = X.copy()
-            self._params = self._get_params().copy()
+            self._params = params.copy()
             if X2 is None:
                 self._X2 = None
                 X = X / self.lengthscale
@@ -244,7 +245,7 @@ class RBF(Kernpart):
             self._psi2_Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
             self._psi2_Zdist_sq = np.square(self._psi2_Zdist / self.lengthscale) # M,M,Q
 
-        if not (fast_array_equal(Z, self._Z) and fast_array_equal(mu, self._mu) and fast_array_equal(S, self._S)):
+        if not fast_array_equal(Z, self._Z) or not fast_array_equal(mu, self._mu) or not fast_array_equal(S, self._S):
             # something's changed. recompute EVERYTHING
 
             # psi1