diff --git a/GPy/core/model.py b/GPy/core/model.py index 46cf6ac9..183cf0f0 100644 --- a/GPy/core/model.py +++ b/GPy/core/model.py @@ -363,7 +363,8 @@ class model(parameterised): grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4]) print grad_string - print '' - + if verbose: + print '' + return False return True diff --git a/GPy/models/__init__.py b/GPy/models/__init__.py index ab7ff5b4..3b8b31c8 100644 --- a/GPy/models/__init__.py +++ b/GPy/models/__init__.py @@ -3,7 +3,7 @@ from GP_regression import GP_regression -from sparse_GP_regression import sparse_GP_regression +from sparse_GP_regression import sparse_GP_regression, sgp_debugB, sgp_debugC, sgp_debugE from GPLVM import GPLVM from warped_GP import warpedGP from GP_EP import GP_EP diff --git a/GPy/models/sparse_GP_regression.py b/GPy/models/sparse_GP_regression.py index fe5f7cc1..45376cf2 100644 --- a/GPy/models/sparse_GP_regression.py +++ b/GPy/models/sparse_GP_regression.py @@ -197,3 +197,127 @@ class sparse_GP_regression(GP_regression): pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten())) if self.Q==2: pb.plot(self.Z[:,0],self.Z[:,1],'wo') + +class sgp_debugB(sparse_GP_regression): + def _computations(self): + self.V = self.beta*self.Y + self.psi1V = np.dot(self.psi1, self.V) + self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T) + self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm) + self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T) + self.B = np.eye(self.M) + self.A + self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B) + self.LLambdai = np.dot(self.LBi, self.Lmi) + self.trace_K = self.psi0 - np.trace(self.A)/self.beta + self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi) + self.C = mdot(self.LLambdai, self.psi1V) + self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T) + + # Compute dL_dpsi + self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N) + self.dL_dpsi1 = np.zeros_like(self.psi1) + self.dL_dpsi2 = - 0.5 * self.beta * (self.D*( - self.Kmmi)) + + # Compute dL_dKmm + self.dL_dKmm = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi) # dB + + def log_likelihood(self): + A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta)) + B = -0.5*self.beta*self.D*self.trace_K + C = -0.5*self.D * self.B_logdet + D = -0.5*self.beta*self.trYYT + E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv) + return B + + def dL_dbeta(self): + dA_dbeta = 0.5 * self.N*self.D/self.beta + dB_dbeta = - 0.5 * self.D * self.trace_K + dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta + dD_dbeta = - 0.5 * self.trYYT + tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V) + dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta + return np.squeeze(dB_dbeta) + + +class sgp_debugC(sparse_GP_regression): + def _computations(self): + self.V = self.beta*self.Y + self.psi1V = np.dot(self.psi1, self.V) + self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T) + self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm) + self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T) + self.B = np.eye(self.M) + self.A + self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B) + self.LLambdai = np.dot(self.LBi, self.Lmi) + self.trace_K = self.psi0 - np.trace(self.A)/self.beta + self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi) + self.C = mdot(self.LLambdai, self.psi1V) + self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T) + + # Compute dL_dpsi + self.dL_dpsi0 = np.zeros(self.N) + self.dL_dpsi1 = np.zeros_like(self.psi1) + self.dL_dpsi2 = - 0.5 * self.beta * (self.D*(self.LBL_inv)) + + # Compute dL_dKmm + self.dL_dKmm = -0.5 * self.D * (- self.LBL_inv - 2.*self.beta*mdot(self.LBL_inv, self.psi2, self.Kmmi) + self.Kmmi) # dC + + def log_likelihood(self): + A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta)) + B = -0.5*self.beta*self.D*self.trace_K + C = -0.5*self.D * self.B_logdet + D = -0.5*self.beta*self.trYYT + E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv) + return C + + def dL_dbeta(self): + dA_dbeta = 0.5 * self.N*self.D/self.beta + dB_dbeta = - 0.5 * self.D * self.trace_K + dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta + dD_dbeta = - 0.5 * self.trYYT + tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V) + dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta + return np.squeeze(dC_dbeta) + + +class sgp_debugE(sparse_GP_regression): + def _computations(self): + self.V = self.beta*self.Y + self.psi1V = np.dot(self.psi1, self.V) + self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T) + self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm) + self.A = mdot(self.Lmi, self.beta*self.psi2, self.Lmi.T) + self.B = np.eye(self.M) + self.A + self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B) + self.LLambdai = np.dot(self.LBi, self.Lmi) + self.trace_K = self.psi0 - np.trace(self.A)/self.beta + self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi) + self.C = mdot(self.LLambdai, self.psi1V) + self.G = mdot(self.LBL_inv, self.psi1VVpsi1, self.LBL_inv.T) + + # Compute dL_dpsi + self.dL_dpsi0 = np.zeros(self.N) + self.dL_dpsi1 = np.zeros_like(self.psi1) + self.dL_dpsi2 = - 0.5 * self.beta * (self.G) + + # Compute dL_dKmm + self.dL_dKmm = np.dot(np.dot(self.G,self.beta*self.psi2) - np.dot(self.LBL_inv, self.psi1VVpsi1), self.Kmmi) + 0.5*self.G # dE + + def log_likelihood(self): + A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta)) + B = -0.5*self.beta*self.D*self.trace_K + C = -0.5*self.D * self.B_logdet + D = -0.5*self.beta*self.trYYT + E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv) + return E + + def dL_dbeta(self): + dA_dbeta = 0.5 * self.N*self.D/self.beta + dB_dbeta = - 0.5 * self.D * self.trace_K + dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta + dD_dbeta = - 0.5 * self.trYYT + tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V) + dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta + return np.squeeze(dE_dbeta) + + diff --git a/grid_parameters.py b/grid_parameters.py index 8c4c1e0a..421eef10 100644 --- a/grid_parameters.py +++ b/grid_parameters.py @@ -16,11 +16,14 @@ Y = np.sin(X) + np.random.randn(*X.shape)/np.sqrt(50.) k = GPy.kern.rbf(1) -m = GPy.models.sparse_GP_regression(X,Y,Z=Z,kernel=k) -m.constrain_fixed('iip') +models = [GPy.models.sparse_GP_regression(X,Y,Z=Z,kernel=k), + GPy.models.sgp_debugB(X,Y,Z=Z,kernel=k), + GPy.models.sgp_debugC(X,Y,Z=Z,kernel=k), + GPy.models.sgp_debugE(X,Y,Z=Z,kernel=k)] +#[m.constrain_fixed('iip') for m in models] #m.constrain_fixed('white',1e-6) -m.constrain_fixed('precision',50) -m.ensure_default_constraints() +#[m.constrain_fixed('precision',50) for m in models] +#[m.ensure_default_constraints() for m in models] xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j] @@ -28,24 +31,16 @@ xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j] lls = [] cgs = [] for l,v in zip(xx.flatten(),yy.flatten()): - m.set('lengthscale',l) - m.set('rbf_variance',10.**v) - lls.append(m.log_likelihood()) - cgs.append(m.checkgrad()) - #m.plot() + [m.set('lengthscale',l) for m in models] + [m.set('rbf_variance',10.**v) for m in models] + lls.append(models[0].log_likelihood()) + cgs.append([m.checkgrad(verbose=0) for m in models]) lls = np.array(lls).reshape(resolution,resolution) -cgs = np.array(cgs,dtype=np.float64).reshape(resolution,resolution) +cgs = np.array(zip(*cgs),dtype=np.float64).reshape(-1,resolution,resolution) -pb.contourf(xx,yy,lls,np.linspace(-500,560,100),linewidths=2,cmap=pb.cm.jet) -pb.colorbar() -pb.scatter(xx.flatten(),yy.flatten(),10,cgs.flatten(),linewidth=0,cmap=pb.cm.gray) -pb.figure() -#pb.imshow(lls,origin='upper',cmap=pb.cm.jet,extent=[xx[0,0],xx[-1,0],yy[0].min(),yy[0].max()],vmin=-500) -pb.scatter(xx.flatten(),yy.flatten(),10,lls.flatten(),linewidth=0,cmap=pb.cm.jet) -pb.colorbar() -pb.figure() -#pb.imshow(cgs,origin='upper',cmap=pb.cm.jet,extent=[xx[0,0],xx[-1,0],yy[0].min(),yy[0].max()]) -pb.scatter(xx.flatten(),yy.flatten(),10,cgs.flatten(),linewidth=0,cmap=pb.cm.jet) -pb.colorbar() +for cg in cgs: + pb.figure() + pb.contourf(xx,yy,lls,cmap=pb.cm.jet) + pb.scatter(xx.flatten(),yy.flatten(),12,cg.flatten(),cmap=pb.cm.gray)