Added matrix kernel

MatlabGP/+kernels/EQ_matrix.m

@@ -0,0 +1,40 @@
+classdef EQ_matrix<kernels.Kernel
+
+    properties
+        theta
+    end
+
+    methods
+
+        function obj = EQ_matrix(scale,theta)
+            obj.scale = scale;
+            obj.scales{1} = scale;
+            obj.theta = theta;
+            obj.thetas{1} = theta;
+            obj.kernels{1} = obj;
+            obj.w.map = 'none';
+            obj.warping{1} = obj.w;
+        end
+
+        function [K] = forward_(~,x1,x2,theta)
+
+            n=0;
+            for i = 1:size(x1,2)
+                for j = 1:size(x2,2)
+                    n=n+1;
+                    M(i,j) = theta(n);
+                end
+            end
+
+            for i = 1:size(x1,1)
+                for j = 1:size(x2,1)
+                    d(i,j) = x1(i,:)*M*x2(j,:)';
+                end
+            end
+
+            K = exp(-d.^2);
+
+        end
+
+    end
+end

MatlabGP/GP.m

@@ -247,7 +247,7 @@
             tk0 = obj.kernel.getHPs();
 
             tklb = 0*tk0 + 0.001;
-            tkub = 0*tk0 + 4;
+            tkub = 0*tk0 + 10;
 
             tlb = [tmlb tklb];
             tub = [tmub tkub];
@@ -259,30 +259,30 @@
 
             func = @(x) obj.LL(x,regress,ntm);
 
-            % xxt = tlb + (tub - tlb).*lhsdesign(500*length(tlb),length(tlb));
-            % 
-            % for ii = 1:size(xxt,1)
-            %     LL(ii) = func(xxt(ii,:));
-            % end
-            % 
-            % LL = exp(1 + LL - max(LL));
-            % 
-            % theta = sum(xxt.*LL')/sum(LL);
-
-            for i = 1:2
-                tx0 = tlb + (tub - tlb).*rand(1,length(tlb));
-
-                opts = optimoptions('fmincon','SpecifyObjectiveGradient',true,'Display','none');
-
-                [theta{i},val(i)] = fmincon(func,tx0,[],[],[],[],tlb,tub,[],opts);
-                %[theta{i},val(i)] = bads(func,tx0,tlb,tub);
-                %[theta{i},val(i)] = VSGD(func,tx0,'lr',0.02,'lb',tlb,'ub',tub,'gamma',0.0001,'iters',400,'tol',1*10^(-4));
+            xxt = tlb + (tub - tlb).*lhsdesign(500*length(tlb),length(tlb));
 
+            for ii = 1:size(xxt,1)
+                LL(ii) = func(xxt(ii,:));
             end
 
-            [mval,i] = min(val);
+            LL = exp(1 + LL - max(LL));
+
+            theta = sum(xxt.*LL')/sum(LL);
 
-            theta = theta{i};
+            % for i = 1:2
+            %     tx0 = tlb + (tub - tlb).*rand(1,length(tlb));
+            % 
+            %     opts = optimoptions('fmincon','SpecifyObjectiveGradient',true,'Display','none');
+            % 
+            %     [theta{i},val(i)] = fmincon(func,tx0,[],[],[],[],tlb,tub,[],opts);
+            %     %[theta{i},val(i)] = bads(func,tx0,tlb,tub);
+            %     %[theta{i},val(i)] = VSGD(func,tx0,'lr',0.02,'lb',tlb,'ub',tub,'gamma',0.0001,'iters',400,'tol',1*10^(-4));
+            % 
+            % end
+            % 
+            % [mval,i] = min(val);
+            % 
+            % theta = theta{i};
 
             if regress
                 obj.kernel.signn = theta(end);

MatlabGP/testMatrixKernel.asv

@@ -0,0 +1,43 @@
+
+
+clear all
+close all
+clc
+
+xx = lhsdesign(100,2);
+
+q = kernels.EQ_matrix(1,[2 0 0 3]);
+
+q.signn = 0;
+
+[K, dK] = q.build(xx,xx);
+%%
+f1 = @(x) (6*x(:,1)-2).^2.*sin(12*x(:,2)-4);%.*sin(24*x-1);
+
+x1 = lhsdesign(50,2);
+y1 = f1(x1)+normrnd(0*x1(:,1),0*x1(:,1));
+
+%%
+a = means.const(1);
+
+q = kernels.EQ_matrix(1,[2 -0.5 0.5 3]);
+
+q.signn = 0.01;
+
+Z = GP(a,q);
+
+Z1 = Z.condition(x1,y1);
+
+figure(1)
+clf(1)
+utils.plotSurf(Z1,2,1)
+
+%%
+tic
+[Z2] = Z1.train();
+toc
+%%
+figure
+hold on
+utils.plotSurf(Z2,2,1)
+view(20,20)

MatlabGP/testMatrixKernel.m

@@ -0,0 +1,43 @@
+
+
+clear all
+close all
+clc
+
+xx = lhsdesign(100,2);
+
+q = kernels.EQ_matrix(1,[2 0 0 3]);
+
+q.signn = 0;
+
+[K, dK] = q.build(xx,xx);
+%%
+f1 = @(x) (6*x(:,1)-2).^2.*sin(12*x(:,2)-4);%.*sin(24*x-1);
+
+x1 = lhsdesign(50,2);
+y1 = f1(x1)+normrnd(0*x1(:,1),0*x1(:,1));
+
+%%
+a = means.const(1);
+
+q = kernels.EQ_matrix(1,[1 -1 1 1]);
+
+q.signn = 0.01;
+
+Z = GP(a,q);
+
+Z1 = Z.condition(x1,y1);
+
+figure(1)
+clf(1)
+utils.plotSurf(Z1,2,1)
+
+%%
+tic
+[Z2] = Z1.train();
+toc
+%%
+figure
+hold on
+utils.plotSurf(Z2,2,1)
+view(20,20)