new stuff

MatlabGP/+NN/C2.m

@@ -0,0 +1,35 @@
+classdef C2
+
+    properties
+        filter
+        bias
+    end
+
+    methods
+
+        function obj = C2(F)
+            obj.filter = normrnd(zeros(F,F),sqrt(1/F));
+        end
+
+        function [y] = forward(obj,x)
+
+            y = utils.conv2D(x,obj.filter,obj.bias);
+
+        end
+
+        function V = getHPs(obj)
+
+            V = [obj.filter(:);obj.bias(:)];
+        end
+
+        function obj = setHPs(obj,V)
+
+            nT = numel(obj.filter(:));
+
+            obj.filter = reshape(V(1:nT),size(obj.filter));
+
+            obj.bias = reshape(V(nT+1:end),size(obj.bias));
+
+        end
+    end
+end

MatlabGP/+NN/reshape.m

@@ -0,0 +1,30 @@
+classdef reshape
+
+    properties
+        bias
+    end
+
+    methods
+
+        function obj = reshape()
+
+        end
+
+        function [y] = forward(obj,x)
+
+            y = x(:) + obj.bias;
+
+        end
+
+        function V = getHPs(obj)
+
+            V = [obj.bias(:)];
+        end
+
+        function obj = setHPs(obj,V)
+
+           obj.bias = reshape(V,size(obj.bias));
+
+        end
+    end
+end

MatlabGP/+utils/conv2D.m

@@ -0,0 +1,14 @@
+function [B] = conv2D(A,F,b)
+
+[m,~] = size(F);
+[y, x] = size(A);
+y = y - m + 1;
+x = x - m + 1;
+B = zeros(y,x,'like',F);
+for i=1:y
+    for j=1:x
+        B(i,j) = sum(sum(A(i:i+m-1, j:j+m-1)*F)) + b;
+    end
+end
+
+end

MatlabGP/+utils/isoplotclasses.m

@@ -0,0 +1,62 @@
+function isoplotclasses(kr,dim1,dim2,varargin)
+
+%   dim1 - Vary this dimension
+%   dim2 - plot along this dimension
+%   f - plot this fidelity
+
+input=inputParser;
+input.KeepUnmatched=true;
+input.addOptional('new_fig',false,@islogical);  % Create a new figure
+input.addOptional('lb',kr.lb_x,@isnumeric);     % Lower bound of plot
+input.addOptional('ub',kr.ub_x,@isnumeric);     % Upper bound of plot
+input.addOptional('cmap','thermal');
+input.addOptional('LS','-');
+input.addOptional('nL',50);
+input.addOptional('mb',[]);
+input.addOptional('CI',true);
+input.addOptional('color','b');
+input.parse(varargin{:})
+in=input.Results;
+
+if in.new_fig
+    figure
+end
+
+a1=in.lb(dim1);
+b1=in.ub(dim1);
+
+
+a2=in.lb(dim2);
+b2=in.ub(dim2);
+
+n = in.nL;
+
+xx1 = linspace(a1,b1,n);
+xx2 = linspace(a2,b2,n);
+
+if isempty(in.mb)
+    mb = in.lb + 0.5*(in.ub - in.lb);
+else
+    mb = in.mb;
+end
+
+for i = 1:n
+    for j = 1:n
+
+        XX = mb;
+        XX(dim1) = xx1(i);
+        XX(dim2) = xx2(j);
+
+        Yj = kr.eval([XX;XX]);
+        YY(i,j,:) = exp(Yj(1,:))/sum(Yj(1,:),2);        
+    end
+end
+
+cmap = utils.cmocean('thermal',size(YY,3));
+
+for i = 1:size(YY,3)
+    contour(xx2,xx1,YY(:,:,i),[0.5 0.5],'color',cmap(i,:));
+    hold on
+end
+
+end

MatlabGP/examples/TestAppliedAviationProblem.asv

@@ -0,0 +1,183 @@
+
+clear all
+clc
+
+lb = [0.5 0.5 2.5*10^(-3)];
+ub = [1.5 1.5 7.5*10^(-3)];
+
+xx = lb + (ub - lb).*lhsdesign(50000,3);
+yy = testFuncs.StressedPlate(xx,1);
+
+x1 = lb + (ub - lb).*lhsdesign(5,3);
+y1 = testFuncs.StressedPlate(x1,1);
+
+x2 = [lb + (ub - lb).*lhsdesign(20,3)];
+y2 = testFuncs.StressedPlate(x2,2);
+
+x{1} = x1;
+x{2} = x2;
+
+y{1} = y1;
+y{2} = y2;
+
+%%
+% ma = means.linear([1 1 1 1]);
+% mb = means.linear([1 1 1]);
+
+ma = means.zero([1 1 1 1]);
+mb = means.zero();
+
+a = kernels.RQ(2,1,[0.1 0.2 0.1 0.3]);%.periodic(1,10);
+b = kernels.RQ(2,1,[0.2 0.2 0.2]);
+a.signn = eps;
+b.signn = 0.1;
+
+%%
+tic
+for i = 1:2
+    Z{i} = GP(mb,b);
+    Z{i} = Z{i}.condition(x{i},y{i},lb,ub);
+    Z{i} = Z{i}.train();
+end
+toc
+
+%%
+tic
+MF = NLMFGP(Z,ma,a);
+MF = MF.condition();
+MF = MF.train();
+toc
+
+%%
+
+% layers{1} = NN.FF(3,6);
+% layers{2} = NN.FF(6,6);
+% layers{3} = NN.FF(6,2);
+% 
+% acts{1} = NN.SNAKE(1);
+% acts{2} = NN.SNAKE(1);
+% 
+% lss = NN.CE();
+% 
+% nnc = NN.NN(layers,acts,lss);
+
+%%
+
+% clear delta
+% 
+% delta(:,1) = double(log(abs(MF.LOO))>median(log(abs(MF.LOO))));
+% delta(:,2) = double(log(abs(MF.LOO))<=median(log(abs(MF.LOO))));
+% 
+% tic
+% nnc = nnc.train(x{1},delta);
+% toc
+% 
+% %%
+% figure
+% utils.isoplotclasses(nnc,1,3)
+
+
+%%
+
+% mc = means.linear(ones(1,3));%*means.sine(1,10,0,1);
+% c = kernels.RQ(2,1,ones(1,3));
+% c.signn = eps;
+% 
+% LOOZ = GP(mc,c);
+% LOOZ = LOOZ.condition(x{1},MF.LOO,lb,ub);
+% LOOZ = LOOZ.train();
+
+
+%%
+figure
+hold on
+utils.plotLineOut(Z{1},(lb+ub)/2,1,'color','r')
+utils.plotLineOut(Z{2},(lb+ub)/2,1,'color','b')
+
+%%
+
+figure
+hold on
+utils.plotLineOut(MF,(lb+ub)/2,1)
+
+%%
+
+1 - mean((yy - Z{1}.eval_mu(xx)).^2)./var(yy)
+max(abs(yy - Z{1}.eval_mu(xx)))./std(yy)
+
+1 - mean((yy - MF.eval_mu(xx)).^2)./var(yy)
+max(abs(yy - MF.eval_mu(xx)))./std(yy)
+
+%%
+
+C = [50 1];
+
+%%
+for jj = 1:60
+
+    [xn,Rn] = BO.argmax(@BO.MFSFDelta,MF);
+    %[xn,Rn] = BO.argmax(@BO.UCB,LOOZ);
+
+    sign(1) = Z{1}.eval_var(xn)/C(1);
+    sign(2) = Z{2}.eval_var(xn)/C(2);
+
+    [~,in] = max(sign);
+
+    if in==1
+        x{1} = [x{1}; xn];
+    end
+    x{2} = [x{2}; xn];
+
+    if in==1
+        y{1} = [y{1}; testFuncs.StressedPlate(xn,1)];
+    end
+    y{2} = [y{2}; testFuncs.StressedPlate(xn,2)];
+
+    for ii = 1:2
+        Z{ii} = Z{ii}.condition(x{ii},y{ii},lb,ub);
+    end
+
+    MF.GPs = Z;
+    MF = MF.condition();
+
+    %LOOZ = LOOZ.condition(x{1},MF.LOO,lb,ub);
+
+    pc(jj,1) = size(x{1},1);
+    pc(jj,2) = size(x{2},1);
+
+    R2z(jj) = 1 - mean((yy - Z{1}.eval_mu(xx)).^2)./var(yy);
+    RMAEz(jj) = max(abs(yy - Z{1}.eval_mu(xx)))./std(yy);
+
+    R2MF(jj) = 1 - mean((yy - MF.eval_mu(xx)).^2)./var(yy);
+    RMAEMF(jj) = max(abs(yy - MF.eval_mu(xx)))./std(yy);
+
+    figure(3)
+    clf(3)
+    hold on
+    plot(pc(1:jj,1),R2z)
+    plot(pc(1:jj,1),R2MF)
+
+    figure(4)
+    clf(4)
+    hold on
+    plot(pc(1:jj,1),RMAEz)
+    plot(pc(1:jj,1),RMAEMF)
+
+    drawnow
+
+end
+
+
+%%
+
+xi = lb + (ub - lb).*lhsdesign(size(x{1},1),3);
+yi = testFuncs.StressedPlate(xi,1);
+
+Zi = GP(mb,b);
+Zi = Zi.condition(xi,yi,lb,ub);
+Zi = Zi.train();
+
+%%
+
+1 - mean((yy - Zi.eval_mu(xx)).^2)./var(yy)
+max(abs(yy - Zi.eval_mu(xx)))./std(yy)