kernel_dmd.m predict() tested for heart dataset

examples/kernel_dmd.m

@@ -132,16 +132,16 @@ classdef kernel_dmd < handle
             X2hatScaled = real(X2hatComplex);
             
             X2hat = obj.ReapplyScaling(X2hatScaled);
-            imageSeq = obj.VecSeq2ImSeq(X2hat);
+            imageSeq = uint8(obj.VecSeq2ImSeq(X2hat));
         end
         
-        function xhat = predict(obj,initial_condition,num_timesteps)
+        function [xhat,imageSeq] = predict(obj,initial_condition,num_timesteps)
             % predicts num_timesteps timesteps from an initial condition
             % like reconstruct, but more general.
             % can be used to predict more timesteps than were in the data
             
             tic;
-            if(isempty(obj.U0) || isempty(obj.S0) || isempty(obj.V0))
+            if(isempty(obj.Qfull) || isempty(obj.Sfull) || isempty(obj.Vhat))
                 error('Need to run regression() before running predict().');
             end
             
@@ -149,16 +149,18 @@ classdef kernel_dmd < handle
             xhat = nan(num_timesteps,length(initial_condition));
             xhat(1,:) = initial_condition;
             
+            num_modes = size(obj.Q,1);
             for k = 2:num_timesteps
                 prev_x = xhat(k-1,:);
-                kernelmat = GaussianKernel(prev_x,obj.X);
+                kernelmat = obj.GaussianKernel(prev_x,obj.X);
                 kernelmat = kernelmat(1:num_modes)'; % truncate and turn into row vector
                 phi_at_t = kernelmat*obj.Q/obj.S*obj.Vhat;
                 xhat(k,:) = real(phi_at_t*obj.Lambda*obj.Xi);
             end
             
             assert(norm(imag(xhat)) <= 1e-6,'Predicted xhat has non-negligible imaginary part!');
-            xhat = real(xhat);
+            xhat = obj.ReapplyScaling(real(xhat));
+            imageSeq = uint8(obj.VecSeq2ImSeq(xhat));
             
             predictTime = toc;
             if(obj.verbose)

examples/kernel_dmd_heart.m

@@ -154,6 +154,7 @@ for t = 2:2*size(X,1)
     xhat(t,:) = real(xnext);
 end
 toc;
+[xhatDmd,dmd_reconstruction] = D.predict(D.X(1,:),200);
 
 %% Check
 figure(1)
@@ -199,6 +200,8 @@ reconsructionHdl = implay(uint8(reconstruction));
 set(reconsructionHdl.Parent,'Name',sprintf('Reconstruction: %d modes, sigma2 = %.2e.',r,sigma2));
 originalHdl = implay(grayImages(:,:,image_start:image_start+num_images));
 set(originalHdl.Parent,'Name','Original data');
+dmdReconstructionHdl = implay(dmd_reconstruction);
+set(dmdReconstructionHdl.Parent,'Name','DMD');
 %% 
 
 % figure(3);