testcode.m

global PNUM;
PNUM = 6;
debug = 0;
NORM = 1; %flag=1 normalise for hip centroid
%Hip centre is first joint


for i=1:PNUM
    path = ['C:\Users\liam\Desktop\KINECT\kbox\data\freshdata\' num2str(i) '\'];
    %path = ['C:\Users\liam\Desktop\KINECT\kbox\data\1\'];
    %path = ['C:\Users\liam\Desktop\KINECT\kbox\data\jabtest\' num2str(i) '\'];
    
    
    data = loadKinectData(path,NORM); %flag=1 normalise for hip centroid
    %data = diff(data,1,2); %Columnwise Differentiation - Remove effect of distance from Kinect
    dataAll(i).data = data;
    %dataAll(i).labels to do  
    
    if 0
        close all
        for i=1:size(data,2)
            cla
            a = data(:,i);
            for j=1:3:length(a)
                plot3(a(j),a(j+2),a(j+1),'.');
                hold on
            end
            set(gca,'XLim',[-1 1]);
            set(gca,'YLim',[-1 1]);
            set(gca,'ZLim',[-1 1]);
            pause
        end
    end
    
end

% Big data matrix
M=[];
for i = 1:length(dataAll(i))
    M = [M, dataAll(i).data]; 
end

[Xm1,EV1,Ev1]=createES(M,3); %Create Eigenspace., New data !contribute to eigenspace
%close all
for i=1:PNUM
    dataAll(i).jred=reconstructPose(dataAll(i).data,Xm1,EV1);
    dataAll(i).jredSmooth = kinsmooth2(dataAll(i).jred);
    [valmax,imax,valmin, imin] = getminmax(dataAll(i).jredSmooth(1,:),i,NORM);
    dataAll(i).diff = [];
    dataAll(i).odiff = [];
   % distance = pythagoras(sort(imax)); Going to put in getminmax
    [dataAll(i).imax, a]= sort(imax);
    
end

for j=1
    for i = 1:length(dataAll(j).jred)
        dataAll(j).diff = [dataAll(j).diff (dtw(dataAll(j).jred(:,10), dataAll(j).jred(:,i)))];
        dataAll(j).odiff = [dataAll(j).odiff (dtw(dataAll(j).jred(:,1), dataAll(j+1).jred(:,i)))];
%         F = [F; dataAll(i).jred]; 
%         S = [S; dataAll(i).jredSmooth]; 
    end
end

% dataAll.diffF = [];
% diffS = [];
% for i=1:length(S)
%     diffF = [diffF (dtw(F(:,1), F(:,i)))];
% %     diffS = [diffS dtw(S(:,1), S(:,i))];
% end



% tilefigs();
% pause;
% close all;
% 
nsamples = 15;
ncomponents = 3;

X = [];
Y = [];
lbl = [];
%close all
for i=1:PNUM
    nelem = length(dataAll(i).imax);
    dataAll(i).labels = ones(nelem,1) * i;
    dataAll(i).features =  zeros(nelem,nsamples * ncomponents);%HERE
    for j = 1:nelem - 1
        inds = round(linspace(dataAll(i).imax(j), dataAll(i).imax(j+1), nsamples));
        %dataAll(i).features(j,:) = dataAll(i).jred(1,inds);
        foo = dataAll(i).jred(1:ncomponents,inds);%HERE
        dataAll(i).features(j,:) = foo(:);%HERE
        
        if debug
            plot(dataAll(i).features(j,:))
            pause
        end
    end
    
   X = [X;dataAll(i).features];
   Y = [Y;dataAll(i).labels];
   lbl = [lbl;ceil(0.2*(length(dataAll(i).labels)))]; %for labels
   %lbl = ceil(lbl);
end
% 
% 
% trainPercent = 0.8;
% trainInds = randperm(length(Y));
% trainInds(round(length(Y)*0.8):end) = [];
% testInds = 1:length(Y);
% testInds(trainInds) = [];
% 
% %'autoscale' is true by default 'kernel_function' 'rbf'
% % svmStruct = svmtrain(X(trainInds,:),Y(trainInds),'kernel_function', 'rbf','autoscale','true');
%  svmStruct = svmtrain(Y(trainInds),X(trainInds,:),['-b 1']);
%  %labels = zeros(182,1);
%  [predicted_label, accuracy, probest] = svmpredict(Y(testInds),X(testInds,:),svmStruct,['-b 1']);
%  %close all
%  
% %%
% %Random forest  label generation. 
%  testlabels = [];
%  for i=1:6 %should be 6
%      temp = repmat(dataAll(i).labels(i,1),lbl(i,1),1);
%      testlabels = vertcat(testlabels,temp);
%  end
%  
%  
% %NVarToSample, 'all' deciscion tree, otherwise random forest
% %X = M'; %Changed this, this is full pose pose.
% B = TreeBagger(75,X(trainInds,:),Y(trainInds),'OOBPred','On');
% C = B.predict(X(testInds,:));
% C = cellfun(@str2num,C);
% 
% testlabels(end,:) = [];
% diff = size(testlabels,1) - size(C,1);
% 
% if diff < 0
%     for i=1:abs(diff)
%     testlabels = vertcat(testlabels,6);
%     end
% end
%     if diff > 0
%        testlabels(end-(diff-1):end,:) = [];
%     end
% chklbl = horzcat(testlabels,C);
% 
% count=0;
% for i=1:length(C)
%     if chklbl(i,1) == chklbl(i,2)
%         count = count+1;
%     end
% end
% correct = (count/length(C))*100;
% sprintf('Random Forest Correct: %f%%', correct)
% 
% X = X';
% for i=1:length(X)
%     drel(i)  = dtw(X(:,1),X(:,i));
% end
% % close all
% %Diffusion Maps
% %dtw
% close all
% mappedX = diffusion_maps(X,3,1,1);
% 
% 
% %mappedA = compute_mapping(A, type, no_dims, parameters)
%  
% % C = svmclassify(svmStruct,X(testInds,:),'showplot',true);
% %[C, Y(testInds)]
% 
% % ty = Y(testInds);
% % count = 0;
% % for i=1:length(C)
% %     if C(i) == ty(i)
% %         count = count+1;
% %     end
% % end
% % correct = (count/length(C))*100
% % sprintf('Correct: %f%%', correct)
%