Please i need URGENT help how to make a demo??

I need help to add a few lines to the code to make a ’demo’ of how Adaboost works, by plotting intermediate results in each round – for example the weight distribution on the data points so that we can see how this distribution changes from round to round

      function [estimateclasstotal,model]=adaboost(mode,datafeatures,dataclass_or_model,itt)
      % This function AdaBoost, consist of two parts a simpel weak classifier and
      % a boosting part:
      % The weak classifier tries to find the best treshold in one of the data
      % dimensions to sepparate the data into two classes -1 and 1
      % The boosting part calls the clasifier iteratively, after every classification
      % step it changes the weights of miss-classified examples. This creates a
      % cascade of "weak classifiers" which behaves like a "strong classifier"
      %
      %  Training mode:
      %    [estimateclass,model]=adaboost('train',datafeatures,dataclass,itt)
      %  Apply mode:
      %    estimateclass=adaboost('apply',datafeatures,model)
      % 
      %  inputs/outputs:
      %    datafeatures : An Array with size number_samples x number_features
      %    dataclass : An array with the class off all examples, the class
      %                 can be -1 or 1
      %    itt : The number of training itterations
      %    model : A struct with the cascade of weak-classifiers
      %    estimateclass : The by the adaboost model classified data
      %               
      %  %%Example
      %
      %  example.m
      %
      %  Function is written by D.Kroon University of Twente (August 2010)
      switch(mode)
          case 'train'
              % Train the adaboost model
              % Set the data class 
              dataclass=dataclass_or_model(:);
              model=struct;
              % Weight of training samples, first every sample is even important
              % (same weight)
              D=ones(length(dataclass),1)/length(dataclass);
              % This variable will contain the results of the single weak
              % classifiers weight by their alpha
              estimateclasssum=zeros(size(dataclass));
              % Calculate max min of the data
              boundary=[min(datafeatures,[],1) max(datafeatures,[],1)];
              % Do all model training itterations
              for t=1:itt
                  % Find the best treshold to separate the data in two classes
                  [estimateclass,err,h] = WeightedThresholdClassifier(datafeatures,dataclass,D);
                  % Weak classifier influence on total result is based on the current
                  % classification error
                  alpha=1/2 * log((1-err)/max(err,eps));
                  % Store the model parameters
                  model(t).alpha = alpha;
                  model(t).dimension=h.dimension;
                  model(t).threshold=h.threshold;
                  model(t).direction=h.direction;
                  model(t).boundary = boundary;
                  % We update D so that wrongly classified samples will have more weight
                  D = D.* exp(-model(t).alpha.*dataclass.*estimateclass);
                  D = D./sum(D);
                  % Calculate the current error of the cascade of weak
                  % classifiers
                  estimateclasssum=estimateclasssum +estimateclass*model(t).alpha;
                  estimateclasstotal=sign(estimateclasssum);
                  model(t).error=sum(estimateclasstotal~=dataclass)/length(dataclass);
                  if(model(t).error==0), break; end
              end
          case 'apply' 
              % Apply Model on the test data
              model=dataclass_or_model;
              % Limit datafeatures to orgininal boundaries
              if(length(model)>1);
                  minb=model(1).boundary(1:end/2);
                  maxb=model(1).boundary(end/2+1:end);
                  datafeatures=bsxfun(@min,datafeatures,maxb);
                  datafeatures=bsxfun(@max,datafeatures,minb);
              end
              % Add all results of the single weak classifiers weighted by their alpha 
              estimateclasssum=zeros(size(datafeatures,1),1);
              for t=1:length(model);
                  estimateclasssum=estimateclasssum+model(t).alpha*ApplyClassTreshold(model(t), datafeatures);
              end
              % If the total sum of all weak classifiers
              % is less than zero it is probablly class -1 otherwise class 1;
              estimateclasstotal=sign(estimateclasssum);
          otherwise
              error('adaboost:inputs','unknown mode');
      end
      function [estimateclass,err,h] = WeightedThresholdClassifier(datafeatures,dataclass,dataweight)
      % This is an example of an "Weak Classifier", it caculates the optimal
      % threshold for all data feature dimensions.
      % It then selects the dimension and  treshold which divides the 
      % data into two class with the smallest error.
      % Number of treshold steps
      ntre=2e5;
      % Split the data in two classes 1 and -1
      r1=datafeatures(dataclass<0,:); w1=dataweight(dataclass<0);
      r2=datafeatures(dataclass>0,:); w2=dataweight(dataclass>0);
      % Calculate the min and max for every dimensions
      minr=min(datafeatures,[],1)-1e-10; maxr=max(datafeatures,[],1)+1e-10;
      % Make a weighted histogram of the two classes
      p2c= ceil((bsxfun(@rdivide,bsxfun(@minus,r2,minr),(maxr-minr)))*(ntre-1)+1+1e-9);   p2c(p2c>ntre)=ntre;
      p1f=floor((bsxfun(@rdivide,bsxfun(@minus,r1,minr),(maxr-minr)))*(ntre-1)+1-1e-9);  p1f(p1f<1)=1;
      ndims=size(datafeatures,2);
      i1=repmat(1:ndims,size(p1f,1),1);  i2=repmat(1:ndims,size(p2c,1),1);
      h1f=accumarray([p1f(:) i1(:)],repmat(w1(:),ndims,1),[ntre ndims],[],0);
      h2c=accumarray([p2c(:) i2(:)],repmat(w2(:),ndims,1),[ntre ndims],[],0);
      % This function calculates the error for every all possible treshold value
      % and dimension
      h2ic=cumsum(h2c,1);
      h1rf=cumsum(h1f(end:-1:1,:),1); h1rf=h1rf(end:-1:1,:);
      e1a=h1rf+h2ic;
      e2a=sum(dataweight)-e1a;
      % We want the treshold value and dimension with the minimum error
      [err1a,ind1a]=min(e1a,[],1);  dim1a=(1:ndims); dir1a=ones(1,ndims);
      [err2a,ind2a]=min(e2a,[],1);  dim2a=(1:ndims); dir2a=-ones(1,ndims);
      A=[err1a(:),dim1a(:),dir1a(:),ind1a(:);err2a(:),dim2a(:),dir2a(:),ind2a(:)];
      [err,i]=min(A(:,1)); dim=A(i,2); dir=A(i,3); ind=A(i,4);
      thresholds = linspace(minr(dim),maxr(dim),ntre);
      thr=thresholds(ind);
      % Apply the new treshold
      h.dimension = dim; 
      h.threshold = thr; 
      h.direction = dir;
      estimateclass=ApplyClassTreshold(h,datafeatures);
      function y = ApplyClassTreshold(h, x)
      % Draw a line in one dimension (like horizontal or vertical)
      % and classify everything below the line to one of the 2 classes
      % and everything above the line to the other class.
      if(h.direction == 1)
          y =  double(x(:,h.dimension) >= h.threshold);
      else
          y =  double(x(:,h.dimension) < h.threshold);
      end
      y(y==0) = -1;

[Walter Roberson]

http://www.mathworks.com/matlabcentral/answers/29922-why-your-question-is-not-urgent-or-an-emergency

Answer 1

[Image Analyst]

So just make a script that calls that function several times with different set of input parameters, just like you'd make any demo. Plot the results to make a nice visual display for the user.

[Ayman]

Thanks a lot for your kind reply

please can you guide me how and where to put these lines ?

thanks a lot for your help and kindness

[Image Analyst]

Make a file called adaboost_demo.m and put lines like these in it:

% Make up a set of values for
% mode1,datafeatures1,dataclass_or_model1,itt1 and pass them in.
[estimateclasstotal1,model1]=adaboost(mode1,datafeatures1,dataclass_or_model1,itt1)

% Make up another set of values for
% mode2,datafeatures2,dataclass_or_model2,itt2 and pass them in.
[estimateclasstotal2,model2]=adaboost(mode2,datafeatures2,dataclass_or_model2,itt2)

Answer 2

[per isakson]

Search the File Exchange for adaboost

[Ayman]

thanks a lot for your kind reply

i saw these examples but it didn't show the same idea, if you have any idea i would be greatful

thanks a lot for your help and kindness