som_norm_variable.m

it is matlab code , som(slef organizing map) tool for matlab

  % initialize
  if strcmp(operation,'init') | ...
     (strcmp(operation,'do') & strcmp(sNorm(i).status,'uninit')), 

    % case method = 'hist'
    if strcmp(sNorm(i).method,'hist'), 
      inds = find(~isnan(x) & ~isinf(x));
      if length(unique(x(inds)))>20, sNorm(i).method = 'histC'; 
      else sNorm{i}.method = 'histD'; end
    end

    switch(sNorm(i).method), 
    case 'var',   params = norm_variance_init(x);
    case 'range', params = norm_scale01_init(x);
    case 'log',   params = norm_log_init(x);
    case 'logistic', params = norm_logistic_init(x);
    case 'histD', params = norm_histeqD_init(x);
    case 'histC', params = norm_histeqC_init(x);
    case 'eval',  params = sNorm(i).params; 
    otherwise, 
      error(['Unrecognized method: ' sNorm(i).method]); 
    end
    sNorm(i).params = params;
    sNorm(i).status = 'undone';
  end

  % do / undo
  if strcmp(operation,'do'), 
    switch(sNorm(i).method), 
    case 'var',   x = norm_scale_do(x,sNorm(i).params);
    case 'range', x = norm_scale_do(x,sNorm(i).params);
    case 'log',   x = norm_log_do(x,sNorm(i).params);
    case 'logistic', x = norm_logistic_do(x,sNorm(i).params);
    case 'histD', x = norm_histeqD_do(x,sNorm(i).params);
    case 'histC', x = norm_histeqC_do(x,sNorm(i).params);
    case 'eval',  x = norm_eval_do(x,sNorm(i).params);
    otherwise, 
      error(['Unrecognized method: ' sNorm(i).method]);
    end
    sNorm(i).status = 'done';

  elseif strcmp(operation,'undo'), 

    if strcmp(sNorm(i).status,'uninit'), 
      warning('Could not undo: uninitialized normalization struct.')
      break;
    end
    switch(sNorm(i).method), 
    case 'var',   x = norm_scale_undo(x,sNorm(i).params);
    case 'range', x = norm_scale_undo(x,sNorm(i).params);
    case 'log',   x = norm_log_undo(x,sNorm(i).params);    
    case 'logistic', x = norm_logistic_undo(x,sNorm(i).params);
    case 'histD', x = norm_histeqD_undo(x,sNorm(i).params);
    case 'histC', x = norm_histeqC_undo(x,sNorm(i).params);
    case 'eval',  x = norm_eval_undo(x,sNorm(i).params);
    otherwise, 
      error(['Unrecognized method: ' sNorm(i).method]);
    end
    sNorm(i).status = 'undone';

  elseif ~strcmp(operation,'init'),

    error(['Unrecognized operation: ' operation])

  end
end  

return;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions

% linear scaling

function p = norm_variance_init(x)
  inds = find(~isnan(x) & isfinite(x));
  p = [mean(x(inds)), std(x(inds))];
  if p(2) == 0, p(2) = 1; end
  %end of norm_variance_init

function p = norm_scale01_init(x)
  inds = find(~isnan(x) & isfinite(x));
  mi = min(x(inds)); 
  ma = max(x(inds));
  if mi == ma, p = [mi, 1]; else p = [mi, ma-mi]; end
  %end of norm_scale01_init  

function x = norm_scale_do(x,p)
  x = (x - p(1)) / p(2);
  % end of norm_scale_do

function x = norm_scale_undo(x,p)
  x = x * p(2) + p(1);
  % end of norm_scale_undo

% logarithm

function p = norm_log_init(x)
  inds = find(~isnan(x) & isfinite(x));
  p = min(x(inds));
  % end of norm_log_init

function x = norm_log_do(x,p)
  x = log(x - p +1); 
  % if any(~isreal(x)), ok = 0; end
  % end of norm_log_do 

function x = norm_log_undo(x,p)
  x = exp(x) -1 + p; 
  % end of norm_log_undo 

% logistic

function p = norm_logistic_init(x)
  inds = find(~isnan(x) & isfinite(x));
  p = [mean(x(inds)), std(x(inds))];
  if p(2)==0, p(2) = 1; end
  % end of norm_logistic_init

function x = norm_logistic_do(x,p)
  x = (x-p(1))/p(2);
  x = 1./(1+exp(-x));
  % end of norm_logistic_do

function x = norm_logistic_undo(x,p)
  x = log(x./(1-x));
  x = x*p(2)+p(1);
  % end of norm_logistic_undo

% histogram equalization for discrete values

function p = norm_histeqD_init(x)
  inds = find(~isnan(x) & ~isinf(x));
  p = unique(x(inds));
  % end of norm_histeqD_init

function x = norm_histeqD_do(x,p)
  bins = length(p);
  inds = find(~isnan(x) & ~isinf(x))';
  for i = inds, 
    [dummy ind] = min(abs(x(i) - p));
    % data item closer to the left-hand bin wall is indexed after RH wall
    if x(i) > p(ind) & ind < bins, 
      x(i) = ind + 1;  
    else 
      x(i) = ind;
    end
  end
  x = (x-1)/(bins-1); % normalization between [0,1]
  % end of norm_histeqD_do 

function x = norm_histeqD_undo(x,p)
  bins = length(p);
  x = round(x*(bins-1)+1);
  inds = find(~isnan(x) & ~isinf(x));
  x(inds) = p(x(inds));
  % end of norm_histeqD_undo

% histogram equalization with partially linear functions

function p = norm_histeqC_init(x)
  % investigate x
  inds = find(~isnan(x) & ~isinf(x));
  samples = length(inds);
  xs = unique(x(inds));
  mi = xs(1);
  ma = xs(end);
  % decide number of limits
  lims = ceil(sqrt(length(xs))); % 2->2,100->10,1000->32,10000->100
  % decide limits
  if lims==1,     
    p = [mi, mi+1];
    lims = 2; 
  elseif lims==2, 
    p = [mi, ma];
  else
    p = zeros(lims,1);   
    p(1) = mi; 
    p(end) = ma;
    binsize = zeros(lims-1,1); b = 1; avebinsize = samples/(lims-1);
    for i=1:(length(xs)-1), 
      binsize(b) = binsize(b) + sum(x==xs(i)); 
      if binsize(b) >= avebinsize, 
        b = b + 1;
        p(b) = (xs(i)+xs(i+1))/2;
      end
      if b==(lims-1), 
        binsize(b) = samples-sum(binsize); break;
      else
        avebinsize = (samples-sum(binsize))/(lims-1-b);
      end
    end
  end
  % end of norm_histeqC_init

function x = norm_histeqC_do(x,p)
  xnew = x; 
  lims = length(p);
  % handle values below minimum
  r = p(2)-p(1); 
  inds = find(x<=p(1) & isfinite(x)); 
  if any(inds), xnew(inds) = 0-(p(1)-x(inds))/r; end 
  % handle values above maximum
  r = p(end)-p(end-1); 
  inds = find(x>p(end) & isfinite(x)); 
  if any(inds), xnew(inds) = lims-1+(x(inds)-p(end))/r; end
  % handle all other values
  for i=1:(lims-1), 
    r0 = p(i); r1 = p(i+1); r = r1-r0; 
    inds = find(x>r0 & x<=r1); 
    if any(inds), xnew(inds) = i-1+(x(inds)-r0)/r; end
  end
  % scale so that minimum and maximum correspond to 0 and 1
  x = xnew/(lims-1);
  % end of norm_histeqC_do

function x = norm_histeqC_undo(x,p)
  xnew = x; 
  lims = length(p); 
  % scale so that 0 and 1 correspond to minimum and maximum
  x = x*(lims-1);

  % handle values below minimum
  r = p(2)-p(1); 
  inds = find(x<=0 & isfinite(x)); 
  if any(inds), xnew(inds) = x(inds)*r + p(1); end 
  % handle values above maximum
  r = p(end)-p(end-1); 
  inds = find(x>lims-1 & isfinite(x)); 
  if any(inds), xnew(inds) = (x(inds)-(lims-1))*r+p(end); end
  % handle all other values
  for i=1:(lims-1), 
    r0 = p(i); r1 = p(i+1); r = r1-r0; 
    inds = find(x>i-1 & x<=i); 
    if any(inds), xnew(inds) = (x(inds)-(i-1))*r + r0; end
  end
  x = xnew;
  % end of norm_histeqC_undo

% eval

function p = norm_eval_init(method)
  p = method;
  %end of norm_eval_init

function x = norm_eval_do(x,p)
  x_tmp = eval(p{1});
  if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
     size(x_tmp,2)==1 & size(x,2)==1,
    x = x_tmp;
  end
  %end of norm_eval_do

function x = norm_eval_undo(x,p)
  x_tmp = eval(p{2});
  if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
     size(x_tmp,2)==1 & size(x,2)==1,
    x = x_tmp;
  end
  %end of norm_eval_undo

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%