util_do_operation.m

Standard model object recognition matlab code

function y = util_do_operation (x, w_in, oper_type, varargin)

% FUNCTION y = util_do_operation (x, w, oper_type, varargin)
%
% Sometimes, comp_get_next_layer can be cumbersome, since the input
% and filter/weights need to be in some particular format.  Hence,
% this is a simple function to run on input x and w.  Note that the
% actual computations done here may be different (ie. check with
% init_operation_def.m to make sure).  The inputs x and w are given
% in a format of
%    w = number of afferents x 1
%    x = number of afferents x sample points
% The accepted oper_type are:
%   'gaussf': Gassian function with varargin{1} = sigma.
%   'normsp': Normalized dot product with L2 norm.
%   'normdp': Normalized dot product varargin{1} = [p q r]. 

num_d = size(x,1);
num_x = size(x,2);

switch oper_type
  case ('gaussf')
    % Gaussian function
    w_rep = repmat(w_in, [1 size(x,2)]);
    y     = sum((w_rep-x).^2);
    
    % with this sigma, gaussian function at 0 is 0.01.
    if length(varargin) > 0
      sigma = varargin{1};
    else
      sigma = sum(w_in.^2);
      sigma = sqrt(-sigma/2/log(0.01));
      sigma = 1;
    end
    y     = exp(-y./2/sigma^2);
  
  case ('normdp')
    % Normalized dot product
    if length(varargin) > 0
      o_exp = varargin{1};
      p = o_exp(1);
      q = o_exp(2);
      r = o_exp(3);
      if length(varargin) > 1
	% sigmoid parameters
	o_sig = varargin{2};
	a = o_sig(1);
	b = o_sig(2);
      else
	a = sqrt(length(w_in)^(q*r/p));
	b = 0.9;
      end
    else
      error('Specify p, q, and r for normalized operation.');
    end
    
    w = w_in.^(q-p);
    c  = (sum(w.^(q/(q-p))).^r)*(q*r/p-1)+eps;
    y_max = p/q/r./(sum(w.^(q/(q-p))).^(r-1));

    w_rep = repmat(w, [1 size(x,2)]);
    y = sum(w_rep.*(x.^p))./(c+sum(x.^q).^r);
    y = y./y_max;
    
    y = 1./(1+exp(-a*(y-b)));
    
  case ('dotpro')
    % Just the dot product
    %w_in  = w_in-0.3;
    w_in  = w_in-mean(w_in);
    w_rep = repmat(w_in, [1 size(x,2)]);
    y     = sum(w_rep.*x);
    y     = y.*(y>0); % rectify the response.

    % Absolute y_max is when x=1 and w>0.
    y_max = sum(w_rep.*(w_rep>0))+eps;
    %y_max = repmat(max(y),[size(y,1) 1]);
    y = y./y_max;
    
    if length(varargin) > 0
      % sigmoid parameters
      o_sig = varargin{1};
      a = o_sig(1);
      b = o_sig(2);
      y = 1./(1+exp(-a*(y-b)));
    end
    
    case ('modelo') % Model operation.
    % x and w need to be converted into 3D and 4D matrices,
    % respectively.
    x_r(:,1,:)  = x';
    f1(1,1,:,1) = w_in;
    f2          = ones(size(f1));
    addpath('~/Model/Ver5.0');
    f = filt_package([],f1,f2,1,'s1');
    y = comp_get_next_layer (x_r, f, 'normsp');
end