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Comment:error --> err to avoid name clash with built in function
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SHA3-256: 1288c7a627cbcd4806976ef1ab1ad68a8d47425932a90caacab5561c1e83783b
User & Date: gawthrop@users.sourceforge.net on 2002-04-23 17:50:39
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Context
2002-04-25
09:56:27
\% --> % to avoid awk error message check-in: 833d750d78 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
2002-04-23
17:50:39
error --> err to avoid name clash with built in function check-in: 1288c7a627 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
17:46:05
_sim.m now returns time as third argument check-in: 864f048dbc user: gawthrop@users.sourceforge.net tags: origin/master, trunk
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Modified mttroot/mtt/lib/control/PPP/ppp_optimise.m from [87f475bb1d] to [293204b700].

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  ######################################
  
  ###############################################################
  ## Version control history
  ###############################################################
  ## $Id$
  ## $Log$



  ## Revision 1.6  2001/07/03 22:59:10  gawthrop
  ## Fixed problems with argument passing for CRs
  ##
  ## Revision 1.5  2001/06/06 07:54:38  gawthrop
  ## Further fixes to make nonlinear PPP work ...
  ##
  ## Revision 1.4  2001/05/26 15:46:38  gawthrop








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  ######################################
  
  ###############################################################
  ## Version control history
  ###############################################################
  ## $Id$
  ## $Log$
  ## Revision 1.7  2001/08/10 16:19:06  gawthrop
  ## Tidied up the optimisation stuff
  ##
  ## Revision 1.6  2001/07/03 22:59:10  gawthrop
  ## Fixed problems with argument passing for CRs
  ##
  ## Revision 1.5  2001/06/06 07:54:38  gawthrop
  ## Further fixes to make nonlinear PPP work ...
  ##
  ## Revision 1.4  2001/05/26 15:46:38  gawthrop
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  [n_data,n_y] = size(y_0);
  if n_data<n_y
    error("ppp_optimise: y_0 should be in columns, not rows")
  endif

  n_th = length(i_s);
  error_old = inf;
  error_old_old = inf;
  error = 1e50;
  reduction = inf;
  predicted_reduction = 0;
  par = par_0;
  Par = par_0;
  step = ones(n_th,1);
  Error = [];
  Y = [];
  iterations = 0;
  v = extras.v;			# Levenverg-Marquardt parameter.
  r = 1;			# Step ratio

  if extras.verbose		# Diagnostics
    printf("Iteration: %i\n", iterations);
    printf("  error:  %g\n", error);
    printf("  reduction:  %g\n", reduction);
    printf("  prediction: %g\n", predicted_reduction);
    printf("  ratio:      %g\n", r);
    printf("  L-M param:  %g\n", v);
    printf("  parameters: ");
    for i_th=1:n_th
      printf("%g ", par(i_t(i_th)));
    endfor
    printf("\n");
  endif
  
  while (abs(reduction)>extras.criterion)&&\
	(abs(error)>extras.criterion)&&\
	(iterations<extras.max_iterations)

    iterations = iterations + 1; # Increment iteration counter

    [y,y_par,x] = eval(sim_command); # Simulate
    [N_data,N_y] = size(y);









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  [n_data,n_y] = size(y_0);
  if n_data<n_y
    error("ppp_optimise: y_0 should be in columns, not rows")
  endif

  n_th = length(i_s);
  err_old = inf;
  err_old_old = inf;
  err = 1e50;
  reduction = inf;
  predicted_reduction = 0;
  par = par_0;
  Par = par_0;
  step = ones(n_th,1);
  Error = [];
  Y = [];
  iterations = 0;
  v = extras.v;			# Levenverg-Marquardt parameter.
  r = 1;			# Step ratio

  if extras.verbose		# Diagnostics
    printf("Iteration: %i\n", iterations);
    printf("  error:  %g\n", err);
    printf("  reduction:  %g\n", reduction);
    printf("  prediction: %g\n", predicted_reduction);
    printf("  ratio:      %g\n", r);
    printf("  L-M param:  %g\n", v);
    printf("  parameters: ");
    for i_th=1:n_th
      printf("%g ", par(i_t(i_th)));
    endfor
    printf("\n");
  endif
  
  while (abs(reduction)>extras.criterion)&&\
	(abs(err)>extras.criterion)&&\
	(iterations<extras.max_iterations)

    iterations = iterations + 1; # Increment iteration counter

    [y,y_par,x] = eval(sim_command); # Simulate
    [N_data,N_y] = size(y);
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    if extras.verbose		# Diagnostics
##      printf("y and y_0\n");
##      [y,y_0]
    endif
    
    ##Evaluate error, cost derivative J and cost second derivative JJ
    error = 0; 
    J = zeros(n_th,1);
    JJ = zeros(n_th,n_th);
    
    for i = 1:n_y
      E = y(:,i) - y_0(:,i);	#  Error in ith output
      error = error + (E'*E);	# Sum the squared error over outputs
      y_par_i = y_par(:,i:n_y:n_y*n_th); # sensitivity function (ith output)
      J  = J + y_par_i'*E;	# Jacobian
      JJ = JJ + y_par_i'*y_par_i; # Newton Euler approx Hessian
    endfor

    if iterations>1 # Adjust the Levenberg-Marquardt parameter
      reduction = error_old-error;
      predicted_reduction =  2*J'*step + step'*JJ*step;
      r = predicted_reduction/reduction;
      if (r<0.25)||(reduction<0)
	v = 4*v;
      elseif r>0.75
	v = v/2;
      endif

      if reduction<0		# Its getting worse
	par(i_t) = par(i_t) + step; # rewind parameter
	error = error_old;	# rewind error
	error_old = error_old_old; # rewind old error
	if extras.verbose
	  printf(" Rewinding ....\n");
	endif
      endif
    endif

    ## Compute step using pseudo inverse
    JJL = JJ + v*eye(n_th);	# Levenberg-Marquardt term
    step =  pinv(JJL)*J;	# Step size
    par(i_t) = par(i_t) - step; # Increment parameters
    error_old_old = error_old;	# Save old error
    error_old = error;		# Save error

    ##Some diagnostics
    Error = [Error error];	# Save error
    Par = [Par par];		# Save parameters
    Y = [Y y];			# Save output

    if extras.verbose		# Diagnostics
      printf("Iteration: %i\n", iterations);
      printf("  error:  %g\n", error);
      printf("  reduction:  %g\n", reduction);
      printf("  prediction: %g\n", predicted_reduction);
      printf("  ratio:      %g\n", r);
      printf("  L-M param:  %g\n", v);
      printf("  parameters: ");
      for i_th=1:n_th
	printf("%g ", par(i_t(i_th)));








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    if extras.verbose		# Diagnostics
##      printf("y and y_0\n");
##      [y,y_0]
    endif
    
    ##Evaluate error, cost derivative J and cost second derivative JJ
    err = 0; 
    J = zeros(n_th,1);
    JJ = zeros(n_th,n_th);
    
    for i = 1:n_y
      E = y(:,i) - y_0(:,i);	#  Error in ith output
      err = err + (E'*E);	# Sum the squared error over outputs
      y_par_i = y_par(:,i:n_y:n_y*n_th); # sensitivity function (ith output)
      J  = J + y_par_i'*E;	# Jacobian
      JJ = JJ + y_par_i'*y_par_i; # Newton Euler approx Hessian
    endfor

    if iterations>1 # Adjust the Levenberg-Marquardt parameter
      reduction = err_old-err;
      predicted_reduction =  2*J'*step + step'*JJ*step;
      r = predicted_reduction/reduction;
      if (r<0.25)||(reduction<0)
	v = 4*v;
      elseif r>0.75
	v = v/2;
      endif

      if reduction<0		# Its getting worse
	par(i_t) = par(i_t) + step; # rewind parameter
	err = err_old;	# rewind error
	err_old = err_old_old; # rewind old error
	if extras.verbose
	  printf(" Rewinding ....\n");
	endif
      endif
    endif

    ## Compute step using pseudo inverse
    JJL = JJ + v*eye(n_th);	# Levenberg-Marquardt term
    step =  pinv(JJL)*J;	# Step size
    par(i_t) = par(i_t) - step; # Increment parameters
    err_old_old = err_old;	# Save old error
    err_old = err;		# Save error

    ##Some diagnostics
    Error = [Error err];	# Save error
    Par = [Par par];		# Save parameters
    Y = [Y y];			# Save output

    if extras.verbose		# Diagnostics
      printf("Iteration: %i\n", iterations);
      printf("  error:  %g\n", err);
      printf("  reduction:  %g\n", reduction);
      printf("  prediction: %g\n", predicted_reduction);
      printf("  ratio:      %g\n", r);
      printf("  L-M param:  %g\n", v);
      printf("  parameters: ");
      for i_th=1:n_th
	printf("%g ", par(i_t(i_th)));
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