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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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