1 ;;; calc-nlfit.el --- nonlinear curve fitting for Calc
3 ;; Copyright (C) 2007-2017 Free Software Foundation, Inc.
5 ;; This file is part of GNU Emacs.
7 ;; GNU Emacs is free software: you can redistribute it and/or modify
8 ;; it under the terms of the GNU General Public License as published by
9 ;; the Free Software Foundation, either version 3 of the License, or
10 ;; (at your option) any later version.
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13 ;; but WITHOUT ANY WARRANTY; without even the implied warranty of
14 ;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 ;; GNU General Public License for more details.
17 ;; You should have received a copy of the GNU General Public License
18 ;; along with GNU Emacs. If not, see <http://www.gnu.org/licenses/>.
22 ;; This code uses the Levenberg-Marquardt method, as described in
23 ;; _Numerical Analysis_ by H. R. Schwarz, to fit data to
24 ;; nonlinear curves. Currently, the only the following curves are
26 ;; The logistic S curve, y=a/(1+exp(b*(t-c)))
27 ;; Here, y is usually interpreted as the population of some
28 ;; quantity at time t. So we will think of the data as consisting
29 ;; of quantities q0, q1, ..., qn and their respective times
32 ;; The logistic bell curve, y=A*exp(B*(t-C))/(1+exp(B*(t-C)))^2
33 ;; Note that this is the derivative of the formula for the S curve.
34 ;; We get A=-a*b, B=b and C=c. Here, y is interpreted as the rate
35 ;; of growth of a population at time t. So we will think of the
36 ;; data as consisting of rates p0, p1, ..., pn and their
37 ;; respective times t0, t1, ..., tn.
39 ;; The Hubbert Linearization, y/x=A*(1-x/B)
40 ;; Here, y is thought of as the rate of growth of a population
41 ;; and x represents the actual population. This is essentially
42 ;; the differential equation describing the actual population.
44 ;; The Levenberg-Marquardt method is an iterative process: it takes
45 ;; an initial guess for the parameters and refines them. To get an
46 ;; initial guess for the parameters, we'll use a method described by
47 ;; Luis de Sousa in "Hubbert's Peak Mathematics". The idea is that
48 ;; given quantities Q and the corresponding rates P, they should
49 ;; satisfy P/Q= mQ+a. We can use the parameter a for an
50 ;; approximation for the parameter a in the S curve, and
51 ;; approximations for b and c are found using least squares on the
52 ;; linearization log((a/y)-1) = log(bb) + cc*t of
53 ;; y=a/(1+bb*exp(cc*t)), which is equivalent to the above s curve
54 ;; formula, and then translating it to b and c. From this, we can
55 ;; also get approximations for the bell curve parameters.
62 ;; Declare functions which are defined elsewhere.
63 (declare-function calc-get-fit-variables
"calcalg3" (nv nc
&optional defv defc with-y homog
))
64 (declare-function math-map-binop
"calcalg3" (binop args1 args2
))
66 (defun math-nlfit-least-squares (xdata ydata
&optional sdata sigmas
)
67 "Return the parameters A and B for the best least squares fit y=a+bx."
68 (let* ((n (length xdata
))
70 (mapcar 'calcFunc-sqr sdata
)
82 (setq Sx
(math-add Sx
(if s
(math-div x s
) x
)))
83 (setq Sy
(math-add Sy
(if s
(math-div y s
) y
)))
84 (setq Sxx
(math-add Sxx
(if s
(math-div (math-mul x x
) s
)
86 (setq Sxy
(math-add Sxy
(if s
(math-div (math-mul x y
) s
)
89 (setq S
(math-add S
(math-div 1 s
)))))
90 (setq xdata
(cdr xdata
))
91 (setq ydata
(cdr ydata
))
92 (setq s2data
(cdr s2data
)))
93 (setq D
(math-sub (math-mul S Sxx
) (math-mul Sx Sx
)))
94 (let ((A (math-div (math-sub (math-mul Sxx Sy
) (math-mul Sx Sxy
)) D
))
95 (B (math-div (math-sub (math-mul S Sxy
) (math-mul Sx Sy
)) D
)))
97 (let ((C11 (math-div Sxx D
))
98 (C12 (math-neg (math-div Sx D
)))
100 (list (list 'sdev A
(calcFunc-sqrt C11
))
101 (list 'sdev B
(calcFunc-sqrt C22
))
104 (list 'vec C12 C22
))))
107 ;;; The methods described by de Sousa require the cumulative data qdata
108 ;;; and the rates pdata. We will assume that we are given either
109 ;;; qdata and the corresponding times tdata, or pdata and the corresponding
110 ;;; tdata. The following two functions will find pdata or qdata,
111 ;;; given the other..
113 ;;; First, given two lists; one of values q0, q1, ..., qn and one of
114 ;;; corresponding times t0, t1, ..., tn; return a list
115 ;;; p0, p1, ..., pn of the rates of change of the qi with respect to t.
116 ;;; p0 is the right hand derivative (q1 - q0)/(t1 - t0).
117 ;;; pn is the left hand derivative (qn - q(n-1))/(tn - t(n-1)).
118 ;;; The other pis are the averages of the two:
119 ;;; (1/2)((qi - q(i-1))/(ti - t(i-1)) + (q(i+1) - qi)/(t(i+1) - ti)).
121 (defun math-nlfit-get-rates-from-cumul (tdata qdata
)
124 (math-sub (nth 1 qdata
)
126 (math-sub (nth 1 tdata
)
128 (while (> (length qdata
) 2)
135 (math-sub (nth 2 qdata
)
137 (math-sub (nth 2 tdata
)
140 (math-sub (nth 1 qdata
)
142 (math-sub (nth 1 tdata
)
145 (setq qdata
(cdr qdata
)))
149 (math-sub (nth 1 qdata
)
151 (math-sub (nth 1 tdata
)
156 ;;; Next, given two lists -- one of rates p0, p1, ..., pn and one of
157 ;;; corresponding times t0, t1, ..., tn -- and an initial values q0,
158 ;;; return a list q0, q1, ..., qn of the cumulative values.
159 ;;; q0 is the initial value given.
160 ;;; For i>0, qi is computed using the trapezoid rule:
161 ;;; qi = q(i-1) + (1/2)(pi + p(i-1))(ti - t(i-1))
163 (defun math-nlfit-get-cumul-from-rates (tdata pdata q0
)
164 (let* ((qdata (list q0
)))
168 (math-add (car qdata
)
172 (math-add (nth 1 pdata
) (nth 0 pdata
)))
173 (math-sub (nth 1 tdata
)
176 (setq pdata
(cdr pdata
))
177 (setq tdata
(cdr tdata
)))
180 ;;; Given the qdata, pdata and tdata, find the parameters
181 ;;; a, b and c that fit q = a/(1+b*exp(c*t)).
182 ;;; a is found using the method described by de Sousa.
183 ;;; b and c are found using least squares on the linearization
184 ;;; log((a/q)-1) = log(b) + c*t
185 ;;; In some cases (where the logistic curve may well be the wrong
186 ;;; model), the computed a will be less than or equal to the maximum
187 ;;; value of q in qdata; in which case the above linearization won't work.
188 ;;; In this case, a will be replaced by a number slightly above
189 ;;; the maximum value of q.
191 (defun math-nlfit-find-qmax (qdata pdata tdata
)
192 (let* ((ratios (math-map-binop 'math-div pdata qdata
))
193 (lsdata (math-nlfit-least-squares ratios tdata
))
194 (qmax (math-max-list (car qdata
) (cdr qdata
)))
195 (a (math-neg (math-div (nth 1 lsdata
) (nth 0 lsdata
)))))
196 (if (math-lessp a qmax
)
197 (math-add '(float 5 -
1) qmax
)
200 (defun math-nlfit-find-logistic-parameters (qdata pdata tdata
)
201 (let* ((a (math-nlfit-find-qmax qdata pdata tdata
))
203 (mapcar (lambda (q) (calcFunc-ln (math-sub (math-div a q
) 1)))
205 (bandc (math-nlfit-least-squares tdata newqdata
)))
208 (calcFunc-exp (nth 0 bandc
))
211 ;;; Next, given the pdata and tdata, we can find the qdata if we know q0.
212 ;;; We first try to find q0, using the fact that when p takes on its largest
213 ;;; value, q is half of its maximum value. So we'll find the maximum value
214 ;;; of q given various q0, and use bisection to approximate the correct q0.
216 ;;; First, given pdata and tdata, find what half of qmax would be if q0=0.
218 (defun math-nlfit-find-qmaxhalf (pdata tdata
)
219 (let ((pmax (math-max-list (car pdata
) (cdr pdata
)))
221 (while (math-lessp (car pdata
) pmax
)
227 (math-add (nth 1 pdata
) (nth 0 pdata
)))
228 (math-sub (nth 1 tdata
)
230 (setq pdata
(cdr pdata
))
231 (setq tdata
(cdr tdata
)))
234 ;;; Next, given pdata and tdata, approximate q0.
236 (defun math-nlfit-find-q0 (pdata tdata
)
237 (let* ((qhalf (math-nlfit-find-qmaxhalf pdata tdata
))
238 (q0 (math-mul 2 qhalf
))
239 (qdata (math-nlfit-get-cumul-from-rates tdata pdata q0
)))
240 (while (math-lessp (math-nlfit-find-qmax
242 (lambda (q) (math-add q0 q
))
250 (setq q0
(math-add q0 qhalf
)))
251 (let* ((qmin (math-sub q0 qhalf
))
253 (qt (math-nlfit-find-qmax
255 (lambda (q) (math-add q0 q
))
260 (setq q0
(math-mul '(float 5 -
1) (math-add qmin qmax
)))
262 (math-nlfit-find-qmax
264 (lambda (q) (math-add q0 q
))
267 (math-mul '(float 5 -
1) (math-add qhalf q0
)))
271 (math-mul '(float 5 -
1) (math-add qmin qmax
)))))
273 ;;; To improve the approximations to the parameters, we can use
274 ;;; Marquardt method as described in Schwarz's book.
276 ;;; Small numbers used in the Givens algorithm
277 (defvar math-nlfit-delta
'(float 1 -
8))
279 (defvar math-nlfit-epsilon
'(float 1 -
5))
281 ;;; Maximum number of iterations
282 (defvar math-nlfit-max-its
100)
284 ;;; Next, we need some functions for dealing with vectors and
285 ;;; matrices. For convenience, we'll work with Emacs lists
286 ;;; as vectors, rather than Calc's vectors.
288 (defun math-nlfit-set-elt (vec i x
)
289 (setcar (nthcdr (1- i
) vec
) x
))
291 (defun math-nlfit-get-elt (vec i
)
294 (defun math-nlfit-make-matrix (i j
)
295 (let ((row (make-list j
0))
299 (setq mat
(cons (copy-sequence row
) mat
))
303 (defun math-nlfit-set-matx-elt (mat i j x
)
304 (setcar (nthcdr (1- j
) (nth (1- i
) mat
)) x
))
306 (defun math-nlfit-get-matx-elt (mat i j
)
307 (nth (1- j
) (nth (1- i
) mat
)))
309 ;;; For solving the linearized system.
310 ;;; (The Givens method, from Schwarz.)
312 (defun math-nlfit-givens (C d
)
313 (let* ((C (copy-tree C
))
327 (let ((cij (math-nlfit-get-matx-elt C i j
))
328 (cjj (math-nlfit-get-matx-elt C j j
)))
329 (when (not (math-equal 0 cij
))
330 (if (math-lessp (calcFunc-abs cjj
)
331 (math-mul math-nlfit-delta
(calcFunc-abs cij
)))
332 (setq w
(math-neg cij
)
341 (math-mul cij cij
))))
342 gamma
(math-div cjj w
)
343 sigma
(math-neg (math-div cij w
)))
344 (if (math-lessp (calcFunc-abs sigma
) gamma
)
346 (setq rho
(math-div (calcFunc-sign sigma
) gamma
))))
349 (math-nlfit-set-matx-elt C j j w
)
350 (math-nlfit-set-matx-elt C i j rho
)
353 (let* ((cjk (math-nlfit-get-matx-elt C j k
))
354 (cik (math-nlfit-get-matx-elt C i k
))
356 (math-mul gamma cjk
) (math-mul sigma cik
))))
359 (math-mul gamma cik
)))
361 (math-nlfit-set-matx-elt C i k cik
)
362 (math-nlfit-set-matx-elt C j k cjk
)
364 (let* ((di (math-nlfit-get-elt d i
))
365 (dj (math-nlfit-get-elt d j
))
368 (math-mul sigma di
))))
371 (math-mul gamma di
)))
373 (math-nlfit-set-elt d i di
)
374 (math-nlfit-set-elt d j dj
))))
380 (math-nlfit-set-elt r i
0)
381 (setq s
(math-nlfit-get-elt d i
))
384 (setq s
(math-add s
(math-mul (math-nlfit-get-matx-elt C i k
)
385 (math-nlfit-get-elt x k
))))
387 (math-nlfit-set-elt x i
390 (math-nlfit-get-matx-elt C i i
))))
394 (math-nlfit-set-elt r i
(math-nlfit-get-elt d i
))
400 (setq rho
(math-nlfit-get-matx-elt C i j
))
401 (if (math-equal rho
1)
404 (if (math-lessp (calcFunc-abs rho
) 1)
407 (math-sub 1 (math-mul sigma sigma
))))
408 (setq gamma
(math-div 1 (calcFunc-abs rho
))
409 sigma
(math-mul (calcFunc-sign rho
)
411 (math-sub 1 (math-mul gamma gamma
)))))))
412 (let ((ri (math-nlfit-get-elt r i
))
413 (rj (math-nlfit-get-elt r j
))
415 (setq h
(math-add (math-mul gamma rj
)
416 (math-mul sigma ri
)))
419 (math-mul sigma rj
)))
421 (math-nlfit-set-elt r i ri
)
422 (math-nlfit-set-elt r j rj
))
428 (defun math-nlfit-jacobian (grad xlist parms
&optional slist
)
431 (let ((row (apply grad
(car xlist
) parms
)))
435 (mapcar (lambda (x) (math-div x
(car slist
))) row
)
438 (setq slist
(cdr slist
))
439 (setq xlist
(cdr xlist
)))
442 (defun math-nlfit-make-ident (l n
)
443 (let ((m (math-nlfit-make-matrix n n
))
446 (math-nlfit-set-matx-elt m i i l
)
450 (defun math-nlfit-chi-sq (xlist ylist parms fn
&optional slist
)
455 (apply fn
(car xlist
) parms
)
458 (setq c
(math-div c
(car slist
))))
462 (setq xlist
(cdr xlist
))
463 (setq ylist
(cdr ylist
))
464 (setq slist
(cdr slist
)))
467 (defun math-nlfit-init-lambda (C)
474 (setq l
(math-add l
(math-mul (car row
) (car row
))))
475 (setq row
(cdr row
))))
477 (calcFunc-sqrt (math-div l
(math-mul n N
)))))
479 (defun math-nlfit-make-Ctilda (C l
)
480 (let* ((n (length (car C
)))
481 (bot (math-nlfit-make-ident l n
)))
484 (defun math-nlfit-make-d (fn xdata ydata parms
&optional sdata
)
488 (let ((dd (math-sub (apply fn
(car xdata
) parms
)
490 (if sdata
(math-div dd
(car sdata
)) dd
))
492 (setq xdata
(cdr xdata
))
493 (setq ydata
(cdr ydata
))
494 (setq sdata
(cdr sdata
)))
497 (defun math-nlfit-make-dtilda (d n
)
498 (append d
(make-list n
0)))
500 (defun math-nlfit-fit (xlist ylist parms fn grad
&optional slist
)
502 ((C (math-nlfit-jacobian grad xlist parms slist
))
503 (d (math-nlfit-make-d fn xlist ylist parms slist
))
504 (chisq (math-nlfit-chi-sq xlist ylist parms fn slist
))
505 (lambda (math-nlfit-init-lambda C
))
510 (< iters math-nlfit-max-its
))
511 (setq iters
(1+ iters
))
514 (let* ((Ctilda (math-nlfit-make-Ctilda C lambda
))
515 (dtilda (math-nlfit-make-dtilda d
(length (car C
))))
516 (zeta (math-nlfit-givens Ctilda dtilda
))
517 (newparms (math-map-binop 'math-add
(copy-tree parms
) zeta
))
518 (newchisq (math-nlfit-chi-sq xlist ylist newparms fn slist
)))
519 (if (math-lessp newchisq chisq
)
523 (math-sub chisq newchisq
) newchisq
) math-nlfit-epsilon
)
524 (setq really-done t
))
525 (setq lambda
(math-div lambda
10))
526 (setq chisq newchisq
)
527 (setq parms newparms
)
529 (setq lambda
(math-mul lambda
10)))))
530 (setq C
(math-nlfit-jacobian grad xlist parms slist
))
531 (setq d
(math-nlfit-make-d fn xlist ylist parms slist
))))
534 ;;; The functions that describe our models, and their gradients.
536 (defun math-nlfit-s-logistic-fn (x a b c
)
537 (math-div a
(math-add 1 (math-mul b
(calcFunc-exp (math-mul c x
))))))
539 (defun math-nlfit-s-logistic-grad (x a b c
)
540 (let* ((ep (calcFunc-exp (math-mul c x
)))
541 (d (math-add 1 (math-mul b ep
)))
545 (math-neg (math-div (math-mul a ep
) d2
))
546 (math-neg (math-div (math-mul a
(math-mul b
(math-mul x ep
))) d2
)))))
548 (defun math-nlfit-b-logistic-fn (x a c d
)
549 (let ((ex (calcFunc-exp (math-mul c
(math-sub x d
)))))
556 (defun math-nlfit-b-logistic-grad (x a c d
)
557 (let* ((ex (calcFunc-exp (math-mul c
(math-sub x d
))))
558 (ex1 (math-add 1 ex
))
566 (math-mul a
(math-mul xd ex
))
569 (math-mul 2 (math-mul a
(math-mul xd
(math-sqr ex
))))
573 (math-mul 2 (math-mul a
(math-mul c
(math-sqr ex
))))
576 (math-mul a
(math-mul c ex
))
579 ;;; Functions to get the final covariance matrix and the sdevs
581 (defun math-nlfit-find-covar (grad xlist pparms
)
584 (setq j
(cons (cons 'vec
(apply grad
(car xlist
) pparms
)) j
))
585 (setq xlist
(cdr xlist
)))
586 (setq j
(cons 'vec
(reverse j
)))
592 (defun math-nlfit-get-sigmas (grad xlist pparms chisq
)
594 (covar (math-nlfit-find-covar grad xlist pparms
))
595 (n (1- (length covar
)))
600 (setq sgs
(cons (calcFunc-sqrt (nth i
(nth i covar
))) sgs
))
602 (setq sgs
(reverse sgs
)))
605 ;;; Now the Calc functions
607 (defun math-nlfit-s-logistic-params (xdata ydata
)
608 (let ((pdata (math-nlfit-get-rates-from-cumul xdata ydata
)))
609 (math-nlfit-find-logistic-parameters ydata pdata xdata
)))
611 (defun math-nlfit-b-logistic-params (xdata ydata
)
612 (let* ((q0 (math-nlfit-find-q0 ydata xdata
))
613 (qdata (math-nlfit-get-cumul-from-rates xdata ydata q0
))
614 (abc (math-nlfit-find-logistic-parameters qdata ydata xdata
))
621 (D (math-neg (math-div (calcFunc-ln B
) C
)))
625 ;;; Some functions to turn the parameter lists and variables
626 ;;; into the appropriate functions.
628 (defun math-nlfit-s-logistic-solnexpr (pms var
)
629 (let ((a (nth 0 pms
))
642 (defun math-nlfit-b-logistic-solnexpr (pms var
)
643 (let ((a (nth 0 pms
))
662 (defun math-nlfit-enter-result (n prefix vals
)
663 (setq calc-aborted-prefix prefix
)
664 (calc-pop-push-record-list n prefix vals
)
667 (defun math-nlfit-fit-curve (fn grad solnexpr initparms
&optional sdv
)
669 (let* ((sdevv (or (eq sdv
'calcFunc-efit
) (eq sdv
'calcFunc-xfit
)))
670 (calc-display-working-message nil
)
672 (xdata (cdr (car (cdr data
))))
673 (ydata (cdr (car (cdr (cdr data
)))))
674 (sdata (if (math-contains-sdev-p ydata
)
675 (mapcar (lambda (x) (math-get-sdev x t
)) ydata
)
677 (ydata (mapcar (lambda (x) (math-get-value x
)) ydata
))
678 (calc-curve-varnames nil
)
679 (calc-curve-coefnames nil
)
681 (fitvars (calc-get-fit-variables 1 3))
682 (var (nth 1 calc-curve-varnames
))
683 (parms (cdr calc-curve-coefnames
))
685 (funcall initparms xdata ydata
))
686 (fit (math-nlfit-fit xdata ydata parmguess fn grad sdata
))
687 (finalparms (nth 1 fit
))
690 (math-nlfit-get-sigmas grad xdata finalparms
(nth 0 fit
))))
697 (lambda (x y
) (list 'sdev x y
)) finalparms sigmas
)
699 (soln (funcall solnexpr finalparms var
)))
700 (let ((calc-fit-to-trail t
)
703 (setq traillist
(cons (list 'calcFunc-eq
(car parms
) (car finalparms
))
705 (setq finalparms
(cdr finalparms
))
706 (setq parms
(cdr parms
)))
707 (setq traillist
(calc-normalize (cons 'vec
(nreverse traillist
))))
708 (cond ((eq sdv
'calcFunc-efit
)
709 (math-nlfit-enter-result 1 "efit" soln
))
710 ((eq sdv
'calcFunc-xfit
)
719 (let ((n (length xdata
))
720 (m (length finalparms
)))
721 (if (and sdata
(> n m
))
722 (calcFunc-utpc (nth 0 fit
)
724 '(var nan var-nan
)))))
725 (math-nlfit-enter-result 1 "xfit" sln
)))
727 (math-nlfit-enter-result 1 "fit" soln
)))
728 (calc-record traillist
"parm")))))
730 (defun calc-fit-s-shaped-logistic-curve (arg)
732 (math-nlfit-fit-curve 'math-nlfit-s-logistic-fn
733 'math-nlfit-s-logistic-grad
734 'math-nlfit-s-logistic-solnexpr
735 'math-nlfit-s-logistic-params
738 (defun calc-fit-bell-shaped-logistic-curve (arg)
740 (math-nlfit-fit-curve 'math-nlfit-b-logistic-fn
741 'math-nlfit-b-logistic-grad
742 'math-nlfit-b-logistic-solnexpr
743 'math-nlfit-b-logistic-params
746 (defun calc-fit-hubbert-linear-curve (&optional sdv
)
748 (let* ((sdevv (or (eq sdv
'calcFunc-efit
) (eq sdv
'calcFunc-xfit
)))
749 (calc-display-working-message nil
)
751 (qdata (cdr (car (cdr data
))))
752 (pdata (cdr (car (cdr (cdr data
)))))
753 (sdata (if (math-contains-sdev-p pdata
)
754 (mapcar (lambda (x) (math-get-sdev x t
)) pdata
)
756 (pdata (mapcar (lambda (x) (math-get-value x
)) pdata
))
757 (poverqdata (math-map-binop 'math-div pdata qdata
))
758 (parmvals (math-nlfit-least-squares qdata poverqdata sdata sdevv
))
759 (finalparms (list (nth 0 parmvals
)
761 (math-div (nth 0 parmvals
)
763 (calc-curve-varnames nil
)
764 (calc-curve-coefnames nil
)
766 (fitvars (calc-get-fit-variables 1 2))
767 (var (nth 1 calc-curve-varnames
))
768 (parms (cdr calc-curve-coefnames
))
769 (soln (list '* (nth 0 finalparms
)
771 (list '/ var
(nth 1 finalparms
))))))
772 (let ((calc-fit-to-trail t
)
776 (list 'calcFunc-eq
(nth 0 parms
) (nth 0 finalparms
))
777 (list 'calcFunc-eq
(nth 1 parms
) (nth 1 finalparms
))))
778 (cond ((eq sdv
'calcFunc-efit
)
779 (math-nlfit-enter-result 1 "efit" soln
))
780 ((eq sdv
'calcFunc-xfit
)
785 (list (nth 1 (nth 0 finalparms
))
786 (nth 1 (nth 1 finalparms
)))
800 '(calcFunc-fitdummy 1)
803 '(calcFunc-fitdummy 1)
804 '(calcFunc-fitdummy 2))))
806 (let ((n (length qdata
)))
807 (if (and sdata
(> n
2))
811 '(var nan var-nan
)))))
812 (math-nlfit-enter-result 1 "xfit" sln
)))
814 (math-nlfit-enter-result 1 "fit" soln
)))
815 (calc-record traillist
"parm")))))
817 (provide 'calc-nlfit
)