Changeset 34 for trunk/solve-minimize-fit

Timestamp:

03/11/08 01:34:13 (8 months ago)

Author:

lhealy

Message:

The classes/types in the different contexts are now gathered together
in one place, in *type-names* for the types and in *data-class-name*
for data classes, populated by #'add-data-class. Both defdata and
defmfun-all use the table and so mapping between various names is
consistent. The data class names are now different, *-double-float
and *-single-float replaces *-double and *-single. The regression
tests give the same results as before.

Location:

trunk/solve-minimize-fit

Files:

• linear-least-squares.lisp (modified) (3 diffs)
• minimization-multi.lisp (modified) (4 diffs)
• nonlinear-least-squares.lisp (modified) (2 diffs)
• roots-multi.lisp (modified) (4 diffs)

trunk/solve-minimize-fit/linear-least-squares.lisp

@@ -1 +1 @@
 ;; Linear least squares, or linear regression
 ;; Liam Healy <2008-01-21 12:41:46EST linear-least-squares.lisp>
-;; Time-stamp: <2008-02-23 19:43:59EST linear-least-squares.lisp>
+;; Time-stamp: <2008-03-09 19:30:56EDT linear-least-squares.lisp>
 ;; $Id$
 
@@ -259 +259 @@
   "First example in Section 36.5 of the GSL manual."
   ;; Results not given in manual so not verified yet.
-  (letm ((x (vector-double #(1970.0d0 1980.0d0 1990.0d0 2000.0d0)))
-         (y (vector-double #(12.0d0 11.0d0 14.0d0 13.0d0)))
-         (w (vector-double #(0.1d0 0.2d0 0.3d0 0.4d0))))
+  (letm ((x (vector-double-float #(1970.0d0 1980.0d0 1990.0d0 2000.0d0)))
+         (y (vector-double-float #(12.0d0 11.0d0 14.0d0 13.0d0)))
+         (w (vector-double-float #(0.1d0 0.2d0 0.3d0 0.4d0))))
         (multiple-value-bind (c0 c1 cov00 cov01 cov11 chisq)
             (weighted-linear-fit x w y)
@@ -302 +302 @@
   "Second example in Section 36.5 of the GSL manual."
   (letm ((n (length data)) chisq
-         (x (matrix-double n 3))
-         (cov (matrix-double 3 3))
-         (y (vector-double n))
-         (w (vector-double n))
-         (c (vector-double 3)))
+         (x (matrix-double-float n 3))
+         (cov (matrix-double-float 3 3))
+         (y (vector-double-float n))
+         (w (vector-double-float n))
+         (c (vector-double-float 3)))
     (loop for i from 0
           for row in data do

trunk/solve-minimize-fit/minimization-multi.lisp

@@ -1 +1 @@
 ;; Multivariate minimization.
 ;; Liam Healy  <Tue Jan  8 2008 - 21:28>
-;; Time-stamp: <2008-02-23 18:49:17EST minimization-multi.lisp>
+;; Time-stamp: <2008-03-09 19:29:20EDT minimization-multi.lisp>
 ;; $Id$
 
@@ -8 +8 @@
 ;; In the parabaloid example, I notice that the consruct 
 ;; (min-test-gradient (mfdfminimizer-gradient minimizer) 1.0d-3)
-;; is constructing a CL vector-double (in mfdfminimizer-gradient) and
+;; is constructing a CL vector-double-float (in mfdfminimizer-gradient) and
 ;; then immediately pulling out the pointer (in min-test-gradient).  It
 ;; is easy enough to eliminate this, but then mfdfminimizer-gradient
@@ -349 +349 @@
           (mfdfminimizer *conjugate-fletcher-reeves* 2 parabaloid
                          initial 0.01d0 1.0d-4))
-         (initial (vector-double #(5.0d0 7.0d0))))
+         (initial (vector-double-float #(5.0d0 7.0d0))))
     (loop with status = T
           for iter from 0 below 100
@@ -376 +376 @@
 
 (defun multimin-example-nelder-mead ()
-  (letm ((initial (vector-double #(5.0d0 7.0d0)))
-         (step-size (vector-double 2)))
+  (letm ((initial (vector-double-float #(5.0d0 7.0d0)))
+         (step-size (vector-double-float 2)))
     (set-all step-size 1.0d0)
     (letm ((minimizer

trunk/solve-minimize-fit/nonlinear-least-squares.lisp

@@ -1 +1 @@
 ;; Nonlinear least squares fitting.
 ;; Liam Healy, 2008-02-09 12:59:16EST nonlinear-least-squares.lisp
-;; Time-stamp: <2008-02-23 18:49:18EST nonlinear-least-squares.lisp>
+;; Time-stamp: <2008-03-09 19:30:55EDT nonlinear-least-squares.lisp>
 ;; $Id$
 
@@ -384 +384 @@
 
 (defun solve-nonlinear-least-squares-example ()
-  (letm ((init (vector-double #(1.0d0 0.0d0 0.0d0)))
+  (letm ((init (vector-double-float #(1.0d0 0.0d0 0.0d0)))
          (covariance
-          (matrix-double *number-of-parameters* *number-of-parameters*))
+          (matrix-double-float *number-of-parameters* *number-of-parameters*))
          (fit (nonlinear-fdffit
                *levenberg-marquardt*

trunk/solve-minimize-fit/roots-multi.lisp

@@ -1 +1 @@
 ;;; Multivariate roots.                
 ;;; Liam Healy 2008-01-12 12:49:08
-;;; Time-stamp: <2008-02-23 18:49:17EST roots-multi.lisp>
+;;; Time-stamp: <2008-03-09 19:29:20EDT roots-multi.lisp>
 ;;; $Id$
 
@@ -407 +407 @@
 (defparameter *rosenbrock-b* 10.0d0)
 
-(defparameter *gsl-vector*
-  (make-instance 'gsl-vector-double :pointer nil :storage-size nil))
-
 #|
 ;;; One alternative way of writing the function, not recommended.
@@ -433 +430 @@
   "Solving Rosenbrock, the example given in Sec. 34.8 of the GSL manual."
   (let ((max-iter 1000))
-    (letm ((vect (vector-double #(-10.0d0 -5.0d0))))
+    (letm ((vect (vector-double-float #(-10.0d0 -5.0d0))))
       (letm ((solver (mfsolver *hybrid-scaled* rosenbrock vect)))
         (let ((fnval (mfsolver-f solver))
@@ -485 +482 @@
                    (maref fnval 1))))
     (let ((max-iter 1000))
-      (letm ((vect (vector-double #(-10.0d0 -5.0d0))))
+      (letm ((vect (vector-double-float #(-10.0d0 -5.0d0))))
         (letm
             ((solver (mfdfsolver *gnewton-mfdfsolver* rosenbrock-f vect)))