Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in / Register
Toggle navigation
G
gsll
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
3
Issues
3
List
Boards
Labels
Service Desk
Milestones
Merge Requests
4
Merge Requests
4
Operations
Operations
Incidents
Analytics
Analytics
Repository
Value Stream
Wiki
Wiki
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Commits
Issue Boards
Open sidebar
antik
gsll
Commits
b75416da
Commit
b75416da
authored
Jan 03, 2013
by
Liam M. Healy
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Improved docstring for #'linear-fit
parent
0e427340
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
11 additions
and
10 deletions
+11
-10
solve-minimize-fit/linear-least-squares.lisp
solve-minimize-fit/linear-least-squares.lisp
+11
-10
No files found.
solve-minimize-fit/linear-least-squares.lisp
View file @
b75416da
;; Linear least squares, or linear regression
;; Liam Healy <2008-01-21 12:41:46EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-0
1 21:29:30
EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-0
3 10:14:04
EST linear-least-squares.lisp>
;;
;; Copyright 2008, 2009, 2010, 2011, 2012, 2013 Liam M. Healy
;; Distributed under the terms of the GNU General Public License
...
...
@@ -51,7 +51,7 @@
(
cov11
(
:pointer
:double
))
(
sumsq
(
:pointer
:double
))))
:inputs
(
x
weight
y
)
:switch
(
weight
weight-stride
)
:documentation
;
FDL
:documentation
;
GSL texi partial
"Compute the best-fit linear regression coefficients
c0, c1 of the model Y = c_0 + c_1 X for the weighted or unweighted
dataset (x, y), two vectors of equal length with strides
...
...
@@ -66,14 +66,15 @@
unweighted sum of squares of the residuals from the best-fit line,
\chi^2, is returned as the last value.
Returns: c0, c1, cov00, cov01, cov11, sumsq."
)
Returns: c0, c1, cov00, cov01, cov11, sumsq.
Returns: intercept, slope, intercept variance, covariance, slope variance, sum square of residuals."
)
(
defmfun
linear-estimate
(
x
c0
c1
cov00
cov01
cov11
)
"gsl_fit_linear_est"
((
x
:double
)
(
c0
:double
)
(
c1
:double
)
(
cov00
:double
)
(
cov01
:double
)
(
cov11
:double
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:documentation
;
FDL
:documentation
;
GSL texi partial
"Use the best-fit linear regression coefficients
c0, c1 and their covariance
cov00, cov01, cov11 to compute the fitted function
...
...
@@ -100,7 +101,7 @@
(
sumsq
(
:pointer
:double
))))
:inputs
(
x
y
weight
)
:switch
(
weight
weight-stride
)
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit linear regression coefficient
c1 of the model Y = c_1 X for the weighted or unweighted datasets
(x, y), two vectors of equal length with strides
...
...
@@ -117,7 +118,7 @@
"gsl_fit_mul_est"
((
x
:double
)
(
c1
:double
)
(
cov11
:double
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:documentation
;
FDL
:documentation
;
GSL texi
"Use the best-fit linear regression coefficient
c1 and its covariance cov11 to compute the fitted function
y and its standard deviation y-error for the model
...
...
@@ -207,7 +208,7 @@
:return
(
parameters
covariance
(
cffi:mem-ref
chisq
:double
))
:export
nil
:index
linear-mfit
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and optional weights
and the model matrix X. The covariance matrix of
...
...
@@ -252,7 +253,7 @@
:return
((
cffi:mem-ref
chisq
:double
)
(
cffi:mem-ref
rank
'
:sizet
))
:export
nil
:index
linear-mfit
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and weights and the model
matrix X. The covariance matrix of the model parameters is
...
...
@@ -276,7 +277,7 @@
((
mpointer
covariance
)
:pointer
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:inputs
(
x
coefficients
covariance
)
:documentation
;
FDL
:documentation
;
GSL texi
"Use the best-fit multilinear regression coefficients
and their covariance matrix to compute the fitted function value
y and its standard deviation for the model y = x.c
...
...
@@ -293,7 +294,7 @@
:inputs
(
x
observations
coefficients
)
:outputs
(
residuals
)
:gsl-version
(
1
11
)
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the vector of residuals r = y - X c for the observations y,
coefficients c and matrix of predictor variables X."
)
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
.
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment