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261 | with Ada.Text_IO; use Ada.Text_IO;
with Ada.Text_IO.C_Streams;
with Ada.Long_Float_Text_IO; use Ada.Long_Float_Text_IO;
with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
with Interfaces.C; use Interfaces.C;
with Interfaces.C.Strings; use Interfaces.C.Strings;
with Ada.Strings.Unbounded; use Ada.Strings.Unbounded;
with Ada.Command_Line; use Ada.Command_Line;
with Ada.Numerics.Elementary_Functions ; use Ada.Numerics.Elementary_Functions ;
with GNAT.Source_Info; use GNAT.Source_Info;
with gsl;
with gsl.fit;
with gsl.multifit;
with gsl.rng ;
with gsl.randist;
with gsl.vector_double ;
with gsl.matrix_double ;
procedure Fit is
use gsl.double_text_Io ;
Verbose : boolean := true ;
Status : Int ;
procedure Test1 (weighted : boolean := true) is
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
-- 40.8.1 Simple Linear Regression Example
x : gsl.double_array := ( 1970.0 , 1980.0 , 1990.0 , 2000.0 );
y : gsl.double_array := ( 12.0 , 11.0 , 14.0 , 13.0 );
w : gsl.double_array := ( 0.1, 0.2, 0.3, 0.4 ) ;
c0, c1 : aliased double ;
cov00, cov01, cov11 : aliased double ;
-- sumsq : aliased double ;
chisq : aliased double ;
N1 : constant := -30 ;
N2 : constant := 130 ;
xf, yf, yf_error : aliased double ;
begin
if Verbose
then
Put_Line(gnat.source_info.enclosing_entity);
end if ;
if weighted
then
Put_Line("Weighted fit");
Status := gsl.fit.wlinear(x , 1 , w , 1 , y , 1 , size_t(x'Length) ,
c0'access , c1'access ,
cov00'access , cov01'access , cov11'access , chisq'access ) ;
else
Status := gsl.fit.linear(x , 1 , y , 1 , size_t(x'Length) ,
c0'access , c1'access ,
cov00'access , cov01'access , cov11'access , chisq'access ) ;
end if ;
Put("c0 "); Put(Long_Float(c0)); Put(" c1 "); Put(Long_Float(c1)); New_Line ;
Put(cov00); Put(" "); Put(cov01); Put(" "); Put(cov01); Put(" "); Put(cov11); New_Line ;
Put("chisq "); Put(Long_Float(chisq)); New_Line ;
if weighted
then
Create(logfile,Out_File,myname & ".w.csv");
else
Create(logfile,Out_File,myname & ".csv");
end if ;
Set_Output(logfile);
for i in x'Range
loop
Put(x(i)); Put(" ; ");
Put(y(i)); Put(" ; ");
Put(1.0/double(Sqrt(Float(w(i))))); Put(" ; ");
New_Line;
end loop;
Set_Output(Standard_Output);
Close(logfile);
Put_Line("Calculated");
for i in x'Range
loop
Put(Long_FLoat(x(i))); Put(" ");
Put(Long_Float(y(i))); Put(" ");
Put(Long_Float(c0 + c1*x(i))); Put(" ");
Put("error "); Put(Abs(Long_Float(y(i)-(c0+c1*x(i)))));
New_Line;
end loop;
if weighted
then
Create(logfile,Out_File,myname & ".w.est.csv");
else
Create(logfile,Out_File,myname & ".est.csv");
end if ;
Set_Output(logfile);
for i in N1..N2
loop
xf := x(x'first) + (double(i)/100.0) * (x(x'Last) - x(x'first)) ;
Put(xf); Put(" ; ");
Status := gsl.fit.linear_est(double(xf) , c0 , c1 ,
cov00 , cov01 , cov11 ,
yf'access , yf_error'access);
Put(yf); Put(" ; ");
Put(yf_error); Put(" ; ");
Put(yf-yf_error); Put(" ; ");
Put(yf+yf_error) ; Put( " ; ");
New_Line;
end loop ;
Set_Output(Standard_Output);
Close(logfile);
end Test1 ;
-- 40.8.2 Multi-parameter Linear Regression Example
procedure Test2 is
use gsl.double_elementary_functions ;
N : constant := 19 ;
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
XData , cov : access gsl.matrix_double.gsl_matrix ;
Y, W, c : access gsl.vector_double.gsl_vector ;
ws : access gsl.multifit.gsl_multifit_linear_workspace ;
chisq : aliased double ;
xfrom, xto, xdelta, x : double ;
yest, yerr : aliased double ;
XCand : access gsl.vector_double.gsl_vector ;
procedure CreateTestData is
rng : access gsl.rng.gsl_rng ;
tdfile : File_Type ;
y0, sigma, dy : double ;
rownum : size_t ;
begin
rng := gsl.rng.alloc(gsl.rng.env_setup);
xfrom := 0.0 ;
xdelta := 0.1 ;
xto := xfrom + double( N - 1 ) * xdelta ;
Create( tdfile , Out_File , myname & ".td.csv");
Set_Output(tdfile);
x := xfrom ;
rownum := 0 ;
loop
y0 := exp(x);
sigma := 0.1 * y0 ;
dy := gsl.randist.gaussian(rng,sigma);
Put(x) ; Put(" ; ");
Put(y0+dy); Put(" ; ");
Put(sigma); Put(" ; ");
New_Line;
x := x + xdelta ;
gsl.matrix_double.set(XData , rownum , 0 , 1.0 );
gsl.matrix_double.set(XData , rownum , 1 , x );
gsl.matrix_double.set(XData , rownum , 2 , x * x );
gsl.vector_double.set (Y , rownum , y0+dy );
gsl.vector_double.set (W , rownum , 1.0/(sigma*sigma)) ;
rownum := rownum + 1 ;
if x > xto
then
exit ;
end if ;
end loop;
Set_Output(Standard_Output);
Close(tdfile);
gsl.rng.free(rng);
end CreateTestData;
begin
if Verbose
then
Put_Line(myname);
end if ;
XData := gsl.matrix_double.alloc(N,3);
cov := gsl.matrix_double.alloc(3,3) ;
Y := gsl.vector_double.alloc(N);
W := gsl.vector_double.alloc(N);
c := gsl.vector_double.alloc(3);
CreateTestData ;
ws := gsl.multifit.linear_alloc(N,3);
Status := gsl.multifit.wlinear (XData, W, Y, c, cov, chisq'Access, ws );
Put("Chisq "); Put( chisq ) ; New_Line ;
for i in 1..3
loop
for j in 1..3
loop
Put( gsl.matrix_double.get(cov,size_t(i-1),size_t(j-1)) ); Put (" ; ");
end loop ;
New_Line ;
end loop;
Create(logfile,Out_File,myname & ".est.csv");
Set_Output(logfile);
xfrom := 0.0 ;
xdelta := 0.05 ;
XCand := gsl.vector_double.alloc( size_t((xto-xfrom)/xdelta) );
x := xfrom ;
for n in 1..XCand.size
loop
gsl.vector_double.set(XCand,n-1,x);
x := x + xdelta ;
end loop ;
declare
xout, yout : double ;
c0, c1, c2 : double ;
begin
c0 := gsl.vector_double.get(C,0);
c1 := gsl.vector_double.get(C,1);
c2 := gsl.vector_double.get(C,2);
for n in 1..Integer(XCand.size)
loop
xout := gsl.vector_double.get(XCand,size_t(n-1)) ;
Put( xout ); Put(" ; ");
Put( c0 + c1 * xout + c2 * xout * xout ); Put(" ; ");
New_Line;
end loop ;
end ;
Set_Output(Standard_Output);
Close(logfile);
gsl.multifit.linear_free(ws);
gsl.matrix_double.free(XData);
gsl.matrix_double.free(cov);
gsl.vector_double.free(Y);
gsl.vector_double.free(W);
gsl.vector_double.free(c);
end Test2 ;
begin
Ada.Long_Float_Text_IO.Default_exp := 0 ;
Ada.Long_Float_Text_IO.Default_aft := 4 ;
gsl.double_text_io.Default_aft := 4 ;
gsl.double_text_Io.Default_exp := 0;
Test1(True);
Test1(False);
Test2;
end Fit;
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