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fitlog_zipf.txt
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fitlog_zipf.txt
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==> zipf/NKJP-2G-D/fit.log <==
*******************************************************************************
Thu Jun 8 14:25:35 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 183
residuals are weighted equally (unit weight)
function used for fitting: TT1(x)
TT1(n)=Constant(n,s(b1))
s(r)=1/(1+abs(r))
Constant(n,b)=n**b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda b1
0 1.6972659129e+11 0.00e+00 5.87e+03 1.000000e+00
7 4.4471347191e+08 -1.71e-02 5.87e-01 -4.545181e-01
After 7 iterations the fit converged.
final sum of squares of residuals : 4.44713e+08
rel. change during last iteration : -1.70788e-07
degrees of freedom (FIT_NDF) : 182
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 1563.16
variance of residuals (reduced chisquare) = WSSR/ndf : 2.44348e+06
Final set of parameters Asymptotic Standard Error
======================= ==========================
b1 = -0.454518 +/- 0.0004607 (0.1014%)
*******************************************************************************
Thu Jun 8 14:25:35 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 183
residuals are weighted equally (unit weight)
function used for fitting: TT2(x)
TT2(n)=Davis(n,s(a2)*20)
s(r)=1/(1+abs(r))
Davis(n,a)=Davis_0(n*exp(-a))/Davis_0(exp(-a))
Davis_0(n)=n*log(n)/(n-1)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a2
0 1.3131504898e+11 0.00e+00 2.22e+04 1.000000e+00
14 3.3842366655e+10 -3.38e-02 2.22e+00 6.271904e-01
After 14 iterations the fit converged.
final sum of squares of residuals : 3.38424e+10
rel. change during last iteration : -3.37559e-07
degrees of freedom (FIT_NDF) : 182
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 13636.2
variance of residuals (reduced chisquare) = WSSR/ndf : 1.85947e+08
Final set of parameters Asymptotic Standard Error
======================= ==========================
a2 = 0.62719 +/- 0.006711 (1.07%)
*******************************************************************************
Thu Jun 8 14:25:35 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 183
residuals are weighted equally (unit weight)
function used for fitting: TT3(x)
TT3(n)=Logistic_C(n,s(c3),s(b3),s(a3)*20)
s(r)=1/(1+abs(r))
Logistic_C(n,c,b,a)=Logistic_C_S(n*exp(-a),c,b)/Logistic_C_S(exp(-a),c,b)
Logistic_C_S(n,c,b)=Logistic_S(n,c)**(1-b)*n**b
Logistic_S(n,c)=(2**(1/c))*n/(n**c+1)**(1/c)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c3 b3 a3
0 5.4955976728e+12 0.00e+00 3.41e+05 1.000000e+00 1.000000e+00 1.000000e+00
4606 8.2113459353e+08 -1.00e+00 3.41e+03 4.720483e-02 4.995320e-01 3.024380e+01
After 4606 iterations the fit converged.
final sum of squares of residuals : 8.21135e+08
rel. change during last iteration : -9.99963e-06
degrees of freedom (FIT_NDF) : 180
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2135.85
variance of residuals (reduced chisquare) = WSSR/ndf : 4.56186e+06
Final set of parameters Asymptotic Standard Error
======================= ==========================
c3 = 0.0472048 +/- 312.7 (6.625e+05%)
b3 = 0.499532 +/- 0.01494 (2.992%)
a3 = 30.2438 +/- 1.528e+04 (5.053e+04%)
correlation matrix of the fit parameters:
c3 b3 a3
c3 1.000
b3 0.210 1.000
a3 1.000 0.208 1.000
*******************************************************************************
Thu Jun 8 14:26:12 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 183
residuals are weighted equally (unit weight)
function used for fitting: TT4(x)
TT4(n)=Linear(n,s(c4)*0.1,s(a4)*20)
s(r)=1/(1+abs(r))
Linear(n,c,a)=Linear_0(n*exp(-a),c)/Linear_0(exp(-a),c)
Linear_0(n,c)=(n<1)?n:(n>exp(1/c))?sqrt(exp(1/c)):exp(log(n)-c/2.0*log(n)**2)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c4 a4
0 6.3452919662e+14 0.00e+00 2.37e+06 1.000000e+00 1.000000e+00
115 1.5515564598e+10 -2.51e-01 2.37e-06 1.638653e+00 2.468387e+07
After 115 iterations the fit converged.
final sum of squares of residuals : 1.55156e+10
rel. change during last iteration : -2.50782e-06
degrees of freedom (FIT_NDF) : 181
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 9258.58
variance of residuals (reduced chisquare) = WSSR/ndf : 8.57214e+07
Final set of parameters Asymptotic Standard Error
======================= ==========================
c4 = 1.63865 +/- 0.1906 (11.63%)
a4 = 2.46839e+07 +/- 1.805e+13 (7.314e+07%)
correlation matrix of the fit parameters:
c4 a4
c4 1.000
a4 0.998 1.000
==> zipf/NKJP-2G/fit.log <==
*******************************************************************************
Thu Jun 8 14:25:28 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 219
residuals are weighted equally (unit weight)
function used for fitting: TT1(x)
TT1(n)=Constant(n,s(b1))
s(r)=1/(1+abs(r))
Constant(n,b)=n**b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda b1
0 3.4509895864e+14 0.00e+00 3.60e+04 1.000000e+00
13 1.1786571188e+13 -1.17e-01 3.60e+02 -3.220617e-01
After 13 iterations the fit converged.
final sum of squares of residuals : 1.17866e+13
rel. change during last iteration : -1.17227e-06
degrees of freedom (FIT_NDF) : 218
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 232523
variance of residuals (reduced chisquare) = WSSR/ndf : 5.40668e+10
Final set of parameters Asymptotic Standard Error
======================= ==========================
b1 = -0.322062 +/- 0.001106 (0.3434%)
*******************************************************************************
Thu Jun 8 14:25:29 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 219
residuals are weighted equally (unit weight)
function used for fitting: TT2(x)
TT2(n)=Davis(n,s(a2)*20)
s(r)=1/(1+abs(r))
Davis(n,a)=Davis_0(n*exp(-a))/Davis_0(exp(-a))
Davis_0(n)=n*log(n)/(n-1)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a2
0 3.4448108985e+14 0.00e+00 3.82e+04 1.000000e+00
5 1.3855097962e+13 -3.39e-02 3.82e+03 1.976880e-01
After 5 iterations the fit converged.
final sum of squares of residuals : 1.38551e+13
rel. change during last iteration : -3.39471e-07
degrees of freedom (FIT_NDF) : 218
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 252102
variance of residuals (reduced chisquare) = WSSR/ndf : 6.35555e+10
Final set of parameters Asymptotic Standard Error
======================= ==========================
a2 = 0.197688 +/- 0.001506 (0.7617%)
*******************************************************************************
Thu Jun 8 14:25:29 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 219
residuals are weighted equally (unit weight)
function used for fitting: TT3(x)
TT3(n)=Logistic_C(n,s(c3),s(b3),s(a3)*20)
s(r)=1/(1+abs(r))
Logistic_C(n,c,b,a)=Logistic_C_S(n*exp(-a),c,b)/Logistic_C_S(exp(-a),c,b)
Logistic_C_S(n,c,b)=Logistic_S(n,c)**(1-b)*n**b
Logistic_S(n,c)=(2**(1/c))*n/(n**c+1)**(1/c)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c3 b3 a3
0 7.4680084137e+12 0.00e+00 2.17e+06 1.000000e+00 1.000000e+00 1.000000e+00
6 1.0219852770e+09 -2.97e-02 2.17e+00 7.266339e-01 9.301558e-01 1.009099e+00
After 6 iterations the fit converged.
final sum of squares of residuals : 1.02199e+09
rel. change during last iteration : -2.97477e-07
degrees of freedom (FIT_NDF) : 216
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 2175.18
variance of residuals (reduced chisquare) = WSSR/ndf : 4.73141e+06
Final set of parameters Asymptotic Standard Error
======================= ==========================
c3 = 0.726634 +/- 0.02215 (3.049%)
b3 = 0.930156 +/- 0.0009297 (0.09995%)
a3 = 1.0091 +/- 0.001059 (0.1049%)
correlation matrix of the fit parameters:
c3 b3 a3
c3 1.000
b3 0.855 1.000
a3 -0.858 -0.999 1.000
*******************************************************************************
Thu Jun 8 14:25:29 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 219
residuals are weighted equally (unit weight)
function used for fitting: TT4(x)
TT4(n)=Linear(n,s(c4)*0.1,s(a4)*20)
s(r)=1/(1+abs(r))
Linear(n,c,a)=Linear_0(n*exp(-a),c)/Linear_0(exp(-a),c)
Linear_0(n,c)=(n<1)?n:(n>exp(1/c))?sqrt(exp(1/c)):exp(log(n)-c/2.0*log(n)**2)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c4 a4
0 2.9590753151e+16 0.00e+00 2.53e+07 1.000000e+00 1.000000e+00
11 1.2712559898e+11 -4.39e-03 2.53e-04 2.839244e+00 2.616828e+01
After 11 iterations the fit converged.
final sum of squares of residuals : 1.27126e+11
rel. change during last iteration : -4.38716e-08
degrees of freedom (FIT_NDF) : 217
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 24204
variance of residuals (reduced chisquare) = WSSR/ndf : 5.85832e+08
Final set of parameters Asymptotic Standard Error
======================= ==========================
c4 = 2.83924 +/- 0.02373 (0.8358%)
a4 = 26.1683 +/- 2.199 (8.403%)
correlation matrix of the fit parameters:
c4 a4
c4 1.000
a4 0.999 1.000
==> zipf/NKJP-300M/fit.log <==
*******************************************************************************
Thu Jun 8 14:26:21 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 202
residuals are weighted equally (unit weight)
function used for fitting: TT1(x)
TT1(n)=Constant(n,s(b1))
s(r)=1/(1+abs(r))
Constant(n,b)=n**b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda b1
0 4.4941386737e+13 0.00e+00 1.53e+04 1.000000e+00
5 2.4443011796e+12 -4.09e-01 1.53e+03 3.039299e-01
After 5 iterations the fit converged.
final sum of squares of residuals : 2.4443e+12
rel. change during last iteration : -4.09094e-06
degrees of freedom (FIT_NDF) : 201
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 110276
variance of residuals (reduced chisquare) = WSSR/ndf : 1.21607e+10
Final set of parameters Asymptotic Standard Error
======================= ==========================
b1 = 0.30393 +/- 0.001553 (0.5111%)
*******************************************************************************
Thu Jun 8 14:26:21 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 202
residuals are weighted equally (unit weight)
function used for fitting: TT2(x)
TT2(n)=Davis(n,s(a2)*20)
s(r)=1/(1+abs(r))
Davis(n,a)=Davis_0(n*exp(-a))/Davis_0(exp(-a))
Davis_0(n)=n*log(n)/(n-1)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a2
0 4.4193944223e+13 0.00e+00 3.04e+04 1.000000e+00
7 4.7741078652e+11 -8.19e-03 3.04e+01 -2.577097e-01
After 7 iterations the fit converged.
final sum of squares of residuals : 4.77411e+11
rel. change during last iteration : -8.18639e-08
degrees of freedom (FIT_NDF) : 201
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 48735.8
variance of residuals (reduced chisquare) = WSSR/ndf : 2.37518e+09
Final set of parameters Asymptotic Standard Error
======================= ==========================
a2 = -0.25771 +/- 0.0009491 (0.3683%)
*******************************************************************************
Thu Jun 8 14:26:21 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 202
residuals are weighted equally (unit weight)
function used for fitting: TT3(x)
TT3(n)=Logistic_C(n,s(c3),s(b3),s(a3)*20)
s(r)=1/(1+abs(r))
Logistic_C(n,c,b,a)=Logistic_C_S(n*exp(-a),c,b)/Logistic_C_S(exp(-a),c,b)
Logistic_C_S(n,c,b)=Logistic_S(n,c)**(1-b)*n**b
Logistic_S(n,c)=(2**(1/c))*n/(n**c+1)**(1/c)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c3 b3 a3
0 1.3468029990e+11 0.00e+00 9.13e+05 1.000000e+00 1.000000e+00 1.000000e+00
6 2.3635247050e+08 -6.25e-04 9.13e-01 1.356875e+00 1.262773e+00 8.435533e-01
After 6 iterations the fit converged.
final sum of squares of residuals : 2.36352e+08
rel. change during last iteration : -6.25351e-09
degrees of freedom (FIT_NDF) : 199
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 1089.82
variance of residuals (reduced chisquare) = WSSR/ndf : 1.1877e+06
Final set of parameters Asymptotic Standard Error
======================= ==========================
c3 = 1.35688 +/- 0.01434 (1.057%)
b3 = 1.26277 +/- 0.003911 (0.3097%)
a3 = 0.843553 +/- 0.002148 (0.2546%)
correlation matrix of the fit parameters:
c3 b3 a3
c3 1.000
b3 0.957 1.000
a3 -0.968 -0.999 1.000
*******************************************************************************
Thu Jun 8 14:26:21 2023
FIT: data read from 'herdan_hapaxes_exp.csv' u 1:2
format = x:z
x range restricted to [1.00000 : *]
#datapoints = 202
residuals are weighted equally (unit weight)
function used for fitting: TT4(x)
TT4(n)=Linear(n,s(c4)*0.1,s(a4)*20)
s(r)=1/(1+abs(r))
Linear(n,c,a)=Linear_0(n*exp(-a),c)/Linear_0(exp(-a),c)
Linear_0(n,c)=(n<1)?n:(n>exp(1/c))?sqrt(exp(1/c)):exp(log(n)-c/2.0*log(n)**2)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda c4 a4
0 5.2557522466e+15 0.00e+00 9.02e+06 1.000000e+00 1.000000e+00
9 1.2366868191e+10 -1.66e-02 9.02e-03 1.735926e+00 5.334799e+00
After 9 iterations the fit converged.
final sum of squares of residuals : 1.23669e+10
rel. change during last iteration : -1.662e-07
degrees of freedom (FIT_NDF) : 200
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7863.48
variance of residuals (reduced chisquare) = WSSR/ndf : 6.18343e+07
Final set of parameters Asymptotic Standard Error
======================= ==========================
c4 = 1.73593 +/- 0.01338 (0.7708%)
a4 = 5.3348 +/- 0.07453 (1.397%)
correlation matrix of the fit parameters:
c4 a4
c4 1.000
a4 0.998 1.000