185 lines
5.0 KiB
Plaintext
185 lines
5.0 KiB
Plaintext
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*******************************************************************************
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Tue Oct 24 02:36:17 2017
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FIT: data read from "gradient.csv" every ::1 using 2:5
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format = x:z
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#datapoints = 305
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residuals are weighted equally (unit weight)
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function used for fitting: f(x)
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fitted parameters initialized with current variable values
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Iteration 0
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WSSR : 1.35967e+07 delta(WSSR)/WSSR : 0
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delta(WSSR) : 0 limit for stopping : 1e-05
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lambda : 0.707243
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initial set of free parameter values
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a = 1
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b = 1
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After 4 iterations the fit converged.
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final sum of squares of residuals : 773379
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rel. change during last iteration : -8.7995e-07
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degrees of freedom (FIT_NDF) : 303
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rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 50.5213
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variance of residuals (reduced chisquare) = WSSR/ndf : 2552.4
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Final set of parameters Asymptotic Standard Error
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======================= ==========================
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a = -3455.11 +/- 597.4 (17.29%)
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b = 271.216 +/- 11.74 (4.33%)
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correlation matrix of the fit parameters:
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a b
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a 1.000
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b -0.969 1.000
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*******************************************************************************
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Tue Oct 24 02:36:17 2017
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FIT: data read from "gradient.csv" every ::1 using 4:5
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format = x:z
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#datapoints = 305
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residuals are weighted equally (unit weight)
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function used for fitting: g(x)
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fitted parameters initialized with current variable values
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Iteration 0
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WSSR : 1.3481e+07 delta(WSSR)/WSSR : 0
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delta(WSSR) : 0 limit for stopping : 1e-05
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lambda : 0.974091
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initial set of free parameter values
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aa = 1
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bb = 1
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After 4 iterations the fit converged.
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final sum of squares of residuals : 799152
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rel. change during last iteration : -9.18053e-10
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degrees of freedom (FIT_NDF) : 303
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rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 51.3562
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variance of residuals (reduced chisquare) = WSSR/ndf : 2637.46
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Final set of parameters Asymptotic Standard Error
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======================= ==========================
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aa = 647.632 +/- 136.2 (21.03%)
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bb = -408.074 +/- 129.1 (31.63%)
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correlation matrix of the fit parameters:
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aa bb
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aa 1.000
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bb -1.000 1.000
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*******************************************************************************
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Tue Oct 24 02:36:17 2017
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FIT: data read from "gradient.csv" every ::1 using 4:6
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format = x:z
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#datapoints = 305
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residuals are weighted equally (unit weight)
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function used for fitting: h(x)
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fitted parameters initialized with current variable values
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Iteration 0
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WSSR : 1.4212e+07 delta(WSSR)/WSSR : 0
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delta(WSSR) : 0 limit for stopping : 1e-05
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lambda : 0.974091
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initial set of free parameter values
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aaa = 1
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bbb = 1
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After 4 iterations the fit converged.
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final sum of squares of residuals : 146702
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rel. change during last iteration : -2.79543e-10
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degrees of freedom (FIT_NDF) : 303
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rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 22.0038
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variance of residuals (reduced chisquare) = WSSR/ndf : 484.166
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Final set of parameters Asymptotic Standard Error
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======================= ==========================
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aaa = -20.6354 +/- 58.36 (282.8%)
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bbb = 236.239 +/- 55.3 (23.41%)
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correlation matrix of the fit parameters:
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aaa bbb
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aaa 1.000
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bbb -1.000 1.000
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*******************************************************************************
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Tue Oct 24 02:36:17 2017
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FIT: data read from "gradient.csv" every ::1 using 3:6
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format = x:z
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#datapoints = 305
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residuals are weighted equally (unit weight)
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function used for fitting: i(x)
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fitted parameters initialized with current variable values
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Iteration 0
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WSSR : 1.43362e+07 delta(WSSR)/WSSR : 0
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delta(WSSR) : 0 limit for stopping : 1e-05
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lambda : 0.707107
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initial set of free parameter values
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aaaa = 1
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bbbb = 1
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After 3 iterations the fit converged.
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final sum of squares of residuals : 146763
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rel. change during last iteration : -6.86135e-14
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degrees of freedom (FIT_NDF) : 303
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rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 22.0083
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variance of residuals (reduced chisquare) = WSSR/ndf : 484.366
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Final set of parameters Asymptotic Standard Error
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======================= ==========================
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aaaa = 1.23966 +/- 4.167e+17 (3.362e+19%)
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bbbb = 216.691 +/- 4.63e+14 (2.137e+14%)
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correlation matrix of the fit parameters:
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aaaa bbbb
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aaaa 1.000
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bbbb -1.000 1.000
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