[Octave-bug-tracker] [bug #62393] nonlin_curvefit fails in 7.1.0 with "function called with too many inputs"

[anonymous]

URL:
  <https://savannah.gnu.org/bugs/?62393>

                 Summary: nonlin_curvefit fails in 7.1.0 with "function called
with too many inputs"
                 Project: GNU Octave
            Submitted by: None
            Submitted on: Mon 02 May 2022 04:13:09 PM UTC
                Category: Octave Forge Package
                Severity: 3 - Normal
                Priority: 5 - Normal
              Item Group: Segfault, Bus Error, etc.
                  Status: None
             Assigned to: None
         Originator Name: RandomDude
        Originator Email: riander73@gmail.com
             Open/Closed: Open
                 Release: 7.1.0
         Discussion Lock: Any
        Operating System: Microsoft Windows

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Details:

All my scripts that use "nlinfit" and "lsqcurvefit" fail with the following
error:


error: @<anonymous>: function called with too many inputs
error: called from
    nonlin_curvefit>@<anonymous>
    __nonlin_residmin__>@<anonymous> at line 316 column 42
    __dfdp__ at line 304 column 15
    __nonlin_residmin__>@<anonymous> at line 329 column 26
    __jacobian_constants__>@<anonymous> at line 121 column 11
    __lm_svd__ at line 196 column 9
    __nonlin_residmin__ at line 452 column 25
    nonlin_curvefit at line 83 column 22
    nlinfit at line 169 column 20


Here is example code that results in error, taken from
"https://octave.sourceforge.io/optim/function/nlinfit.html":

modelfun = @(b, x) (b(1) + b(2) * exp (- b(3) * x));
 %% actual value
 beta_without_noise = [1; 3; 2];
 x = [3.49622; 0.33751; 1.25675; 3.66981; 0.26237; 5.51095; ...
      2.11407; 1.48774; 6.22436; 2.04519];
 y_actual = modelfun (beta_without_noise, x);
 noise = [0.176110; -0.066850; 0.231000; -0.047570; -0.108230; ...
          0.122790; 0.062940; 0.151510; 0.116010; -0.097460];
 y_noisy = y_actual + noise;
 %% initial guess
 beta0 = [2; 2; 2];
 %% weights vector
 weights = [5; 16; 1; 20; 12; 11; 17; 8; 11; 13];
 [beta, R, J, covb, mse] = nlinfit (x, y_noisy, modelfun, beta0)
 [beta_w, R_w, J_w, covb_w, mse_w] = nlinfit (x, y_noisy, modelfun, beta0, [],
"weights", weights)






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