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# LMLSQFitter

class astropy.modeling.fitting.LMLSQFitter(calc_uncertainties=False)

  Bases: `_NLLSQFitter`

  `scipy.optimize.least_squares` Levenberg-Marquardt algorithm and least squares statistic.

  Parameters:
    calc_uncertainties bool

      If the covariance matrix should be computed and set in the fit_info. Default: False

  Attributes:
    fit_info

      A `scipy.optimize.OptimizeResult` class which contains all of the most recent fit information

  Attributes Summary

  `supported_constraints` | The constraint types supported by this fitter type.

  Methods Summary

  `__call__`(model, x, y[, z, weights, maxiter, ...]) | Fit data to this model.
  `objective_function`(fps, *args[, ...]) | Function to minimize.

  Attributes Documentation

  supported_constraints = ['fixed', 'tied', 'bounds']

    The constraint types supported by this fitter type.

  Methods Documentation

  __call__(model, x, y, z=None, weights=None, maxiter=100, acc=1e-07, epsilon=np.float64(1.4901161193847656e-08), estimate_jacobian=False, filter_non_finite=False, inplace=False)

    Fit data to this model.

    Parameters:
      model `FittableModel`

        model to fit to x, y, z

      x `array`

        input coordinates

      y `array`

        input coordinates

      z `array`, optional

        input coordinates

      weights `array`, optional

        Weights for fitting. For data with Gaussian uncertainties, the weights should be 1/sigma.

        Changed in version 5.3: Calculate parameter covariances while accounting for `weights` as “absolute” inverse uncertainties. To recover the old behavior, choose `weights=None`.

      maxiter `python:int`

        maximum number of iterations

      acc `python:float`

        Relative error desired in the approximate solution

      epsilon `python:float`

        A suitable step length for the forward-difference approximation of the Jacobian (if model.fjac=None). If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.

      estimate_jacobian bool

        If False (default) and if the model has a fit_deriv method, it will be used. Otherwise the Jacobian will be estimated. If True, the Jacobian will be estimated in any case.

      equivalencies `python:list` or `python:None`, optional, keyword-only

        List of additional equivalencies that are should be applied in case x, y and/or z have units. Default is None.

      filter_non_finite bool, optional

        Whether or not to filter data with non-finite values. Default is False

      inplace bool, optional

        If `False` (the default), a copy of the model with the fitted parameters set will be returned. If `True`, the returned model will be the same instance as the model passed in, and the parameter values will be changed inplace.

    Returns:
      fitted_model `FittableModel`

        If `inplace` is `False` (the default), this is a copy of the input model with parameters set by the fitter. If `inplace` is `True`, this is the same model as the input model, with parameters updated to be those set by the fitter.

  objective_function(fps, *args, fit_param_indices=None)

    Function to minimize.

    Parameters:
      fps `python:list`

        parameters returned by the fitter

      args `python:list`

        [model, [weights], [input coordinates]]

      fit_param_indices `python:list`, optional

        The `fit_param_indices` as returned by `model_to_fit_params`. This is a list of the parameter indices being fit, so excluding any tied or fixed parameters. This can be passed in to the objective function to prevent it having to be computed on every call. This must be optional as not all fitters support passing kwargs to the objective function.