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scipy.optimize.

# leastsq

scipy.optimize.leastsq(func, x0, args=(), Dfun=None, full_output=False, col_deriv=False, ftol=1.49012e-08, xtol=1.49012e-08, gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None)

  Minimize the sum of squares of a set of equations.

      x = arg min(sum(func(y)**2,axis=0))
               y

  Parameters:
    func callable

      Should take at least one (possibly length `N` vector) argument and returns `M` floating point numbers. It must not return NaNs or fitting might fail. `M` must be greater than or equal to `N`.

    x0 ndarray

      The starting estimate for the minimization.

    args tuple, optional

      Any extra arguments to func are placed in this tuple.

    Dfun callable, optional

      A function or method to compute the Jacobian of func with derivatives across the rows. If this is None, the Jacobian will be estimated.

    full_output bool, optional

      If `True`, return all optional outputs (not just x and ier).

    col_deriv bool, optional

      If `True`, specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).

    ftol float, optional

      Relative error desired in the sum of squares.

    xtol float, optional

      Relative error desired in the approximate solution.

    gtol float, optional

      Orthogonality desired between the function vector and the columns of the Jacobian.

    maxfev int, optional

      The maximum number of calls to the function. If Dfun is provided, then the default maxfev is 100*(N+1) where N is the number of elements in x0, otherwise the default maxfev is 200*(N+1).

    epsfcn float, optional

      A variable used in determining a suitable step length for the forward- difference approximation of the Jacobian (for Dfun=None). Normally the actual step length will be sqrt(epsfcn)*x If epsfcn is less than the machine precision, it is assumed that the relative errors are of the order of the machine precision.

    factor float, optional

      A parameter determining the initial step bound (`factor * || diag * x||`). Should be in interval `(0.1, 100)`.

    diag sequence, optional

      N positive entries that serve as a scale factors for the variables.

  Returns:
    x ndarray

      The solution (or the result of the last iteration for an unsuccessful call).

    cov_x ndarray

      The inverse of the Hessian. fjac and ipvt are used to construct an estimate of the Hessian. A value of None indicates a singular matrix, which means the curvature in parameters x is numerically flat. To obtain the covariance matrix of the parameters x, cov_x must be multiplied by the variance of the residuals – see curve_fit. Only returned if full_output is `True`.

    infodict dict

      a dictionary of optional outputs with the keys:

      `nfev`

        The number of function calls

      `fvec`

        The function evaluated at the output

      `fjac`

        A permutation of the R matrix of a QR factorization of the final approximate Jacobian matrix, stored column wise. Together with ipvt, the covariance of the estimate can be approximated.

      `ipvt`

        An integer array of length N which defines a permutation matrix, p, such that fjac*p = q*r, where r is upper triangular with diagonal elements of nonincreasing magnitude. Column j of p is column ipvt(j) of the identity matrix.

      `qtf`

        The vector (transpose(q) * fvec).

      Only returned if full_output is `True`.

    mesg str

      A string message giving information about the cause of failure. Only returned if full_output is `True`.

    ier int

      An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. Otherwise, the solution was not found. In either case, the optional output variable ‘mesg’ gives more information.

  See also

  `least_squares`

    Newer interface to solve nonlinear least-squares problems with bounds on the variables. See `method='lm'` in particular.

  Notes

  “leastsq” is a wrapper around MINPACK’s lmdif and lmder algorithms.

  cov_x is a Jacobian approximation to the Hessian of the least squares objective function. This approximation assumes that the objective function is based on the difference between some observed target data (ydata) and a (non-linear) function of the parameters f(xdata, params)

      func(params) = ydata - f(xdata, params)

  so that the objective function is

        min   sum((ydata - f(xdata, params))**2, axis=0)
      params

  The solution, x, is always a 1-D array, regardless of the shape of x0, or whether x0 is a scalar.

  Examples

      >>> from scipy.optimize import leastsq
      >>> def func(x):
      ...     return 2*(x-3)**2+1
      >>> leastsq(func, 0)
      (array([2.99999999]), 1)