Doing non-linear regression for a set of data

In the next section, we will see how to do the non-linear regression for a set of data.

Getting ready

The following is the main requirement that we need:

scipy.optimize.curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False, check_finite=True, bounds=(-inf, inf), method=None, jac=None, **kwargs)

How to do it...

We present the main functions and parameters:

The parameters are:

• f: Callable. The model function, f(x, ...). It must take the independent variable as the first argument and the parameters to fit as separate remaining arguments.
• xdata: An M-length sequence or an (k,M)-shaped array for functions with k predictors. The independent variable where the data is measured.
• ydata: M-length sequence. The dependent data—nominally f(xdata, ...).
• p0: None, scalar, or N-length sequence, optional. Initial guess for the parameters. If none, then the initial values will all be 1 (if the number of parameters for the function can be determined using...