pyrtid.inverse.eval_loss_ls#
- pyrtid.inverse.eval_loss_ls(d_obs: ndarray[Any, dtype[float64]], d_calc: ndarray[Any, dtype[float64]], x_sigma: ndarray[Any, dtype[float64]]) float[source]#
Return the objective function with regard to x.
\[\mathcal{J} = \dfrac{1}{2} \sum_{n=0}^{N} \left(\dfrac{d_{\mathrm{obs}}^{n} - d_{\mathrm{calc}}^{n}}{\sigma_{\mathrm{obs}}^{n}} \right)^{2}\]with $n$, a time with an observation, and $lvert bm{d}_{mathrm{obs}} rvert$ the number of observation points.
- Parameters
d_obs (NDArrayFloat) – 1D vector of observed values.
d_calc (NDArrayFloat) – 1D vector of calculated values.
d_obs – 1D vector of uncertainties on observed values.
- Returns
objective – the value of the objective function
- Return type