fitbenchmarking.cost_func.nlls_base_cost_func module
Implements the base non-linear least squares cost function
- class fitbenchmarking.cost_func.nlls_base_cost_func.BaseNLLSCostFunc(problem)
Bases:
fitbenchmarking.cost_func.base_cost_func.CostFuncThis defines a base cost function for objectives of the type
\[\min_p \sum_{i=1}^n r(y_i, x_i, p)^2\]where \(p\) is a vector of length \(m\), and we start from a given initial guess for the optimal parameters.
- eval_cost(params, **kwargs)
Evaluate the square of the L2 norm of the residuals, \(\sum_i r(x_i,y_i,p)^2\) at the given parameters
- Parameters
params (list) – The parameters, \(p\), to calculate residuals for
- Returns
The sum of squares of residuals for the datapoints at the given parameters
- Return type
numpy array
- abstract eval_r(params, **kwargs)
Calculate residuals used in Least-Squares problems
- Parameters
params (list) – The parameters to calculate residuals for
- Returns
The residuals for the datapoints at the given parameters
- Return type
numpy array
- hes_cost(params, **kwargs)
Uses the Hessian of the model to evaluate the Hessian of the cost function, \(\nabla_p^2 F(r(x,y,p))\), at the given parameters.
- Parameters
params (list) – The parameters at which to calculate Hessians
- Returns
evaluated Hessian of the cost function
- Return type
2D numpy array
- jac_cost(params, **kwargs)
Uses the Jacobian of the model to evaluate the Jacobian of the cost function, \(\nabla_p F(r(x,y,p))\), at the given parameters. :param params: The parameters at which to calculate Jacobians :type params: list :return: evaluated Jacobian of the cost function :rtype: 1D numpy array