:py:mod:`ccs_fit.fitting.objective`
===================================

.. py:module:: ccs_fit.fitting.objective

.. autoapi-nested-parse::

   This module constructs and solves the spline objective.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   ccs_fit.fitting.objective.Objective




Attributes
~~~~~~~~~~

.. autoapisummary::

   ccs_fit.fitting.objective.logger


.. py:data:: logger

   

.. py:class:: Objective(l_twb, l_one, sto, energy_ref, force_ref, gen_params, energy_ewald=[], force_ewald=[])

   Objective function for the ccs method.

   .. py:method:: reduce_stoichiometry()


   .. py:method:: solution()

      Function to solve the objective with constraints.


   .. py:method:: predict(xx)

      Predict results.

      :param xx: Solution array from training.
      :type xx: ndarrray
      :param Needs to be updated to handle merging and dissolving intervals:


   .. py:method:: solver(pp, qq, gg, hh, aa, bb, maxiter=300, tol=(1e-10, 1e-10, 1e-10))
      :staticmethod:

      The solver for the objective.

      :param pp: P matrix as per standard Quadratic Programming(QP)
                 notation.
      :type pp: matrix
      :param qq: q matrix as per standard QP notation.
      :type qq: matrix
      :param gg: G matrix as per standard QP notation.
      :type gg: matrix
      :param hh: h matrix as per standard QP notation
      :type hh: matrix
      :param aa: A matrix as per standard QP notation.
      :type aa: matrix
      :param bb: b matrix as per standard QP notation
      :type bb: matrix
      :param maxiter: maximum iteration steps (default: 300).
      :type maxiter: int, optional
      :param tol: tolerance value of the solution
                  (default: (1e-10, 1e-10, 1e-10)).
      :type tol: tuple, optional

      :returns: dictionary containing solution details
      :rtype: sol (dict)


   .. py:method:: merge_intervals()


   .. py:method:: unfold_intervals()


   .. py:method:: eval_obj(xx)

      Mean squared error function.

      :param xx: the solution for the objective
      :type xx: ndarray

      :returns: mean square error
      :rtype: float


   .. py:method:: assign_parameter_values(xx)


   .. py:method:: list_iterator()

      Iterates over the self.np attribute.


   .. py:method:: get_m()

      Returns the M matrix.

      :returns: The M matrix.
      :rtype: ndarray


   .. py:method:: get_g(n_switch)

      Returns constraints matrix.

      :param n_switch: switching point to change signs of curvatures.
      :type n_switch: int

      :returns: returns G and A matrix
      :rtype: ndarray


   .. py:method:: write_error(fname='CCS_error.out')

      Prints the errors in a file.

      :param mdl_eng: Energy prediction values from splines.
      :type mdl_eng: ndarray
      :param ref_eng: Reference energy values.
      :type ref_eng: ndarray
      :param mse: Mean square error.
      :type mse: float
      :param fname: Output filename (default: 'error.out').
      :type fname: str, optional


   .. py:method:: write_error_forces(mdl_for, ref_for, fname='CCS_error_forces.out')

      Prints the errors in a file.

      :param mdl_eng: Energy prediction values from splines.
      :type mdl_eng: ndarray
      :param ref_eng: Reference energy values.
      :type ref_eng: ndarray
      :param mse: Mean square error.
      :type mse: float
      :param fname: Output filename (default: 'error.out').
      :type fname: str, optional


   .. py:method:: write_CCS_params(fname='CCS_params.json')


   .. py:method:: gen_Buckingham()


   .. py:method:: unconstrained_fit()


   .. py:method:: ridge_regresssion()