ccs_fit.fitting.objective

This module constructs and solves the spline objective.

Module Contents

Classes

Objective

Objective function for the ccs method.

Attributes

logger

ccs_fit.fitting.objective.logger
class ccs_fit.fitting.objective.Objective(l_twb, l_one, sto, energy_ref, force_ref, gen_params, energy_ewald=[], force_ewald=[])[source]

Objective function for the ccs method.

reduce_stoichiometry()[source]
solution()[source]

Function to solve the objective with constraints.

predict(xx)[source]

Predict results.

Parameters:

  • xx (ndarrray) – Solution array from training.

  • intervals (Needs to be updated to handle merging and dissolving) –

static solver(pp, qq, gg, hh, aa, bb, maxiter=300, tol=(1e-10, 1e-10, 1e-10))[source]

The solver for the objective.

Parameters:

  • pp (matrix) – P matrix as per standard Quadratic Programming(QP) notation.

  • qq (matrix) – q matrix as per standard QP notation.

  • gg (matrix) – G matrix as per standard QP notation.

  • hh (matrix) – h matrix as per standard QP notation

  • aa (matrix) – A matrix as per standard QP notation.

  • bb (matrix) – b matrix as per standard QP notation

  • maxiter (int, optional) – maximum iteration steps (default: 300).

  • tol (tuple, optional) – tolerance value of the solution (default: (1e-10, 1e-10, 1e-10)).

Returns:

dictionary containing solution details

Return type:

sol (dict)

merge_intervals()[source]
unfold_intervals()[source]
eval_obj(xx)[source]

Mean squared error function.

Parameters:

xx (ndarray) – the solution for the objective

Returns:

mean square error

Return type:

float

assign_parameter_values(xx)[source]
list_iterator()[source]

Iterates over the self.np attribute.

get_m()[source]

Returns the M matrix.

Returns:

The M matrix.

Return type:

ndarray

get_g(n_switch)[source]

Returns constraints matrix.

Parameters:

n_switch (int) – switching point to change signs of curvatures.

Returns:

returns G and A matrix

Return type:

ndarray

write_error(fname='CCS_error.out')[source]

Prints the errors in a file.

Parameters:

  • mdl_eng (ndarray) – Energy prediction values from splines.

  • ref_eng (ndarray) – Reference energy values.

  • mse (float) – Mean square error.

  • fname (str, optional) – Output filename (default: ‘error.out’).

write_error_forces(mdl_for, ref_for, fname='CCS_error_forces.out')[source]

Prints the errors in a file.

Parameters:

  • mdl_eng (ndarray) – Energy prediction values from splines.

  • ref_eng (ndarray) – Reference energy values.

  • mse (float) – Mean square error.

  • fname (str, optional) – Output filename (default: ‘error.out’).

write_CCS_params(fname='CCS_params.json')[source]
gen_Buckingham()[source]
unconstrained_fit()[source]
ridge_regresssion()[source]