CNLSRDF

class pystoned.CNLSRDF.CNLSRDF(y, x, z=None, rdf='DI', rts='vrs')

Convex Nonparametric Least Square with radial distance function

__init__(y, x, z=None, rdf='DI', rts='vrs')

CNLSRDF model :param y: output variable. :type y: float :param x: input variables. :type x: float :param z: control variables. Defaults to None. :type z: float, optional :param cet: CET_ADDI (additive composite error term) or CET_MULT (multiplicative composite error term). Defaults to CET_ADDI. :type cet: String, optional :param rdf: RDF_DI (input distance function) or RDF_DO (output distance function). Defaults to RDF_DI. :type rdf: String, optional :param rts: RTS_VRS (variable returns to scale) or RTS_CRS (constant returns to scale). Defaults to RTS_VRS. :type rts: String, optional

display_alpha()

Display alpha value

display_beta()

Display beta value

display_gamma()

Display gamma value

display_kappa()

Display kappa value

display_lamda()

Display lamda value

display_residual()

Dispaly residual value

display_status()

Display the status of problem

get_adjusted_alpha()

Return the shifted constatnt(alpha) term by CCNLS

get_adjusted_residual()

Return the shifted residuals(epsilon) tern by CCNLS

get_alpha()

Return alpha value by array

get_beta()

Return beta value by array

get_chi()

Return estimated radial distance function by array

get_frontier()

Return estimated frontier value by array

get_gamma()

Return gamma value by array

get_kappa()

Return kappa value by array

get_lamda()

Return lamda value by array

get_predict(x_test)

Return the estimated function in testing sample

get_residual()

Return residual value by array

get_status()

Return status

optimize(email='local', solver=None)

Optimize the function by requested method :param email: The email address for remote optimization. It will optimize locally if OPT_LOCAL is given. :type email: string :param solver: The solver chosen for optimization. It will optimize with default solver if OPT_DEFAULT is given. :type solver: string