CNLSDDF¶
- class pystoned.CNLSDDF.CNLSDDF(y, x, b=None, gy=[1], gx=[1], gb=None, fun='prod')[source]¶
Convex Nonparametric Least Square with directional distance function
- __init__(y, x, b=None, gy=[1], gx=[1], gb=None, fun='prod')[source]¶
CNLS DDF model
- Parameters:
y (float) – output variable.
x (float) – input variables.
b (float), optional) – undesirable output variables. Defaults to None.
gy (list, optional) – output directional vector. Defaults to [1].
gx (list, optional) – input directional vector. Defaults to [1].
gb (list, optional) – undesirable output directional vector. Defaults to None.
fun (String, optional) – FUN_PROD (production frontier) or FUN_COST (cost frontier). Defaults to FUN_PROD.
- display_alpha()¶
Display alpha value
- display_beta()¶
Display beta 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_frontier()¶
Return estimated frontier 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)[source]¶
Optimize the function by requested method
- Parameters:
email (string) – The email address for remote optimization. It will optimize locally if OPT_LOCAL is given.
solver (string) – The solver chosen for optimization. It will optimize with default solver if OPT_DEFAULT is given.