Surrogate

Surrogate provides methods for different surrogate formulations of the original problem. More...

Classes
class	SUAVE.Surrogate.Surrogate_Problem.Surrogate_Problem

Functions
def	SUAVE.Surrogate.kriging_surrogate_functions.build_kriging_models (obj_values, inputs, constraints)
def	SUAVE.Surrogate.scikit_surrogate_functions.build_scikit_models (surrogate_optimization, obj_values, inputs, constraints)
def	SUAVE.Surrogate.svr_surrogate_functions.build_svr_models (obj_values, inputs, constraints, kernel='rbf', C=1E5, epsilon=.01)
def	SUAVE.Surrogate.svr_surrogate_functions.check_svr_accuracy (x, data_inputs, data_outputs, imin=-1)

Detailed Description

Surrogate provides methods for different surrogate formulations of the original problem.

Function Documentation

build_kriging_models()

def SUAVE.Surrogate.kriging_surrogate_functions.build_kriging_models	(		obj_values,
			inputs,
			constraints
	)

Uses the pyKriging package to build a surrogate formulation of an optimization problem

Inputs:
obj_values          [array]
inputs              [array]
constraints         [array]

Outputs:
obj_surrogate            callable function(inputs)
constraints_surrogates   [array(callable function(inputs))]
surrogate_function       callable function(inputs): returns the objective, constraints, and whether it succeeded as an int 

build_scikit_models()

def SUAVE.Surrogate.scikit_surrogate_functions.build_scikit_models	(		surrogate_optimization,
			obj_values,
			inputs,
			constraints
	)

Uses the scikit-learn package to build a surrogate formulation of an optimization problem

Inputs:
obj_values          [array]
inputs              [array]
constraints         [array]

Outputs:
obj_surrogate            callable function(inputs)
constraints_surrogates   [array(callable function(inputs))]
surrogate_function       callable function(inputs): returns the objective, constraints, and whether it succeeded as an int 

build_svr_models()

def SUAVE.Surrogate.svr_surrogate_functions.build_svr_models	(		obj_values,
			inputs,
			constraints,
			kernel = 'rbf',
			C = 1E5,
			epsilon = .01
	)

Uses the scikit-learn package to build a surrogate formulation of an optimization problem,
using support vector regression. Includes additional options specific to support vector regression

Inputs:
obj_values          [array]
inputs              [array]
constraints         [array]
kernel              [string]
C                   [float]
epsilon             [float]

Outputs:
obj_surrogate            callable function(inputs)
constraints_surrogates   [array(callable function(inputs))]
surrogate_function       callable function(inputs): returns the objective, constraints, and whether it succeeded as an int 

check_svr_accuracy()

def SUAVE.Surrogate.svr_surrogate_functions.check_svr_accuracy	(		x,
			data_inputs,
			data_outputs,
			imin = -1
	)

Determines how accurate the SVR function is at a given point

Inputs:
x            [array]
data_inputs  [array]
data_outputs [array]
imin         [int]

Outputs:
output       [float]