bayesoptmodule.BayesOptDiscrete Class Reference

Python Module for BayesOptDiscrete. More...

Public Member Functions
def	__init__ (self, x_set=None, n_dim=None, n_samples=None)
	Let's define the parameters. More...
def	parameters (self)
def	parameters (self, params)
def	evaluateSample (self, x_in)
	Function for testing. More...
def	optimize (self)
	Main function. More...

Public Attributes
	params
	Library parameters.
	x_set
	Set of discrete points.

Detailed Description

Python Module for BayesOptDiscrete.

Python module to get run BayesOpt library in a OO pattern. The objective module should inherit this one and override evaluateSample.

Definition at line 97 of file bayesoptmodule.py.

Constructor & Destructor Documentation

__init__()

def bayesoptmodule.BayesOptDiscrete.__init__	(		self,
			x_set = None,
			n_dim = None,
			n_samples = None
	)

Let's define the parameters.

For different options: see parameters.h and parameters.cpp . If a parameter is not define, it will be automatically set to a default value.

Definition at line 104 of file bayesoptmodule.py.

Member Function Documentation

evaluateSample()

def bayesoptmodule.BayesOptDiscrete.evaluateSample	(		self,
			x_in
	)

Function for testing.

It should be overriden.

Definition at line 125 of file bayesoptmodule.py.

Referenced by bayesoptmodule.BayesOptDiscrete.optimize(), and bayesoptmodule.BayesOptCategorical.optimize().

optimize()

def bayesoptmodule.BayesOptDiscrete.optimize

(

self

)

Main function.

Starts the optimization process.

Definition at line 129 of file bayesoptmodule.py.

References SystemCallsBranin.evaluateSample(), ExampleQuadratic.evaluateSample(), ExampleDisc.evaluateSample(), bayesoptmodule.BayesOptContinuous.evaluateSample(), bayesopt::BayesOptBase.evaluateSample(), XMLCallsBranin.evaluateSample(), bayesoptmodule.BayesOptDiscrete.evaluateSample(), bayesoptmodule.BayesOptContinuous.params, bayesopt::AtomicKernel.params, bayesoptmodule.BayesOptDiscrete.params, and bayesoptmodule.BayesOptDiscrete.x_set.

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The documentation for this class was generated from the following file:

• bayesoptmodule.py