BayesOpt
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Public Member Functions | |
ExampleQuadratic (size_t dim, bayesopt::Parameters param) | |
double | evaluateSample (const vectord &Xi) |
Function that defines the actual function to be optimized. More... | |
bool | checkReachability (const vectord &query) |
This function checks if the query is valid or not. More... | |
Public Member Functions inherited from bayesopt::ContinuousModel | |
ContinuousModel (size_t dim, Parameters params) | |
Constructor. More... | |
virtual | ~ContinuousModel () |
Default destructor. | |
void | setBoundingBox (const vectord &lowerBound, const vectord &upperBound) |
Sets the bounding box. More... | |
Public Member Functions inherited from bayesopt::BayesOptBase | |
BayesOptBase (size_t dim, Parameters params) | |
Constructor. More... | |
virtual | ~BayesOptBase () |
Default destructor. | |
void | optimize (vectord &bestPoint) |
Execute the optimization process of the function defined in evaluateSample. More... | |
void | stepOptimization () |
Execute ONE step the optimization process of the function defined in evaluateSample. | |
void | initializeOptimization () |
Initialize the optimization process. More... | |
vectord | getFinalResult () |
Once the optimization has been perfomed, return the optimal point. More... | |
void | saveOptimization (BOptState &state) |
Saves the current state of the optimization process into a state class. More... | |
void | restoreOptimization (BOptState state) |
Restores the optimization process of a previous execution. | |
ProbabilityDistribution * | getPrediction (const vectord &query) |
const Dataset * | getData () |
Parameters * | getParameters () |
double | getValueAtMinimum () |
size_t | getCurrentIter () |
double | evaluateCriteria (const vectord &query) |
Additional Inherited Members | |
Protected Member Functions inherited from bayesopt::ContinuousModel | |
vectord | samplePoint () |
Sample a single point in the input space. More... | |
void | findOptimal (vectord &xOpt) |
Call the inner optimization method to find the optimal point acording to the criteria. More... | |
vectord | remapPoint (const vectord &x) |
Remap the point x to the original space (e.g. More... | |
void | generateInitialPoints (matrixd &xPoints) |
Selects the initial set of points to build the surrogate model. More... | |
Protected Member Functions inherited from bayesopt::BayesOptBase | |
vectord | getPointAtMinimum () |
Get optimal point in the inner space (e.g. More... | |
double | evaluateSampleInternal (const vectord &query) |
Wrapper for the target function adding any preprocessing or constraint. More... | |
void | plotStepData (size_t iteration, const vectord &xNext, double yNext) |
Print data for every step according to the verbose level. More... | |
void | saveInitialSamples (matrixd xPoints) |
Eases the process of saving a state during initial samples. | |
void | saveResponse (double yPoint, bool clear) |
Protected Attributes inherited from bayesopt::BayesOptBase | |
Parameters | mParameters |
Configuration parameters. | |
size_t | mDims |
Number of dimensions. | |
size_t | mCurrentIter |
Current iteration number. | |
boost::mt19937 | mEngine |
Random number generator. | |
Definition at line 43 of file bo_cont.cpp.
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inlinevirtual |
This function checks if the query is valid or not.
It can be used to introduce arbitrary constrains. Since the Gaussian process assumes smoothness, constrains are managed by the inner optimizer (e.g.:DIRECT), being highly time consuming. If the constrain is very tricky, DIRECT will need much more function evaluations.
Note: This function is experimental. Thus it is not made pure virtual. Using it is completely optional.
query | point to be evaluated. |
Reimplemented from bayesopt::BayesOptBase.
Definition at line 61 of file bo_cont.cpp.
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inlinevirtual |
Function that defines the actual function to be optimized.
This function must be modified (overriden) according to the specific problem.
query | point to be evaluated. |
Implements bayesopt::BayesOptBase.
Definition at line 50 of file bo_cont.cpp.
Referenced by bayesoptmodule.BayesOptContinuous::optimize(), bayesoptmodule.BayesOptDiscrete::optimize(), and bayesoptmodule.BayesOptCategorical::optimize().