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template<class InputType , class LabelType , class OutputType > |
| ErrorFunction (LabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType > *model, AbstractLoss< LabelType, OutputType > *loss) |
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| ErrorFunction (const ErrorFunction &op) |
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ErrorFunction & | operator= (const ErrorFunction &op) |
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std::string | name () const |
| returns the name of the object More...
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void | setRegularizer (double factor, SingleObjectiveFunction *regularizer) |
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SearchPointType | proposeStartingPoint () const |
| Proposes a starting point in the feasible search space of the function. More...
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std::size_t | numberOfVariables () const |
| Accesses the number of variables. More...
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double | eval (RealVector const &input) const |
| Evaluates the objective function for the supplied argument. More...
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ResultType | evalDerivative (const SearchPointType &input, FirstOrderDerivative &derivative) const |
| Evaluates the objective function and calculates its gradient. More...
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const Features & | features () const |
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virtual void | updateFeatures () |
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bool | hasValue () const |
| returns whether this function can calculate it's function value More...
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bool | hasFirstDerivative () const |
| returns whether this function can calculate the first derivative More...
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bool | hasSecondDerivative () const |
| returns whether this function can calculate the second derivative More...
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bool | canProposeStartingPoint () const |
| returns whether this function can propose a starting point. More...
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bool | isConstrained () const |
| returns whether this function can return More...
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bool | hasConstraintHandler () const |
| returns whether this function can return More...
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bool | canProvideClosestFeasible () const |
| Returns whether this function can calculate thee closest feasible to an infeasible point. More...
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bool | isThreadSafe () const |
| Returns true, when the function can be usd in parallel threads. More...
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| AbstractObjectiveFunction () |
| Default ctor. More...
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virtual | ~AbstractObjectiveFunction () |
| Virtual destructor. More...
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virtual void | init () |
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virtual bool | hasScalableDimensionality () const |
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virtual void | setNumberOfVariables (std::size_t numberOfVariables) |
| Adjusts the number of variables if the function is scalable. More...
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virtual std::size_t | numberOfObjectives () const |
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virtual bool | hasScalableObjectives () const |
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virtual void | setNumberOfObjectives (std::size_t numberOfObjectives) |
| Adjusts the number of objectives if the function is scalable. More...
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std::size_t | evaluationCounter () const |
| Accesses the evaluation counter of the function. More...
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AbstractConstraintHandler< SearchPointType > const & | getConstraintHandler () const |
| Returns the constraint handler of the function if it has one. More...
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virtual bool | isFeasible (const SearchPointType &input) const |
| Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled. More...
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virtual void | closestFeasible (SearchPointType &input) const |
| If supported, the supplied point is repaired such that it satisfies all of the function's constraints. More...
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ResultType | operator() (const SearchPointType &input) const |
| Evaluates the function. Useful together with STL-Algorithms like std::transform. More...
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virtual ResultType | evalDerivative (const SearchPointType &input, SecondOrderDerivative &derivative) const |
| Evaluates the objective function and calculates its gradient. More...
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virtual | ~INameable () |
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Objective function for supervised learning.
- An ErrorFunction object is an objective function for learning the parameters of a model from data by means of minimization of a cost function. The value of the objective function is the cost of the model predictions on the training data, given the targets.
- The class detects automatically when an AbstractLoss is used as Costfunction. In this case, it uses faster algorithms for empirical risk minimization
- It also automatically infers the input und label type from the given dataset and the output type of the model in the constructor and ensures that Model and loss match. Thus the user does not need to provide the types as template parameters.
Definition at line 65 of file ErrorFunction.h.