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template<class InputType , class LabelType , class OutputType > |
| NoisyErrorFunction (LabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType > *model, AbstractLoss< LabelType, OutputType > *loss, std::size_t batchSize=1) |
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| NoisyErrorFunction (NoisyErrorFunction const &op1) |
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NoisyErrorFunction & | operator= (NoisyErrorFunction const &op1) |
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std::string | name () const |
| From INameable: return the class name. More...
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void | setBatchSize (std::size_t batchSize) |
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std::size_t | batchSize () const |
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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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void | init () |
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void | setRegularizer (double factor, SingleObjectiveFunction *regularizer) |
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double | eval (RealVector const &input) const |
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ResultType | evalDerivative (SearchPointType const &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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bool | isNoisy () 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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void | setRng (random::rng_type *rng) |
| Sets the Rng used by the objective function. More...
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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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virtual ResultType | eval (SearchPointType const &input) const |
| Evaluates the objective function for the supplied argument. More...
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ResultType | operator() (SearchPointType const &input) const |
| Evaluates the function. Useful together with STL-Algorithms like std::transform. More...
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virtual ResultType | evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const |
| Evaluates the objective function and calculates its gradient. More...
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virtual | ~INameable () |
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Error Function which only uses a random fraction of data.
Conceptionally, this is the same as the normal ErrorFunction, with the only difference, that only a fraction of the training examples is chosen randomly out of the set and thus noise is introduced. This can be used to perform stochastic gradient descent or to introduce some noise to a problem.
Setting the batch size to 0 is equivalent to performing minibatch learning where one random batch is picked from the dataset instead of sampling points from it
Definition at line 70 of file NoisyErrorFunction.h.