At each iteration this algorithm moves in only one dimension. If the situation doesn't improve it tries the opposite direction. If both directions are worse, it decreases the step size for that dimension, otherwise it increases the step size for that dimension.
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| GMomentumGreedySearch (GTargetFunction *pCritic) |
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virtual | ~GMomentumGreedySearch () |
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virtual const GVec & | currentVector () |
| Returns a pointer to the state vector. More...
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virtual double | iterate () |
| Makes another attempt to find a better vector. Returns the heuristic error. (Usually you will call this method in a loop until your stopping criteria has been met.) More...
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void | setAllStepSizes (double dStepSize) |
| Set all the current step sizes to this value. More...
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void | setChangeFactor (double d) |
| d should be a value between 0 and 1 More...
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GVec & | stepSizes () |
| Returns the vector of step sizes. More...
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| GOptimizer (GTargetFunction *pCritic) |
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virtual | ~GOptimizer () |
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void | basicTest (double minAccuracy, double warnRange=0.001) |
| This is a helper method used by the unit tests of several model learners. More...
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double | searchUntil (size_t nBurnInIterations, size_t nIterations, double dImprovement) |
| This will first call iterate() nBurnInIterations times, then it will repeatedly call iterate() in blocks of nIterations times. If the error heuristic has not improved by the specified ratio after a block of iterations, it will stop. (For example, if the error before the block of iterations was 50, and the error after is 49, then training will stop if dImprovement is > 0.02.) If the error heuristic is not stable, then the value of nIterations should be large. More...
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