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| PamiToy (unsigned int size_useful=5, unsigned int size_noise=5, double noise_position=0.0, double noise_variance=1.0) |
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void | draw (RealVector &input, unsigned int &label) const |
| Generates a single pair of input and label. More...
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virtual | ~LabeledDataDistribution () |
| Virtual destructor. More...
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std::pair< RealVector, unsigned int > | operator() () |
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LabeledData< RealVector, unsigned int > | generateDataset (std::size_t size, std::size_t maximumBatchSize) const |
| Generates a dataset with samples from from the distribution. More...
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LabeledData< RealVector, unsigned int > | generateDataset (std::size_t size) const |
| Generates a data set with samples from from the distribution. More...
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"Pami Toy" problem for binary classification, as used in the article "Glasmachers and C. Igel. Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010." In summary, the first M dimensions are correlated to the labels, the last N dimensions are not.
Definition at line 217 of file DataDistribution.h.