added temperature, and coverage increases!
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@ -48,8 +48,7 @@ def methods():
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# yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True),
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# yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True),
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# yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper),
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# yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper),
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for T in [1., 10., 100., 1000.]:
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for T in [1., 10., 100., 1000.]:
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yield (f'BaKDE-CLR-T{T}', KDEyCLR(LR()), kdey_hyper_clr,
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yield f'BaKDE-CLR-T{T}', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, temperature=T, step_size=.15, **hyper),
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lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, temperature=T, step_size=.15, **hyper)),
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@ -66,7 +65,7 @@ if __name__ == '__main__':
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}
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}
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setup = multiclass
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setup = multiclass
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data_name = 'digits'
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data_name = 'isolet'
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qp.environ['SAMPLE_SIZE'] = setup['sample_size']
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qp.environ['SAMPLE_SIZE'] = setup['sample_size']
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print(f'dataset={data_name}')
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print(f'dataset={data_name}')
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