adding experiment with ILR
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@ -166,7 +166,7 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
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recent_accept_rate = np.mean(acceptance_history[-100:])
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step_size *= np.exp(adapt_rate * (recent_accept_rate - target_acceptance))
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# step_size = float(np.clip(step_size, min_step, max_step))
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print(f'acceptance-rate={recent_accept_rate*100:.3f}%, step-size={step_size:.5f}')
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# print(f'acceptance-rate={recent_accept_rate*100:.3f}%, step-size={step_size:.5f}')
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# remove "warmup" initial iterations
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samples = np.asarray(samples[self.num_warmup:])
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