219652c649a687d04d9075d288f0e3ea72ca34e2,pymc3/smc/smc.py,,sample_smc,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,34

Before Change

        pm._log.info("Stage: {:3d} Beta: {:.3f} Steps: {:3d}".format(stage, beta, n_steps))
        // Apply Metropolis kernel (mutation)
        proposed = draws * n_steps
        priors = np.array([prior_logp(sample) for sample in posterior]).squeeze()
        tempered_logp = priors + likelihoods * beta

        parameters = (
            proposal,

After Change

        priors = [prior_logp(sample) for sample in posterior]
        likelihoods = [likelihood_logp(sample) for sample in posterior]

    priors = np.array(priors).squeeze()
    likelihoods = np.array(likelihoods).squeeze()

    while beta < 1:
        beta, old_beta, weights, sj = calc_beta(beta, likelihoods, threshold)

        model.marginal_likelihood *= sj
        // resample based on plausibility weights (selection)
        resampling_indexes = np.random.choice(np.arange(draws), size=draws, p=weights)
        posterior = posterior[resampling_indexes]
        priors = priors[resampling_indexes]
        likelihoods = likelihoods[resampling_indexes]

        // compute proposal distribution based on weights
        covariance = _calc_covariance(posterior, weights)
        proposal = MultivariateNormalProposal(covariance)

        // compute scaling (optional) and number of Markov chains steps (optional), based on the
        // acceptance rate of the previous stage
        if (tune_scaling or tune_steps) and stage > 0:
            scaling, n_steps = _tune(
                acc_rate, proposed, tune_scaling, tune_steps, scaling, max_steps, p_acc_rate
            )

        pm._log.info("Stage: {:3d} Beta: {:.3f} Steps: {:3d}".format(stage, beta, n_steps))
        // Apply Metropolis kernel (mutation)
        proposed = draws * n_steps
        tempered_logp = priors + likelihoods * beta

        parameters = (
            proposal,
            scaling,
            accepted,
            any_discrete,
            all_discrete,
            discrete,
            n_steps,
            prior_logp,
            likelihood_logp,
            beta,
        )
        if parallel and cores > 1:
            results = pool.starmap(
                metrop_kernel,
                [
                    (
                        posterior[draw],
                        tempered_logp[draw],
                        priors[draw],
                        likelihoods[draw],
                        *parameters,
                    )
                    for draw in range(draws)
                ],
            )
        else:
            results = [
                metrop_kernel(
                    posterior[draw],
                    tempered_logp[draw],
                    priors[draw],
                    likelihoods[draw],
                    *parameters
                )
                for draw in tqdm(range(draws), disable=not progressbar)
            ]

        posterior, acc_list, priors, likelihoods = zip(*results)
        posterior = np.array(posterior)
        priors = np.array(priors)
        likelihoods = np.array(likelihoods)
        acc_rate = sum(acc_list) / proposed
        stage += 1

Instances