85e8cf0742048b55a20418441e4e9e51444c3f54,gpytorch/likelihoods/multitask_gaussian_likelihood.py,_MultitaskGaussianLikelihoodBase,_eval_corr_matrix,#_MultitaskGaussianLikelihoodBase#,76

Before Change

        corr_factor = self.task_noise_corr_factor.squeeze(0)
        corr_diag = self.task_noise_corr_diag.squeeze(0)
        M = corr_factor.matmul(corr_factor.transpose(-1, -2))
        idx = torch.arange(M.shape[-1], dtype=torch.long, device=M.device)
        M[..., idx, idx] += corr_diag
        sem_inv = 1 / torch.diagonal(M, dim1=-2, dim2=-1).sqrt().unsqueeze(-1)
        return M * sem_inv.matmul(sem_inv.transpose(-1, -2))

After Change

    def _eval_corr_matrix(self):
        tnc = self.task_noise_corr
        fac_diag = torch.ones(*tnc.shape[:-1], self.num_tasks, device=tnc.device, dtype=tnc.dtype)
        Cfac = torch.diag_embed(fac_diag)
        Cfac[..., self.tidcs[0], self.tidcs[1]] = self.task_noise_corr
        // squared rows must sum to one for this to be a correlation matrix
        C = Cfac / Cfac.pow(2).sum(dim=-1, keepdim=True).sqrt()
        return C @ C.transpose(-1, -2)

Instances