Python torch.diagonal() Examples

def _is_valid_correlation_matrix(Sigma, tol=1e-6):
    """Check if supplied matrix is a valid correlation matrix

    A matrix is a valid correlation matrix if it is positive semidefinite, and
    if all diagonal elements are equal to 1.

    Args:
        Sigma: A n x n correlation matrix, or a batch of b correlation matrices
            with shape b x n x n
        tol: The tolerance with which to check unit value of the diagonal elements

    Returns:
        True if Sigma is a valid correlation matrix, False otherwise (in batch
            mode, all matrices in the batch need to be valid correlation matrices)

    """
    pdef = torch.all(constraints.positive_definite.check(Sigma))
    return pdef and all(torch.all(torch.abs(S.diag() - 1) < tol) for S in Sigma.view(-1, *Sigma.shape[-2:])) 

def _apply_loss(d, d_gt):
    """
    LOSS CALCULATION OF THE BATCH

    Arguments:
    ----------
        - d: Computed displacements
        - d_gt: Ground truth displacements

    Returns:
    --------
        - loss: calculate loss according to the specified loss function
    
    """

    # Set all pixel entries to 0 whose displacement magnitude is bigger than 10px
    pixel_thresh = 10
    dispMagnitude = torch.sqrt(torch.pow(d_gt[:,:,0],2) + torch.pow(d_gt[:,:,1], 2)).unsqueeze(-1).expand(-1,-1,2)
    idx = dispMagnitude > pixel_thresh
    z = torch.zeros(dispMagnitude.shape)
    d = torch.where(idx, z, d)
    d_gt = torch.where(idx, z, d_gt)

    # Calculate loss according to formula in paper
    return torch.sum(torch.sqrt(torch.diagonal(torch.bmm(d - d_gt, (d-d_gt).permute(0,2,1)), dim1=-2, dim2=-1)), dim = 1) 

def power_series_full_jac_exact_trace(Fx, x, k):
    """
    Fast-boi Tr(Ln(d(Fx)/dx)) using power-series approximation with full
    jacobian and exact trace
    
    :param Fx: output of f(x)
    :param x: input
    :param k: number of power-series terms  to use
    :return: Tr(Ln(I + df/dx))
    """
    _, jac = compute_log_det(x, Fx)
    jacPower = jac
    summand = torch.zeros_like(jacPower)
    for i in range(1, k+1):
        if i > 1:
            jacPower = torch.matmul(jacPower, jac)
        if (i + 1) % 2 == 0:
            summand += jacPower / (np.float(i))
        else: 
            summand -= jacPower / (np.float(i)) 
    trace = torch.diagonal(summand, dim1=1, dim2=2).sum(1)
    return trace 

def exact_matrix_logarithm_trace(Fx, x):
    """
    Computes slow-ass Tr(Ln(d(Fx)/dx))
    :param Fx: output of f(x)
    :param x: input
    :return: Tr(Ln(I + df/dx))
    """
    bs = Fx.size(0)
    outVector = torch.sum(Fx, 0).view(-1)
    outdim = outVector.size()[0]
    indim = x.view(bs, -1).size()
    jac = torch.empty([bs, outdim, indim[1]], dtype=torch.float)
    # for each output Fx[i] compute d(Fx[i])/d(x)
    for i in range(outdim):
        zero_gradients(x)
        jac[:, i, :] = torch.autograd.grad(outVector[i], x,
                                           retain_graph=True)[0].view(bs, outdim)
    jac = jac.cpu().numpy()
    iden = np.eye(jac.shape[1])
    log_jac = np.stack([logm(jac[i] + iden) for i in range(bs)])
    trace_jac = np.diagonal(log_jac, axis1=1, axis2=2).sum(1)
    return trace_jac 

def norm_to_lognorm(mu: Tensor, Cov: Tensor) -> Tuple[Tensor, Tensor]:
    """Compute mean and covariance of a log-MVN from its MVN sufficient statistics

    If `X ~ N(mu, Cov)` and `Y = exp(X)`, then `Y` is log-normal with

        mu_ln_{i} = exp(mu_{i} + 0.5 * Cov_{ii})
        Cov_ln_{ij} = exp(mu_{i} + mu_{j} + 0.5 * (Cov_{ii} + Cov_{jj})) *
        (exp(Cov_{ij}) - 1)

    Args:
        mu: A `batch_shape x n` mean vector of the Normal distribution.
        Cov: A `batch_shape x n x n` covariance matrix of the Normal distribution.

    Returns:
        A two-tuple containing:

        - The `batch_shape x n` mean vector of the log-Normal distribution.
        - The `batch_shape x n x n` covariance matrix of the log-Normal
            distribution.
    """
    diag = torch.diagonal(Cov, dim1=-1, dim2=-2)
    b = mu + 0.5 * diag
    mu_ln = torch.exp(b)
    Cov_ln = (torch.exp(Cov) - 1) * torch.exp(b.unsqueeze(-1) + b.unsqueeze(-2))
    return mu_ln, Cov_ln 

def lognorm_to_norm(mu: Tensor, Cov: Tensor) -> Tuple[Tensor, Tensor]:
    """Compute mean and covariance of a MVN from those of the associated log-MVN

    If `Y` is log-normal with mean mu_ln and covariance Cov_ln, then
    `X ~ N(mu_n, Cov_n)` with

        Cov_n_{ij} = log(1 + Cov_ln_{ij} / (mu_ln_{i} * mu_n_{j}))
        mu_n_{i} = log(mu_ln_{i}) - 0.5 * log(1 + Cov_ln_{ii} / mu_ln_{i}**2)

    Args:
        mu: A `batch_shape x n` mean vector of the log-Normal distribution.
        Cov: A `batch_shape x n x n` covariance matrix of the log-Normal
            distribution.

    Returns:
        A two-tuple containing:

        - The `batch_shape x n` mean vector of the Normal distribution
        - The `batch_shape x n x n` covariance matrix of the Normal distribution
    """
    Cov_n = torch.log(1 + Cov / (mu.unsqueeze(-1) * mu.unsqueeze(-2)))
    mu_n = torch.log(mu) - 0.5 * torch.diagonal(Cov_n, dim1=-1, dim2=-2)
    return mu_n, Cov_n 

def deprecate_task_noise_corr(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
    if prefix + "task_noise_corr_factor" in state_dict:
        # Remove after 1.0
        warnings.warn(
            "Loading a deprecated parameterization of _MultitaskGaussianLikelihoodBase. Consider re-saving your model.",
            OldVersionWarning,
        )
        # construct the task correlation matrix from the factors using the old parameterization
        corr_factor = state_dict.pop(prefix + "task_noise_corr_factor").squeeze(0)
        corr_diag = state_dict.pop(prefix + "task_noise_corr_diag").squeeze(0)
        num_tasks, rank = corr_factor.shape[-2:]
        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)
        C = M * sem_inv.matmul(sem_inv.transpose(-1, -2))
        # perform a Cholesky decomposition and extract the required entries
        L = torch.cholesky(C)
        tidcs = torch.tril_indices(num_tasks, rank)[:, 1:]
        task_noise_corr = L[..., tidcs[0], tidcs[1]]
        state_dict[prefix + "task_noise_corr"] = task_noise_corr 

def _is_valid_correlation_matrix_cholesky_factor(L, tol=1e-6):
    """Check if supplied matrix is a Cholesky factor of a valid correlation matrix

    A matrix is a Cholesky fator of a valid correlation matrix if it is lower
    triangular, has positive diagonal, and unit row-sum

    Args:
        L: A n x n lower-triangular matrix, or a batch of b lower-triangular
            matrices with shape b x n x n
        tol: The tolerance with which to check positivity of the diagonal and
            unit-sum of the rows

    Returns:
        True if L is a Cholesky factor of a valid correlation matrix, False
            otherwise (in batch mode, all matrices in the batch need to be
            Cholesky factors of valid correlation matrices)

    """
    unit_row_length = torch.all((torch.norm(L, dim=-1) - 1).abs() < tol)
    return unit_row_length and torch.all(constraints.lower_cholesky.check(L)) 

def test_lkj_covariance_prior_log_prob_hetsd(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        a = torch.tensor([exp(-1), exp(-2)], device=device)
        b = torch.tensor([exp(1), exp(2)], device=device)
        sd_prior = SmoothedBoxPrior(a, b)
        prior = LKJCovariancePrior(2, torch.tensor(0.5, device=device), sd_prior)
        S = torch.eye(2, device=device)
        self.assertAlmostEqual(prior.log_prob(S).item(), -4.71958, places=4)
        S = torch.stack([S, torch.tensor([[1.0, 0.5], [0.5, 1]], device=S.device)])
        self.assertTrue(approx_equal(prior.log_prob(S), torch.tensor([-4.71958, -4.57574], device=S.device)))
        with self.assertRaises(ValueError):
            prior.log_prob(torch.eye(3, device=device))

        # For eta=1.0 log_prob is flat over all covariance matrices
        prior = LKJCovariancePrior(2, torch.tensor(1.0, device=device), sd_prior)
        marginal_sd = torch.diagonal(S, dim1=-2, dim2=-1).sqrt()
        log_prob_expected = prior.correlation_prior.C + prior.sd_prior.log_prob(marginal_sd)
        self.assertTrue(approx_equal(prior.log_prob(S), log_prob_expected)) 

def _components(self) -> Dict[Tuple[str, str, str], Tuple[Tensor, Tensor]]:
        states_per_measure = defaultdict(list)
        for state_belief in self.state_beliefs:
            for m, measure in enumerate(self.design.measures):
                H = state_belief.H[:, m, :].data
                m = H * state_belief.means.data
                std = H * torch.diagonal(state_belief.covs.data, dim1=-2, dim2=-1).sqrt()
                states_per_measure[measure].append((m, std))

        out = {}
        for measure, means_and_stds in states_per_measure.items():
            means, stds = zip(*means_and_stds)
            means = torch.stack(means).permute(1, 0, 2)
            stds = torch.stack(stds).permute(1, 0, 2)
            for s, (process_name, state_element) in enumerate(self.design.state_elements):
                if ~torch.isclose(means[:, :, s].abs().max(), torch.zeros(1)):
                    out[(measure, process_name, state_element)] = (means[:, :, s], stds[:, :, s])
        return out 

def test_lkj_covariance_prior_log_prob(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        sd_prior = SmoothedBoxPrior(exp(-1), exp(1))
        if cuda:
            sd_prior = sd_prior.cuda()
        prior = LKJCovariancePrior(2, torch.tensor(0.5, device=device), sd_prior)
        S = torch.eye(2, device=device)
        self.assertAlmostEqual(prior.log_prob(S).item(), -3.59981, places=4)
        S = torch.stack([S, torch.tensor([[1.0, 0.5], [0.5, 1]], device=S.device)])
        self.assertTrue(approx_equal(prior.log_prob(S), torch.tensor([-3.59981, -3.45597], device=S.device)))
        with self.assertRaises(ValueError):
            prior.log_prob(torch.eye(3, device=device))

        # For eta=1.0 log_prob is flat over all covariance matrices
        prior = LKJCovariancePrior(2, torch.tensor(1.0, device=device), sd_prior)
        marginal_sd = torch.diagonal(S, dim1=-2, dim2=-1).sqrt()
        log_prob_expected = prior.correlation_prior.C + prior.sd_prior.log_prob(marginal_sd)
        self.assertTrue(approx_equal(prior.log_prob(S), log_prob_expected)) 

def test_multivariate_normal_batch_lazy(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        for dtype in (torch.float, torch.double):
            mean = torch.tensor([0, 1, 2], device=device, dtype=dtype).repeat(2, 1)
            covmat = torch.diag(torch.tensor([1, 0.75, 1.5], device=device, dtype=dtype)).repeat(2, 1, 1)
            covmat_chol = torch.cholesky(covmat)
            mvn = MultivariateNormal(mean=mean, covariance_matrix=NonLazyTensor(covmat))
            self.assertTrue(torch.is_tensor(mvn.covariance_matrix))
            self.assertIsInstance(mvn.lazy_covariance_matrix, LazyTensor)
            self.assertAllClose(mvn.variance, torch.diagonal(covmat, dim1=-2, dim2=-1))
            self.assertAllClose(mvn._unbroadcasted_scale_tril, covmat_chol)
            mvn_plus1 = mvn + 1
            self.assertAllClose(mvn_plus1.mean, mvn.mean + 1)
            self.assertAllClose(mvn_plus1.covariance_matrix, mvn.covariance_matrix)
            self.assertAllClose(mvn_plus1._unbroadcasted_scale_tril, covmat_chol)
            mvn_times2 = mvn * 2
            self.assertAllClose(mvn_times2.mean, mvn.mean * 2)
            self.assertAllClose(mvn_times2.covariance_matrix, mvn.covariance_matrix * 4)
            self.assertAllClose(mvn_times2._unbroadcasted_scale_tril, covmat_chol * 2)
            mvn_divby2 = mvn / 2
            self.assertAllClose(mvn_divby2.mean, mvn.mean / 2)
            self.assertAllClose(mvn_divby2.covariance_matrix, mvn.covariance_matrix / 4)
            self.assertAllClose(mvn_divby2._unbroadcasted_scale_tril, covmat_chol / 2)
            # TODO: Add tests for entropy, log_prob, etc. - this an issue b/c it
            # uses using root_decomposition which is not very reliable
            # self.assertTrue(torch.allclose(mvn.entropy(), 4.3157 * torch.ones(2)))
            # self.assertTrue(
            #     torch.allclose(mvn.log_prob(torch.zeros(2, 3)), -4.8157 * torch.ones(2))
            # )
            # self.assertTrue(
            #     torch.allclose(mvn.log_prob(torch.zeros(2, 2, 3)), -4.8157 * torch.ones(2, 2))
            # )
            conf_lower, conf_upper = mvn.confidence_region()
            self.assertAllClose(conf_lower, mvn.mean - 2 * mvn.stddev)
            self.assertAllClose(conf_upper, mvn.mean + 2 * mvn.stddev)
            self.assertTrue(mvn.sample().shape == torch.Size([2, 3]))
            self.assertTrue(mvn.sample(torch.Size([2])).shape == torch.Size([2, 2, 3]))
            self.assertTrue(mvn.sample(torch.Size([2, 4])).shape == torch.Size([2, 4, 2, 3])) 

def tobit_probs(mean: Tensor,
                cov: Tensor,
                lower: Optional[Tensor] = None,
                upper: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
    # CDF not well behaved at tails, truncate
    clamp = lambda z: torch.clamp(z, -5., 5.)

    if upper is None:
        upper = torch.empty_like(mean)
        upper[:] = float('inf')
    if lower is None:
        lower = torch.empty_like(mean)
        lower[:] = float('-inf')

    std = torch.diagonal(cov, dim1=-2, dim2=-1)
    probs_up = torch.zeros_like(mean)
    is_cens_up = torch.isfinite(upper)
    upper_z = (upper[is_cens_up] - mean[is_cens_up]) / std[is_cens_up]
    probs_up[is_cens_up] = 1. - std_normal.cdf(clamp(upper_z))

    probs_lo = torch.zeros_like(mean)
    is_cens_lo = torch.isfinite(lower)
    lower_z = (lower[is_cens_lo] - mean[is_cens_lo]) / std[is_cens_lo]
    probs_lo[is_cens_lo] = std_normal.cdf(clamp(lower_z))

    return probs_lo, probs_up 

def forward(self, src, adj, tgt_seq, binary_tgt,return_attns=False,int_preds=False):
        batch_size = src[0].size(0)
        src_seq, src_pos = src
        if self.decoder_type in ['sa_m','rnn_m']: 
            tgt_seq = tgt_seq[:, :-1]

        enc_output, *enc_self_attns = self.encoder(src_seq, adj, src_pos,return_attns=return_attns)
        dec_output, *dec_output2 = self.decoder(tgt_seq,src_seq,enc_output,return_attns=return_attns,int_preds=int_preds)

        if self.decoder_type == 'rnn_m':
            seq_logit = dec_output
        elif self.decoder_type == 'mlp':
            seq_logit = dec_output
        else:
            seq_logit = self.tgt_word_proj(dec_output)
            if self.decoder_type == 'graph':
                seq_logit = torch.diagonal(seq_logit,0,1,2)
        if int_preds:
            intermediate_preds = []
            tgt_word_proj_copy = self.tgt_word_proj.linear.weight.data.detach().repeat(batch_size,1,1)
            for int_idx,int_out in enumerate(dec_output2[0][:-1]):
                int_out = torch.bmm(int_out,tgt_word_proj_copy.transpose(1,2))
                intermediate_preds += [torch.diagonal(int_out,0,1,2)]
            return seq_logit.view(-1, seq_logit.size(-1)),enc_output, intermediate_preds
        elif return_attns:
            return seq_logit.view(-1,seq_logit.size(-1)),enc_output,enc_self_attns,dec_output2
        else:
            return seq_logit.view(-1,seq_logit.size(-1)),enc_output,None 

def generate_labels(pairs, rel_dict):
    pool = ThreadPool(8)
    def func(pair):
        tmp = rel_dict.get((pair[0], pair[1]), [0])
        out = np.zeros(cfg.MODEL.NUM_RELATIONS, dtype=np.int32)
        if pair[0] != -1 and pair[1] != -1: # If pair = (-1,-1) then this pair is diagonal pair, we don't need the labels
            out[tmp] = 1
        return out
    results = pool.map(func, pairs)
    pool.close()
    pool.join()
    results = np.stack(results)
    return results 

def test_compute_linear_truncated_kernel_no_batch(self):
        x1 = torch.tensor([[1, 0.1, 0.2], [2, 0.3, 0.4]])
        x2 = torch.tensor([[3, 0.5, 0.6], [4, 0.7, 0.8]])
        t_1 = torch.tensor([[0.3584, 0.1856], [0.2976, 0.1584]])
        for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
            kernel = LinearTruncatedFidelityKernel(
                fidelity_dims=fidelity_dims, dimension=3, nu=nu
            )
            kernel.power = 1
            n_fid = len(fidelity_dims)
            if n_fid > 1:
                active_dimsM = [0]
                t_2 = torch.tensor([[0.4725, 0.2889], [0.4025, 0.2541]])
                t_3 = torch.tensor([[0.1685, 0.0531], [0.1168, 0.0386]])
                t = 1 + t_1 + t_2 + t_3
            else:
                active_dimsM = [0, 1]
                t = 1 + t_1

            matern_ker = MaternKernel(nu=nu, active_dims=active_dimsM)
            matern_term = matern_ker(x1, x2).evaluate()
            actual = t * matern_term
            res = kernel(x1, x2).evaluate()
            self.assertLess(torch.norm(res - actual), 1e-4)
            # test diagonal mode
            res_diag = kernel(x1, x2, diag=True)
            self.assertLess(torch.norm(res_diag - actual.diag()), 1e-4)
        # make sure that we error out if last_dim_is_batch=True
        with self.assertRaises(NotImplementedError):
            kernel(x1, x2, diag=True, last_dim_is_batch=True) 

def log_det_by_cholesky(matrix):
	"""
	Args:
		matrix: matrix must be a positive define matrix.
				shape [N, C, D, D].
	Ref:
		https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/ops/linalg/linalg_impl.py
	"""
	# This uses the property that the log det(A) = 2 * sum(log(real(diag(C))))
	# where C is the cholesky decomposition of A.
	chol = torch.cholesky(matrix)
	#return 2.0 * torch.sum(torch.log(torch.diagonal(chol, dim1=-2, dim2=-1) + 1e-6), dim=-1)
	return 2.0 * torch.sum(torch.log(torch.diagonal(chol, dim1=-2, dim2=-1) + 1e-8), dim=-1) 

def generate_gt_rel_labels(rois, roi_labels, roi_feats, pairs, roidb):
    # rois and roi_labels has to be original
    proposal_num = get_proposal_num(rois)
    head_pointer = 0
    rel_labels = []
    for i in range(len(proposal_num)):
        if proposal_num[i] == 0:
            continue
        tmp_rel_labels = torch.zeros(proposal_num[i], proposal_num[i], cfg.MODEL.NUM_RELATIONS).long()
        tmp_rel_labels[:,:,0].fill_(1)
        for rel, rel_ids in roidb[i]['gt_relationships'].items():
            tmp_rel_labels[rel[0],rel[1],0] = 0
            tmp_rel_labels[rel[0],rel[1]][rel_ids] = 1
        # remove diagonals
        torch.diagonal(tmp_rel_labels, dim1=0, dim2=1).fill_(0)
        # Remove roi_label == 0 ones
        remaining_index = roi_labels[head_pointer: head_pointer + proposal_num[i]] > 0
        remaining_index = torch.from_numpy(remaining_index.astype('uint8'))
        if remaining_index.sum() >= 0:
            tmp_rel_labels = tmp_rel_labels[remaining_index][:, remaining_index]
            rel_labels.append(tmp_rel_labels.reshape(-1, cfg.MODEL.NUM_RELATIONS))
        head_pointer += proposal_num[i]
    
    out = torch.cat(rel_labels, 0).numpy()
    if out.shape[0] != pairs.shape[0]:
        import pdb;pdb.set_trace()
    return out 

def __init__(
        self, num_tasks, rank=0, task_prior=None, batch_shape=torch.Size(), noise_prior=None, noise_constraint=None
    ):
        """
        Args:
            num_tasks (int): Number of tasks.

            rank (int): The rank of the task noise covariance matrix to fit. If `rank` is set to 0,
            then a diagonal covariance matrix is fit.

            task_prior (:obj:`gpytorch.priors.Prior`): Prior to use over the task noise covariance matrix if
            `rank` > 0, or a prior over the log of just the diagonal elements, if `rank` == 0.

        """
        super(Likelihood, self).__init__()
        if noise_constraint is None:
            noise_constraint = GreaterThan(1e-4)
        self.register_parameter(name="raw_noise", parameter=torch.nn.Parameter(torch.zeros(*batch_shape, 1)))
        if rank == 0:
            self.register_parameter(
                name="raw_task_noises", parameter=torch.nn.Parameter(torch.zeros(*batch_shape, num_tasks))
            )
            if task_prior is not None:
                raise RuntimeError("Cannot set a `task_prior` if rank=0")
        else:
            self.register_parameter(
                name="task_noise_covar_factor", parameter=torch.nn.Parameter(torch.randn(*batch_shape, num_tasks, rank))
            )
            if task_prior is not None:
                self.register_prior("MultitaskErrorCovariancePrior", task_prior, self._eval_covar_matrix)
        self.num_tasks = num_tasks
        self.rank = rank

        self.register_constraint("raw_noise", noise_constraint) 

def __init__(
        self,
        num_tasks,
        rank=0,
        task_correlation_prior=None,
        batch_shape=torch.Size(),
        noise_prior=None,
        noise_constraint=None,
    ):
        """
        Args:
            num_tasks (int): Number of tasks.

            rank (int): The rank of the task noise covariance matrix to fit. If `rank` is set to 0,
            then a diagonal covariance matrix is fit.

            task_correlation_prior (:obj:`gpytorch.priors.Prior`): Prior to use over the task noise correlaton matrix.
            Only used when `rank` > 0.

        """
        if noise_constraint is None:
            noise_constraint = GreaterThan(1e-4)

        noise_covar = MultitaskHomoskedasticNoise(
            num_tasks=num_tasks, noise_prior=noise_prior, noise_constraint=noise_constraint, batch_shape=batch_shape
        )
        super().__init__(
            num_tasks=num_tasks,
            noise_covar=noise_covar,
            rank=rank,
            task_correlation_prior=task_correlation_prior,
            batch_shape=batch_shape,
        )

        self.register_parameter(name="raw_noise", parameter=torch.nn.Parameter(torch.zeros(*batch_shape, 1)))
        self.register_constraint("raw_noise", noise_constraint) 

def _batch_form_diag(tsr):
    """Form diagonal matrices in batch mode."""
    eye = torch.eye(tsr.shape[-1], dtype=tsr.dtype, device=tsr.device)
    M = tsr.unsqueeze(-1).expand(tsr.shape + tsr.shape[-1:])
    return eye * M 

def log_prob(self, X):
        marginal_var = torch.diagonal(X, dim1=-2, dim2=-1)
        if not torch.all(marginal_var >= 0):
            raise ValueError("Variance(s) cannot be negative")
        marginal_sd = marginal_var.sqrt()
        sd_diag_mat = _batch_form_diag(1 / marginal_sd)
        correlations = torch.matmul(torch.matmul(sd_diag_mat, X), sd_diag_mat)
        log_prob_corr = self.correlation_prior.log_prob(correlations)
        log_prob_sd = self.sd_prior.log_prob(marginal_sd)
        return log_prob_corr + log_prob_sd 

def log_prob(self, X):
        if any(s != self.n for s in X.shape[-2:]):
            raise ValueError("Cholesky factor is not of size n={}".format(self.n.item()))
        if not _is_valid_correlation_matrix_cholesky_factor(X):
            raise ValueError("Input is not a Cholesky factor of a valid correlation matrix")
        log_diag_sum = torch.diagonal(X, dim1=-2, dim2=-1).log().sum(-1)
        return self.C + (self.eta - 1) * 2 * log_diag_sum 

def log_prob(self, X):
        logdetp = torch.logdet(X)
        pinvK = torch.solve(self.K, X)[0]
        trpinvK = torch.diagonal(pinvK, dim1=-2, dim2=-1).sum(-1)  # trace in batch mode
        return self.C - 0.5 * ((self.nu + 2 * self.n) * logdetp + trpinvK) 

def log_prob(self, X):
        # I'm sure this could be done more elegantly
        logdetp = torch.logdet(X)
        Kinvp = torch.matmul(self.K_inv, X)
        trKinvp = torch.diagonal(Kinvp, dim1=-2, dim2=-1).sum(-1)
        return self.C + 0.5 * (self.nu - self.n - 1) * logdetp - trKinvp 

def marginal(self, function_dist, *params, **kwargs):
        r"""
        Adds the task noises to the diagonal of the covariance matrix of the supplied
        :obj:`gpytorch.distributions.MultivariateNormal` or :obj:`gpytorch.distributions.MultitaskMultivariateNormal`,
        in case of `rank` == 0. Otherwise, adds a rank `rank` covariance matrix to it.

        To accomplish this, we form a new :obj:`gpytorch.lazy.KroneckerProductLazyTensor` between :math:`I_{n}`,
        an identity matrix with size equal to the data and a (not necessarily diagonal) matrix containing the task
        noises :math:`D_{t}`.

        We also incorporate a shared `noise` parameter from the base
        :class:`gpytorch.likelihoods.GaussianLikelihood` that we extend.

        The final covariance matrix after this method is then :math:`K + D_{t} \otimes I_{n} + \sigma^{2}I_{nt}`.

        Args:
            function_dist (:obj:`gpytorch.distributions.MultitaskMultivariateNormal`): Random variable whose covariance
                matrix is a :obj:`gpytorch.lazy.LazyTensor` we intend to augment.
        Returns:
            :obj:`gpytorch.distributions.MultitaskMultivariateNormal`: A new random variable whose covariance
            matrix is a :obj:`gpytorch.lazy.LazyTensor` with :math:`D_{t} \otimes I_{n}` and :math:`\sigma^{2}I_{nt}`
            added.
        """
        mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix

        if self.rank == 0:
            task_noises = self.raw_noise_constraint.transform(self.raw_task_noises)
            task_var_lt = DiagLazyTensor(task_noises)
            dtype, device = task_noises.dtype, task_noises.device
        else:
            task_noise_covar_factor = self.task_noise_covar_factor
            task_var_lt = RootLazyTensor(task_noise_covar_factor)
            dtype, device = task_noise_covar_factor.dtype, task_noise_covar_factor.device

        eye_lt = DiagLazyTensor(
            torch.ones(*covar.batch_shape, covar.size(-1) // self.num_tasks, dtype=dtype, device=device)
        )
        task_var_lt = task_var_lt.expand(*covar.batch_shape, *task_var_lt.matrix_shape)

        covar_kron_lt = KroneckerProductLazyTensor(eye_lt, task_var_lt)
        covar = covar + covar_kron_lt

        noise = self.noise
        covar = add_diag(covar, noise)
        return function_dist.__class__(mean, covar) 

def update_qparam(self, a_i, V_ji, R_ij):

        # Out ← [?, B, C, 1, 1, F, F, K, K]
        R_ij = R_ij * a_i # broadcast a_i 1->C, and R_ij (1,1,1,1)->(F,F,K,K), 1->batch

        # Out ← [?, 1, C, 1, 1, F, F, 1, 1]
        self.R_j = self.reduce_icaps(R_ij)

        # Out ← [?, 1, C, 1, 1, F, F, 1, 1]
        self.alpha_j = self.alpha0 + self.R_j
        # self.alpha_j = torch.exp(self.alpha0) + self.R_j # when alpha's a param
        self.kappa_j = self.kappa0 + self.R_j
        self.nu_j = self.nu0 + self.R_j

        # Out ← [?, 1, C, P*P, 1, F, F, 1, 1]
        mu_j = (1./self.R_j) * self.reduce_icaps(R_ij * V_ji)

        # Out ← [?, 1, C, P*P, 1, F, F, 1, 1]
        # self.m_j = (1./self.kappa_j) * (self.R_j * mu_j + self.kappa0 * self.m0) # use this if self.m0 != 0
        self.m_j = (1./self.kappa_j) * (self.R_j * mu_j) # priors removed for faster computation

        if self.cov == 'diag':
            # Out ← [?, 1, C, P*P, 1, F, F, 1, 1] (1./R_j) not needed because Psi_j calc
            sigma_j = self.reduce_icaps(R_ij * (V_ji - mu_j).pow(2))

            # Out ← [?, 1, C, P*P, 1, F, F, 1, 1]
            # self.invPsi_j = self.Psi0 + sigma_j + (self.kappa0*self.R_j / self.kappa_j) \
            #     * (mu_j - self.m0).pow(2) # use this if m0 != 0 or kappa0 != 1
            self.invPsi_j = self.Psi0 + sigma_j + (self.R_j / self.kappa_j) * (mu_j).pow(2) # priors removed for faster computation

            # Out ← [?, 1, C, 1, 1, F, F, 1, 1] (-) sign as inv. Psi_j
            self.lndet_Psi_j = -self.reduce_poses(torch.log(self.invPsi_j)) # log det of diag precision matrix

        elif self.cov == 'full':
            #[?, B, C, P*P, P*P, F, F, K, K]
            sigma_j = self.reduce_icaps(
                R_ij * (V_ji - mu_j) * (V_ji - mu_j).transpose(3,4))

            # Out ← [?, 1, C, P*P, P*P, F, F, 1, 1] full cov, torch.inverse(self.Psi0)
            self.invPsi_j = self.Psi0 + sigma_j + (self.kappa0*self.R_j / self.kappa_j) \
                * (mu_j - self.m0) * (mu_j - self.m0).transpose(3,4)

            # Out ← [?, 1, C, F, F, 1, 1 , P*P, P*P]
            # needed for pytorch (*,n,n) dim requirements in .cholesky and .inverse
            self.invPsi_j = self.invPsi_j.permute(0,1,2,5,6,7,8,3,4)

            # Out ← [?, 1, 1, 1, C, F, F, 1, 1] (-) sign as inv. Psi_j
            self.lndet_Psi_j = -2*torch.diagonal(torch.cholesky(
                self.invPsi_j), dim1=-2, dim2=-1).log().sum(-1, keepdim=True)[...,None] 

def tobit_adjustment(mean: Tensor,
                     cov: Tensor,
                     lower: Optional[Tensor] = None,
                     upper: Optional[Tensor] = None,
                     probs: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]:
    assert cov.shape[-1] == cov.shape[-2]  # symmetrical

    if upper is None:
        upper = torch.full_like(mean, float('inf'))
    if lower is None:
        lower = torch.full_like(mean, -float('inf'))

    assert lower.shape == upper.shape == mean.shape

    is_cens_up = torch.isfinite(upper)
    is_cens_lo = torch.isfinite(lower)

    if not is_cens_up.any() and not is_cens_lo.any():
        return mean, cov

    F1, F2 = _F1F2(mean, cov, lower, upper)

    std = torch.diagonal(cov, dim1=-2, dim2=-1).sqrt()
    sqrt_pi = pi ** .5

    # prob censoring:
    if probs is None:
        prob_lo, prob_up = tobit_probs(mean=mean,
                                       cov=cov,
                                       lower=lower,
                                       upper=upper)
    else:
        prob_lo, prob_up = probs

    # adjust mean:
    lower_adj = torch.zeros_like(mean)
    lower_adj[is_cens_lo] = prob_lo[is_cens_lo] * lower[is_cens_lo]
    upper_adj = torch.zeros_like(mean)
    upper_adj[is_cens_up] = prob_up[is_cens_up] * upper[is_cens_up]
    mean_if_uncens = mean + (sqrt(2. / pi) * F1) * std
    mean_uncens_adj = (1. - prob_up - prob_lo) * mean_if_uncens
    mean_adj = mean_uncens_adj + upper_adj + lower_adj

    # adjust cov:
    diag_adj = torch.zeros_like(mean)
    for m in range(mean.shape[-1]):
        diag_adj[..., m] = (1. + 2. / sqrt_pi * F2[..., m] - 2. / pi * (F1[..., m] ** 2)) * cov[..., m, m]

    cov_adj = torch.diag_embed(diag_adj)

    return mean_adj, cov_adj 

def lw_shrink(X_t):
    """
    Estimates the shrunk Ledoit-Wolf covariance matrix

    Args:
      X_t: samples x variants

    Returns:
      shrunk_cov_t: shrunk covariance
      shrinkage_t:  shrinkage coefficient

    Adapted from scikit-learn:
    https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/shrunk_covariance_.py
    """
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if len(X_t.shape) == 2:
        n_samples, n_features = X_t.shape  # samples x variants
        X_t = X_t - X_t.mean(0)
        X2_t = X_t.pow(2)
        emp_cov_trace_sum = X2_t.sum() / n_samples
        delta_ = torch.mm(X_t.t(), X_t).pow(2).sum() / n_samples**2
        beta_ = torch.mm(X2_t.t(), X2_t).sum()
        beta = 1. / (n_features * n_samples) * (beta_ / n_samples - delta_)
        delta = delta_ - 1. * emp_cov_trace_sum**2 / n_features
        delta /= n_features
        beta = torch.min(beta, delta)
        shrinkage_t = 0 if beta == 0 else beta / delta
        emp_cov_t = torch.mm(X_t.t(), X_t) / n_samples
        mu_t = torch.trace(emp_cov_t) / n_features
        shrunk_cov_t = (1. - shrinkage_t) * emp_cov_t
        shrunk_cov_t.view(-1)[::n_features + 1] += shrinkage_t * mu_t  # add to diagonal
    else:  # broadcast along first dimension
        n_samples, n_features = X_t.shape[1:]  # samples x variants
        X_t = X_t - X_t.mean(1, keepdim=True)
        X2_t = X_t.pow(2)
        emp_cov_trace_sum = X2_t.sum([1,2]) / n_samples
        delta_ = torch.matmul(torch.transpose(X_t, 1, 2), X_t).pow(2).sum([1,2]) / n_samples**2
        beta_ = torch.matmul(torch.transpose(X2_t, 1, 2), X2_t).sum([1,2])
        beta = 1. / (n_features * n_samples) * (beta_ / n_samples - delta_)
        delta = delta_ - 1. * emp_cov_trace_sum**2 / n_features
        delta /= n_features
        beta = torch.min(beta, delta)
        shrinkage_t = torch.where(beta==0, torch.zeros(beta.shape).to(device), beta/delta)
        emp_cov_t = torch.matmul(torch.transpose(X_t, 1, 2), X_t) / n_samples
        mu_t = torch.diagonal(emp_cov_t, dim1=1, dim2=2).sum(1) / n_features
        shrunk_cov_t = (1 - shrinkage_t.reshape([shrinkage_t.shape[0], 1, 1])) * emp_cov_t

        ix = torch.LongTensor(np.array([np.arange(0, n_features**2, n_features+1)+i*n_features**2 for i in range(X_t.shape[0])])).to(device)
        shrunk_cov_t.view(-1)[ix] += (shrinkage_t * mu_t).unsqueeze(-1)  # add to diagonal

    return shrunk_cov_t, shrinkage_t 

def test_compute_linear_truncated_kernel_with_batch(self):
        x1 = torch.tensor(
            [[[1.0, 0.1, 0.2], [3.0, 0.3, 0.4]], [[5.0, 0.5, 0.6], [7.0, 0.7, 0.8]]]
        )
        x2 = torch.tensor(
            [[[2.0, 0.8, 0.7], [4.0, 0.6, 0.5]], [[6.0, 0.4, 0.3], [8.0, 0.2, 0.1]]]
        )
        t_1 = torch.tensor(
            [[[0.2736, 0.4400], [0.2304, 0.3600]], [[0.3304, 0.3816], [0.1736, 0.1944]]]
        )
        batch_shape = torch.Size([2])
        for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
            kernel = LinearTruncatedFidelityKernel(
                fidelity_dims=fidelity_dims, dimension=3, nu=nu, batch_shape=batch_shape
            )
            kernel.power = 1
            if len(fidelity_dims) > 1:
                active_dimsM = [0]
                t_2 = torch.tensor(
                    [
                        [[0.0527, 0.1670], [0.0383, 0.1159]],
                        [[0.1159, 0.1670], [0.0383, 0.0527]],
                    ]
                )
                t_3 = torch.tensor(
                    [
                        [[0.1944, 0.3816], [0.1736, 0.3304]],
                        [[0.3600, 0.4400], [0.2304, 0.2736]],
                    ]
                )
                t = 1 + t_1 + t_2 + t_3
            else:
                active_dimsM = [0, 1]
                t = 1 + t_1

            matern_ker = MaternKernel(
                nu=nu, active_dims=active_dimsM, batch_shape=batch_shape
            )
            matern_term = matern_ker(x1, x2).evaluate()
            actual = t * matern_term
            res = kernel(x1, x2).evaluate()
            self.assertLess(torch.norm(res - actual), 1e-4)
            # test diagonal mode
            res_diag = kernel(x1, x2, diag=True)
            self.assertLess(
                torch.norm(res_diag - torch.diagonal(actual, dim1=-1, dim2=-2)), 1e-4
            )
        # make sure that we error out if last_dim_is_batch=True
        with self.assertRaises(NotImplementedError):
            kernel(x1, x2, diag=True, last_dim_is_batch=True)