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inverse_approx_multihop_chain_interdependence

Bases: chain_interdependence

Source code in tinybig/interdependence/topological_interdependence.py 
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class inverse_approx_multihop_chain_interdependence(chain_interdependence):

    def __init__(self, name: str = 'inverse_approx_multihop_chain_interdependence', normalization: bool = False, normalization_mode: str = 'row', *args, **kwargs):
        super().__init__(name=name, normalization=normalization, normalization_mode=normalization_mode, *args, **kwargs)

    def calculate_A(self, x: torch.Tensor = None, w: torch.nn.Parameter = None, device: str = 'cpu', *args, **kwargs):
        if not self.require_data and not self.require_parameters and self.A is not None:
            return self.A
        else:
            adj, mappings = self.chain.to_matrix(normalization=self.normalization, normalization_mode=self.normalization_mode, device=device)
            self.node_id_index_map = mappings['node_id_index_map']
            self.node_index_id_map = mappings['node_index_id_map']

            if self.is_bi_directional():
                A = accumulative_matrix_power(adj, n=adj.shape[0] - 1)
                A = degree_based_normalize_matrix(mx=A, mode='column')
            else:
                A = torch.inverse(torch.eye(adj.shape[0], device=device) - adj)

            A = self.post_process(x=A, device=device)

            if self.interdependence_type in ['column', 'right', 'attribute', 'attribute_interdependence']:
                assert A.shape == (self.m, self.calculate_m_prime())
            elif self.interdependence_type in ['row', 'left', 'instance', 'instance_interdependence']:
                assert A.shape == (self.b, self.calculate_b_prime())

            if not self.require_data and not self.require_parameters and self.A is None:
                self.A = A

            return A