o ˷e @s@dZddlZddlmZddlmZdgZedd d dZdS) uFunction for computing Local and global consistency algorithm by Zhou et al. References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. N)_get_label_info)not_implemented_forlocal_and_global_consistencydirectedGz?labelcCs"ddl}ddl}ddl}t|}t||\}} |jddkr(td|d|jd} | jd} || | f} |j dd} d| | dk<| |j |j j d| dd}||||}|| | f}d|||dddf|dddff<t|D]}|| |} q}| |j| ddS) uNode classification by Local and Global Consistency Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNz*No node on the input graph is labeled by 'z'.)axisg?)offsets)numpyscipy scipy.sparsenxto_scipy_sparse_arrayrshape NetworkXErrorzerossumsqrtsparse csr_arraydiagsrangeargmaxtolist)Galphamax_iter label_namenpspr Xlabels label_dict n_samples n_classesFdegreesD2PB_r-b/var/www/ideatree/venv/lib/python3.10/site-packages/networkx/algorithms/node_classification/lgc.pyrs*,      "( )rrr) __doc__networkxr-networkx.algorithms.node_classification.utilsrnetworkx.utils.decoratorsr__all__rr-r-r-r.s