o ˷e+ @s@dZddlZddlmZddlmZdgZedd d dZdS) zFunction for computing Harmonic function algorithm by Zhu et al. References ---------- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919). N)_get_label_info)not_implemented_forharmonic_functiondirectedlabelcCs(ddl}ddl}ddl}t|}t||\}}|jddkr(td|d|jd} |jd} || | f} |j dd} d| | dk<|j |j j d| dd} | | }d||dddf<|| | f}d||dddf|dddff<t|D]}|| |} q||j| ddS) aBNode classification by Harmonic function Parameters ---------- G : NetworkX Graph 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.harmonic_function(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919). rNz*No node on the input graph is labeled by 'z'.)axisg?)offsets)numpyscipy scipy.sparsenxto_scipy_sparse_arrayrshape NetworkXErrorzerossumsparse csr_arraydiagstolilrangeargmaxtolist)Gmax_iter label_namenpspr Xlabels label_dict n_samples n_classesFdegreesDPB_r+b/var/www/ideatree/venv/lib/python3.10/site-packages/networkx/algorithms/node_classification/hmn.pyrs,*       $ )rr) __doc__networkxr-networkx.algorithms.node_classification.utilsrnetworkx.utils.decoratorsr__all__rr+r+r+r,s