3 Md@sLdZddlZddlmZddlmZmZmZm Z dgZ edd d dZ dS) 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)not_implemented_for)_get_label_info_init_label_matrix _propagate_predictharmonic_functionZdirectedlabelc s4y ddlWn,tk r8}ztd|WYdd}~XnXyddlmWn,tk rv}ztd|WYdd}~XnXfdd}fdd }tj|}t||\}}|jddkrtjd |d |jd} |jd} t | | } |||} |||| } |}x$|dkr$t | | | } |d 8}qWt | |}|S) a+Node 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 : array, shape = [n_samples] Array of predicted labels Raises ---------- NetworkXError If no nodes on `G` has `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). rNz6harmonic_function() requires numpy: http://numpy.org/ )sparsez6harmonic_function() requires scipy: http://scipy.org/ csV|jddjd}d||dk<jd|dd}|j|j}d||dddf<|S)aBuild propagation matrix of Harmonic function Parameters ---------- X : scipy sparse matrix, shape = [n_samples, n_samples] Adjacency matrix labels : array, shape = [n_samples, 2] Array of pairs of node id and label id Returns ---------- P : scipy sparse matrix, shape = [n_samples, n_samples] Propagation matrix r)Zaxisg?)offsetsN)sumAZdiagsdotZtolil)XlabelsdegreesDP)r ]/var/www/html/virt/lib/python3.6/site-packages/networkx/algorithms/node_classification/hmn.py_build_propagation_matrixMs  z4harmonic_function.._build_propagation_matrixcs@|jd}j||f}d||dddf|dddff<|S)aBuild base matrix of Harmonic function Parameters ---------- X : scipy sparse matrix, shape = [n_samples, n_samples] Adjacency matrix labels : array, shape = [n_samples, 2] Array of pairs of node id and label id n_classes : integer The number of classes (distinct labels) on the input graph Returns ---------- B : array, shape = [n_samples, n_classes] Base matrix rr N)shapeZzeros)rr n_classes n_samplesB)nprr_build_base_matrixds $z-harmonic_function.._build_base_matrixz*No node on the input graph is labeled by 'z'.r ) Znumpy ImportErrorZscipyr nxZto_scipy_sparse_matrixrrZ NetworkXErrorrrr)GZmax_iterZ label_nameerrrrZ label_dictrrFrrZremaining_iterZ predictedr)rr rrs<*            )rr ) __doc__ZnetworkxrZnetworkx.utils.decoratorsrZ-networkx.algorithms.node_classification.utilsrrrr__all__rrrrrs