3 MdD@sLdZddlZddlmZddlmZmZmZm Z dgZ edd d dZ dS) 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)not_implemented_for)_get_label_info_init_label_matrix _propagate_predictlocal_and_global_consistencyZdirectedGz?labelc!s>y ddlWn.tk r:}ztdd|WYdd}~XnXyddlmWn.tk rz}ztdd|WYdd}~XnXfdd }fd d }tj|}t||\}} |jddkrtjd |d |jd} | jd} t | | } ||||} ||||| }|}x$|dkr.t | | |} |d8}q Wt | | }|S)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 : 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.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/local_and_global_consistency() requires numpy: zhttp://numpy.org/ )sparsez/local_and_global_consistensy() requires scipy: zhttp://scipy.org/ csN|jddjd}d||dk<jjd|dd}||j|j|}|S)aBuild propagation matrix of Local and global consistency 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 alpha : float Clamping factor Returns ---------- S : scipy sparse matrix, shape = [n_samples, n_samples] Propagation matrix r)Zaxisg?)offsets)sumAsqrtZdiagsdot)XlabelsalphadegreesZD2S)npr ]/var/www/html/virt/lib/python3.6/site-packages/networkx/algorithms/node_classification/lgc.py_build_propagation_matrixPs  z?local_and_global_consistency.._build_propagation_matrixcsD|jd}j||f}d|||dddf|dddff<|S)a#Build base matrix of Local and global consistency 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 alpha : float Clamping factor 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)rrr n_classes n_samplesB)rrr_build_base_matrixhs (z8local_and_global_consistency.._build_base_matrixz*No node on the input graph is labeled by 'z'.r ) Znumpy ImportErrorZscipyr nxZto_scipy_sparse_matrixrrZ NetworkXErrorrrr)GrZmax_iterZ label_nameerrrrZ label_dictrrFPrZremaining_iterZ predictedr)rr rrs<-          )rr r ) __doc__Znetworkxr!Znetworkx.utils.decoratorsrZ-networkx.algorithms.node_classification.utilsrrrr__all__rrrrrs