+ )iARt^RItRR.t]PP R4]P !RR7R Rl44t]PP R4]P !RR7R Rl44tR t R#) aThis module provides the functions for node classification problem. The functions in this module are not imported into the top level `networkx` namespace. You can access these functions by importing the `networkx.algorithms.node_classification` modules, then accessing the functions as attributes of `node_classification`. For example: >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> node_classification.harmonic_function(G) ['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). Nharmonic_functionlocal_and_global_consistencydirected label_name) node_attrsc^RIp^RIp\P!V4p\ W4wrgVP ^,^8Xd\P !RV R24hVP ^,pVP ^,p VPW34p VP^R7p ^W^8H&VPPRV , ^3W3R7P4p W,P4p ^WR,&VPW34p^WR,VR,3&\V4FpW,V,p K WsPV ^R7,P4#) aNode classification by Harmonic function Function for computing Harmonic function algorithm by Zhu et al. 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). N*No node on the input graph is labeled by ''.axis?shapeNNNr)numpyscipynxto_scipy_sparse_array_get_label_infor NetworkXErrorzerossumsparse dia_arraytocsrtolilrangeargmaxtolist)Gmax_iterrnpspXlabels label_dict n_samples n_classesFdegreesDPB_s&&& e/var/www/html/photoedit/myenv/lib/python3.14/site-packages/networkx/algorithms/node_classification.pyrrsJZ   #A(7F ||A!8 B G   I  #I )'(AeeemGGqL S7]A.y6LMSSUA  AATlO )'(A$%ATlF4L ! 8_ UaK iii* + 2 2 44c^RIp^RIp\P!V4p\ W4wrxVP ^,^8Xd\P !RV R24hVP ^,p VP ^,p VPW34p VP^R7p ^W^8H&VPPRVPV 4, ^3W3R7P4p WV,V ,,pVPW34p^V, WR,VR,3&\V4FpW,V,p K WPV ^R7,P4#) u[Node classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. Nrr r r r rr)rrrrrrrrrrrsqrtrr r!r")r#alphar$rr%r&r'r(r)r*r+r,r-D2r/r0r1s&&&& r2rrksX^   #A(7F ||A!8 B G   I  #I )'(AeeemGGqL    rwww #I+A   eg q&BA )'(A$%IATlF4L ! 8_ UaK iii* + 2 2 44r3c^RIp.p/p^p\VPRR74FPwrgW^,9gKV^,V,pW9dWTV&V^, pVPWdV,.4KR VP V4pTP \ VP 4RR7UU u.uFwrVNK up p4p W:3#uup pi)aGet and return information of labels from the input graph Parameters ---------- G : Network X graph label_name : string Name of the target label Returns ------- labels : numpy array, shape = [n_labeled_samples, 2] Array of pairs of labeled node ID and label ID label_dict : numpy array, shape = [n_classes] Array of labels i-th element contains the label corresponding label ID `i` NT)datacV^,#)r)xs&r2!_get_label_info..s1r3)key)r enumeratenodesappendarraysorteditems) r#rr%r( label_to_idlidinlabelr1r)s && r2rrs" FK C!''t',- 1 aD$E'%(E"q MM1%01 2 .XXf F%k&7&7&9~NON85NOJ   Ps5 C )rJ)gGz?rKrJ) __doc__networkxr__all__utilsnot_implemented_for _dispatchablerrrr;r3r2rRs2  > ?j)\*G5+*G5Tj)\*J5+*J5Z! r3