9iASrSSKrSS/r\RR S5\R "SS9S Sj55r\RR S5\R "SS9S S j55rS r g) 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_attrscSSKnSSKn[R"U5n[ X5upgUR SS:Xa[R "SUS35eUR SnUR Sn URX45n URSS9n SXS:H'URRSU - S4X4S9R5n X-R5n SXSS2S4'URX45nSXSS2S4USS2S44'[U5H nX-U-n M XsRU SS9R5$) aNode classification by Harmonic function Function for computing Harmonic function algorithm by Zhu et al. Parameters ---------- G : NetworkX Graph max_iter : int maximum number of iterations allowed label_name : string name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.harmonic_function(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919). rN*No node on the input graph is labeled by ''.axis?shape)numpyscipynxto_scipy_sparse_array_get_label_infor NetworkXErrorzerossumsparse dia_arraytocsrtolilrangeargmaxtolist)Gmax_iterrnpspXlabels label_dict n_samples n_classesFdegreesDPB_s g/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/networkx/algorithms/node_classification.pyrrsVZ   #A(7F ||A!8 B G   I  #I )'(AeeemGGqL S7]A.y6LMSSUA  AAQTlO )'(A$%AQTlF1a4L ! 8_ UaK iii* + 2 2 44cSSKnSSKn[R"U5n[ X5upxUR SS:Xa[R "SUS35eUR Sn UR Sn URX45n URSS9n SXS:H'URRSURU 5- S4X4S9R5n XU-U --nURX45nSU- XSS2S4USS2S44'[U5H nX-U-n M XRU SS9R5$) u[Node classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNr r r r rr)rrrrrrrrrrrsqrtrrrr)r alphar!rr"r#r$r%r&r'r(r)r*D2r,r-r.s r/rrks`^   #A(7F ||A!8 B G   I  #I )'(AeeemGGqL    rwww #I+A   eg q&BA )'(A$%IAQTlF1a4L ! 8_ UaK iii* + 2 2 44r0cpSSKn/n0nSn[URSS95H:upgXS;dMUSUnX;a XTU'US- nURXdU/5 M< UR U5nUR [ UR 5SS9VV s/sHupUPM sn n5n X:4$s sn nf)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` rNT)datar c US$)Nr )xs r/!_get_label_info..s1r0)key)r enumeratenodesappendarraysorteditems) r rr"r% label_to_idlidinlabelr.r&s r/rrs" FK C!''t',- 1 aD$E'%(E"q MM1%01 2 .XXf F%k&7&7&9~NON85NOJ   PsB2 )rG)gGz?rHrG) __doc__networkxr__all__utilsnot_implemented_for _dispatchablerrrr8r0r/rOs2  > ?j)\*G5+*G5Tj)\*J5+*J5Z! r0