wgEdZddlZddgZejj dej dd dZejj dej dd d Zd Z y) 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_attrscddl}ddl}tj|}t ||\}}|j ddk(rtj d|d|j d}|j d} |j|| f} |jd} d| | dk(<|jj|jjd| z d} | |zj} d| |dddf<|j|| f}d||dddf|dddff<t|D] }| | z|z} ||j| djS) 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?offsets)numpyscipynxto_scipy_sparse_array_get_label_infoshape NetworkXErrorzerossumsparse csr_arraydiagstolilrangeargmaxtolist)Gmax_iterrnpspXlabels label_dict n_samples n_classesFdegreesDPB_s l/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/networkx/algorithms/node_classification.pyrrsjZ   #A(J7FJ ||A!8 B G   I  #I )Y'(AeeemGGGqL BIIOOS7]QOGHA Q AAfQTlO )Y'(A$%AfQTlF1a4L ! 8_ UaK biii* + 2 2 44cddl}ddl}tj|}t ||\}}|j ddk(rtj d|d|j d} |j d} |j| | f} |jd} d| | dk(<|j|jj|jjd| z d} || |z| zz}|j| | f}d|z ||dddf|dddff<t|D] }|| z|z} ||j| djS) uNode classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNr r r r rr)rrrrrrrrrsqrtrrrrrr )r!alphar"rr#r$r%r&r'r(r)r*r+D2r-r.r/s r0rrlsi^   #A(J7FJ ||A!8 B G   I  #I )Y'(AeeemGGGqL $$RYY__cGma_%PQ RB "q&BA )Y'(A$%IAfQTlF1a4L ! 8_ UaK biii* + 2 2 44r1cvddl}g}i}d}t|jdD]9\}}||dvs|d|}||vr |||<|dz }|j|||g;|j |}|j t |j dD cgc]\}} | c} }} || fScc} }w)aGet and return information of labels from the input graph Parameters ---------- G : Network X graph label_name : string Name of the target label Returns ------- labels : numpy array, shape = [n_labeled_samples, 2] Array of pairs of labeled node ID and label ID label_dict : numpy array, shape = [n_classes] Array of labels i-th element contains the label corresponding label ID `i` rNT)datar c |dS)Nr )xs r0z!_get_label_info..s 1r1)key)r enumeratenodesappendarraysorteditems) r!rr#r& label_to_idlidinlabelr/r's r0rrs" FK C!''t',-31 1 aD$EK'%( E"q MM1k%01 2 3XXf F%k&7&7&9~NO85!OJ J  Ps B5 )rG)gGz?rHrG) __doc__networkxr__all__utilsnot_implemented_for _dispatchablerrrr9r1r0rOs2  > ?j)\*H5+*H5Vj)\*I5+*I5X! r1