o  hE@sddZddlZddgZejdejdddd dZejdejdddd dZd dZ dS)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_attrslabelcCs ddl}ddl}t|}t||\}}|jddkr$td|d|jd}|jd} ||| f} |jdd} d| | dk<|j |j j d| dd} | | } d| |dddf<||| f}d||dddf|dddff<t |D]}| | |} q|||j| ddS) 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_r0[/var/www/vscode/kcb/lib/python3.10/site-packages/networkx/algorithms/node_classification.pyrs*-       $ Gz?cCsddl}ddl}t|}t||\}}|jddkr$td|d|jd} |jd} || | f} |jdd} d| | dk<| |j |j j d| dd} || || }|| | f}d|||dddf|dddff<t |D]}|| |} qy||j| ddS) uNode classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNr r r r rr)rrrrrrrrrsqrtrrrrrr )r!alphar"rr#r$r%r&r'r(r)r*r+D2r-r.r/r0r0r1rls(/      "( c Csddl}g}i}d}t|jddD]%\}}||dvr7|d|}||vr.|||<|d7}||||gq||}|ddt|dd d D} || fS) 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 cSsg|]\}}|qSr0r0).0rr/r0r0r1 sz#_get_label_info..cSs|dS)Nr r0)xr0r0r1sz!_get_label_info..)key)r enumeratenodesappendarraysorteditems) r!rr#r& label_to_idlidinrr'r0r0r1rs"   r)rr)r2rr) __doc__networkxr__all__utilsnot_implemented_for _dispatchablerrrr0r0r0r1s   K  L