btjA dZddlZddgZejdejddd Zejdejddd Zd Z 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_attrslabelcddl}ddl}tj|}t ||\}}|jddkrtjd|d|jd}|jd} ||| f} |d} d| | dk<|j d| z df||f } | |z } d| |dddf<||| f}d||dddf|dddff<t|D] }| | z|z} ||| dS) 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 e/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/networkx/algorithms/node_classification.pyrrsZ  ##A(J77FJ |A! G G G G    I #I )Y'((AeeemmGGGqL S7]A.y)6LMMSSUUA Q AAfQQQTlO )Y'((A$%AfQQQTlF111a4L ! 8__ UaK biii** + 2 2 4 44Gz?cddl}ddl}tj|}t ||\}}|jddkrtjd|d|jd} |jd} || | f} |d} d| | dk<|j d| | z df| | f } || |z| zz}|| | f}d|z ||dddf|dddff<t|D] }|| z|z} ||| dS) uNode classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNr r r rrr)rrrrrrrrrrrsqrtrrr r!)r"alphar#rr$r%r&r'r(r)r*r+r,D2r.r/r0s r1rrks^  ##A(J77FJ |A! G G G G    I #I )Y'((AeeemmGGGqL    rwww #Iy+A    egg "q&BA )Y'((A$%IAfQQQTlF111a4L ! 8__ UaK biii** + 2 2 4 44r2cddl}g}i}d}t|dD]H\}}||dvr9|d|}||vr |||<|dz }||||gI||}|dt |dD} || fS) aGet and return information of labels from the input graph Parameters ---------- G : Network X graph label_name : string Name of the target label Returns ------- labels : numpy array, shape = [n_labeled_samples, 2] Array of pairs of labeled node ID and label ID label_dict : numpy array, shape = [n_classes] Array of labels i-th element contains the label corresponding label ID `i` rNT)datarcg|]\}}|Sr;).0r r0s r1 z#_get_label_info..sOOO85!OOOr2c|dS)Nrr;)xs r1z!_get_label_info..s 1r2)key)r enumeratenodesappendarraysorteditems) r"rr$r' label_to_idlidinr r(s r1rrs" FK C!''t',,--331 1  aD$EK''%( E"q MM1k%01 2 2 2 XXf  FOOvk&7&7&9&9~~NNNOOOJ J r2)rr )r3rr ) __doc__networkxr__all__utilsnot_implemented_for _dispatchablerrrr;r2r1rRs2  > ?j))\***G5G5G5+**)G5Tj))\***J5J5J5+**)J5Z! ! ! ! ! r2