### Replace 'N' by 'V' in docstrings, when referring to number of nodes

This improves the consistency in the documentation.
parent a5fd88ec
Pipeline #70 passed with stage
 ... ... @@ -1843,7 +1843,7 @@ class Graph(object): .. note:: If the option fast == False is given, this operation is :math:O(N + E) (this is the default). Otherwise it is :math:O(V + E) (this is the default). Otherwise it is :math:O(k + k_{\text{last}}), where :math:k is the (total) degree of the vertex being deleted, and :math:k_{\text{last}} is the (total) degree of the vertex with the largest index. ... ... @@ -2587,13 +2587,13 @@ class Graph(object): If the option in_place == True is given, the algorithm will remove the filtered vertices and re-index all property maps which are tied with the graph. This is a slow operation which has an :math:O(N^2) the graph. This is a slow operation which has an :math:O(V^2) complexity. If in_place == False, the graph and its vertex and edge property maps are temporarily copied to a new unfiltered graph, which will replace the contents of the original graph. This is a fast operation with an :math:O(N + E) complexity. This is the default behaviour if no with an :math:O(V + E) complexity. This is the default behaviour if no option is given. .. note : ... ... @@ -2679,7 +2679,7 @@ class Graph(object): .. note:: If the vertices are being filtered, and ignore_filter == False, this operation is :math:O(N). Otherwise it is :math:O(1). this operation is :math:O(V). Otherwise it is :math:O(1). """ return self.__graph.get_num_vertices(not ignore_filter) ... ...
 ... ... @@ -394,11 +394,11 @@ def closeness(g, weight=None, source=None, vprop=None, norm=True, harmonic=False If norm == True, the values of :math:c_i are normalized by :math:n_i-1 where :math:n_i is the size of the (out-) component of :math:i. If harmonic == True, they are instead simply normalized by :math:N-1. :math:V-1. The algorithm complexity of :math:O(N(N + E)) for unweighted graphs and :math:O(N(N+E) \log N) for weighted graphs. If the option source is specified, this drops to :math:O(N + E) and :math:O((N+E)\log N) The algorithm complexity of :math:O(V(V + E)) for unweighted graphs and :math:O(V(v+E) \log V) for weighted graphs. If the option source is specified, this drops to :math:O(V + E) and :math:O((V+E)\log V) respectively. If enabled during compilation, this algorithm runs in parallel. ... ... @@ -1079,11 +1079,11 @@ def trust_transitivity(g, trust_map, source=None, target=None, vprop=None): maximum weight, using Dijkstra's algorithm, to all in-neighbours of a given target. This search needs to be performed repeatedly for every target, since it needs to be removed from the graph first. For each given source, the resulting complexity is therefore :math:O(N^2\log N) for all targets, and :math:O(N\log N) for a single target. For a given target, the complexity for obtaining the trust from all given sources is :math:O(kN\log N), where resulting complexity is therefore :math:O(V^2\log V) for all targets, and :math:O(V\log V) for a single target. For a given target, the complexity for obtaining the trust from all given sources is :math:O(kV\log V), where :math:k is the in-degree of the target. Thus, the complexity for obtaining the complete trust matrix is :math:O(EN\log N), where :math:E is the the complete trust matrix is :math:O(EV\log V), where :math:E is the number of edges in the network. If enabled during compilation, this algorithm runs in parallel. ... ...
 ... ... @@ -1649,7 +1649,7 @@ def multilevel_minimize(state, B, nsweeps=10, adaptive_sweeps=True, epsilon=0, the :func:mcmc_sweep moves, at different scales. See [peixoto-efficient-2014]_ for more details. This algorithm has a complexity of :math:O(N\ln^2 N), where :math:N is the This algorithm has a complexity of :math:O(V\ln^2 V), where :math:V is the number of nodes in the network. Examples ... ... @@ -2102,8 +2102,8 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None, one-dimensional Fibonacci search on :math:B. See [peixoto-parsimonious-2013]_ and [peixoto-efficient-2014]_ for more details. This algorithm has a complexity of :math:O(\tau N\ln^2 B_{\text{max}}), where :math:N is the number of nodes in the network, :math:\tau is the This algorithm has a complexity of :math:O(\tau V\ln^2 B_{\text{max}}), where :math:V is the number of nodes in the network, :math:\tau is the mixing time of the MCMC, and :math:B_{\text{max}} is the maximum number of blocks considered. If :math:B_{\text{max}} is not supplied, it is computed as :math:\sim\sqrt{E} via :func:get_max_B, in which case the complexity ... ...
 ... ... @@ -919,10 +919,9 @@ def nested_tree_sweep(state, min_B=None, max_B=None, max_b=None, nsweeps=10, This algorithm performs a constrained agglomerative heuristic on each level of the network, via the function :func:~graph_tool.community.multilevel_minimize. This algorithm has worst-case complexity of :math:O(N\ln^2 N \times L), where :math:N is the number of nodes in the network, and :math:L is the depth of the hierarchy. """ This algorithm has worst-case complexity of :math:O(V\ln^2 V \times L), where :math:V is the number of nodes in the network, and :math:L is the depth of the hierarchy. """ dl_ent = kwargs.get("dl_ent", False) ... ... @@ -1102,10 +1101,9 @@ def init_nested_state(g, Bs, ec=None, deg_corr=True, overlap=False, This algorithm performs an agglomerative heuristic on each level of the network, via the function :func:~graph_tool.community.multilevel_minimize. This algorithm has worst-case complexity of :math:O(N\ln^2 N \times L), where :math:N is the number of nodes in the network, and :math:L is the depth of the hierarchy. """ This algorithm has worst-case complexity of :math:O(V\ln^2 V \times L), where :math:V is the number of nodes in the network, and :math:L is the depth of the hierarchy. """ dl_ent = kwargs.get("dl_ent", False) ignore_degrees = kwargs.get("ignore_degrees", None) ... ... @@ -1376,8 +1374,8 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None, See [peixoto-hierarchical-2014]_ for details on the algorithm. This algorithm has a complexity of :math:O(N \ln^2 N), where :math:N is the number of nodes in the network. This algorithm has a complexity of :math:O(V \ln^2 V), where :math:V is the number of nodes in the network. Examples -------- ... ...
 ... ... @@ -1544,11 +1544,11 @@ def price_network(N, m=1, c=None, gamma=1, directed=True, seed_graph=None): Note that if seed_graph is not given, the algorithm will *always* start with one node if :math:c > 0, or with two nodes with a link between them otherwise. If :math:m > 1, the degree of the newly added vertices will be vary dynamically as :math:m'(t) = \min(m, N(t)), where :math:N(t) is the vary dynamically as :math:m'(t) = \min(m, V(t)), where :math:V(t) is the number of vertices added so far. If this behaviour is undesired, a proper seed graph with :math:N \ge m vertices must be provided. seed graph with :math:V \ge m vertices must be provided. This algorithm runs in :math:O(N\log N) time. This algorithm runs in :math:O(V\log V) time. See Also -------- ... ...
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