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void | set_type (const std::string &t) |
| Specifies whether the type of the output simplicial complex. More...
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void | set_verbose (bool verb=false) |
| Specifies whether the program should display information or not. More...
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void | set_subsampling (double constant, double power) |
| Sets the constants used to subsample the data set. These constants are explained in [17]. More...
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void | set_mask (int nodemask) |
| Sets the mask, which is a threshold integer such that nodes in the complex that contain a number of data points which is less than or equal to this threshold are not displayed. More...
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void | set_point_cloud_from_range (const std::vector< std::vector< double > > &point_cloud) |
| Reads and stores the input point cloud from vector stored in memory. More...
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bool | read_point_cloud (const std::string &off_file_name) |
| Reads and stores the input point cloud from .(n)OFF file. More...
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void | set_graph_from_file (const std::string &graph_file_name) |
| Creates a graph G from a file containing the edges. More...
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void | set_graph_from_OFF () |
| Creates a graph G from the triangulation given by the input .OFF file.
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template<typename Distance > |
void | set_graph_from_rips (double threshold, Distance distance) |
| Creates a graph G from a Rips complex. More...
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void | set_distances_from_range (const std::vector< std::vector< double > > &distance_matrix) |
| Reads and stores the distance matrices from vector stored in memory. More...
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template<typename Distance > |
double | set_graph_from_automatic_rips (Distance distance, int N=100) |
| Creates a graph G from a Rips complex whose threshold value is automatically tuned with subsampling—see [17]. More...
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void | set_function_from_file (const std::string &func_file_name) |
| Creates the function f from a file containing the function values. More...
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void | set_function_from_coordinate (int k) |
| Creates the function f from the k-th coordinate of the point cloud P. More...
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template<class InputRange > |
void | set_function_from_range (InputRange const &function) |
| Creates the function f from a vector stored in memory. More...
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double | set_automatic_resolution () |
| Computes the optimal length of intervals (i.e. the smallest interval length avoiding discretization artifacts—see [17]) for a functional cover. More...
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void | set_resolution_with_interval_length (double reso) |
| Sets a length of intervals from a value stored in memory. More...
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void | set_resolution_with_interval_number (int reso) |
| Sets a number of intervals from a value stored in memory. More...
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void | set_gain (double g=0.3) |
| Sets a gain from a value stored in memory (default value 0.3). More...
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void | set_cover_from_function () |
| Creates a cover C from the preimages of the function f.
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void | set_cover_from_file (const std::string &cover_file_name) |
| Creates the cover C from a file containing the cover elements of each point (the order has to be the same as in the input file!). More...
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template<class AssignmentRange > |
void | set_cover_from_range (AssignmentRange const &assignments) |
| Creates the cover C from a vector of assignments stored in memory. The assignments, or clusters IDs, are vectors of integers. More...
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template<typename Distance > |
void | set_cover_from_Voronoi (Distance distance, int m=100) |
| Creates the cover C from the Voronoï cells of a subsampling of the point cloud. More...
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const std::vector< int > & | subpopulation (int c) |
| Returns the data subset corresponding to a specific node of the created complex. More...
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double | subcolor (int c) |
| Returns the mean color corresponding to a specific node of the created complex. More...
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void | set_color_from_file (const std::string &color_file_name) |
| Computes the function used to color the nodes of the simplicial complex from a file containing the function values. More...
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void | set_color_from_coordinate (int k=0) |
| Computes the function used to color the nodes of the simplicial complex from the k-th coordinate. More...
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void | set_color_from_range (std::vector< double > color) |
| Computes the function used to color the nodes of the simplicial complex from a vector stored in memory. More...
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void | plot_DOT () |
| Creates a .dot file called SC.dot for neato (part of the graphviz package) once the simplicial complex is computed to get a visualization of its 1-skeleton in a .pdf file.
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void | write_info () |
| Creates a .txt file called SC.txt describing the 1-skeleton, which can then be plotted with e.g. KeplerMapper.
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void | plot_OFF () |
| Creates a .off file called SC.off for 3D visualization, which contains the 2-skeleton of the GIC. This function assumes that the cover has been computed with Voronoi. If data points are in 1D or 2D, the remaining coordinates of the points embedded in 3D are set to 0.
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Persistence_diagram | compute_PD () |
| Computes the extended persistence diagram of the complex.
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void | compute_distribution (unsigned int N=100) |
| Computes bootstrapped distances distribution. More...
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double | compute_distance_from_confidence_level (double alpha) |
| Computes the bottleneck distance threshold corresponding to a specific confidence level. More...
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double | compute_confidence_level_from_distance (double d) |
| Computes the confidence level of a specific bottleneck distance threshold. More...
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double | compute_p_value () |
| Computes the p-value, i.e. the opposite of the confidence level of the largest bottleneck distance preserving the points in the persistence diagram of the output simplicial complex.
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template<typename SimplicialComplex > |
void | create_complex (SimplicialComplex &complex) |
| Creates the simplicial complex. More...
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void | find_simplices () |
| Computes the simplices of the simplicial complex.
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template<typename Point>
class Gudhi::cover_complex::Cover_complex< Point >
Cover complex data structure.
The data structure is a simplicial complex, representing a Graph Induced simplicial Complex (GIC) or a Nerve, and whose simplices are computed with a cover C of a point cloud P, which often comes from the preimages of intervals covering the image of a function f defined on P. These intervals are parameterized by their resolution (either their length or their number) and their gain (percentage of overlap). To compute a GIC, one also needs a graph G built on top of P, whose cliques with vertices belonging to different elements of C correspond to the simplices of the GIC.
- Examples
- CoordGIC.cpp, FuncGIC.cpp, Nerve.cpp, and VoronoiGIC.cpp.