AENC/switchchain Changeset - b8a998539881 · Centrum Wiskunde & Informatica (CWI)

Changeset - b8a998539881

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Tom Bannink - 8 years ago 2017-06-10 18:23:10
tombannink@gmail.com

Add class to compute graph spectrum

4 files changed with 77 insertions and 4 deletions:

cpp/Makefile

cpp/graph.hpp

cpp/graph_spectrum.hpp

cpp/switchchain.cpp

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cpp/Makefile

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 #CXX=clang++
 INCLUDES += -I.
 CXXFLAGS += -std=c++14 -O3 -Wall -Wextra -Wfatal-errors -Werror -pedantic -Wno-deprecated-declarations $(INCLUDES)
 INCLUDES += -I. \
 			-I/usr/include/eigen3
 CXXFLAGS += -std=c++14 -O3
 CXXFLAGS += -Wall -Wextra -Wfatal-errors -Werror -pedantic -Wno-deprecated-declarations
 CXXFLAGS += $(INCLUDES)
 # Disable Eigen's debug info and disable a warning generated by Eigen
 CXXFLAGS += -DNDEBUG
 CXXFLAGS += -Wno-int-in-bool-context
 all: switchchain switchchain_exponent switchchain_initialtris switchchain_dsp

cpp/graph.hpp

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@@ @@ -58,6 +58,8 @@ class Graph { @@
     const auto& getAdj() const { return adj; }
     const auto& getBooleanAdj() const { return badj; }
     // When the degree sequence is not graphics, the Graph can be
     // in any state, it is not neccesarily empty
     bool createFromDegreeSequence(const DegreeSequence &d) {

cpp/graph_spectrum.hpp

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@@ new file 100644 @@
 #include "graph.hpp"
 #include <Eigen/Dense>
 #include <Eigen/Eigenvalues>
 using MatrixType =
     Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
 // A: Adjacency matrix
 //    lambda_max <= d_max
 //
 // L: Laplacian
 //    L = D - A
 ///
 // P: Random walk matrix
 //    lambda_max = 1
 class GraphSpectrum {
   public:
     GraphSpectrum(const Graph& g) : graph(g) {}
     ~GraphSpectrum() {}
     std::vector<float> computeAdjacencySpectrum() const {
         // matrix stored as std::vector<std::vector<bool>>
         auto& badj = graph.getBooleanAdj();
         // Convert it to MatrixType
         auto n = badj.size();
         MatrixType m(n, n);
         for (auto i = 0u; i < n; ++i)
             for (auto j = 0u; j < n; ++j)
                 m(i, j) = badj[i][j] ? 1.0f : 0.0f;
         return getEigenvalues_(m);
+    }
     std::vector<float> computeLaplacianSpectrum() const {
         // matrix stored as std::vector<std::vector<bool>>
         auto& badj = graph.getBooleanAdj();
         auto& adj = graph.getAdj();
         // - A
         auto n = badj.size();
         MatrixType m(n, n);
         for (auto i = 0u; i < n; ++i)
             for (auto j = 0u; j < n; ++j)
                 m(i, j) = badj[i][j] ? -1.0f : 0.0f;
         // + D
         for (auto i = 0u; i < n; ++i)
             m(i, i) = float(adj[i].size());
         return getEigenvalues_(m);
+    }
   private:
     const Graph& graph;
     std::vector<float> getEigenvalues_(const MatrixType& m) const {
         Eigen::SelfAdjointEigenSolver<MatrixType> es(
             m, Eigen::DecompositionOptions::EigenvaluesOnly);
         auto ev = es.eigenvalues();
         return std::vector<float>(ev.data(), ev.data() + ev.rows() * ev.cols());
+    }
 };

cpp/switchchain.cpp

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 #include "exports.hpp"
 #include "graph.hpp"
 #include "powerlaw.hpp"
 #include "graph_spectrum.hpp"
 #include <algorithm>
 #include <array>
 #include <fstream>

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