From 6308773c0b6b745d49d20dc2afd6ab7ec63cb996 Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Tue, 11 Oct 2022 14:25:11 +0200
Subject: Refactoring.

---
 rbmp.hpp | 138 ---------------------------------------------------------------
 1 file changed, 138 deletions(-)
 delete mode 100644 rbmp.hpp

(limited to 'rbmp.hpp')

diff --git a/rbmp.hpp b/rbmp.hpp
deleted file mode 100644
index 88f5900..0000000
--- a/rbmp.hpp
+++ /dev/null
@@ -1,138 +0,0 @@
-#include <vector>
-#include <stack>
-
-#include "randutils/randutils.hpp"
-#include "pcg-cpp/include/pcg_random.hpp"
-
-#include "blossom5-v2.05.src/PerfectMatching.h"
-
-using Rng = randutils::random_generator<pcg32>;
-using Real = long double;
-
-class AztecDiamond {
-public:
-  using Coordinate = std::array<int, 2>;
-
-  typedef struct Vertex {
-    unsigned index;
-    Coordinate coordinate;
-  } Vertex;
-
-  typedef struct Edge {
-    Vertex* tail;
-    Vertex* head;
-    Real weight;
-    std::stack<Real> weights;
-    Real probability = 0;
-  } Edge;
-
-private:
-  std::tuple<Edge&, Edge&, Edge&, Edge&> face(unsigned i, unsigned j) {
-    unsigned x0 = n - i;
-    unsigned x = x0 + 2 * (j % i);
-    unsigned y = x0 + 2 * (j / i);
-
-    Edge& e1 = edges[2 * n * y + x];
-    Edge& e2 = edges[2 * n * y + x + 1];
-    Edge& e3 = edges[2 * n * (y + 1) + x];
-    Edge& e4 = edges[2 * n * (y + 1) + x + 1];
-
-    return {e1, e2, e3, e4};
-  }
-
-public:
-  unsigned n;
-  std::vector<Vertex> vertices;
-  std::vector<Edge> edges;
-
-  AztecDiamond(int n) : n(n), vertices(2 * n * (n + 1)), edges(pow(2 * n, 2)) {
-    unsigned M = vertices.size() / 2;
-    for (int i = 0; i < M; i++) {
-      vertices[i].index = i;
-      vertices[M + i].index = M + i;
-      vertices[i].coordinate = {2 * (i % (n + 1)), 2 * (i / (n + 1)) + 1};
-      vertices[M + i].coordinate = {2 * (i % n) + 1, 2 * (i / n)};
-    }
-    for (unsigned i = 0; i < edges.size(); i++) {
-      edges[i].tail = &vertices[(1 + (i % (2 * n))) / 2 + (n + 1) * ((i / 4) / n)];
-      edges[i].head = &vertices[M + (i % (2 * n)) / 2 + n * (((i + 2 * n) / 4) / n)];
-    }
-  }
-
-  void setWeights(Rng& r) {
-    for (Edge& e : edges) {
-      e.weight = r.variate<Real, std::exponential_distribution>(1);
-    }
-  }
-
-  void computeWeights(Real T) {
-    for (Edge& e : edges) {
-      e.weights.push(exp(-e.weight / T));
-    }
-
-    for (unsigned i = n; i > 0; i--) {
-#pragma omp parallel for
-      for (unsigned j = 0; j < pow(i, 2); j++) {
-        auto [e1, e2, e3, e4] = face(i, j);
-
-        Real w = e1.weights.top();
-        Real x = e2.weights.top();
-        Real y = e3.weights.top();
-        Real z = e4.weights.top();
-
-        Real cellFactor = std::max(std::numeric_limits<Real>::min(), w * z + x * y);
-
-        e1.weights.push(z / cellFactor);
-        e2.weights.push(y / cellFactor);
-        e3.weights.push(x / cellFactor);
-        e4.weights.push(w / cellFactor);
-      }
-    }
-
-    // This process computes one extra weight per edge.
-    for (Edge& e : edges) {
-      e.weights.pop();
-    }
-  }
-
-  Real computeProbabilities() { // destroys *all* weights
-    for (Edge& e : edges) {
-      e.probability = 0;
-    }
-    Real logPartitionFunction = 0;
-
-    for (unsigned i = 1; i <= n; i++) {
-#pragma omp parallel for reduction(+:logPartitionFunction)
-      for (unsigned j = 0; j < pow(i, 2); j++) {
-        auto [e1, e2, e3, e4] = face(i, j);
-
-        Real p = e1.probability;
-        Real q = e2.probability;
-        Real r = e3.probability;
-        Real s = e4.probability;
-
-        Real w = e1.weights.top();
-        Real x = e2.weights.top();
-        Real y = e3.weights.top();
-        Real z = e4.weights.top();
-
-        Real cellFactor = w * z + x * y;
-        Real deficit = 1 - p - q - r - s;
-
-        e1.probability = s + deficit * w * z / cellFactor;
-        e2.probability = r + deficit * x * y / cellFactor;
-        e3.probability = q + deficit * x * y / cellFactor;
-        e4.probability = p + deficit * w * z / cellFactor;
-
-        e1.weights.pop();
-        e2.weights.pop();
-        e3.weights.pop();
-        e4.weights.pop();
-
-        logPartitionFunction += log(cellFactor);
-      }
-    }
-
-    return logPartitionFunction;
-  }
-};
-- 
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