Merge pull request #1898 from borglab/improve-memory

gtsam/discrete/DecisionTreeFactor.cpp

@@ -239,7 +239,7 @@ namespace gtsam {
     };
 
     // Go through the tree
-    this->apply(op);
+    this->visitWith(op);
 
     return probs;
   }
@@ -349,22 +349,110 @@ namespace gtsam {
       : DiscreteFactor(keys.indices(), keys.cardinalities()),
         AlgebraicDecisionTree<Key>(keys, table) {}
 
+  /**
+   * @brief Min-Heap class to help with pruning.
+   * The `top` element is always the smallest value.
+   */
+  class MinHeap {
+    std::vector<double> v_;
+
+   public:
+    /// Default constructor
+    MinHeap() {}
+
+    /// Push value onto the heap
+    void push(double x) {
+      v_.push_back(x);
+      std::push_heap(v_.begin(), v_.end(), std::greater<double>{});
+    }
+
+    /// Push value `x`, `n` number of times.
+    void push(double x, size_t n) {
+      for (size_t i = 0; i < n; ++i) {
+        v_.push_back(x);
+        std::push_heap(v_.begin(), v_.end(), std::greater<double>{});
+      }
+    }
+
+    /// Pop the top value of the heap.
+    double pop() {
+      std::pop_heap(v_.begin(), v_.end(), std::greater<double>{});
+      double x = v_.back();
+      v_.pop_back();
+      return x;
+    }
+
+    /// Return the top value of the heap without popping it.
+    double top() { return v_.at(0); }
+
+    /**
+     * @brief Print the heap as a sequence.
+     *
+     * @param s A string to prologue the output.
+     */
+    void print(const std::string& s = "") {
+      std::cout << (s.empty() ? "" : s + " ");
+      for (size_t i = 0; i < v_.size(); i++) {
+        std::cout << v_.at(i);
+        if (v_.size() > 1 && i < v_.size() - 1) std::cout << ", ";
+      }
+      std::cout << std::endl;
+    }
+
+    /// Return true if heap is empty.
+    bool empty() const { return v_.empty(); }
+
+    /// Return the size of the heap.
+    size_t size() const { return v_.size(); }
+  };
+
   /* ************************************************************************ */
   DecisionTreeFactor DecisionTreeFactor::prune(size_t maxNrAssignments) const {
     const size_t N = maxNrAssignments;
 
-    // Get the probabilities in the decision tree so we can threshold.
+    // Set of all keys
-    std::vector<double> probabilities = this->probabilities();
+    std::set<Key> allKeys(keys().begin(), keys().end());
+    MinHeap min_heap;
 
-    // The number of probabilities can be lower than max_leaves
+    auto op = [&](const Assignment<Key>& a, double p) {
-    if (probabilities.size() <= N) {
+      // Get all the keys in the current assignment
-      return *this;
+      std::set<Key> assignment_keys;
+      for (auto&& [k, _] : a) {
+        assignment_keys.insert(k);
       }
 
-    std::sort(probabilities.begin(), probabilities.end(),
+      // Find the keys missing in the assignment
-              std::greater<double>{});
+      std::vector<Key> diff;
+      std::set_difference(allKeys.begin(), allKeys.end(),
+                          assignment_keys.begin(), assignment_keys.end(),
+                          std::back_inserter(diff));
 
-    double threshold = probabilities[N - 1];
+      // Compute the total number of assignments in the (pruned) subtree
+      size_t nrAssignments = 1;
+      for (auto&& k : diff) {
+        nrAssignments *= cardinalities_.at(k);
+      }
+
+      if (min_heap.empty()) {
+        min_heap.push(p, std::min(nrAssignments, N));
+
+      } else {
+        // If p is larger than the smallest element,
+        // then we insert into the max heap.
+        if (p > min_heap.top()) {
+          for (size_t i = 0; i < std::min(nrAssignments, N); ++i) {
+            if (min_heap.size() == N) {
+              min_heap.pop();
+            }
+            min_heap.push(p);
+          }
+        }
+      }
+      return p;
+    };
+    this->visitWith(op);
+
+    double threshold = min_heap.top();
 
     // Now threshold the decision tree
     size_t total = 0;