Merge pull request #1696 from borglab/model-selection-integration

AlgebraicDecisionTree Helpers

gtsam/discrete/AlgebraicDecisionTree.h

@@ -196,6 +196,42 @@ namespace gtsam {
       return this->apply(g, &Ring::div);
     }
 
+    /// Compute sum of all values
+    double sum() const {
+      double sum = 0;
+      auto visitor = [&](double y) { sum += y; };
+      this->visit(visitor);
+      return sum;
+    }
+
+    /**
+     * @brief Helper method to perform normalization such that all leaves in the
+     * tree sum to 1
+     *
+     * @param sum
+     * @return AlgebraicDecisionTree
+     */
+    AlgebraicDecisionTree normalize(double sum) const {
+      return this->apply([&sum](const double& x) { return x / sum; });
+    }
+
+    /// Find the minimum values amongst all leaves
+    double min() const {
+      double min = std::numeric_limits<double>::max();
+      auto visitor = [&](double x) { min = x < min ? x : min; };
+      this->visit(visitor);
+      return min;
+    }
+
+    /// Find the maximum values amongst all leaves
+    double max() const {
+      // Get the most negative value
+      double max = -std::numeric_limits<double>::max();
+      auto visitor = [&](double x) { max = x > max ? x : max; };
+      this->visit(visitor);
+      return max;
+    }
+
     /** sum out variable */
     AlgebraicDecisionTree sum(const L& label, size_t cardinality) const {
       return this->combine(label, cardinality, &Ring::add);

gtsam/discrete/tests/testAlgebraicDecisionTree.cpp

@@ -593,6 +593,55 @@ TEST(ADT, zero) {
   EXPECT_DOUBLES_EQUAL(0, anotb(x11), 1e-9);
 }
 
+/// Example ADT from 0 to 11.
+ADT exampleADT() {
+  DiscreteKey A(0, 2), B(1, 3), C(2, 2);
+  ADT f(A & B & C, "0 6  2 8  4 10    1 7  3 9  5 11");
+  return f;
+}
+/* ************************************************************************** */
+// Test sum
+TEST(ADT, Sum) {
+  ADT a = exampleADT();
+  double expected_sum = 0;
+  for (double i = 0; i < 12; i++) {
+    expected_sum += i;
+  }
+  EXPECT_DOUBLES_EQUAL(expected_sum, a.sum(), 1e-9);
+}
+
+/* ************************************************************************** */
+// Test normalize
+TEST(ADT, Normalize) {
+  ADT a = exampleADT();
+  double sum = a.sum();
+  auto actual = a.normalize(sum);
+
+  DiscreteKey A(0, 2), B(1, 3), C(2, 2);
+  DiscreteKeys keys = DiscreteKeys{A, B, C};
+  std::vector<double> cpt{0 / sum, 6 / sum,  2 / sum, 8 / sum,
+                          4 / sum, 10 / sum, 1 / sum, 7 / sum,
+                          3 / sum, 9 / sum,  5 / sum, 11 / sum};
+  ADT expected(keys, cpt);
+  EXPECT(assert_equal(expected, actual));
+}
+
+/* ************************************************************************** */
+// Test min
+TEST(ADT, Min) {
+  ADT a = exampleADT();
+  double min = a.min();
+  EXPECT_DOUBLES_EQUAL(0.0, min, 1e-9);
+}
+
+/* ************************************************************************** */
+// Test max
+TEST(ADT, Max) {
+  ADT a = exampleADT();
+  double max = a.max();
+  EXPECT_DOUBLES_EQUAL(11.0, max, 1e-9);
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;

gtsam/hybrid/GaussianMixture.h

@@ -67,7 +67,8 @@ class GTSAM_EXPORT GaussianMixture
   double logConstant_;         ///< log of the normalization constant.
 
   /**
-   * @brief Convert a DecisionTree of factors into a DT of Gaussian FGs.
+   * @brief Convert a DecisionTree of factors into
+   * a DecisionTree of Gaussian factor graphs.
    */
   GaussianFactorGraphTree asGaussianFactorGraphTree() const;
 
@@ -214,7 +215,8 @@ class GTSAM_EXPORT GaussianMixture
    * @return AlgebraicDecisionTree<Key> A decision tree on the discrete keys
    * only, with the leaf values as the error for each assignment.
    */
-  AlgebraicDecisionTree<Key> errorTree(const VectorValues &continuousValues) const;
+  AlgebraicDecisionTree<Key> errorTree(
+      const VectorValues &continuousValues) const;
 
   /**
    * @brief Compute the logProbability of this Gaussian Mixture.

gtsam/hybrid/GaussianMixtureFactor.h

@@ -135,7 +135,8 @@ class GTSAM_EXPORT GaussianMixtureFactor : public HybridFactor {
    * @return AlgebraicDecisionTree<Key> A decision tree with the same keys
    * as the factors involved, and leaf values as the error.
    */
-  AlgebraicDecisionTree<Key> errorTree(const VectorValues &continuousValues) const;
+  AlgebraicDecisionTree<Key> errorTree(
+      const VectorValues &continuousValues) const;
 
   /**
    * @brief Compute the log-likelihood, including the log-normalizing constant.

gtsam/hybrid/HybridValues.h

@@ -12,6 +12,7 @@
 /**
  *  @file HybridValues.h
  *  @date Jul 28, 2022
+ *  @author Varun Agrawal
  *  @author Shangjie Xue
  */
 
@@ -54,13 +55,13 @@ class GTSAM_EXPORT HybridValues {
   HybridValues() = default;
 
   /// Construct from DiscreteValues and VectorValues.
-  HybridValues(const VectorValues &cv, const DiscreteValues &dv)
-      : continuous_(cv), discrete_(dv){}
+  HybridValues(const VectorValues& cv, const DiscreteValues& dv)
+      : continuous_(cv), discrete_(dv) {}
 
   /// Construct from all values types.
   HybridValues(const VectorValues& cv, const DiscreteValues& dv,
                const Values& v)
-      : continuous_(cv), discrete_(dv), nonlinear_(v){}
+      : continuous_(cv), discrete_(dv), nonlinear_(v) {}
 
   /// @}
   /// @name Testable
@@ -101,9 +102,7 @@ class GTSAM_EXPORT HybridValues {
   bool existsDiscrete(Key j) { return (discrete_.find(j) != discrete_.end()); }
 
   /// Check whether a variable with key \c j exists in values.
-  bool existsNonlinear(Key j) {
-    return nonlinear_.exists(j);
-  }
+  bool existsNonlinear(Key j) { return nonlinear_.exists(j); }
 
   /// Check whether a variable with key \c j exists.
   bool exists(Key j) {
@@ -128,9 +127,7 @@ class GTSAM_EXPORT HybridValues {
   }
 
   /// insert_or_assign() , similar to Values.h
-  void insert_or_assign(Key j, size_t value) {
-    discrete_[j] = value;
-  }
+  void insert_or_assign(Key j, size_t value) { discrete_[j] = value; }
 
   /** Insert all continuous values from \c values.  Throws an invalid_argument
    * exception if any keys to be inserted are already used. */

gtsam/hybrid/tests/testHybridEstimation.cpp

@@ -333,7 +333,6 @@ TEST(HybridEstimation, Probability) {
   for (auto discrete_conditional : *discreteBayesNet) {
     bayesNet->add(discrete_conditional);
   }
-  auto discreteConditional = discreteBayesNet->at(0)->asDiscrete();
 
   HybridValues hybrid_values = bayesNet->optimize();
 

gtsam/nonlinear/nonlinear.i

@@ -381,17 +381,22 @@ typedef gtsam::GncOptimizer<gtsam::GncParams<gtsam::LevenbergMarquardtParams>> G
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 virtual class LevenbergMarquardtOptimizer : gtsam::NonlinearOptimizer {
   LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph,
-                              const gtsam::Values& initialValues);
+                              const gtsam::Values& initialValues,
+                              const gtsam::LevenbergMarquardtParams& params =
+                                  gtsam::LevenbergMarquardtParams());
   LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph,
                               const gtsam::Values& initialValues,
-                              const gtsam::LevenbergMarquardtParams& params);
+                              const gtsam::Ordering& ordering,
+                              const gtsam::LevenbergMarquardtParams& params =
+                                  gtsam::LevenbergMarquardtParams());
+
   double lambda() const;
   void print(string s = "") const;
 };
 
 #include <gtsam/nonlinear/ISAM2.h>
 class ISAM2GaussNewtonParams {
-  ISAM2GaussNewtonParams();
+  ISAM2GaussNewtonParams(double _wildfireThreshold = 0.001);
 
   void print(string s = "") const;