Merge pull request #1039 from borglab/feature/DiscreteDistribution

gtsam/discrete/DiscreteBayesNet.h

@@ -19,7 +19,7 @@
 #pragma once
 
 #include <gtsam/discrete/DiscreteConditional.h>
-#include <gtsam/discrete/DiscretePrior.h>
+#include <gtsam/discrete/DiscreteDistribution.h>
 #include <gtsam/inference/BayesNet.h>
 #include <gtsam/inference/FactorGraph.h>
 
@@ -79,9 +79,9 @@ namespace gtsam {
     // Add inherited versions of add.
     using Base::add;
 
-    /** Add a DiscretePrior using a table or a string */
+    /** Add a DiscreteDistribution using a table or a string */
     void add(const DiscreteKey& key, const std::string& spec) {
-      emplace_shared<DiscretePrior>(key, spec);
+      emplace_shared<DiscreteDistribution>(key, spec);
     }
 
     /** Add a DiscreteCondtional */

gtsam/discrete/DiscreteConditional.h

@@ -89,7 +89,7 @@ class GTSAM_EXPORT DiscreteConditional
                       const std::string& spec)
       : DiscreteConditional(Signature(key, parents, spec)) {}
 
-  /// No-parent specialization; can also use DiscretePrior.
+  /// No-parent specialization; can also use DiscreteDistribution.
   DiscreteConditional(const DiscreteKey& key, const std::string& spec)
       : DiscreteConditional(Signature(key, {}, spec)) {}
 

gtsam/discrete/DiscreteDistribution.cpp

@@ -10,21 +10,23 @@
  * -------------------------------------------------------------------------- */
 
 /**
- *  @file DiscretePrior.cpp
+ *  @file DiscreteDistribution.cpp
  *  @date December 2021
  *  @author Frank Dellaert
  */
 
-#include <gtsam/discrete/DiscretePrior.h>
+#include <gtsam/discrete/DiscreteDistribution.h>
+
+#include <vector>
 
 namespace gtsam {
 
-void DiscretePrior::print(const std::string& s,
+void DiscreteDistribution::print(const std::string& s,
-                          const KeyFormatter& formatter) const {
+                                 const KeyFormatter& formatter) const {
   Base::print(s, formatter);
 }
 
-double DiscretePrior::operator()(size_t value) const {
+double DiscreteDistribution::operator()(size_t value) const {
   if (nrFrontals() != 1)
     throw std::invalid_argument(
         "Single value operator can only be invoked on single-variable "
@@ -34,10 +36,10 @@ double DiscretePrior::operator()(size_t value) const {
   return Base::operator()(values);
 }
 
-std::vector<double> DiscretePrior::pmf() const {
+std::vector<double> DiscreteDistribution::pmf() const {
   if (nrFrontals() != 1)
     throw std::invalid_argument(
-        "DiscretePrior::pmf only defined for single-variable priors");
+        "DiscreteDistribution::pmf only defined for single-variable priors");
   const size_t nrValues = cardinalities_.at(keys_[0]);
   std::vector<double> array;
   array.reserve(nrValues);

gtsam/discrete/DiscreteDistribution.h

@@ -10,7 +10,7 @@
  * -------------------------------------------------------------------------- */
 
 /**
- *  @file DiscretePrior.h
+ *  @file DiscreteDistribution.h
  *  @date December 2021
  *  @author Frank Dellaert
  */
@@ -20,6 +20,7 @@
 #include <gtsam/discrete/DiscreteConditional.h>
 
 #include <string>
+#include <vector>
 
 namespace gtsam {
 
@@ -27,7 +28,7 @@ namespace gtsam {
  * A prior probability on a set of discrete variables.
  * Derives from DiscreteConditional
  */
-class GTSAM_EXPORT DiscretePrior : public DiscreteConditional {
+class GTSAM_EXPORT DiscreteDistribution : public DiscreteConditional {
  public:
   using Base = DiscreteConditional;
 
@@ -35,35 +36,36 @@ class GTSAM_EXPORT DiscretePrior : public DiscreteConditional {
   /// @{
 
   /// Default constructor needed for serialization.
-  DiscretePrior() {}
+  DiscreteDistribution() {}
 
   /// Constructor from factor.
-  DiscretePrior(const DecisionTreeFactor& f) : Base(f.size(), f) {}
+  explicit DiscreteDistribution(const DecisionTreeFactor& f)
+      : Base(f.size(), f) {}
 
   /**
    * Construct from a Signature.
    *
-   * Example: DiscretePrior P(D % "3/2");
+   * Example: DiscreteDistribution P(D % "3/2");
    */
-  DiscretePrior(const Signature& s) : Base(s) {}
+  explicit DiscreteDistribution(const Signature& s) : Base(s) {}
 
   /**
    * Construct from key and a vector of floats specifying the probability mass
    * function (PMF).
    *
-   * Example: DiscretePrior P(D, {0.4, 0.6});
+   * Example: DiscreteDistribution P(D, {0.4, 0.6});
    */
-  DiscretePrior(const DiscreteKey& key, const std::vector<double>& spec)
+  DiscreteDistribution(const DiscreteKey& key, const std::vector<double>& spec)
-      : DiscretePrior(Signature(key, {}, Signature::Table{spec})) {}
+      : DiscreteDistribution(Signature(key, {}, Signature::Table{spec})) {}
 
   /**
    * Construct from key and a string specifying the probability mass function
    * (PMF).
    *
-   * Example: DiscretePrior P(D, "9/1 2/8 3/7 1/9");
+   * Example: DiscreteDistribution P(D, "9/1 2/8 3/7 1/9");
    */
-  DiscretePrior(const DiscreteKey& key, const std::string& spec)
+  DiscreteDistribution(const DiscreteKey& key, const std::string& spec)
-      : DiscretePrior(Signature(key, {}, spec)) {}
+      : DiscreteDistribution(Signature(key, {}, spec)) {}
 
   /// @}
   /// @name Testable
@@ -102,10 +104,10 @@ class GTSAM_EXPORT DiscretePrior : public DiscreteConditional {
 
   /// @}
 };
-// DiscretePrior
+// DiscreteDistribution
 
 // traits
 template <>
-struct traits<DiscretePrior> : public Testable<DiscretePrior> {};
+struct traits<DiscreteDistribution> : public Testable<DiscreteDistribution> {};
 
 }  // namespace gtsam

gtsam/discrete/discrete.i

@@ -128,12 +128,12 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
               std::map<gtsam::Key, std::vector<std::string>> names) const;
 };
 
-#include <gtsam/discrete/DiscretePrior.h>
+#include <gtsam/discrete/DiscreteDistribution.h>
-virtual class DiscretePrior : gtsam::DiscreteConditional {
+virtual class DiscreteDistribution : gtsam::DiscreteConditional {
-  DiscretePrior();
+  DiscreteDistribution();
-  DiscretePrior(const gtsam::DecisionTreeFactor& f);
+  DiscreteDistribution(const gtsam::DecisionTreeFactor& f);
-  DiscretePrior(const gtsam::DiscreteKey& key, string spec);
+  DiscreteDistribution(const gtsam::DiscreteKey& key, string spec);
-  DiscretePrior(const gtsam::DiscreteKey& key, std::vector<double> spec);
+  DiscreteDistribution(const gtsam::DiscreteKey& key, std::vector<double> spec);
   void print(string s = "Discrete Prior\n",
              const gtsam::KeyFormatter& keyFormatter =
                  gtsam::DefaultKeyFormatter) const;

gtsam/discrete/tests/testDecisionTreeFactor.cpp

@@ -20,7 +20,7 @@
 #include <CppUnitLite/TestHarness.h>
 #include <gtsam/base/Testable.h>
 #include <gtsam/discrete/DecisionTreeFactor.h>
-#include <gtsam/discrete/DiscretePrior.h>
+#include <gtsam/discrete/DiscreteDistribution.h>
 #include <gtsam/discrete/Signature.h>
 
 #include <boost/assign/std/map.hpp>
@@ -56,8 +56,8 @@ TEST( DecisionTreeFactor, constructors)
 TEST(DecisionTreeFactor, multiplication) {
   DiscreteKey v0(0, 2), v1(1, 2), v2(2, 2);
 
-  // Multiply with a DiscretePrior, i.e., Bayes Law!
+  // Multiply with a DiscreteDistribution, i.e., Bayes Law!
-  DiscretePrior prior(v1 % "1/3");
+  DiscreteDistribution prior(v1 % "1/3");
   DecisionTreeFactor f1(v0 & v1, "1 2 3 4");
   DecisionTreeFactor expected(v0 & v1, "0.25 1.5 0.75 3");
   CHECK(assert_equal(expected, static_cast<DecisionTreeFactor>(prior) * f1));

gtsam/discrete/tests/testDiscreteDistribution.cpp

@@ -11,42 +11,41 @@
 
 /*
  * @file    testDiscretePrior.cpp
- * @brief   unit tests for DiscretePrior
+ * @brief   unit tests for DiscreteDistribution
  * @author  Frank dellaert
  * @date    December 2021
  */
 
 #include <CppUnitLite/TestHarness.h>
-#include <gtsam/discrete/DiscretePrior.h>
+#include <gtsam/discrete/DiscreteDistribution.h>
 #include <gtsam/discrete/Signature.h>
 
-using namespace std;
 using namespace gtsam;
 
 static const DiscreteKey X(0, 2);
 
 /* ************************************************************************* */
-TEST(DiscretePrior, constructors) {
+TEST(DiscreteDistribution, constructors) {
   DecisionTreeFactor f(X, "0.4 0.6");
-  DiscretePrior expected(f);
+  DiscreteDistribution expected(f);
 
-  DiscretePrior actual(X % "2/3");
+  DiscreteDistribution actual(X % "2/3");
   EXPECT_LONGS_EQUAL(1, actual.nrFrontals());
   EXPECT_LONGS_EQUAL(0, actual.nrParents());
   EXPECT(assert_equal(expected, actual, 1e-9));
 
-  const vector<double> pmf{0.4, 0.6};
+  const std::vector<double> pmf{0.4, 0.6};
-  DiscretePrior actual2(X, pmf);
+  DiscreteDistribution actual2(X, pmf);
   EXPECT_LONGS_EQUAL(1, actual2.nrFrontals());
   EXPECT_LONGS_EQUAL(0, actual2.nrParents());
   EXPECT(assert_equal(expected, actual2, 1e-9));
 }
 
 /* ************************************************************************* */
-TEST(DiscretePrior, Multiply) {
+TEST(DiscreteDistribution, Multiply) {
   DiscreteKey A(0, 2), B(1, 2);
   DiscreteConditional conditional(A | B = "1/2 2/1");
-  DiscretePrior prior(B, "1/2");
+  DiscreteDistribution prior(B, "1/2");
   DiscreteConditional actual = prior * conditional;  // P(A|B) * P(B)
 
   EXPECT_LONGS_EQUAL(2, actual.nrFrontals());  // = P(A,B)
@@ -56,22 +55,22 @@ TEST(DiscretePrior, Multiply) {
 }
 
 /* ************************************************************************* */
-TEST(DiscretePrior, operator) {
+TEST(DiscreteDistribution, operator) {
-  DiscretePrior prior(X % "2/3");
+  DiscreteDistribution prior(X % "2/3");
   EXPECT_DOUBLES_EQUAL(prior(0), 0.4, 1e-9);
   EXPECT_DOUBLES_EQUAL(prior(1), 0.6, 1e-9);
 }
 
 /* ************************************************************************* */
-TEST(DiscretePrior, pmf) {
+TEST(DiscreteDistribution, pmf) {
-  DiscretePrior prior(X % "2/3");
+  DiscreteDistribution prior(X % "2/3");
-  vector<double> expected {0.4, 0.6};
+  std::vector<double> expected{0.4, 0.6};
-  EXPECT(prior.pmf() ==  expected);
+  EXPECT(prior.pmf() == expected);
 }
 
 /* ************************************************************************* */
-TEST(DiscretePrior, sample) {
+TEST(DiscreteDistribution, sample) {
-  DiscretePrior prior(X % "2/3");
+  DiscreteDistribution prior(X % "2/3");
   prior.sample();
 }
 

python/gtsam/tests/test_DecisionTreeFactor.py

@@ -13,7 +13,7 @@ Author: Frank Dellaert
 
 import unittest
 
-from gtsam import DecisionTreeFactor, DiscreteValues, DiscretePrior, Ordering
+from gtsam import DecisionTreeFactor, DiscreteValues, DiscreteDistribution, Ordering
 from gtsam.utils.test_case import GtsamTestCase
 
 
@@ -36,8 +36,8 @@ class TestDecisionTreeFactor(GtsamTestCase):
         v1 = (1, 2)
         v2 = (2, 2)
 
-        # Multiply with a DiscretePrior, i.e., Bayes Law!
+        # Multiply with a DiscreteDistribution, i.e., Bayes Law!
-        prior = DiscretePrior(v1, [1, 3])
+        prior = DiscreteDistribution(v1, [1, 3])
         f1 = DecisionTreeFactor([v0, v1], "1 2 3 4")
         expected = DecisionTreeFactor([v0, v1], "0.25 1.5 0.75 3")
         self.gtsamAssertEquals(DecisionTreeFactor(prior) * f1, expected)

python/gtsam/tests/test_DiscreteBayesNet.py

@@ -14,7 +14,7 @@ Author: Frank Dellaert
 import unittest
 
 from gtsam import (DiscreteBayesNet, DiscreteConditional, DiscreteFactorGraph,
-                   DiscreteKeys, DiscretePrior, DiscreteValues, Ordering)
+                   DiscreteKeys, DiscreteDistribution, DiscreteValues, Ordering)
 from gtsam.utils.test_case import GtsamTestCase
 
 
@@ -74,7 +74,7 @@ class TestDiscreteBayesNet(GtsamTestCase):
         for j in range(8):
             ordering.push_back(j)
         chordal = fg.eliminateSequential(ordering)
-        expected2 = DiscretePrior(Bronchitis, "11/9")
+        expected2 = DiscreteDistribution(Bronchitis, "11/9")
         self.gtsamAssertEquals(chordal.at(7), expected2)
 
         # solve

python/gtsam/tests/test_DiscreteDistribution.py

@@ -14,7 +14,7 @@ Author: Frank Dellaert
 import unittest
 
 import numpy as np
-from gtsam import DecisionTreeFactor, DiscreteKeys, DiscretePrior
+from gtsam import DecisionTreeFactor, DiscreteKeys, DiscreteDistribution
 from gtsam.utils.test_case import GtsamTestCase
 
 X = 0, 2
@@ -28,33 +28,33 @@ class TestDiscretePrior(GtsamTestCase):
         keys = DiscreteKeys()
         keys.push_back(X)
         f = DecisionTreeFactor(keys, "0.4 0.6")
-        expected = DiscretePrior(f)
+        expected = DiscreteDistribution(f)
-        
+
-        actual = DiscretePrior(X, "2/3")
+        actual = DiscreteDistribution(X, "2/3")
         self.gtsamAssertEquals(actual, expected)
-        
+
-        actual2 = DiscretePrior(X, [0.4, 0.6])
+        actual2 = DiscreteDistribution(X, [0.4, 0.6])
         self.gtsamAssertEquals(actual2, expected)
 
     def test_operator(self):
-        prior = DiscretePrior(X, "2/3")
+        prior = DiscreteDistribution(X, "2/3")
         self.assertAlmostEqual(prior(0), 0.4)
         self.assertAlmostEqual(prior(1), 0.6)
 
     def test_pmf(self):
-        prior = DiscretePrior(X, "2/3")
+        prior = DiscreteDistribution(X, "2/3")
         expected = np.array([0.4, 0.6])
         np.testing.assert_allclose(expected, prior.pmf())
 
     def test_sample(self):
-        prior = DiscretePrior(X, "2/3")
+        prior = DiscreteDistribution(X, "2/3")
         actual = prior.sample()
         self.assertIsInstance(actual, int)
 
     def test_markdown(self):
         """Test the _repr_markdown_ method."""
 
-        prior = DiscretePrior(X, "2/3")
+        prior = DiscreteDistribution(X, "2/3")
         expected = " *P(0):*\n\n" \
             "|0|value|\n" \
             "|:-:|:-:|\n" \