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Wrapper and tests for logProbability

release/4.3a0
Frank Dellaert 2023-01-10 23:13:40 -08:00
parent eb37d080d4
commit 87ff4af32d
6 changed files with 58 additions and 14 deletions
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8
gtsam/discrete/discrete.i
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@ -95,6 +95,9 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
DiscreteConditional(const gtsam::DecisionTreeFactor& joint,
const gtsam::DecisionTreeFactor& marginal,
const gtsam::Ordering& orderedKeys);
double logProbability(const gtsam::DiscreteValues& values) const;
double evaluate(const gtsam::DiscreteValues& values) const;
double operator()(const gtsam::DiscreteValues& values) const;
gtsam::DiscreteConditional operator*(
const gtsam::DiscreteConditional& other) const;
gtsam::DiscreteConditional marginal(gtsam::Key key) const;
@ -157,7 +160,12 @@ class DiscreteBayesNet {
const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter) const;
bool equals(const gtsam::DiscreteBayesNet& other, double tol = 1e-9) const;
// Standard interface.
double logProbability(const gtsam::DiscreteValues& values) const;
double evaluate(const gtsam::DiscreteValues& values) const;
double operator()(const gtsam::DiscreteValues& values) const;
gtsam::DiscreteValues sample() const;
gtsam::DiscreteValues sample(gtsam::DiscreteValues given) const;

8
gtsam/hybrid/hybrid.i
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@ -61,6 +61,9 @@ virtual class HybridConditional {
size_t nrParents() const;
// Standard interface:
double logProbability(const gtsam::HybridValues& values) const;
double evaluate(const gtsam::HybridValues& values) const;
double operator()(const gtsam::HybridValues& values) const;
gtsam::GaussianMixture* asMixture() const;
gtsam::GaussianConditional* asGaussian() const;
gtsam::DiscreteConditional* asDiscrete() const;
@ -133,7 +136,10 @@ class HybridBayesNet {
gtsam::KeySet keys() const;
const gtsam::HybridConditional* at(size_t i) const;
double evaluate(const gtsam::HybridValues& x) const;
// Standard interface:
double logProbability(const gtsam::HybridValues& values) const;
double evaluate(const gtsam::HybridValues& values) const;
gtsam::HybridValues optimize() const;
gtsam::HybridValues sample(const gtsam::HybridValues &given) const;
gtsam::HybridValues sample() const;

3
gtsam/linear/linear.i
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@ -497,6 +497,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
bool equals(const gtsam::GaussianConditional& cg, double tol) const;
// Standard Interface
double logProbability(const gtsam::VectorValues& x) const;
double evaluate(const gtsam::VectorValues& x) const;
double error(const gtsam::VectorValues& x) const;
gtsam::Key firstFrontalKey() const;
@ -558,6 +559,8 @@ virtual class GaussianBayesNet {
gtsam::GaussianConditional* back() const;
// Standard interface
// Standard Interface
double logProbability(const gtsam::VectorValues& x) const;
double evaluate(const gtsam::VectorValues& x) const;
double error(const gtsam::VectorValues& x) const;

18
python/gtsam/tests/test_DiscreteBayesNet.py
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@ -11,13 +11,15 @@ Author: Frank Dellaert
# pylint: disable=no-name-in-module, invalid-name
import math
import textwrap
import unittest
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import (DiscreteBayesNet, DiscreteConditional, DiscreteDistribution,
DiscreteFactorGraph, DiscreteKeys, DiscreteValues, Ordering)
from gtsam.utils.test_case import GtsamTestCase
# Some keys:
Asia = (0, 2)
@ -111,7 +113,7 @@ class TestDiscreteBayesNet(GtsamTestCase):
self.assertEqual(len(actualSample), 8)
def test_fragment(self):
"""Test sampling and optimizing for Asia fragment."""
"""Test evaluate/sampling/optimizing for Asia fragment."""
# Create a reverse-topologically sorted fragment:
fragment = DiscreteBayesNet()
@ -125,8 +127,14 @@ class TestDiscreteBayesNet(GtsamTestCase):
given[key[0]] = 0
# Now sample from fragment:
actual = fragment.sample(given)
self.assertEqual(len(actual), 5)
values = fragment.sample(given)
self.assertEqual(len(values), 5)
for i in [0, 1, 2]:
self.assertAlmostEqual(fragment.at(i).logProbability(values),
math.log(fragment.at(i).evaluate(values)))
self.assertAlmostEqual(fragment.logProbability(values),
math.log(fragment.evaluate(values)))
def test_dot(self):
"""Check that dot works with position hints."""
@ -139,7 +147,7 @@ class TestDiscreteBayesNet(GtsamTestCase):
# Make sure we can *update* position hints
writer = gtsam.DotWriter()
ph: dict = writer.positionHints
ph['a'] = 2 # hint at symbol position
ph['a'] = 2 # hint at symbol position
writer.positionHints = ph
# Check the output of dot

18
python/gtsam/tests/test_GaussianBayesNet.py
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@ -12,13 +12,15 @@ Author: Frank Dellaert
from __future__ import print_function
import math
import unittest
import gtsam
import numpy as np
from gtsam import GaussianBayesNet, GaussianConditional
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import GaussianBayesNet, GaussianConditional
# some keys
_x_ = 11
_y_ = 22
@ -45,6 +47,18 @@ class TestGaussianBayesNet(GtsamTestCase):
np.testing.assert_equal(R, R1)
np.testing.assert_equal(d, d1)
def test_evaluate(self):
"""Test evaluate method"""
bayesNet = smallBayesNet()
values = gtsam.VectorValues()
values.insert(_x_, np.array([9.0]))
values.insert(_y_, np.array([5.0]))
for i in [0, 1]:
self.assertAlmostEqual(bayesNet.at(i).logProbability(values),
math.log(bayesNet.at(i).evaluate(values)))
self.assertAlmostEqual(bayesNet.logProbability(values),
math.log(bayesNet.evaluate(values)))
def test_sample(self):
"""Test sample method"""
bayesNet = smallBayesNet()

17
python/gtsam/tests/test_HybridBayesNet.py
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@ -10,14 +10,15 @@ Author: Frank Dellaert
"""
# pylint: disable=invalid-name, no-name-in-module, no-member
import math
import unittest
import numpy as np
from gtsam.symbol_shorthand import A, X
from gtsam.utils.test_case import GtsamTestCase
from gtsam import (DiscreteKeys, GaussianMixture, DiscreteConditional, GaussianConditional, GaussianMixture,
HybridBayesNet, HybridValues, noiseModel)
from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
GaussianMixture, HybridBayesNet, HybridValues, noiseModel)
class TestHybridBayesNet(GtsamTestCase):
@ -30,8 +31,8 @@ class TestHybridBayesNet(GtsamTestCase):
# Create the continuous conditional
I_1x1 = np.eye(1)
gc = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4],
5.0)
conditional = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4],
5.0)
# Create the noise models
model0 = noiseModel.Diagonal.Sigmas([2.0])
@ -45,7 +46,7 @@ class TestHybridBayesNet(GtsamTestCase):
# Create hybrid Bayes net.
bayesNet = HybridBayesNet()
bayesNet.push_back(gc)
bayesNet.push_back(conditional)
bayesNet.push_back(GaussianMixture(
[X(1)], [], discrete_keys, [conditional0, conditional1]))
bayesNet.push_back(DiscreteConditional(Asia, "99/1"))
@ -56,13 +57,17 @@ class TestHybridBayesNet(GtsamTestCase):
values.insert(X(0), [-6])
values.insert(X(1), [1])
conditionalProbability = gc.evaluate(values.continuous())
conditionalProbability = conditional.evaluate(values.continuous())
mixtureProbability = conditional0.evaluate(values.continuous())
self.assertAlmostEqual(conditionalProbability * mixtureProbability *
0.99,
bayesNet.evaluate(values),
places=5)
# Check logProbability
self.assertAlmostEqual(bayesNet.logProbability(values),
math.log(bayesNet.evaluate(values)))
if __name__ == "__main__":
unittest.main()