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fix print tests

release/4.3a0
Varun Agrawal 2022-08-10 04:37:26 -04:00
parent 588f56ef3e
commit 60c88e338b
2 changed files with 31 additions and 39 deletions
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4
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
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@ -74,7 +74,7 @@ TEST(GaussianMixtureFactor, Sum) {
// Check that number of keys is 3
EXPECT_LONGS_EQUAL(3, mixtureFactorA.keys().size());
// Check that number of discrete keys is 1 // TODO(Frank): should not exist?
// Check that number of discrete keys is 1
EXPECT_LONGS_EQUAL(1, mixtureFactorA.discreteKeys().size());
// Create sum of two mixture factors: it will be a decision tree now on both
@ -104,7 +104,7 @@ TEST(GaussianMixtureFactor, Printing) {
GaussianMixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors);
std::string expected =
R"(Hybrid x1 x2; 1 ]{
R"(Hybrid [x1 x2; 1]{
Choice(1)
0 Leaf :
A[x1] = [

66
gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp
View File

@ -175,9 +175,8 @@ TEST(HybridFactorGraph, PushBack) {
TEST(HybridFactorGraph, Switching) {
Switching self(3);
EXPECT_LONGS_EQUAL(8, self.nonlinearFactorGraph.size());
EXPECT_LONGS_EQUAL(8, self.linearizedFactorGraph.size());
EXPECT_LONGS_EQUAL(7, self.nonlinearFactorGraph.size());
EXPECT_LONGS_EQUAL(7, self.linearizedFactorGraph.size());
}
/****************************************************************************
@ -190,7 +189,7 @@ TEST(HybridFactorGraph, Linearization) {
HybridGaussianFactorGraph actualLinearized =
self.nonlinearFactorGraph.linearize(self.linearizationPoint);
EXPECT_LONGS_EQUAL(8, actualLinearized.size());
EXPECT_LONGS_EQUAL(7, actualLinearized.size());
}
/****************************************************************************
@ -495,15 +494,15 @@ TEST(HybridFactorGraph, Printing) {
linearizedFactorGraph.eliminatePartialSequential(ordering);
string expected_hybridFactorGraph = R"(
size: 8
factor 0: Continuous x1;
size: 7
factor 0: Continuous [x1]
A[x1] = [
10
]
b = [ -10 ]
No noise model
factor 1: Hybrid x1 x2 m1; m1 ]{
factor 1: Hybrid [x1 x2; m1]{
Choice(m1)
0 Leaf :
A[x1] = [
@ -526,7 +525,7 @@ factor 1: Hybrid x1 x2 m1; m1 ]{
No noise model
}
factor 2: Hybrid x2 x3 m2; m2 ]{
factor 2: Hybrid [x2 x3; m2]{
Choice(m2)
0 Leaf :
A[x2] = [
@ -549,32 +548,25 @@ factor 2: Hybrid x2 x3 m2; m2 ]{
No noise model
}
factor 3: Continuous x1;
A[x1] = [
10
]
b = [ -10 ]
No noise model
factor 4: Continuous x2;
factor 3: Continuous [x2]
A[x2] = [
10
]
b = [ -10 ]
No noise model
factor 5: Continuous x3;
factor 4: Continuous [x3]
A[x3] = [
10
]
b = [ -10 ]
No noise model
factor 6: Discrete m1
factor 5: Discrete [m1]
P( m1 ):
Leaf 0.5
factor 7: Discrete m2 m1
factor 6: Discrete [m2 m1]
P( m2 | m1 ):
Choice(m2)
0 Choice(m1)
@ -594,15 +586,15 @@ factor 0: Hybrid P( x1 | x2 m1)
Discrete Keys = (m1, 2),
Choice(m1)
0 Leaf p(x1 | x2)
R = [ 14.1774 ]
S[x2] = [ -0.0705346 ]
d = [ -14.0364 ]
R = [ 10.0499 ]
S[x2] = [ -0.0995037 ]
d = [ -9.85087 ]
No noise model
1 Leaf p(x1 | x2)
R = [ 14.1774 ]
S[x2] = [ -0.0705346 ]
d = [ -14.1069 ]
R = [ 10.0499 ]
S[x2] = [ -0.0995037 ]
d = [ -9.95037 ]
No noise model
factor 1: Hybrid P( x2 | x3 m1 m2)
@ -610,28 +602,28 @@ factor 1: Hybrid P( x2 | x3 m1 m2)
Choice(m2)
0 Choice(m1)
0 0 Leaf p(x2 | x3)
R = [ 10.0993 ]
S[x3] = [ -0.0990172 ]
d = [ -9.99975 ]
R = [ 10.099 ]
S[x3] = [ -0.0990196 ]
d = [ -9.99901 ]
No noise model
0 1 Leaf p(x2 | x3)
R = [ 10.0993 ]
S[x3] = [ -0.0990172 ]
d = [ -9.90122 ]
R = [ 10.099 ]
S[x3] = [ -0.0990196 ]
d = [ -9.90098 ]
No noise model
1 Choice(m1)
1 0 Leaf p(x2 | x3)
R = [ 10.0993 ]
S[x3] = [ -0.0990172 ]
d = [ -10.0988 ]
R = [ 10.099 ]
S[x3] = [ -0.0990196 ]
d = [ -10.098 ]
No noise model
1 1 Leaf p(x2 | x3)
R = [ 10.0993 ]
S[x3] = [ -0.0990172 ]
d = [ -10.0002 ]
R = [ 10.099 ]
S[x3] = [ -0.0990196 ]
d = [ -10 ]
No noise model
factor 2: Hybrid P( x3 | m1 m2)