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Revert "enumerate all assignments for computing probabilities to prune" 

This reverts commit 8c38e45c83.
release/4.3a0
Varun Agrawal 2023-07-10 19:39:36 -04:00
parent 2f4133fd49
commit ddb36c2e7b
2 changed files with 21 additions and 21 deletions
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4
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
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@ -108,7 +108,7 @@ TEST(GaussianMixtureFactor, Printing) {
std::string expected = std::string expected =
R"(Hybrid [x1 x2; 1]{ R"(Hybrid [x1 x2; 1]{
Choice(1) Choice(1)
0 Leaf : 0 Leaf [1]:
A[x1] = [ A[x1] = [
0; 0;
0 0
@ -120,7 +120,7 @@ TEST(GaussianMixtureFactor, Printing) {
b = [ 0 0 ] b = [ 0 0 ]
No noise model No noise model
1 Leaf : 1 Leaf [1]:
A[x1] = [ A[x1] = [
0; 0;
0 0

38
gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp
View File

@ -492,7 +492,7 @@ factor 0:
factor 1: factor 1:
Hybrid [x0 x1; m0]{ Hybrid [x0 x1; m0]{
Choice(m0) Choice(m0)
0 Leaf : 0 Leaf [1]:
A[x0] = [ A[x0] = [
-1 -1
] ]
@ -502,7 +502,7 @@ Hybrid [x0 x1; m0]{
b = [ -1 ] b = [ -1 ]
No noise model No noise model
1 Leaf : 1 Leaf [1]:
A[x0] = [ A[x0] = [
-1 -1
] ]
@ -516,7 +516,7 @@ Hybrid [x0 x1; m0]{
factor 2: factor 2:
Hybrid [x1 x2; m1]{ Hybrid [x1 x2; m1]{
Choice(m1) Choice(m1)
0 Leaf : 0 Leaf [1]:
A[x1] = [ A[x1] = [
-1 -1
] ]
@ -526,7 +526,7 @@ Hybrid [x1 x2; m1]{
b = [ -1 ] b = [ -1 ]
No noise model No noise model
1 Leaf : 1 Leaf [1]:
A[x1] = [ A[x1] = [
-1 -1
] ]
@ -550,16 +550,16 @@ factor 4:
b = [ -10 ] b = [ -10 ]
No noise model No noise model
factor 5: P( m0 ): factor 5: P( m0 ):
Leaf 0.5 Leaf [2] 0.5
factor 6: P( m1 | m0 ): factor 6: P( m1 | m0 ):
Choice(m1) Choice(m1)
0 Choice(m0) 0 Choice(m0)
0 0 Leaf 0.33333333 0 0 Leaf [1]0.33333333
0 1 Leaf 0.6 0 1 Leaf [1] 0.6
1 Choice(m0) 1 Choice(m0)
1 0 Leaf 0.66666667 1 0 Leaf [1]0.66666667
1 1 Leaf 0.4 1 1 Leaf [1] 0.4
)"; )";
EXPECT(assert_print_equal(expected_hybridFactorGraph, linearizedFactorGraph)); EXPECT(assert_print_equal(expected_hybridFactorGraph, linearizedFactorGraph));
@ -570,13 +570,13 @@ size: 3
conditional 0: Hybrid P( x0 | x1 m0) conditional 0: Hybrid P( x0 | x1 m0)
Discrete Keys = (m0, 2), Discrete Keys = (m0, 2),
Choice(m0) Choice(m0)
0 Leaf p(x0 | x1) 0 Leaf [1] p(x0 | x1)
R = [ 10.0499 ] R = [ 10.0499 ]
S[x1] = [ -0.0995037 ] S[x1] = [ -0.0995037 ]
d = [ -9.85087 ] d = [ -9.85087 ]
No noise model No noise model
1 Leaf p(x0 | x1) 1 Leaf [1] p(x0 | x1)
R = [ 10.0499 ] R = [ 10.0499 ]
S[x1] = [ -0.0995037 ] S[x1] = [ -0.0995037 ]
d = [ -9.95037 ] d = [ -9.95037 ]
@ -586,26 +586,26 @@ conditional 1: Hybrid P( x1 | x2 m0 m1)
Discrete Keys = (m0, 2), (m1, 2), Discrete Keys = (m0, 2), (m1, 2),
Choice(m1) Choice(m1)
0 Choice(m0) 0 Choice(m0)
0 0 Leaf p(x1 | x2) 0 0 Leaf [1] p(x1 | x2)
R = [ 10.099 ] R = [ 10.099 ]
S[x2] = [ -0.0990196 ] S[x2] = [ -0.0990196 ]
d = [ -9.99901 ] d = [ -9.99901 ]
No noise model No noise model
0 1 Leaf p(x1 | x2) 0 1 Leaf [1] p(x1 | x2)
R = [ 10.099 ] R = [ 10.099 ]
S[x2] = [ -0.0990196 ] S[x2] = [ -0.0990196 ]
d = [ -9.90098 ] d = [ -9.90098 ]
No noise model No noise model
1 Choice(m0) 1 Choice(m0)
1 0 Leaf p(x1 | x2) 1 0 Leaf [1] p(x1 | x2)
R = [ 10.099 ] R = [ 10.099 ]
S[x2] = [ -0.0990196 ] S[x2] = [ -0.0990196 ]
d = [ -10.098 ] d = [ -10.098 ]
No noise model No noise model
1 1 Leaf p(x1 | x2) 1 1 Leaf [1] p(x1 | x2)
R = [ 10.099 ] R = [ 10.099 ]
S[x2] = [ -0.0990196 ] S[x2] = [ -0.0990196 ]
d = [ -10 ] d = [ -10 ]
@ -615,14 +615,14 @@ conditional 2: Hybrid P( x2 | m0 m1)
Discrete Keys = (m0, 2), (m1, 2), Discrete Keys = (m0, 2), (m1, 2),
Choice(m1) Choice(m1)
0 Choice(m0) 0 Choice(m0)
0 0 Leaf p(x2) 0 0 Leaf [1] p(x2)
R = [ 10.0494 ] R = [ 10.0494 ]
d = [ -10.1489 ] d = [ -10.1489 ]
mean: 1 elements mean: 1 elements
x2: -1.0099 x2: -1.0099
No noise model No noise model
0 1 Leaf p(x2) 0 1 Leaf [1] p(x2)
R = [ 10.0494 ] R = [ 10.0494 ]
d = [ -10.1479 ] d = [ -10.1479 ]
mean: 1 elements mean: 1 elements
@ -630,14 +630,14 @@ conditional 2: Hybrid P( x2 | m0 m1)
No noise model No noise model
1 Choice(m0) 1 Choice(m0)
1 0 Leaf p(x2) 1 0 Leaf [1] p(x2)
R = [ 10.0494 ] R = [ 10.0494 ]
d = [ -10.0504 ] d = [ -10.0504 ]
mean: 1 elements mean: 1 elements
x2: -1.0001 x2: -1.0001
No noise model No noise model
1 1 Leaf p(x2) 1 1 Leaf [1] p(x2)
R = [ 10.0494 ] R = [ 10.0494 ]
d = [ -10.0494 ] d = [ -10.0494 ]
mean: 1 elements mean: 1 elements