def test_priorConstraint(self):
mln = MLN([os.path.join(self.smokingDir, "wts.smoking.mln"), os.path.join(self.smokingDir, "smoking-prior.emln")])
mln.write(sys.stdout)
mrf = mln.groundMRF(os.path.join(self.smokingDir, "smoking-test-peopleonly.db"))
results = map(float, (mrf.inferEnumerationAsk("Smokes")))
correctResults = [0.5, 0.5]
self.assertApproxListEqual(results, correctResults, 0.01)
def test_inferExact_inferEnumerationAsk(self):
mln = MLN(os.path.join(self.smokingDir, "wts.smoking.mln"))
mln.write(sys.stdout)
mrf = mln.groundMRF(os.path.join(self.smokingDir, "smoking-test-peopleonly.db"))
results = map(float, (mrf.inferExact(["Smokes", "Cancer"])))
results2 = map(float, (mrf.inferEnumerationAsk(["Smokes", "Cancer"])))
correctResults = [0.3537297423387627, 0.3537297423387627, 0.5902867352545694, 0.5902867352545694]
self.assertApproxListEqual(results, correctResults)
self.assertApproxListEqual(results2, correctResults)
def test_priorConstraint(self):
mln = MLN([
os.path.join(self.smokingDir, "wts.smoking.mln"),
os.path.join(self.smokingDir, "smoking-prior.emln")
])
mln.write(sys.stdout)
mrf = mln.groundMRF(
os.path.join(self.smokingDir, "smoking-test-peopleonly.db"))
results = map(float, (mrf.inferEnumerationAsk("Smokes")))
correctResults = [0.5, 0.5]
self.assertApproxListEqual(results, correctResults, 0.01)
def test_groundAMLN(self):
mln = MLN(os.path.join(self.studentDir, "student_course2.a.mln"))
mrf = mln.groundMRF(self.studentDB)
weights = map(lambda p: float(p[0]), mrf.getGroundFormulas())
correctWeights = [
1.5604290525533999, 3.6511741317144004, -0.7968133588156,
7.0145072690164003, 0.151252120947, -1.6360797755282002,
-1.5131502151878, -8.4313192249819995, -0.38454063647300002,
-1.16283955905, 1.3519705598595, -2.8760315103522003
]
self.assertApproxListEqual(weights, correctWeights)
def test_groundWithVars(self):
mln = MLN(
os.path.join(self.studentDir, "student_course2_simLearned.mln"))
mrf = mln.groundMRF(self.studentDB)
weights = map(lambda p: float(p[0]), mrf.getGroundFormulas())
correctWeights = [
-0.40546510810816444, -1.0986122886681098, -0.22314355131420957,
-1.6094379124341012, -7.3508372506616082, -0.0006422607799453446,
-1.0986122886681098, -0.40546510810816427, -6.1468644463356723,
-0.0021424753776469402, 27.350837250661606, 27.350837250661606
]
self.assertApproxListEqual(weights, correctWeights)
def test_inferExact_inferEnumerationAsk(self):
mln = MLN(os.path.join(self.smokingDir, "wts.smoking.mln"))
mln.write(sys.stdout)
mrf = mln.groundMRF(
os.path.join(self.smokingDir, "smoking-test-peopleonly.db"))
results = map(float, (mrf.inferExact(["Smokes", "Cancer"])))
results2 = map(float, (mrf.inferEnumerationAsk(["Smokes", "Cancer"])))
correctResults = [
0.3537297423387627, 0.3537297423387627, 0.5902867352545694,
0.5902867352545694
]
self.assertApproxListEqual(results, correctResults)
self.assertApproxListEqual(results2, correctResults)
def test_groundWithVars(self):
mln = MLN(os.path.join(self.studentDir, "student_course2_simLearned.mln"))
mrf = mln.groundMRF(self.studentDB)
weights = map(lambda p: float(p[0]), mrf.getGroundFormulas())
correctWeights = [-0.40546510810816444, -1.0986122886681098, -0.22314355131420957, -1.6094379124341012, -7.3508372506616082, -0.0006422607799453446, -1.0986122886681098, -0.40546510810816427, -6.1468644463356723, -0.0021424753776469402, 27.350837250661606, 27.350837250661606]
self.assertApproxListEqual(weights, correctWeights)
def test_groundAMLN(self):
mln = MLN(os.path.join(self.studentDir, "student_course2.a.mln"))
mrf = mln.groundMRF(self.studentDB)
weights = map(lambda p: float(p[0]), mrf.getGroundFormulas())
correctWeights = [1.5604290525533999, 3.6511741317144004, -0.7968133588156, 7.0145072690164003, 0.151252120947, -1.6360797755282002, -1.5131502151878, -8.4313192249819995, -0.38454063647300002, -1.16283955905, 1.3519705598595, -2.8760315103522003]
self.assertApproxListEqual(weights, correctWeights)
def test_softEvidenceMCSATPC(self):
mln = MLN(os.path.join(self.posteriorCDir, "simple.mln"))
mrf = mln.groundMRF(os.path.join(self.posteriorCDir, "test.db"))
results = map(float, (mrf.inferMCSAT("attr")))
correctResults = [0.6666666666666666, 0.10]
self.assertApproxListEqual(results, correctResults, 0.04)
def getSimpleLearningModel(self):
return MLN(os.path.join(self.simpleLearnDir, "simple.mln"))
def getSmokersModel(self):
return MLN(os.path.join(self.smokingDir, "smoking.mln"))
def test_softEvidenceMCSATPC(self):
mln = MLN(os.path.join(self.posteriorCDir, "simple.mln"))
mrf = mln.groundMRF(os.path.join(self.posteriorCDir, "test.db"))
results = map(float, (mrf.inferMCSAT("attr")))
correctResults = [0.6666666666666666, 0.10]
self.assertApproxListEqual(results, correctResults, 0.04)