[pyAgrum] adapt test for pyAgrum with new GibbsSampling

wrappers/pyAgrum/testunits/tests/GibbsTestSuite.py

@@ -80,16 +80,16 @@ class TestDictFeature(GibbsTestCase):
   def testDictOfSequences(self):
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'s': [0, 1], 'w': (1, 0)})
     ie.makeInference()
     result = ie.posterior(self.r)
 
     ie2 = gum.GibbsSampling(self.bn)
     ie2.setVerbosity(False)
-    ie2.setEpsilon(0.0001)
-    ie2.setMinEpsilonRate(0.0001)
+    ie2.setEpsilon(0.01)
+    ie2.setMinEpsilonRate(0.01)
     ie2.setEvidence({'s': 1, 'w': 0})
     ie2.makeInference()
     result2 = ie2.posterior(self.r)
@@ -99,16 +99,16 @@ class TestDictFeature(GibbsTestCase):
   def testDictOfLabels(self):
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'s': 0, 'w': 1})
     ie.makeInference()
     result = ie.posterior(self.r).tolist()
 
     ie2 = gum.GibbsSampling(self.bn)
     ie2.setVerbosity(False)
-    ie2.setEpsilon(0.0001)
-    ie2.setMinEpsilonRate(0.0001)
+    ie2.setEpsilon(0.01)
+    ie2.setMinEpsilonRate(0.01)
     ie2.setEvidence({'s': 'no', 'w': 'yes'})
     ie2.makeInference()
     result2 = ie2.posterior(self.r).tolist()
@@ -131,16 +131,16 @@ class TestDictFeature(GibbsTestCase):
   def testWithDifferentVariables(self):
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'r': [0, 1], 'w': (1, 0)})
     ie.makeInference()
     result = ie.posterior(self.s).tolist()
 
     ie = gum.GibbsSampling(self.bni)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'ri': [0, 1], 'wi': (1, 0)})
     ie.makeInference()
     result2 = ie.posterior(self.si).tolist()
@@ -148,8 +148,8 @@ class TestDictFeature(GibbsTestCase):
 
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'r': 1, 'w': 0})
     ie.makeInference()
     result = ie.posterior(self.s).tolist()
@@ -157,8 +157,8 @@ class TestDictFeature(GibbsTestCase):
 
     ie = gum.GibbsSampling(self.bni)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'ri': "6", 'wi': "0.33"})
     ie.makeInference()
     result2 = ie.posterior(self.si).tolist()
@@ -169,15 +169,15 @@ class TestInferenceResults(GibbsTestCase):
   def testOpenBayesSiteExamples(self):
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     result = ie.posterior(self.w)
     self.assertDelta(result.tolist(), [0.3529, 0.6471])
 
     ie = gum.GibbsSampling(self.bn)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.01)
+    ie.setMinEpsilonRate(0.01)
     ie.setEvidence({'s': 1, 'c': 0})
     ie.makeInference()
     result = ie.posterior(self.w)
@@ -186,8 +186,8 @@ class TestInferenceResults(GibbsTestCase):
   def testWikipediaExample(self):
     ie = gum.GibbsSampling(self.bn2)
     ie.setVerbosity(False)
-    ie.setEpsilon(0.0001)
-    ie.setMinEpsilonRate(0.0001)
+    ie.setEpsilon(0.001)
+    ie.setMinEpsilonRate(0.001)
     ie.setEvidence({'w2': 1})
     ie.makeInference()
     result = ie.posterior(self.r2)