System.out.println("Testing Accuracy after saving = " + postTestAcc); assertEquals(postTrainAcc, preTrainAcc, 0.0001); assertEquals(postTestAcc, preTestAcc, 0.0001);
System.out.println("Testing Accuracy after saving = " + postTestAcc); assertEquals(postTrainAcc, preTrainAcc, 0.0001); assertEquals(postTestAcc, preTestAcc, 0.0001);
+ " minGrad=" + minimizableGradientNorm); assertEquals (true, Math.abs (minimizableCost - 35770) < 0.001); assertEquals (true, Math.abs (minimizableGradientNorm - 520) < 0.001);
+ " minGrad=" + minimizableGradientNorm); assertEquals (true, Math.abs (minimizableCost - 35770) < 0.001); assertEquals (true, Math.abs (minimizableGradientNorm - 520) < 0.001);
lik2 < lik3); assertEquals (-167.335971702, lik1, 0.0001); assertEquals (-166.212235389, lik2, 0.0001); assertEquals ( -90.386005741, lik3, 0.0001);
lik2 < lik3); assertEquals (-167.335971702, lik1, 0.0001); assertEquals (-166.212235389, lik2, 0.0001); assertEquals ( -90.386005741, lik3, 0.0001);
public void testSpaceSerializable () throws IOException, ClassNotFoundException { Pipe p = makeSpacePredictionPipe (); InstanceList training = new InstanceList (p); training.addThruPipe (new ArrayIterator (data)); MEMM memm = new MEMM (p, null); memm.addFullyConnectedStatesForLabels (); memm.addStartState(); memm.setWeightsDimensionAsIn(training); MEMMTrainer memmt = new MEMMTrainer (memm); memmt.train (training, 10); MEMM memm2 = (MEMM) TestSerializable.cloneViaSerialization (memm); Optimizable.ByGradientValue mcrf1 = memmt.getOptimizableMEMM(training); double val1 = mcrf1.getValue (); Optimizable.ByGradientValue mcrf2 = memmt.getOptimizableMEMM(training); double val2 = mcrf2.getValue (); assertEquals (val1, val2, 1e-5); }
public void testSpaceSerializable () throws IOException, ClassNotFoundException { Pipe p = makeSpacePredictionPipe (); InstanceList training = new InstanceList (p); training.addThruPipe (new ArrayIterator (data)); MEMM memm = new MEMM (p, null); memm.addFullyConnectedStatesForLabels (); memm.addStartState(); memm.setWeightsDimensionAsIn(training); MEMMTrainer memmt = new MEMMTrainer (memm); memmt.train (training, 10); MEMM memm2 = (MEMM) TestSerializable.cloneViaSerialization (memm); Optimizable.ByGradientValue mcrf1 = memmt.getOptimizableMEMM(training); double val1 = mcrf1.getValue (); Optimizable.ByGradientValue mcrf2 = memmt.getOptimizableMEMM(training); double val2 = mcrf2.getValue (); assertEquals (val1, val2, 1e-5); }