public void testTestValueAndGradient () { SimplePoly maxable = new SimplePoly (); testValueAndGradient (maxable); try { WrongSimplePoly badMaxable = new WrongSimplePoly (); testValueAndGradient (badMaxable); fail ("WrongSimplyPoly should fail testMaxmiziable!"); } catch (Exception e) {} }
public void testTestValueAndGradient () { SimplePoly maxable = new SimplePoly (); testValueAndGradient (maxable); try { WrongSimplePoly badMaxable = new WrongSimplePoly (); testValueAndGradient (badMaxable); fail ("WrongSimplyPoly should fail testMaxmiziable!"); } catch (Exception e) {} }
TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = " + crf.averageTokenAccuracy(lists[0]));
TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = " + crf.averageTokenAccuracy(lists[0]));
Optimizable.ByGradientValue minable = crft .getOptimizableCRF(lists[0]); TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = "
Optimizable.ByGradientValue minable = crft .getOptimizableCRF(lists[0]); TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = "
public void testRandomMaximizable () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist); TestOptimizable.testValueAndGradient (maxable); }
public void testRandomMaximizable () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist); TestOptimizable.testValueAndGradient (maxable); }
if (testValueAndGradient) { Optimizable.ByGradientValue minable = memmt.getOptimizableMEMM(lists[0]); TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = " + memm.averageTokenAccuracy(lists[0]));
if (testValueAndGradient) { Optimizable.ByGradientValue minable = memmt.getOptimizableMEMM(lists[0]); TestOptimizable.testValueAndGradient(minable); } else { System.out.println("Training Accuracy before training = " + memm.averageTokenAccuracy(lists[0]));
TestOptimizable.testValueAndGradient(optable); // This tests at
TestOptimizable.testValueAndGradient(optable); // This tests at
public void testSpaceMaximizable () { Pipe p = makeSpacePredictionPipe (); InstanceList training = new InstanceList (p); // String[] data = { TestMEMM.data[0], }; // TestMEMM.data[1], TestMEMM.data[2], TestMEMM.data[3], }; // String[] data = { "ab" }; training.addThruPipe (new ArrayIterator (data)); // CRF4 memm = new CRF4 (p, null); MEMM memm = new MEMM (p, null); memm.addFullyConnectedStatesForLabels (); memm.addStartState(); memm.setWeightsDimensionAsIn(training); MEMMTrainer memmt = new MEMMTrainer (memm); // memm.gatherTrainingSets (training); // ANNOYING: Need to set up per-instance training sets memmt.train (training, 1); // Set weights dimension, gathers training sets, etc. // memm.print(); // memm.printGradient = true; // memm.printInstanceLists(); // memm.setGaussianPriorVariance (Double.POSITIVE_INFINITY); Optimizable.ByGradientValue mcrf = memmt.getOptimizableMEMM(training); TestOptimizable.setNumComponents (150); TestOptimizable.testValueAndGradient (mcrf); }
public void testSpaceMaximizable () { Pipe p = makeSpacePredictionPipe (); InstanceList training = new InstanceList (p); // String[] data = { TestMEMM.data[0], }; // TestMEMM.data[1], TestMEMM.data[2], TestMEMM.data[3], }; // String[] data = { "ab" }; training.addThruPipe (new ArrayIterator (data)); // CRF4 memm = new CRF4 (p, null); MEMM memm = new MEMM (p, null); memm.addFullyConnectedStatesForLabels (); memm.addStartState(); memm.setWeightsDimensionAsIn(training); MEMMTrainer memmt = new MEMMTrainer (memm); // memm.gatherTrainingSets (training); // ANNOYING: Need to set up per-instance training sets memmt.train (training, 1); // Set weights dimension, gathers training sets, etc. // memm.print(); // memm.printGradient = true; // memm.printInstanceLists(); // memm.setGaussianPriorVariance (Double.POSITIVE_INFINITY); Optimizable.ByGradientValue mcrf = memmt.getOptimizableMEMM(training); TestOptimizable.setNumComponents (150); TestOptimizable.testValueAndGradient (mcrf); }