public void testParameterSetting () { Factory f = new Factory () {{gamma=3;}}; TestStaticParameters g = f.newTSP(); System.out.println ("g.gamma="+g.gamma); assertTrue("gamma="+g.gamma, g.gamma == 3); }
public TestStaticParameters newTSP () { System.out.println ("Factory gamma="+this.gamma); TestStaticParameters t = new TestStaticParameters(); t.gamma = this.gamma; return t; } }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public void testSetGetParameters () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist); TestOptimizable.testGetSetParameters (maxable); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public void testRandomTrained () { ClassifierTrainer[] trainers = new ClassifierTrainer[1]; //trainers[0] = new NaiveBayesTrainer(); trainers[0] = new MaxEntTrainer(); //trainers[2] = new DecisionTreeTrainer(); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 200); InstanceList lists[] = ilist.split (new java.util.Random(2), new double[] {.5, .5}); //System.out.println ("Training set size = "+lists[0].size()); //System.out.println ("Testing set size = "+lists[1].size()); Classifier[] classifiers = new Classifier[trainers.length]; for (int i = 0; i < trainers.length; i++) classifiers[i] = trainers[i].train (lists[0]); System.out.println ("Accuracy on training set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[0]).getAccuracy()); System.out.println ("Accuracy on testing set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[1]).getAccuracy()); }
public void testParameterSetting () { Factory f = new Factory () {{gamma=3;}}; TestStaticParameters g = f.newTSP(); System.out.println ("g.gamma="+g.gamma); assertTrue("gamma="+g.gamma, g.gamma == 3); }
public void testSetGetParameters () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist); TestOptimizable.testGetSetParameters (maxable); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public TestStaticParameters newTSP () { System.out.println ("Factory gamma="+this.gamma); TestStaticParameters t = new TestStaticParameters(); t.gamma = this.gamma; return t; } }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public static void main (String[] args) { junit.textui.TestRunner.run (suite()); }
public void testRandomTrained () { ClassifierTrainer[] trainers = new ClassifierTrainer[1]; //trainers[0] = new NaiveBayesTrainer(); trainers[0] = new MaxEntTrainer(); //trainers[2] = new DecisionTreeTrainer(); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 200); InstanceList lists[] = ilist.split (new java.util.Random(2), new double[] {.5, .5}); //System.out.println ("Training set size = "+lists[0].size()); //System.out.println ("Testing set size = "+lists[1].size()); Classifier[] classifiers = new Classifier[trainers.length]; for (int i = 0; i < trainers.length; i++) classifiers[i] = trainers[i].train (lists[0]); System.out.println ("Accuracy on training set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[0]).getAccuracy()); System.out.println ("Accuracy on testing set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[1]).getAccuracy()); }
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); }
public void testTrainedMaximizable () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); MaxEnt me = (MaxEnt)trainer.train(ilist); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist, me); TestOptimizable.testValueAndGradientCurrentParameters (maxable); }
public void testTrainedMaximizable () { MaxEntTrainer trainer = new MaxEntTrainer(); Alphabet fd = dictOfSize (6); String[] classNames = new String[] {"class0", "class1"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 20); MaxEnt me = (MaxEnt)trainer.train(ilist); Optimizable.ByGradientValue maxable = trainer.getOptimizable (ilist, me); TestOptimizable.testValueAndGradientCurrentParameters (maxable); }