private static void ensembleLearningDemo() {
System.out.println(Util.ntimes("*", 100));
System.out.println(
"\n Ensemble Decision Demo - Weak Learners co operating to give Superior decisions ");
System.out.println(Util.ntimes("*", 100));
try {
DataSet ds = DataSetFactory.getRestaurantDataSet();
List stumps = DecisionTree.getStumpsFor(ds, "Yes", "No");
List<Learner> learners = new ArrayList<Learner>();
System.out.println("\nStump Learners vote to decide in this algorithm");
for (Object stump : stumps) {
DecisionTree sl = (DecisionTree) stump;
StumpLearner stumpLearner = new StumpLearner(sl, "No");
learners.add(stumpLearner);
}
AdaBoostLearner learner = new AdaBoostLearner(learners, ds);
learner.train(ds);
int[] result = learner.test(ds);
System.out.println(
"\nThis Ensemble Learner classifies the data set with "
+ result[0]
+ " successes"
+ " and "
+ result[1]
+ " failures");
System.out.println("\n");
} catch (Exception e) {
}
}
private static void backPropogationDemo() {
try {
System.out.println(Util.ntimes("*", 100));
System.out.println(
"\n BackpropagationDemo - Running BackProp on Iris data Set with 10 epochs of learning ");
System.out.println(Util.ntimes("*", 100));
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
NNConfig config = new NNConfig();
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.setConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.setConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.setTrainingScheme(new BackPropLearning(0.1, 0.9));
ffnn.trainOn(innds, 10);
innds.refreshDataset();
int[] result = ffnn.testOnDataSet(innds);
System.out.println(result[0] + " right, " + result[1] + " wrong");
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
private static void decisionListDemo() {
try {
System.out.println(Util.ntimes("*", 100));
System.out.println(
"DecisionList Demo - Inducing a DecisionList from the Restaurant DataSet\n ");
System.out.println(Util.ntimes("*", 100));
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionListLearner learner = new DecisionListLearner("Yes", "No", new DLTestFactory());
learner.train(ds);
System.out.println("The Induced DecisionList is");
System.out.println(learner.getDecisionList());
int[] result = learner.test(ds);
System.out.println(
"\nThis Decision List classifies the data set with "
+ result[0]
+ " successes"
+ " and "
+ result[1]
+ " failures");
System.out.println("\n");
} catch (Exception e) {
System.out.println("Decision ListDemo Failed");
}
}
private static void perceptronDemo() {
try {
System.out.println(Util.ntimes("*", 100));
System.out.println(
"\n Perceptron Demo - Running Perceptron on Iris data Set with 10 epochs of learning ");
System.out.println(Util.ntimes("*", 100));
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
Perceptron perc = new Perceptron(3, 4);
perc.trainOn(innds, 10);
innds.refreshDataset();
int[] result = perc.testOnDataSet(innds);
System.out.println(result[0] + " right, " + result[1] + " wrong");
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}