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 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();
}
}