/**
* sdf The test main
*
* @param args ignored
*/
public static void main(String[] args) {
// Instance[] instances = new Instance[100];
// for (int i = 0; i < instances.length; i++) {
// double[] data = new double[2];
// data[0] = Math.sin(i/2.0);
// data[1] = (Math.random() - .5)*2;
// instances[i] = new Instance(data);
// }
DataSet set = new DataSet(trainInstances);
System.out.println("Before randomizing");
System.out.println(set);
// Matrix projection = new RectangularMatrix(new double[][]{ {.6, .6}, {.4, .6}});
Matrix projection = new RectangularMatrix(new double[][] {{.1, .1}, {.1, .1}});
for (int i = 0; i < set.size(); i++) {
Instance instance = set.get(i);
instance.setData(projection.times(instance.getData()));
}
System.out.println("Before ICA");
System.out.println(set);
IndependentComponentAnalysis filter = new IndependentComponentAnalysis(set, 1);
filter.filter(set);
System.out.println("After ICA");
System.out.println(set);
}
/**
* Tests out the perceptron with the classic xor test
*
* @param args ignored
*/
public static void main(String[] args) {
double[][][] data = {
{{1, 1, 1, 1}, {0}},
{{1, 0, 1, 0}, {1}},
{{0, 1, 0, 1}, {1}},
{{0, 0, 0, 0}, {0}}
};
Instance[] patterns = new Instance[data.length];
for (int i = 0; i < patterns.length; i++) {
patterns[i] = new Instance(data[i][0]);
patterns[i].setLabel(new Instance(data[i][1]));
}
FeedForwardNeuralNetworkFactory factory = new FeedForwardNeuralNetworkFactory();
FeedForwardNetwork network = factory.createClassificationNetwork(new int[] {4, 3, 1});
ErrorMeasure measure = new SumOfSquaresError();
DataSet set = new DataSet(patterns);
NeuralNetworkOptimizationProblem nno =
new NeuralNetworkOptimizationProblem(set, network, measure);
OptimizationAlgorithm o = new RandomizedHillClimbing(nno);
FixedIterationTrainer fit = new FixedIterationTrainer(o, 5000);
fit.train();
Instance opt = o.getOptimal();
network.setWeights(opt.getData());
for (int i = 0; i < patterns.length; i++) {
network.setInputValues(patterns[i].getData());
network.run();
System.out.println("~~");
System.out.println(patterns[i].getLabel());
System.out.println(network.getOutputValues());
}
}
private static void saveAsArff(DataSet dataSet, String outputFile, List<String> labels) {
writeArffHeader(dataSet, outputFile);
Instance[] instances = dataSet.getInstances();
for (Instance instance : instances) {
Vector vector = instance.getData();
for (int i = 0; i < vector.size(); i++) {
MLAssignmentUtils.writeToFile(outputFile, String.valueOf(vector.get(i)) + ",", true);
}
MLAssignmentUtils.writeToFile(outputFile, getLabel(instance.getLabel(), labels) + "\n", true);
}
}
private static String getLabel(Instance label, List<String> labels) {
Vector vector = label.getData();
for (int i = 0; i < vector.size(); i++) {
if (vector.get(i) == 1) {
return labels.get(i);
}
}
throw new RuntimeException("No label with value found");
}
public void filter(DataSet data) {
int foldSize = data.size() / foldCount;
Random rand = new Random();
for (int currentFold = 0; currentFold < foldCount; currentFold++) {
DataSet currentSet = new DataSet(new Instance[foldSize], data.getDescription());
int i = 0;
while (i < foldSize) {
int position = rand.nextInt(data.size());
Instance instance = data.get(position);
if (instance != null && instance.getData() != null) {
currentSet.set(i, instance);
data.set(position, null);
i++;
}
}
this.folds.add(currentSet);
}
}
public void run(int iterations) throws Exception {
// 1) Construct data instances for training. These will also be run
// through the network at the bottom to verify the output
CSVDataSetReader reader = new CSVDataSetReader("data/letter_training_new.data");
DataSet set = reader.read();
LabelSplitFilter flt = new LabelSplitFilter();
flt.filter(set);
DataSetLabelBinarySeperator.seperateLabels(set);
DataSetDescription desc = set.getDescription();
DataSetDescription labelDesc = desc.getLabelDescription();
// 2) Instantiate a network using the FeedForwardNeuralNetworkFactory. This network
// will be our classifier.
FeedForwardNeuralNetworkFactory factory = new FeedForwardNeuralNetworkFactory();
// 2a) These numbers correspond to the number of nodes in each layer.
// This network has 4 input nodes, 3 hidden nodes in 1 layer, and 1 output node in the
// output layer.
FeedForwardNetwork network =
factory.createClassificationNetwork(
new int[] {
desc.getAttributeCount(),
factory.getOptimalHiddenLayerNodes(desc, labelDesc),
labelDesc.getDiscreteRange()
});
// 3) Instantiate a measure, which is used to evaluate each possible set of weights.
ErrorMeasure measure = new SumOfSquaresError();
// 4) Instantiate a DataSet, which adapts a set of instances to the optimization problem.
// DataSet set = new DataSet(patterns);
// 5) Instantiate an optimization problem, which is used to specify the dataset, evaluation
// function, mutator and crossover function (for Genetic Algorithms), and any other
// parameters used in optimization.
NeuralNetworkOptimizationProblem nno =
new NeuralNetworkOptimizationProblem(set, network, measure);
// 6) Instantiate a specific OptimizationAlgorithm, which defines how we pick our next potential
// hypothesis.
OptimizationAlgorithm o = new RandomizedHillClimbing(nno);
// 7) Instantiate a trainer. The FixtIterationTrainer takes another trainer (in this case,
// an OptimizationAlgorithm) and executes it a specified number of times.
FixedIterationTrainer fit = new FixedIterationTrainer(o, iterations);
// 8) Run the trainer. This may take a little while to run, depending on the
// OptimizationAlgorithm,
// size of the data, and number of iterations.
fit.train();
// 9) Once training is done, get the optimal solution from the OptimizationAlgorithm. These are
// the
// optimal weights found for this network.
Instance opt = o.getOptimal();
network.setWeights(opt.getData());
// 10) Run the training data through the network with the weights discovered through
// optimization, and
// print out the expected label and result of the classifier for each instance.
int[] labels = {0, 1};
TestMetric acc = new AccuracyTestMetric();
TestMetric cm = new ConfusionMatrixTestMetric(labels);
Tester t = new NeuralNetworkTester(network, acc, cm);
t.test(set.getInstances());
acc.printResults();
}