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();
}