/**
* Prints network output for the each element from the specified training set.
*
* @param neuralNet neural network
* @param testSet test data set
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.print(" Output: " + Arrays.toString(networkOutput));
}
}
/**
* Calculates predict values for every possible class that instance can be classified as that
*
* @param instnc Instance
* @return Map<Object, Double>
*/
@Override
public Map<Object, Double> classDistribution(Instance instnc) {
// Convert instance to double array
double[] item = convertInstanceToDoubleArray(instnc);
// set neural network input
neuralNet.setInput(item);
// calculate neural network output
neuralNet.calculate();
// find neuron with highest output
Map<Object, Double> possibilities = new HashMap<Object, Double>();
for (Neuron neuron : neuralNet.getOutputNeurons()) {
possibilities.put(neuron.getLabel(), neuron.getOutput());
}
return possibilities;
}
/**
* Classifies instance as one of possible classes
*
* @param instnc Instance to classify
* @return Object class as Object
*/
@Override
public Object classify(Instance instnc) {
double[] item = convertInstanceToDoubleArray(instnc);
// set neural network input
neuralNet.setInput(item);
// calculate neural network output
neuralNet.calculate();
// find neuron with highest output
Neuron maxNeuron = null;
double maxOut = Double.NEGATIVE_INFINITY;
for (Neuron neuron : neuralNet.getOutputNeurons()) {
if (neuron.getOutput() > maxOut) {
maxNeuron = neuron;
maxOut = neuron.getOutput();
}
}
// and return its label
return maxNeuron.getLabel();
}