/**
* Trains a network against a given dataset
*
* @param input
* @param expected_output
* @return the number of iterations spent training the network
* @throws SizeDifferenceException
* @throws WrongSizeException
*/
public int train(DataList[] inputs, DataList[] outputs, double target_error_rate)
throws SizeDifferenceException, WrongSizeException {
if (inputs.length != outputs.length) {
throw new SizeDifferenceException("Must have the same number of training inputs and outputs");
}
// train until average error rate is below target error rate
int iterations = 0;
double average_error_rate = 1.0;
do {
iterations++;
double error_sum = 0.0;
for (int i = 0; i < inputs.length; i++) {
DataList output = this.use(inputs[i]);
error_sum += DataList.getErrorRate(output, outputs[i]);
// train
this.last().backpropagate(outputs[i]);
}
// calculate error rate
average_error_rate = error_sum / inputs.length;
} while (average_error_rate > target_error_rate);
// return
return iterations;
}
/**
* Test a single dataset for correctness
*
* @param input
* @param expected_output
* @return a proportion, correct results to total results
* @throws SizeDifferenceException
* @throws WrongSizeException
*/
public double test(DataList input, DataList expected_output)
throws SizeDifferenceException, WrongSizeException {
DataList out = this.use(input);
return DataList.getErrorRate(out, expected_output);
}