@Override
public void performExperiment() throws IOException {
BilinearLearnerParameters params = new BilinearLearnerParameters();
params.put(BilinearLearnerParameters.ETA0_U, 0.02);
params.put(BilinearLearnerParameters.ETA0_W, 0.02);
params.put(BilinearLearnerParameters.LAMBDA, 0.001);
params.put(BilinearLearnerParameters.BICONVEX_TOL, 0.01);
params.put(BilinearLearnerParameters.BICONVEX_MAXITER, 10);
params.put(BilinearLearnerParameters.BIAS, true);
params.put(BilinearLearnerParameters.ETA0_BIAS, 0.5);
params.put(BilinearLearnerParameters.WINITSTRAT, new SingleValueInitStrat(0.1));
params.put(BilinearLearnerParameters.UINITSTRAT, new SparseZerosInitStrategy());
BillMatlabFileDataGenerator bmfdg =
new BillMatlabFileDataGenerator(new File(BILL_DATA()), 98, true);
prepareExperimentLog(params);
for (int i = 0; i < bmfdg.nFolds(); i++) {
logger.debug("Fold: " + i);
BilinearSparseOnlineLearner learner = new BilinearSparseOnlineLearner(params);
learner.reinitParams();
bmfdg.setFold(i, Mode.TEST);
List<Pair<Matrix>> testpairs = new ArrayList<Pair<Matrix>>();
while (true) {
Pair<Matrix> next = bmfdg.generate();
if (next == null) break;
testpairs.add(next);
}
logger.debug("...training");
bmfdg.setFold(i, Mode.TRAINING);
int j = 0;
while (true) {
Pair<Matrix> next = bmfdg.generate();
if (next == null) break;
logger.debug("...trying item " + j++);
learner.process(next.firstObject(), next.secondObject());
Matrix u = learner.getU();
Matrix w = learner.getW();
Matrix bias = MatrixFactory.getDenseDefault().copyMatrix(learner.getBias());
BilinearEvaluator eval = new RootMeanSumLossEvaluator();
eval.setLearner(learner);
double loss = eval.evaluate(testpairs);
logger.debug(String.format("Saving learner, Fold %d, Item %d", i, j));
File learnerOut = new File(FOLD_ROOT(i), String.format("learner_%d", j));
IOUtils.writeBinary(learnerOut, learner);
logger.debug("W row sparcity: " + SandiaMatrixUtils.rowSparcity(w));
logger.debug("U row sparcity: " + SandiaMatrixUtils.rowSparcity(u));
Boolean biasMode = learner.getParams().getTyped(BilinearLearnerParameters.BIAS);
if (biasMode) {
logger.debug("Bias: " + SandiaMatrixUtils.diag(bias));
}
logger.debug(String.format("... loss: %f", loss));
}
}
}
/** Test of getDenseDefault method, of class MatrixFactory. */
public void testGetDenseDefault() {
System.out.println("getDenseDefault");
MatrixFactory<? extends Matrix> result = MatrixFactory.getDenseDefault();
assertSame(MatrixFactory.DEFAULT_DENSE_INSTANCE, result);
}