public Xor(Matrix m1, Matrix m2) {
super(m1, m2);
if (m2.isScalar() && !Coordinates.equals(m1.getSize(), m2.getSize())) {
getSources()[1] = MatrixFactory.fill(m2.getAsBoolean(0, 0), m1.getSize());
} else if (m1.isScalar() && !Coordinates.equals(m1.getSize(), m2.getSize())) {
getSources()[0] = MatrixFactory.fill(m1.getAsBoolean(0, 0), m2.getSize());
}
}
private void createMatrix() {
try {
ExecutorService executor =
Executors.newFixedThreadPool(UJMPSettings.getInstance().getNumberOfThreads());
Matrix x = getSource();
double valueCount = x.getValueCount();
long missingCount = (long) x.countMissing(Ret.NEW, Matrix.ALL).getEuklideanValue();
double percent = ((int) Math.round((missingCount * 1000.0 / valueCount))) / 10.0;
System.out.println("missing values: " + missingCount + " (" + percent + "%)");
System.out.println("============================================");
if (bestGuess == null) {
bestGuess = getSource().impute(Ret.NEW, ImputationMethod.RowMean);
}
int run = 0;
double d;
do {
System.out.println("Iteration " + run++);
List<Future<Long>> futures = new ArrayList<Future<Long>>();
imputed = Matrix.Factory.zeros(x.getSize());
long t0 = System.currentTimeMillis();
for (long c = 0; c < x.getColumnCount(); c++) {
if (containsMissingValues(c)) {
futures.add(executor.submit(new PredictColumn(c)));
}
}
for (Future<Long> f : futures) {
Long completedCols = f.get();
long elapsedTime = System.currentTimeMillis() - t0;
long remainingCols = x.getColumnCount() - completedCols;
double colsPerMillisecond = (double) (completedCols + 1) / (double) elapsedTime;
long remainingTime = (long) (remainingCols / colsPerMillisecond / 1000.0);
System.out.println(
(completedCols * 1000 / x.getColumnCount() / 10.0)
+ "% completed ("
+ remainingTime
+ " seconds remaining)");
}
Matrix newBestGuess = bestGuess.times(decay).plus(imputed.times(1 - decay));
for (int r = 0; r < getSource().getRowCount(); r++) {
for (int c = 0; c < getSource().getColumnCount(); c++) {
double value = getSource().getAsDouble(r, c);
if (!MathUtil.isNaNOrInfinite(value)) {
newBestGuess.setAsDouble(value, r, c);
}
}
}
d = newBestGuess.euklideanDistanceTo(bestGuess, true) / missingCount;
System.out.println("delta: " + d);
System.out.println("============================================");
bestGuess = newBestGuess;
bestGuess.exportTo().file(tempFile).asDenseCSV();
} while (delta < d);
executor.shutdown();
imputed = bestGuess;
if (imputed.containsMissingValues()) {
throw new RuntimeException("Matrix has still missing values after imputation");
}
} catch (Exception e) {
throw new RuntimeException(e);
}
}
public Xor(boolean v1, Matrix m2) throws MatrixException {
this(MatrixFactory.fill(v1, m2.getSize()), m2);
}
public Xor(Matrix m1, boolean v2) throws MatrixException {
this(m1, MatrixFactory.fill(v2, m1.getSize()));
}
public long[] getSize() {
if (result == null) {
result = calculate();
}
return result.getSize();
}
