public RepeatingLSH(List<LSH> lshList) throws MathException {
super(lshList.get(0).getDim(), lshList.get(0).getRandomGenerator());
this.lshList = lshList;
RandomGenerator rg = lshList.get(0).getRandomGenerator();
RandomData rd = new RandomDataImpl(rg);
/*
* Compute a random vector of lshList.size() with each component taken from U(0,10)
*/
randomVec = new ArrayRealVector(lshList.size());
for (int i = 0; i < randomVec.getDimension(); ++i) {
randomVec.setEntry(i, rd.nextUniform(0, 10.0));
}
}
/**
* Modifies this map through a single backpropagation iteration using the given error values on
* the output nodes.
*
* @param error
*/
public void train(List<Double> error, double learningRate) {
RealVector eOut = new ArrayRealVector(error.size());
for (int i : series(error.size())) eOut.setEntry(i, error.get(i));
// * gHidden: delta for the non-bias nodes of the hidden layer
gHidden.setSubVector(0, stateHidden.getSubVector(0, n)); // optimize
for (int i : Series.series(gHidden.getDimension()))
gHidden.setEntry(i, activation.derivative(gHidden.getEntry(i)));
eHiddenL = weights1.transpose().operate(eOut);
eHidden.setSubVector(0, eHiddenL.getSubVector(0, h));
for (int i : series(h)) eHidden.setEntry(i, eHidden.getEntry(i) * gHidden.getEntry(i));
weights1Delta = MatrixTools.outer(eOut, stateHidden);
weights1Delta = weights1Delta.scalarMultiply(-1.0 * learningRate); // optimize
weights0Delta = MatrixTools.outer(eHidden, stateIn);
weights0Delta = weights0Delta.scalarMultiply(-1.0 * learningRate);
weights0 = weights0.add(weights0Delta);
weights1 = weights1.add(weights1Delta);
}
public static String realVecToString(RealVector r) {
String res = "RealVector(" + r.getDimension() + ") of type '" + r.getClass().getName() + "'\n";
return res += Arrays.toString(r.getData()) + "\n";
}