private void stochasticUpdateStep(Pair<Integer, Set<Integer>> wordPlusContexts, int s) {
double eta = learningRateDecay(s);
int wordIndex = wordPlusContexts.getFirst(); // actual center word
// Set h vector equal to the kth row of weight matrix W1. h = x' * W = W[k,:] = v(input)
RealVector h = W1.getRowVector(wordIndex); // 1xN row vector
for (int contextWordIndex : wordPlusContexts.getSecond()) {
Set<Integer> negativeContexts;
if (sampleUnigram) {
negativeContexts = negativeSampleContexts(wordIndex, noiseSampler);
} else {
negativeContexts = negativeSampleContexts(wordIndex);
}
// wordIndex is the input word
// negativeContexts is the k negative contexts
// contextWordIndex is 1 positive context
// First update the output vectors for 1 positive context
RealVector vPrime_j = W2.getColumnVector(contextWordIndex); // Nx1 column vector
double u = h.dotProduct(vPrime_j); // u_j = vPrime(output) * v(input)
double t_j = 1.0; // t_j := 1{j == contextWordIndex}
double scale = sigmoid(u) - t_j;
scale = eta * scale;
RealVector gradientOut2Hidden = h.mapMultiply(scale);
vPrime_j = vPrime_j.subtract(gradientOut2Hidden);
W2.setColumnVector(contextWordIndex, vPrime_j);
// Next backpropagate the error to the hidden layer and update the input vectors
RealVector v_I = h;
u = h.dotProduct(vPrime_j);
scale = sigmoid(u) - t_j;
scale = eta * scale;
RealVector gradientHidden2In = vPrime_j.mapMultiply(scale);
v_I = v_I.subtract(gradientHidden2In);
h = v_I;
W1.setRowVector(wordIndex, v_I);
// Repeat update process for k negative contexts
t_j = 0.0; // t_j := 1{j == contextWordIndex}
for (int negContext : negativeContexts) {
vPrime_j = W2.getColumnVector(negContext);
u = h.dotProduct(vPrime_j);
scale = sigmoid(u) - t_j;
scale = eta * scale;
gradientOut2Hidden = h.mapMultiply(scale);
vPrime_j = vPrime_j.subtract(gradientOut2Hidden);
W2.setColumnVector(negContext, vPrime_j);
// Backpropagate the error to the hidden layer and update the input vectors
v_I = h;
u = h.dotProduct(vPrime_j);
scale = sigmoid(u) - t_j;
scale = eta * scale;
gradientHidden2In = vPrime_j.mapMultiply(scale);
v_I = v_I.subtract(gradientHidden2In);
h = v_I;
W1.setRowVector(wordIndex, v_I);
}
}
}
public static double Mahalanobis(double[] data, WormGene g) {
// Compute Mahalanobis distance from worm gene data
assert data.length == g.dim;
int dim = g.dim;
Vector<double[]> eigvec = DataConverter.mat2Jvector(g.eigenvectors);
double[] eigval = DataConverter.colMat2Double(g.eigenvalues);
for (int i = 0; i < dim; i++) eigval[i] += Configs.MDIS_EPSILON;
for (int i = 0; i < dim; i++) assert eigval[i] != 0;
RealVector v1 = MatrixUtils.createRealVector(data);
RealVector[] vec = new RealVector[dim];
for (int i = 0; i < dim; i++) {
vec[i] = MatrixUtils.createRealVector(eigvec.elementAt(i));
}
double[] d = new double[dim];
for (int i = 0; i < dim; i++) {
d[i] = v1.dotProduct(vec[i]) / Math.sqrt(eigval[i]);
}
RealVector dis = MatrixUtils.createRealVector(d);
return vectorLength(dis);
}
public static double Mahalanobis(double[] data, Vector<double[]> eigvec, double[] eigval) {
// Compute Mahalanobis distance from double[]s
assert data.length == eigvec.elementAt(0).length && data.length == eigval.length;
int dim = eigvec.elementAt(0).length;
for (int i = 0; i < dim; i++) eigval[i] += Configs.MDIS_EPSILON;
for (int i = 0; i < dim; i++) assert eigval[i] != 0;
RealVector v1 = MatrixUtils.createRealVector(data);
RealVector[] vec = new RealVector[dim];
for (int i = 0; i < dim; i++) {
vec[i] = MatrixUtils.createRealVector(eigvec.elementAt(i));
}
double[] d = new double[dim];
for (int i = 0; i < dim; i++) {
d[i] = v1.dotProduct(vec[i]) / Math.sqrt(eigval[i]);
}
RealVector dis = MatrixUtils.createRealVector(d);
return vectorLength(dis);
}
private static double sigmoid(RealVector x, RealVector y) {
double z = x.dotProduct(y);
return sigmoid(z);
}
public void estimateRSS() {
rss = resid.dotProduct(resid);
}
