/**
* @param X
* @param nDims
* @param perplexity
*/
public INDArray calculate(INDArray X, int nDims, double perplexity) {
if (usePca) X = PCA.pca(X, Math.min(50, X.columns()), normalize);
// normalization (don't normalize again after pca)
if (normalize) {
X.subi(X.min(Integer.MAX_VALUE));
X = X.divi(X.max(Integer.MAX_VALUE));
X = X.subiRowVector(X.mean(0));
}
if (nDims > X.columns()) nDims = X.columns();
INDArray sumX = pow(X, 2).sum(1);
INDArray D = X.mmul(X.transpose()).muli(-2).addRowVector(sumX).transpose().addRowVector(sumX);
// output
if (y == null) y = randn(X.rows(), nDims, Nd4j.getRandom()).muli(1e-3f);
INDArray p = computeGaussianPerplexity(D, perplexity);
// lie for better local minima
p.muli(4);
// init adagrad where needed
if (useAdaGrad) {
if (adaGrad == null) {
adaGrad = new AdaGrad(y.shape());
adaGrad.setMasterStepSize(learningRate);
}
}
for (int i = 0; i < maxIter; i++) {
step(p, i);
if (i == switchMomentumIteration) momentum = finalMomentum;
if (i == stopLyingIteration) p.divi(4);
if (iterationListener != null) iterationListener.iterationDone(null, i);
}
return y;
}
