How to use

org.deeplearning4j.clustering.sptree.SpTree

constructor

@Override
public Gradient gradient() {
  if (yIncs == null)
    yIncs = zeros(Y.shape());
  if (gains == null)
    gains = ones(Y.shape());
  AtomicDouble sumQ = new AtomicDouble(0);
  /* Calculate gradient based on barnes hut approximation with positive and negative forces */
  INDArray posF = Nd4j.create(Y.shape());
  INDArray negF = Nd4j.create(Y.shape());
  if (tree == null)
    tree = new SpTree(Y);
  tree.computeEdgeForces(rows, cols, vals, N, posF);
  for (int n = 0; n < N; n++)
    tree.computeNonEdgeForces(n, theta, negF.slice(n), sumQ);
  INDArray dC = posF.subi(negF.divi(sumQ));
  Gradient ret = new DefaultGradient();
  ret.gradientForVariable().put(Y_GRAD, dC);
  return ret;
}

children[i] = new SpTree(this, data, newCorner, newWidth, indices);

@Override
public Gradient gradient() {
  MemoryWorkspace workspace =
      workspaceMode == WorkspaceMode.NONE ? new DummyWorkspace()
          : Nd4j.getWorkspaceManager().getWorkspaceForCurrentThread(
          workspaceConfigurationExternal,
          workspaceExternal);
  try (MemoryWorkspace ws = workspace.notifyScopeEntered()) {
    if (yIncs == null)
      yIncs = zeros(Y.shape());
    if (gains == null)
      gains = ones(Y.shape());
    AtomicDouble sumQ = new AtomicDouble(0);
    /* Calculate gradient based on barnes hut approximation with positive and negative forces */
    INDArray posF = Nd4j.create(Y.shape());
    INDArray negF = Nd4j.create(Y.shape());
    if (tree == null) {
      tree = new SpTree(Y);
      tree.setWorkspaceMode(workspaceMode);
    }
    tree.computeEdgeForces(rows, cols, vals, N, posF);
    for (int n = 0; n < N; n++)
      tree.computeNonEdgeForces(n, theta, negF.slice(n), sumQ);
    INDArray dC = posF.subi(negF.divi(sumQ));
    Gradient ret = new DefaultGradient();
    ret.gradientForVariable().put(Y_GRAD, dC);
    return ret;
  }
}