@Override
public void reduce(Sampler other) {
_sampled = ArrayUtils.append(_sampled, other._sampled);
}
// Initialize cluster centers
double[][] initial_centers(
KMeansModel model,
final Vec[] vecs,
final double[] means,
final double[] mults,
final int[] modes) {
// Categoricals use a different distance metric than numeric columns.
model._output._categorical_column_count = 0;
_isCats = new String[vecs.length][];
for (int v = 0; v < vecs.length; v++) {
_isCats[v] = vecs[v].isCategorical() ? new String[0] : null;
if (_isCats[v] != null) model._output._categorical_column_count++;
}
Random rand = water.util.RandomUtils.getRNG(_parms._seed - 1);
double centers[][]; // Cluster centers
if (null != _parms._user_points) { // User-specified starting points
Frame user_points = _parms._user_points.get();
int numCenters = (int) user_points.numRows();
int numCols = model._output.nfeatures();
centers = new double[numCenters][numCols];
Vec[] centersVecs = user_points.vecs();
// Get the centers and standardize them if requested
for (int r = 0; r < numCenters; r++) {
for (int c = 0; c < numCols; c++) {
centers[r][c] = centersVecs[c].at(r);
centers[r][c] = data(centers[r][c], c, means, mults, modes);
}
}
} else { // Random, Furthest, or PlusPlus initialization
if (_parms._init == Initialization.Random) {
// Initialize all cluster centers to random rows
centers = new double[_parms._k][model._output.nfeatures()];
for (double[] center : centers) randomRow(vecs, rand, center, means, mults, modes);
} else {
centers = new double[1][model._output.nfeatures()];
// Initialize first cluster center to random row
randomRow(vecs, rand, centers[0], means, mults, modes);
model._output._iterations = 0;
while (model._output._iterations < 5) {
// Sum squares distances to cluster center
SumSqr sqr = new SumSqr(centers, means, mults, modes, _isCats).doAll(vecs);
// Sample with probability inverse to square distance
Sampler sampler =
new Sampler(
centers,
means,
mults,
modes,
_isCats,
sqr._sqr,
_parms._k * 3,
_parms._seed,
hasWeightCol())
.doAll(vecs);
centers = ArrayUtils.append(centers, sampler._sampled);
// Fill in sample centers into the model
if (!isRunning()) return null; // Stopped/cancelled
model._output._centers_raw = destandardize(centers, _isCats, means, mults);
model._output._tot_withinss = sqr._sqr / _train.numRows();
model._output._iterations++; // One iteration done
model.update(
_key); // Make early version of model visible, but don't update progress using
// update(1)
}
// Recluster down to k cluster centers
centers = recluster(centers, rand, _parms._k, _parms._init, _isCats);
model._output._iterations = 0; // Reset iteration count
}
}
return centers;
}