// Call builder specific score code and then correct probabilities
// if it is necessary.
void score2(Chunk chks[], double weight, double offset, double fs[ /*nclass*/], int row) {
double sum = score1(chks, weight, offset, fs, row);
if (isClassifier()) {
if (!Double.isInfinite(sum) && sum > 0f && sum != 1f) ArrayUtils.div(fs, sum);
if (_parms._balance_classes)
GenModel.correctProbabilities(
fs, _model._output._priorClassDist, _model._output._modelClassDist);
}
}
@Override
public void map(Chunk[] cs) {
int N = cs.length - (_hasWeight ? 1 : 0);
assert _centers[0].length == N;
_cMeans = new double[_k][N];
_cSqr = new double[_k];
_size = new long[_k];
// Space for cat histograms
_cats = new long[_k][N][];
for (int clu = 0; clu < _k; clu++)
for (int col = 0; col < N; col++)
_cats[clu][col] = _isCats[col] == null ? null : new long[cs[col].vec().cardinality()];
_worst_err = 0;
// Find closest cluster center for each row
double[] values = new double[N]; // Temp data to hold row as doubles
ClusterDist cd = new ClusterDist();
for (int row = 0; row < cs[0]._len; row++) {
double weight = _hasWeight ? cs[N].atd(row) : 1;
if (weight == 0) continue; // skip holdout rows
assert (weight == 1); // K-Means only works for weight 1 (or weight 0 for holdout)
data(values, cs, row, _means, _mults, _modes); // Load row as doubles
closest(_centers, values, _isCats, cd); // Find closest cluster center
int clu = cd._cluster;
assert clu != -1; // No broken rows
_cSqr[clu] += cd._dist;
// Add values and increment counter for chosen cluster
for (int col = 0; col < N; col++)
if (_isCats[col] != null) _cats[clu][col][(int) values[col]]++; // Histogram the cats
else _cMeans[clu][col] += values[col]; // Sum the column centers
_size[clu]++;
// Track worst row
if (cd._dist > _worst_err) {
_worst_err = cd._dist;
_worst_row = cs[0].start() + row;
}
}
// Scale back down to local mean
for (int clu = 0; clu < _k; clu++)
if (_size[clu] != 0) ArrayUtils.div(_cMeans[clu], _size[clu]);
_centers = null;
_means = _mults = null;
_modes = null;
}
