public final Row extractDenseRow(Chunk[] chunks, int rid, Row row) {
row.bad = false;
row.rid = rid + chunks[0].start();
row.cid = rid;
if (_weights) row.weight = chunks[weightChunkId()].atd(rid);
if (row.weight == 0) return row;
if (_skipMissing) {
int N = _cats + _nums;
for (int i = 0; i < N; ++i)
if (chunks[i].isNA(rid)) {
row.bad = true;
return row;
}
}
int nbins = 0;
for (int i = 0; i < _cats; ++i) {
int cid = getCategoricalId(i, chunks[i].isNA(rid) ? _catModes[i] : (int) chunks[i].at8(rid));
if (cid >= 0) row.binIds[nbins++] = cid;
}
row.nBins = nbins;
final int n = _nums;
int numValsIdx = 0; // since we're dense, need a second index to track interaction nums
for (int i = 0; i < n; i++) {
if (isInteractionVec(
_cats + i)) { // categorical-categorical interaction is handled as plain categorical
// (above)... so if we have interactions either v1 is categorical, v2 is
// categorical, or neither are categorical
int offset = getInteractionOffset(chunks, _cats + i, rid);
row.numVals[numValsIdx + offset] =
chunks[_cats + i].atd(
rid); // essentially: chunks[v1].atd(rid) * chunks[v2].atd(rid) (see
// InteractionWrappedVec)
numValsIdx += nextNumericIdx(i);
} else {
double d = chunks[_cats + i].atd(rid); // can be NA if skipMissing() == false
if (Double.isNaN(d)) d = _numMeans[i];
if (_normMul != null && _normSub != null)
d = (d - _normSub[numValsIdx]) * _normMul[numValsIdx];
row.numVals[numValsIdx++] = d;
}
}
for (int i = 0; i < _responses; ++i) {
try {
row.response[i] = chunks[responseChunkId(i)].atd(rid);
} catch (Throwable t) {
throw new RuntimeException(t);
}
if (_normRespMul != null)
row.response[i] = (row.response[i] - _normRespSub[i]) * _normRespMul[i];
if (Double.isNaN(row.response[i])) {
row.bad = true;
return row;
}
}
if (_offset) row.offset = chunks[offsetChunkId()].atd(rid);
return row;
}
public final Row extractDenseRow(Chunk[] chunks, int rid, Row row) {
row.bad = false;
row.rid = rid + chunks[0].start();
if (_weights) row.weight = chunks[weightChunkId()].atd(rid);
if (row.weight == 0) return row;
if (_skipMissing)
for (Chunk c : chunks)
if (c.isNA(rid)) {
row.bad = true;
return row;
}
int nbins = 0;
for (int i = 0; i < _cats; ++i) {
if (chunks[i].isNA(rid)) {
if (_imputeMissing) {
int c = getCategoricalId(i, _catModes[i]);
if (c >= 0) row.binIds[nbins++] = c;
} else // TODO: What if missingBucket = false?
row.binIds[nbins++] =
_catOffsets[i + 1] - 1; // missing value turns into extra (last) factor
} else {
int c = getCategoricalId(i, (int) chunks[i].at8(rid));
if (c >= 0) row.binIds[nbins++] = c;
}
}
row.nBins = nbins;
final int n = _nums;
for (int i = 0; i < n; ++i) {
double d = chunks[_cats + i].atd(rid); // can be NA if skipMissing() == false
if (_imputeMissing && Double.isNaN(d)) d = _numMeans[i];
if (_normMul != null && _normSub != null) d = (d - _normSub[i]) * _normMul[i];
row.numVals[i] = d;
}
for (int i = 0; i < _responses; ++i) {
row.response[i] = chunks[responseChunkId()].atd(rid);
if (_normRespMul != null)
row.response[i] = (row.response[i] - _normRespSub[i]) * _normRespMul[i];
if (Double.isNaN(row.response[i])) {
row.bad = true;
return row;
}
}
if (_offset) row.offset = chunks[offsetChunkId()].atd(rid);
return row;
}