// Slow-path append data
private void append2slow() {
if (_len > Vec.CHUNK_SZ) throw new ArrayIndexOutOfBoundsException(_len);
assert _ds == null;
if (_len2 == _len) { // Check for sparse-ness now & then
int nzcnt = 0;
for (int i = 0; i < _len; i++) {
if (_ls[i] != 0) nzcnt++;
if (_xs[i] != 0) {
nzcnt = Vec.CHUNK_SZ;
break;
} // Only non-specials sparse
}
if (_len >= 32 && nzcnt * 8 <= _len) { // Heuristic for sparseness
_len = 0;
for (int i = 0; i < _len2; i++)
if (_ls[i] != 0) {
_xs[_len] = i; // Row number in xs
_ls[_len++] = _ls[i]; // Sparse value in ls
}
return; // Compressed, so lots of room now
}
}
_xs = _ls == null ? MemoryManager.malloc4(4) : MemoryManager.arrayCopyOf(_xs, _len << 1);
_ls = _ls == null ? MemoryManager.malloc8(4) : MemoryManager.arrayCopyOf(_ls, _len << 1);
}
protected void cancel_sparse() {
if (sparseLen() != _len) {
if (_is != null) {
int[] is = MemoryManager.malloc4(_len);
for (int i = 0; i < _len; i++) is[i] = -1;
for (int i = 0; i < sparseLen(); i++) is[_id[i]] = _is[i];
_is = is;
} else if (_ds == null) {
int[] xs = MemoryManager.malloc4(_len);
long[] ls = MemoryManager.malloc8(_len);
for (int i = 0; i < sparseLen(); ++i) {
xs[_id[i]] = _xs[i];
ls[_id[i]] = _ls[i];
}
_xs = xs;
_ls = ls;
} else {
double[] ds = MemoryManager.malloc8d(_len);
for (int i = 0; i < sparseLen(); ++i) ds[_id[i]] = _ds[i];
_ds = ds;
}
set_sparseLen(_len);
}
_id = null;
}
private void cancel_sparse() {
long ls[] = MemoryManager.malloc8(_len2 + 1);
for (int i = 0; i < _len; i++) // Inflate ls to hold values
ls[_xs[i]] = _ls[i];
_ls = ls;
_xs = MemoryManager.malloc4(_len2 + 1);
_len = _len2; // Not compressed now!
}
@Override
NewChunk inflate_impl(NewChunk nc) {
double dx = Math.log10(_scale);
assert DParseTask.fitsIntoInt(dx);
Arrays.fill(nc._xs = MemoryManager.malloc4(_len), (int) dx);
nc._ls = MemoryManager.malloc8(_len);
for (int i = 0; i < _len; i++) {
int res = UDP.get2(_mem, (i << 1) + OFF);
if (res == C2Chunk._NA) nc.setNA_impl2(i);
else nc._ls[i] = res + _bias;
}
return nc;
}
@Override
protected boolean chunkInit() {
final int n_coef = _beta.length;
sumWeightedCatX = MemoryManager.malloc8d(n_coef - (_dinfo._nums - _n_offsets));
sumWeightedNumX = MemoryManager.malloc8d(_dinfo._nums);
sizeRiskSet = MemoryManager.malloc8d(_n_time);
sizeCensored = MemoryManager.malloc8d(_n_time);
sizeEvents = MemoryManager.malloc8d(_n_time);
countEvents = MemoryManager.malloc8(_n_time);
sumRiskEvents = MemoryManager.malloc8d(_n_time);
sumLogRiskEvents = MemoryManager.malloc8d(_n_time);
rcumsumRisk = MemoryManager.malloc8d(_n_time);
sumXEvents = malloc2DArray(_n_time, n_coef);
sumXRiskEvents = malloc2DArray(_n_time, n_coef);
rcumsumXRisk = malloc2DArray(_n_time, n_coef);
sumXXRiskEvents = malloc3DArray(_n_time, n_coef, n_coef);
rcumsumXXRisk = malloc3DArray(_n_time, n_coef, n_coef);
return true;
}
protected void initStats(final CoxPHModel model, final DataInfo dinfo) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n = p.stop_column.length();
o.data_info = dinfo;
final int n_offsets = (p.offset_columns == null) ? 0 : p.offset_columns.length;
final int n_coef = o.data_info.fullN() - n_offsets;
final String[] coefNames = o.data_info.coefNames();
o.coef_names = new String[n_coef];
System.arraycopy(coefNames, 0, o.coef_names, 0, n_coef);
o.coef = MemoryManager.malloc8d(n_coef);
o.exp_coef = MemoryManager.malloc8d(n_coef);
o.exp_neg_coef = MemoryManager.malloc8d(n_coef);
o.se_coef = MemoryManager.malloc8d(n_coef);
o.z_coef = MemoryManager.malloc8d(n_coef);
o.gradient = MemoryManager.malloc8d(n_coef);
o.hessian = malloc2DArray(n_coef, n_coef);
o.var_coef = malloc2DArray(n_coef, n_coef);
o.x_mean_cat = MemoryManager.malloc8d(n_coef - (o.data_info._nums - n_offsets));
o.x_mean_num = MemoryManager.malloc8d(o.data_info._nums - n_offsets);
o.mean_offset = MemoryManager.malloc8d(n_offsets);
o.offset_names = new String[n_offsets];
System.arraycopy(coefNames, n_coef, o.offset_names, 0, n_offsets);
final Vec start_column = p.start_column;
final Vec stop_column = p.stop_column;
o.min_time =
p.start_column == null ? (long) stop_column.min() : (long) start_column.min() + 1;
o.max_time = (long) stop_column.max();
final int n_time = new Vec.CollectDomain().doAll(stop_column).domain().length;
o.time = MemoryManager.malloc8(n_time);
o.n_risk = MemoryManager.malloc8d(n_time);
o.n_event = MemoryManager.malloc8d(n_time);
o.n_censor = MemoryManager.malloc8d(n_time);
o.cumhaz_0 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_1 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_2 = malloc2DArray(n_time, n_coef);
}
protected void set_sparse(int nzeros) {
if (sparseLen() == nzeros && _len != 0) return;
if (_id != null) { // we have sparse representation but some 0s in it!
int[] id = MemoryManager.malloc4(nzeros);
int j = 0;
if (_ds != null) {
double[] ds = MemoryManager.malloc8d(nzeros);
for (int i = 0; i < sparseLen(); ++i) {
if (_ds[i] != 0) {
ds[j] = _ds[i];
id[j] = _id[i];
++j;
}
}
_ds = ds;
} else if (_is != null) {
int[] is = MemoryManager.malloc4(nzeros);
for (int i = 0; i < sparseLen(); i++) {
if (_is[i] != -1) {
is[j] = _is[i];
id[j] = id[i];
++j;
}
}
} else {
long[] ls = MemoryManager.malloc8(nzeros);
int[] xs = MemoryManager.malloc4(nzeros);
for (int i = 0; i < sparseLen(); ++i) {
if (_ls[i] != 0) {
ls[j] = _ls[i];
xs[j] = _xs[i];
id[j] = _id[i];
++j;
}
}
_ls = ls;
_xs = xs;
}
_id = id;
assert j == nzeros;
set_sparseLen(nzeros);
return;
}
assert sparseLen() == _len
: "_len = " + sparseLen() + ", _len2 = " + _len + ", nzeros = " + nzeros;
int zs = 0;
if (_is != null) {
assert nzeros < _is.length;
_id = MemoryManager.malloc4(_is.length);
for (int i = 0; i < sparseLen(); i++) {
if (_is[i] == -1) zs++;
else {
_is[i - zs] = _is[i];
_id[i - zs] = i;
}
}
} else if (_ds == null) {
if (_len == 0) {
_ls = new long[0];
_xs = new int[0];
_id = new int[0];
set_sparseLen(0);
return;
} else {
assert nzeros < sparseLen();
_id = alloc_indices(_ls.length);
for (int i = 0; i < sparseLen(); ++i) {
if (_ls[i] == 0 && _xs[i] == 0) ++zs;
else {
_ls[i - zs] = _ls[i];
_xs[i - zs] = _xs[i];
_id[i - zs] = i;
}
}
}
} else {
assert nzeros < _ds.length;
_id = alloc_indices(_ds.length);
for (int i = 0; i < sparseLen(); ++i) {
if (_ds[i] == 0) ++zs;
else {
_ds[i - zs] = _ds[i];
_id[i - zs] = i;
}
}
}
assert zs == (sparseLen() - nzeros);
set_sparseLen(nzeros);
}
long[] alloc_mantissa(int l) {
return _ls = MemoryManager.malloc8(l);
}
