@Override
protected SBPrintStream toJavaInit(SBPrintStream sb, CodeGeneratorPipeline fileCtx) {
sb = super.toJavaInit(sb, fileCtx);
sb.ip("public boolean isSupervised() { return " + isSupervised() + "; }").nl();
sb.ip("public int nfeatures() { return " + _output.nfeatures() + "; }").nl();
sb.ip("public int nclasses() { return " + _output.nclasses() + "; }").nl();
JCodeGen.toStaticVar(sb, "RESCNT", _output._rescnt, "Count of categorical levels in response.");
JCodeGen.toStaticVar(
sb, "APRIORI", _output._apriori_raw, "Apriori class distribution of the response.");
JCodeGen.toStaticVar(sb, "PCOND", _output._pcond_raw, "Conditional probability of predictors.");
double[] dlen = null;
if (_output._ncats > 0) {
dlen = new double[_output._ncats];
for (int i = 0; i < _output._ncats; i++) dlen[i] = _output._domains[i].length;
}
JCodeGen.toStaticVar(
sb, "DOMLEN", dlen, "Number of unique levels for each categorical predictor.");
return sb;
}
@Override
protected SB toJavaInit(SB sb, SB fileContextSB) {
sb = super.toJavaInit(sb, fileContextSB);
sb.ip("public boolean isSupervised() { return " + isSupervised() + "; }").nl();
sb.ip("public int nfeatures() { return " + _output.nfeatures() + "; }").nl();
sb.ip("public int nclasses() { return " + _parms._k + "; }").nl();
if (_output._nnums > 0) {
JCodeGen.toStaticVar(
sb,
"NORMMUL",
_output._normMul,
"Standardization/Normalization scaling factor for numerical variables.");
JCodeGen.toStaticVar(
sb,
"NORMSUB",
_output._normSub,
"Standardization/Normalization offset for numerical variables.");
}
JCodeGen.toStaticVar(sb, "CATOFFS", _output._catOffsets, "Categorical column offsets.");
JCodeGen.toStaticVar(sb, "PERMUTE", _output._permutation, "Permutation index vector.");
JCodeGen.toStaticVar(sb, "EIGVECS", _output._eigenvectors_raw, "Eigenvector matrix.");
return sb;
}