@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; }