/** * Project each archetype into original feature space * * @param frame Original training data with m rows and n columns * @param destination_key Frame Id for output * @return Frame containing k rows and n columns, where each row corresponds to an archetype */ public Frame scoreArchetypes(Frame frame, Key destination_key, boolean reverse_transform) { final int ncols = _output._names.length; Frame adaptedFr = new Frame(frame); adaptTestForTrain(adaptedFr, true, false); assert ncols == adaptedFr.numCols(); String[][] adaptedDomme = adaptedFr.domains(); double[][] proj = new double[_parms._k][_output._nnums + _output._ncats]; // Categorical columns for (int d = 0; d < _output._ncats; d++) { double[][] block = _output._archetypes_raw.getCatBlock(d); for (int k = 0; k < _parms._k; k++) proj[k][_output._permutation[d]] = _parms.mimpute(block[k], _output._lossFunc[d]); } // Numeric columns for (int d = _output._ncats; d < (_output._ncats + _output._nnums); d++) { int ds = d - _output._ncats; for (int k = 0; k < _parms._k; k++) { double num = _output._archetypes_raw.getNum(ds, k); proj[k][_output._permutation[d]] = _parms.impute(num, _output._lossFunc[d]); if (reverse_transform) proj[k][_output._permutation[d]] = proj[k][_output._permutation[d]] / _output._normMul[ds] + _output._normSub[ds]; } } // Convert projection of archetypes into a frame with correct domains Frame f = ArrayUtils.frame( (null == destination_key ? Key.make() : destination_key), adaptedFr.names(), proj); for (int i = 0; i < ncols; i++) f.vec(i).setDomain(adaptedDomme[i]); return f; }