@Override protected void buildModel() throws Exception { for (int iter = 1; iter <= numIters; iter++) { // update W by fixing H for (int u = 0; u < W.numRows(); u++) { SparseVector uv = V.row(u); if (uv.getCount() > 0) { SparseVector euv = new SparseVector(V.numColumns()); for (int j : uv.getIndex()) euv.set(j, predict(u, j)); for (int f = 0; f < W.numColumns(); f++) { DenseVector fv = H.row(f, false); double real = fv.inner(uv); double estm = fv.inner(euv) + 1e-9; W.set(u, f, W.get(u, f) * (real / estm)); } } } // update H by fixing W DenseMatrix trW = W.transpose(); for (int j = 0; j < H.numColumns(); j++) { SparseVector jv = V.column(j); if (jv.getCount() > 0) { SparseVector ejv = new SparseVector(V.numRows()); for (int u : jv.getIndex()) ejv.set(u, predict(u, j)); for (int f = 0; f < H.numRows(); f++) { DenseVector fv = trW.row(f, false); double real = fv.inner(jv); double estm = fv.inner(ejv) + 1e-9; H.set(f, j, H.get(f, j) * (real / estm)); } } } // compute errors loss = 0; errs = 0; for (MatrixEntry me : V) { int u = me.row(); int j = me.column(); double ruj = me.get(); if (ruj > 0) { double euj = predict(u, j) - ruj; errs += euj * euj; loss += euj * euj; } } errs *= 0.5; loss *= 0.5; if (isConverged(iter)) break; } }