| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| #------------------------------------------------------------- |
| |
| X = read($in_X) |
| T = read($in_T) |
| CL = read($in_CL) |
| k = $in_k |
| |
| [NNR, PR, FI] = knn(Train=X, Test=T, CL=CL, k_value=k, predict_con_tg=1); |
| |
| PR_val = matrix(0, 0, ncol(T)); |
| for(i in 1:nrow(T)) { |
| PR_val = rbind(PR_val, X[as.scalar(PR[i]), ]); |
| } |
| |
| write(NNR, $out_NNR); |
| write(PR_val, $out_PR); |
