| #------------------------------------------------------------- |
| # |
| # 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($1) # Training data |
| Y = read($2) # response values |
| X_test = read($3) # random data to predict |
| Y_test = read($4) # random data labels |
| |
| w = multiLogReg(X=X, Y=Y, icpt=2, tol=0.00000001, reg=1.0, maxi=100, maxii=0, verbose=FALSE) |
| [prob, y, accuracy] = multiLogRegPredict(X=X_test, B=w, Y=Y_test, verbose=TRUE) |
| acc = matrix(accuracy, 1, 1) |
| write(acc, $5) |
