| #------------------------------------------------------------- |
| # |
| # 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); |
| y = read($2); |
| |
| [prior, means, covs, det] = gaussianClassifier(D=X, C=y, varSmoothing=$3); |
| |
| #Cbind the inverse covariance matrices, to make them comparable in the unit tests |
| invcovs = as.matrix(covs[1]) |
| for (i in 2:max(y)) |
| { |
| invcovs = cbind(invcovs, as.matrix(covs[i])) |
| } |
| |
| write(prior, $4); |
| write(means, $5); |
| write(det, $6); |
| write(invcovs, $7); |
