| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| # The lmPredict-function predicts the class of a feature vector |
| # |
| # INPUT: |
| # -------------------------------------------------------------------------------------- |
| # X Matrix of feature vectors |
| # B 1-column matrix of weights. |
| # ytest test labels, used only for verbose output. can be set to matrix(0,1,1) |
| # if verbose output is not wanted |
| # icpt Intercept presence, shifting and rescaling the columns of X |
| # verbose If TRUE print messages are activated |
| # -------------------------------------------------------------------------------------- |
| # |
| # OUTPUT: |
| # ----------------------------------------------------------------------------------- |
| # yhat 1-column matrix of classes |
| # ----------------------------------------------------------------------------------- |
| |
| m_lmPredict = function(Matrix[Double] X, Matrix[Double] B, |
| Matrix[Double] ytest = matrix(0,1,1), Integer icpt = 0, Boolean verbose = FALSE) |
| return (Matrix[Double] yhat) |
| { |
| intercept = ifelse(icpt>0 | ncol(X)+1==nrow(B), as.scalar(B[nrow(B),]), 0); |
| yhat = X %*% B[1:ncol(X),] + intercept; |
| |
| if( verbose ) |
| lmPredictStats(yhat, ytest, TRUE); |
| } |