| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| m_lmPredict = function(Matrix[Double] X, Matrix[Double] B, |
| Matrix[Double] ytest, 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 ) { |
| y_residual = ytest - yhat; |
| avg_res = sum(y_residual) / nrow(ytest); |
| ss_res = sum(y_residual^2); |
| ss_avg_res = ss_res - nrow(ytest) * avg_res^2; |
| R2 = 1 - ss_res / (sum(ytest^2) - nrow(ytest) * (sum(ytest)/nrow(ytest))^2); |
| print("\nAccuracy:" + |
| "\n--sum(ytest) = " + sum(ytest) + |
| "\n--sum(yhat) = " + sum(yhat) + |
| "\n--AVG_RES_Y: " + avg_res + |
| "\n--SS_AVG_RES_Y: " + ss_avg_res + |
| "\n--R2: " + R2 ); |
| } |
| } |