blob: f53e32645516cab0514d86e69cf39b052646b1aa [file] [log] [blame]
#-------------------------------------------------------------
#
# 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 );
}
}