blob: c1727949a49317585afe698f26af37d766827b35 [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.
#
#-------------------------------------------------------------
l2norm = function(Matrix[Double] X, Matrix[Double] y, Matrix[Double] B)
return (Matrix[Double] loss)
{
yhat = lmPredict(X=X, B=B, ytest=y)
loss = as.matrix(sum((y - yhat)^2));
}
X = read($1);
y = read($2);
N = 200;
Xtrain = X[1:N,];
ytrain = y[1:N,];
Xtest = X[(N+1):nrow(X),];
ytest = y[(N+1):nrow(X),];
params = list("icpt","reg", "tol", "maxi");
paramRanges = list(seq(0,2),10^seq(0,-4), 10^seq(-6,-12), 10^seq(1,3));
[B1, opt] = gridSearch(X=Xtrain, y=ytrain, train="lm", predict="l2norm",
numB=ncol(X)+1, params=params, paramValues=paramRanges);
B2 = lm(X=Xtrain, y=ytrain, verbose=FALSE);
l1 = l2norm(Xtest, ytest, B1);
l2 = l2norm(Xtest, ytest, B2);
R = as.scalar(l1 < l2);
write(R, $3)