blob: 2d7cd3ae3250ee08864716df05dda2014955f79d [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) {
loss = as.matrix(sum((y - X%*%B)^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("reg", "tol", "maxi");
paramRanges = list(10^seq(0,-4), 10^seq(-6,-12), 10^seq(1,3));
[B1, opt] = gridSearch(Xtrain, ytrain, "lm", "l2norm", params, paramRanges, TRUE);
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)