/** * Copyright 2013-2015 Pierre Merienne * * Licensed 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. */ package com.github.pmerienne.trident.ml.regression; import static org.junit.Assert.assertTrue; import org.junit.Ignore; import org.junit.Test; import com.github.pmerienne.trident.ml.regression.PerceptronRegressor; import com.github.pmerienne.trident.ml.testing.data.Datasets; public class PerceptronRegressorTest extends RegressorTest { @Test public void testWithRandomData() { double error = this.eval(new PerceptronRegressor(), Datasets.generateDataForRegression(2000, 10)); assertTrue("Error " + error + " is to big!", error <= 0.001); } @Ignore("Regressors are not ready for real data") @Test public void testWithBirthsData() { double error = this.eval(new PerceptronRegressor(), Datasets.getBIRTHSSamples()); assertTrue("Error " + error + " is to big!", error <= 0.01); } }