/**
* 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);
}
}