/**
* 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.
*/
package org.apache.mahout.classifier.discriminative;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.Vector;
import org.junit.Before;
import org.junit.Test;
@Deprecated
public final class WinnowTrainerTest extends MahoutTestCase {
private WinnowTrainer trainer;
@Override
@Before
public void setUp() throws Exception {
super.setUp();
trainer = new WinnowTrainer(3);
}
@Test
public void testUpdate() throws Exception {
double[] labels = { 0.0, 0.0, 0.0, 1.0 };
Vector labelset = new DenseVector(labels);
double[][] values = new double[3][4];
for (int i = 0; i < 3; i++) {
values[i][0] = 1.0;
values[i][1] = 1.0;
values[i][2] = 1.0;
values[i][3] = 1.0;
}
values[1][0] = 0.0;
values[2][0] = 0.0;
values[1][1] = 0.0;
values[2][2] = 0.0;
Matrix dataset = new DenseMatrix(values);
trainer.train(labelset, dataset);
assertTrue(trainer.getModel().classify(dataset.viewColumn(3)));
assertFalse(trainer.getModel().classify(dataset.viewColumn(0)));
}
}