/*
* Artificial Intelligence for Humans
* Volume 1: Fundamental Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
* Copyright 2013 by Jeff Heaton
*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package com.heatonresearch.aifh.learning.score;
import com.heatonresearch.aifh.AIFH;
import com.heatonresearch.aifh.error.ErrorCalculation;
import com.heatonresearch.aifh.error.ErrorCalculationSSE;
import com.heatonresearch.aifh.general.data.BasicData;
import org.junit.Test;
import java.util.List;
import static org.junit.Assert.assertEquals;
/**
* Test regression data.
*/
public class TestScoreRegressionData {
public static final double[][] TEST_INPUT = {
{0.0, 0.0},
{1.0, 0.0},
{0.0, 1.0},
{1.0, 1.0}
};
public static final double[][] TEST_IDEAL = {
{0.0},
{1.0},
{1.0},
{0.0}
};
@Test
public void testGeneral() {
final List<BasicData> training = BasicData.convertArrays(TEST_INPUT, TEST_IDEAL);
final ScoreRegressionData score = new ScoreRegressionData(training);
final ErrorCalculation ec = new ErrorCalculationSSE();
score.setErrorCalc(ec);
assertEquals(ec, score.getErrorCalc());
}
@Test
public void testRegression() {
final double[] ACTUAL = {0.0, 1.0, 0.0, 0.0};
final List<BasicData> training = BasicData.convertArrays(TEST_INPUT, TEST_IDEAL);
final ScoreRegressionData score = new ScoreRegressionData(training);
final SimpleAlgo simple = new SimpleAlgo(ACTUAL);
final double s = score.calculateScore(simple);
assertEquals(training, score.getTrainingData());
assertEquals(1.0, s, AIFH.DEFAULT_PRECISION);
}
}