/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* 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 smile.classification;
import smile.data.NominalAttribute;
import java.text.ParseException;
import smile.data.parser.DelimitedTextParser;
import smile.data.AttributeDataset;
import smile.data.parser.ArffParser;
import java.io.IOException;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import static org.junit.Assert.*;
/**
*
* @author Haifeng Li
*/
public class KNNTest {
public KNNTest() {
}
@BeforeClass
public static void setUpClass() throws Exception {
}
@AfterClass
public static void tearDownClass() throws Exception {
}
@Before
public void setUp() {
}
@After
public void tearDown() {
}
/**
* Test of learn method, of class KNN.
*/
@Test
public void testLearn_3args() {
System.out.println("learn");
ArffParser arffParser = new ArffParser();
arffParser.setResponseIndex(4);
try {
AttributeDataset iris = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/iris.arff"));
double[][] x = iris.toArray(new double[0][]);
int[] y = iris.toArray(new int[0]);
KNN<double[]> knn = KNN.learn(x, y, 1);
int error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("1-nn error = " + error);
assertEquals(6, error);
knn = KNN.learn(x, y, 3);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("3-nn error = " + error);
assertEquals(6, error);
knn = KNN.learn(x, y, 5);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("5-nn error = " + error);
assertEquals(5, error);
knn = KNN.learn(x, y, 7);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("7-nn error = " + error);
assertEquals(5, error);
knn = KNN.learn(x, y, 9);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("9-nn error = " + error);
assertEquals(5, error);
knn = KNN.learn(x, y, 11);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("11-nn error = " + error);
assertEquals(4, error);
knn = KNN.learn(x, y, 13);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("13-nn error = " + error);
assertEquals(5, error);
knn = KNN.learn(x, y, 15);
error = 0;
for (int i = 0; i < x.length; i++) {
if (knn.predict(x[i]) != y[i]) {
error++;
}
}
System.out.println("15-nn error = " + error);
assertEquals(4, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class KNN.
*/
@Test
public void testSegment() throws ParseException {
System.out.println("Segment");
ArffParser parser = new ArffParser();
parser.setResponseIndex(19);
try {
AttributeDataset train = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-challenge.arff"));
AttributeDataset test = parser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/segment-test.arff"));
double[][] x = train.toArray(new double[0][]);
int[] y = train.toArray(new int[0]);
double[][] testx = test.toArray(new double[0][]);
int[] testy = test.toArray(new int[0]);
KNN<double[]> knn = KNN.learn(x, y);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (knn.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("Segment error rate = %.2f%%%n", 100.0 * error / testx.length);
assertEquals(39, error);
} catch (IOException ex) {
System.err.println(ex);
}
}
/**
* Test of learn method, of class KNN.
*/
@Test
public void testUSPS() {
System.out.println("USPS");
DelimitedTextParser parser = new DelimitedTextParser();
parser.setResponseIndex(new NominalAttribute("class"), 0);
try {
AttributeDataset train = parser.parse("USPS Train", smile.data.parser.IOUtils.getTestDataFile("usps/zip.train"));
AttributeDataset test = parser.parse("USPS Test", smile.data.parser.IOUtils.getTestDataFile("usps/zip.test"));
double[][] x = train.toArray(new double[train.size()][]);
int[] y = train.toArray(new int[train.size()]);
double[][] testx = test.toArray(new double[test.size()][]);
int[] testy = test.toArray(new int[test.size()]);
KNN<double[]> knn = KNN.learn(x, y);
int error = 0;
for (int i = 0; i < testx.length; i++) {
if (knn.predict(testx[i]) != testy[i]) {
error++;
}
}
System.out.format("USPS error rate = %.2f%%%n", 100.0 * error / testx.length);
assertEquals(113, error);
} catch (Exception ex) {
System.err.println(ex);
}
}
}