Source Code Cross Referenced for RandomUtilsTest.java in  » Library » Apache-common-lang » org » apache » commons » lang » math » Java Source Code / Java DocumentationJava Source Code and Java Documentation

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Java Source Code / Java Documentation » Library » Apache common lang » org.apache.commons.lang.math 
Source Cross Referenced  Class Diagram Java Document (Java Doc) 


001:        /*
002:         * Licensed to the Apache Software Foundation (ASF) under one or more
003:         * contributor license agreements.  See the NOTICE file distributed with
004:         * this work for additional information regarding copyright ownership.
005:         * The ASF licenses this file to You under the Apache License, Version 2.0
006:         * (the "License"); you may not use this file except in compliance with
007:         * the License.  You may obtain a copy of the License at
008:         * 
009:         *      http://www.apache.org/licenses/LICENSE-2.0
010:         * 
011:         * Unless required by applicable law or agreed to in writing, software
012:         * distributed under the License is distributed on an "AS IS" BASIS,
013:         * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014:         * See the License for the specific language governing permissions and
015:         * limitations under the License.
016:         */
017:        package org.apache.commons.lang.math;
018:
019:        import java.util.Random;
020:
021:        import junit.framework.Test;
022:        import junit.framework.TestCase;
023:        import junit.framework.TestSuite;
024:
025:        /**
026:         * Test cases for the {@link RandomUtils} class.
027:         *
028:         * @author <a href="mailto:phil@steitz.com">Phil Steitz</a>
029:         * @version $Revision: 437554 $ $Date: 2006-08-27 23:21:41 -0700 (Sun, 27 Aug 2006) $
030:         */
031:
032:        public final class RandomUtilsTest extends TestCase {
033:
034:            public RandomUtilsTest(String name) {
035:                super (name);
036:            }
037:
038:            public void setUp() {
039:            }
040:
041:            public static Test suite() {
042:                TestSuite suite = new TestSuite(RandomUtilsTest.class);
043:                suite.setName("RandomUtils Tests");
044:                return suite;
045:            }
046:
047:            /** test distribution of nextInt() */
048:            public void testNextInt() {
049:                tstNextInt(null);
050:
051:                assertTrue(RandomUtils.nextInt() >= 0);
052:            }
053:
054:            /** test distribution of nextInt(Random) */
055:            public void testNextInt2() {
056:                Random rnd = new Random();
057:                rnd.setSeed(System.currentTimeMillis());
058:                tstNextInt(rnd);
059:            }
060:
061:            /** test distribution of JVMRandom.nextInt() */
062:            public void testJvmRandomNextInt() {
063:                tstNextInt(RandomUtils.JVM_RANDOM);
064:            }
065:
066:            /** 
067:             * Generate 1000 values for nextInt(bound) and compare
068:             * the observed frequency counts to expected counts using
069:             * a chi-square test.
070:             * @param rnd Random to use if not null
071:             */
072:            private void tstNextInt(Random rnd) {
073:                int bound = 0;
074:                int result = 0;
075:                // test boundary condition: n = Integer.MAX_VALUE;
076:                bound = Integer.MAX_VALUE;
077:                if (rnd == null) {
078:                    result = RandomUtils.nextInt(bound);
079:                } else {
080:                    result = RandomUtils.nextInt(rnd, bound);
081:                }
082:                assertTrue("result less than bound", result < bound);
083:                assertTrue("result non-negative", result >= 0);
084:
085:                // test uniformity -- use Chi-Square test at .01 level
086:                bound = 4;
087:                int[] expected = new int[] { 250, 250, 250, 250 };
088:                int[] observed = new int[] { 0, 0, 0, 0 };
089:                for (int i = 0; i < 1000; i++) {
090:                    if (rnd == null) {
091:                        result = RandomUtils.nextInt(bound);
092:                    } else {
093:                        result = RandomUtils.nextInt(rnd, bound);
094:                    }
095:                    assertTrue(result < bound);
096:                    assertTrue(result >= 0);
097:                    observed[result]++;
098:                }
099:                /* Use ChiSquare dist with df = 4-1 = 3, alpha = .001
100:                 * Change to 11.34 for alpha = .01   
101:                 */
102:                assertTrue(
103:                        "chi-square test -- will fail about 1 in 1000 times",
104:                        chiSquare(expected, observed) < 16.27);
105:            }
106:
107:            /** test distribution of nextLong() */
108:            public void testNextLong() {
109:                tstNextLong(null);
110:            }
111:
112:            /** test distribution of nextLong(Random) BROKEN
113:             *  contract of nextLong(Random) is different from
114:             * nextLong() */
115:            public void testNextLong2() {
116:                Random rnd = new Random();
117:                rnd.setSeed(System.currentTimeMillis());
118:                tstNextLong(rnd);
119:            }
120:
121:            /** 
122:             * Generate 1000 values for nextLong and check that
123:             * p(value < long.MAXVALUE/2) ~ 0.5. Use chi-square test
124:             * with df = 2-1 = 1  
125:             * @param rnd Random to use if not null
126:             */
127:            private void tstNextLong(Random rnd) {
128:                int[] expected = new int[] { 500, 500 };
129:                int[] observed = new int[] { 0, 0 };
130:                long result = 0;
131:                long midPoint = Long.MAX_VALUE / 2;
132:                for (int i = 0; i < 1000; i++) {
133:                    if (rnd == null) {
134:                        result = Math.abs(RandomUtils.nextLong());
135:                    } else {
136:                        result = Math.abs(RandomUtils.nextLong(rnd));
137:                    }
138:                    if (result < midPoint) {
139:                        observed[0]++;
140:                    } else {
141:                        observed[1]++;
142:                    }
143:                }
144:                /* Use ChiSquare dist with df = 2-1 = 1, alpha = .001
145:                 * Change to 6.64 for alpha = .01  
146:                 */
147:                assertTrue(
148:                        "chi-square test -- will fail about 1 in 1000 times",
149:                        chiSquare(expected, observed) < 10.83);
150:            }
151:
152:            /** test distribution of nextBoolean() */
153:            public void testNextBoolean() {
154:                tstNextBoolean(null);
155:            }
156:
157:            /** test distribution of nextBoolean(Random) */
158:            public void testNextBoolean2() {
159:                Random rnd = new Random();
160:                rnd.setSeed(System.currentTimeMillis());
161:                tstNextBoolean(rnd);
162:            }
163:
164:            /** 
165:             * Generate 1000 values for nextBoolean and check that
166:             * p(value = false) ~ 0.5. Use chi-square test
167:             * with df = 2-1 = 1  
168:             * @param rnd Random to use if not null
169:             */
170:            private void tstNextBoolean(Random rnd) {
171:                int[] expected = new int[] { 500, 500 };
172:                int[] observed = new int[] { 0, 0 };
173:                boolean result = false;
174:                for (int i = 0; i < 1000; i++) {
175:                    if (rnd == null) {
176:                        result = RandomUtils.nextBoolean();
177:                    } else {
178:                        result = RandomUtils.nextBoolean(rnd);
179:                    }
180:                    if (result) {
181:                        observed[0]++;
182:                    } else {
183:                        observed[1]++;
184:                    }
185:                }
186:                /* Use ChiSquare dist with df = 2-1 = 1, alpha = .001
187:                 * Change to 6.64 for alpha = .01 
188:                 */
189:                assertTrue(
190:                        "chi-square test -- will fail about 1 in 1000 times",
191:                        chiSquare(expected, observed) < 10.83);
192:            }
193:
194:            /** test distribution of nextFloat() */
195:            public void testNextFloat() {
196:                tstNextFloat(null);
197:            }
198:
199:            /** test distribution of nextFloat(Random) */
200:            public void testNextFloat2() {
201:                Random rnd = new Random();
202:                rnd.setSeed(System.currentTimeMillis());
203:                tstNextFloat(rnd);
204:            }
205:
206:            /** 
207:             * Generate 1000 values for nextFloat and check that
208:             * p(value < 0.5) ~ 0.5. Use chi-square test
209:             * with df = 2-1 = 1  
210:             * @param rnd Random to use if not null
211:             */
212:            private void tstNextFloat(Random rnd) {
213:                int[] expected = new int[] { 500, 500 };
214:                int[] observed = new int[] { 0, 0 };
215:                float result = 0;
216:                for (int i = 0; i < 1000; i++) {
217:                    if (rnd == null) {
218:                        result = RandomUtils.nextFloat();
219:                    } else {
220:                        result = RandomUtils.nextFloat(rnd);
221:                    }
222:                    if (result < 0.5) {
223:                        observed[0]++;
224:                    } else {
225:                        observed[1]++;
226:                    }
227:                }
228:                /* Use ChiSquare dist with df = 2-1 = 1, alpha = .001
229:                 * Change to 6.64 for alpha = .01 
230:                 */
231:                assertTrue(
232:                        "chi-square test -- will fail about 1 in 1000 times",
233:                        chiSquare(expected, observed) < 10.83);
234:            }
235:
236:            /** test distribution of nextDouble() */
237:            public void testNextDouble() {
238:                tstNextDouble(null);
239:            }
240:
241:            /** test distribution of nextDouble(Random) */
242:            public void testNextDouble2() {
243:                Random rnd = new Random();
244:                rnd.setSeed(System.currentTimeMillis());
245:                tstNextDouble(rnd);
246:            }
247:
248:            /** 
249:             * Generate 1000 values for nextFloat and check that
250:             * p(value < 0.5) ~ 0.5. Use chi-square test
251:             * with df = 2-1 = 1  
252:             * @param rnd Random to use if not null
253:             */
254:            private void tstNextDouble(Random rnd) {
255:                int[] expected = new int[] { 500, 500 };
256:                int[] observed = new int[] { 0, 0 };
257:                double result = 0;
258:                for (int i = 0; i < 1000; i++) {
259:                    if (rnd == null) {
260:                        result = RandomUtils.nextDouble();
261:                    } else {
262:                        result = RandomUtils.nextDouble(rnd);
263:                    }
264:                    if (result < 0.5) {
265:                        observed[0]++;
266:                    } else {
267:                        observed[1]++;
268:                    }
269:                }
270:                /* Use ChiSquare dist with df = 2-1 = 1, alpha = .001
271:                 * Change to 6.64 for alpha = .01 
272:                 */
273:                assertTrue(
274:                        "chi-square test -- will fail about 1 in 1000 times",
275:                        chiSquare(expected, observed) < 10.83);
276:            }
277:
278:            /** make sure that unimplemented methods fail */
279:            public void testUnimplementedMethods() {
280:
281:                try {
282:                    RandomUtils.JVM_RANDOM.setSeed(1000);
283:                    fail("expecting UnsupportedOperationException");
284:                } catch (UnsupportedOperationException ex) {
285:                    // empty
286:                }
287:
288:                try {
289:                    RandomUtils.JVM_RANDOM.nextGaussian();
290:                    fail("expecting UnsupportedOperationException");
291:                } catch (UnsupportedOperationException ex) {
292:                    // empty
293:                }
294:
295:                try {
296:                    RandomUtils.JVM_RANDOM.nextBytes(null);
297:                    fail("expecting UnsupportedOperationException");
298:                } catch (UnsupportedOperationException ex) {
299:                    // empty
300:                }
301:
302:            }
303:
304:            /** make sure that illegal arguments fail */
305:            public void testIllegalArguments() {
306:
307:                try {
308:                    RandomUtils.JVM_RANDOM.nextInt(-1);
309:                    fail("expecting IllegalArgumentException");
310:                } catch (IllegalArgumentException ex) {
311:                    // empty
312:                }
313:
314:                try {
315:                    JVMRandom.nextLong(-1L);
316:                    fail("expecting IllegalArgumentException");
317:                } catch (IllegalArgumentException ex) {
318:                    // empty
319:                }
320:
321:            }
322:
323:            /**
324:             * Computes Chi-Square statistic given observed and expected counts
325:             * @param observed array of observed frequency counts
326:             * @param expected array of expected frequency counts
327:             */
328:            private double chiSquare(int[] expected, int[] observed) {
329:                double sumSq = 0.0d;
330:                double dev = 0.0d;
331:                for (int i = 0; i < observed.length; i++) {
332:                    dev = (double) (observed[i] - expected[i]);
333:                    sumSq += dev * dev / (double) expected[i];
334:                }
335:                return sumSq;
336:            }
337:
338:        }
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