source: src/main/java/agents/anac/y2019/harddealer/math3/distribution/AbstractIntegerDistribution.java

Last change on this file was 204, checked in by Katsuhide Fujita, 5 years ago

Fixed errors of ANAC2019 agents

  • Property svn:executable set to *
File size: 9.3 KB
Line 
1/*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17package agents.anac.y2019.harddealer.math3.distribution;
18
19import java.io.Serializable;
20
21import agents.anac.y2019.harddealer.math3.exception.MathInternalError;
22import agents.anac.y2019.harddealer.math3.exception.NotStrictlyPositiveException;
23import agents.anac.y2019.harddealer.math3.exception.NumberIsTooLargeException;
24import agents.anac.y2019.harddealer.math3.exception.OutOfRangeException;
25import agents.anac.y2019.harddealer.math3.exception.util.LocalizedFormats;
26import agents.anac.y2019.harddealer.math3.random.RandomGenerator;
27import agents.anac.y2019.harddealer.math3.util.FastMath;
28
29/**
30 * Base class for integer-valued discrete distributions. Default
31 * implementations are provided for some of the methods that do not vary
32 * from distribution to distribution.
33 *
34 */
35public abstract class AbstractIntegerDistribution implements IntegerDistribution, Serializable {
36
37 /** Serializable version identifier */
38 private static final long serialVersionUID = -1146319659338487221L;
39
40 /**
41 * RandomData instance used to generate samples from the distribution.
42 * @deprecated As of 3.1, to be removed in 4.0. Please use the
43 * {@link #random} instance variable instead.
44 */
45 @Deprecated
46 protected final agents.anac.y2019.harddealer.math3.random.RandomDataImpl randomData =
47 new agents.anac.y2019.harddealer.math3.random.RandomDataImpl();
48
49 /**
50 * RNG instance used to generate samples from the distribution.
51 * @since 3.1
52 */
53 protected final RandomGenerator random;
54
55 /**
56 * @deprecated As of 3.1, to be removed in 4.0. Please use
57 * {@link #AbstractIntegerDistribution(RandomGenerator)} instead.
58 */
59 @Deprecated
60 protected AbstractIntegerDistribution() {
61 // Legacy users are only allowed to access the deprecated "randomData".
62 // New users are forbidden to use this constructor.
63 random = null;
64 }
65
66 /**
67 * @param rng Random number generator.
68 * @since 3.1
69 */
70 protected AbstractIntegerDistribution(RandomGenerator rng) {
71 random = rng;
72 }
73
74 /**
75 * {@inheritDoc}
76 *
77 * The default implementation uses the identity
78 * <p>{@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}</p>
79 */
80 public double cumulativeProbability(int x0, int x1) throws NumberIsTooLargeException {
81 if (x1 < x0) {
82 throw new NumberIsTooLargeException(LocalizedFormats.LOWER_ENDPOINT_ABOVE_UPPER_ENDPOINT,
83 x0, x1, true);
84 }
85 return cumulativeProbability(x1) - cumulativeProbability(x0);
86 }
87
88 /**
89 * {@inheritDoc}
90 *
91 * The default implementation returns
92 * <ul>
93 * <li>{@link #getSupportLowerBound()} for {@code p = 0},</li>
94 * <li>{@link #getSupportUpperBound()} for {@code p = 1}, and</li>
95 * <li>{@link #solveInverseCumulativeProbability(double, int, int)} for
96 * {@code 0 < p < 1}.</li>
97 * </ul>
98 */
99 public int inverseCumulativeProbability(final double p) throws OutOfRangeException {
100 if (p < 0.0 || p > 1.0) {
101 throw new OutOfRangeException(p, 0, 1);
102 }
103
104 int lower = getSupportLowerBound();
105 if (p == 0.0) {
106 return lower;
107 }
108 if (lower == Integer.MIN_VALUE) {
109 if (checkedCumulativeProbability(lower) >= p) {
110 return lower;
111 }
112 } else {
113 lower -= 1; // this ensures cumulativeProbability(lower) < p, which
114 // is important for the solving step
115 }
116
117 int upper = getSupportUpperBound();
118 if (p == 1.0) {
119 return upper;
120 }
121
122 // use the one-sided Chebyshev inequality to narrow the bracket
123 // cf. AbstractRealDistribution.inverseCumulativeProbability(double)
124 final double mu = getNumericalMean();
125 final double sigma = FastMath.sqrt(getNumericalVariance());
126 final boolean chebyshevApplies = !(Double.isInfinite(mu) || Double.isNaN(mu) ||
127 Double.isInfinite(sigma) || Double.isNaN(sigma) || sigma == 0.0);
128 if (chebyshevApplies) {
129 double k = FastMath.sqrt((1.0 - p) / p);
130 double tmp = mu - k * sigma;
131 if (tmp > lower) {
132 lower = ((int) FastMath.ceil(tmp)) - 1;
133 }
134 k = 1.0 / k;
135 tmp = mu + k * sigma;
136 if (tmp < upper) {
137 upper = ((int) FastMath.ceil(tmp)) - 1;
138 }
139 }
140
141 return solveInverseCumulativeProbability(p, lower, upper);
142 }
143
144 /**
145 * This is a utility function used by {@link
146 * #inverseCumulativeProbability(double)}. It assumes {@code 0 < p < 1} and
147 * that the inverse cumulative probability lies in the bracket {@code
148 * (lower, upper]}. The implementation does simple bisection to find the
149 * smallest {@code p}-quantile <code>inf{x in Z | P(X<=x) >= p}</code>.
150 *
151 * @param p the cumulative probability
152 * @param lower a value satisfying {@code cumulativeProbability(lower) < p}
153 * @param upper a value satisfying {@code p <= cumulativeProbability(upper)}
154 * @return the smallest {@code p}-quantile of this distribution
155 */
156 protected int solveInverseCumulativeProbability(final double p, int lower, int upper) {
157 while (lower + 1 < upper) {
158 int xm = (lower + upper) / 2;
159 if (xm < lower || xm > upper) {
160 /*
161 * Overflow.
162 * There will never be an overflow in both calculation methods
163 * for xm at the same time
164 */
165 xm = lower + (upper - lower) / 2;
166 }
167
168 double pm = checkedCumulativeProbability(xm);
169 if (pm >= p) {
170 upper = xm;
171 } else {
172 lower = xm;
173 }
174 }
175 return upper;
176 }
177
178 /** {@inheritDoc} */
179 public void reseedRandomGenerator(long seed) {
180 random.setSeed(seed);
181 randomData.reSeed(seed);
182 }
183
184 /**
185 * {@inheritDoc}
186 *
187 * The default implementation uses the
188 * <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling">
189 * inversion method</a>.
190 */
191 public int sample() {
192 return inverseCumulativeProbability(random.nextDouble());
193 }
194
195 /**
196 * {@inheritDoc}
197 *
198 * The default implementation generates the sample by calling
199 * {@link #sample()} in a loop.
200 */
201 public int[] sample(int sampleSize) {
202 if (sampleSize <= 0) {
203 throw new NotStrictlyPositiveException(
204 LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize);
205 }
206 int[] out = new int[sampleSize];
207 for (int i = 0; i < sampleSize; i++) {
208 out[i] = sample();
209 }
210 return out;
211 }
212
213 /**
214 * Computes the cumulative probability function and checks for {@code NaN}
215 * values returned. Throws {@code MathInternalError} if the value is
216 * {@code NaN}. Rethrows any exception encountered evaluating the cumulative
217 * probability function. Throws {@code MathInternalError} if the cumulative
218 * probability function returns {@code NaN}.
219 *
220 * @param argument input value
221 * @return the cumulative probability
222 * @throws MathInternalError if the cumulative probability is {@code NaN}
223 */
224 private double checkedCumulativeProbability(int argument)
225 throws MathInternalError {
226 double result = Double.NaN;
227 result = cumulativeProbability(argument);
228 if (Double.isNaN(result)) {
229 throw new MathInternalError(LocalizedFormats
230 .DISCRETE_CUMULATIVE_PROBABILITY_RETURNED_NAN, argument);
231 }
232 return result;
233 }
234
235 /**
236 * For a random variable {@code X} whose values are distributed according to
237 * this distribution, this method returns {@code log(P(X = x))}, where
238 * {@code log} is the natural logarithm. In other words, this method
239 * represents the logarithm of the probability mass function (PMF) for the
240 * distribution. Note that due to the floating point precision and
241 * under/overflow issues, this method will for some distributions be more
242 * precise and faster than computing the logarithm of
243 * {@link #probability(int)}.
244 * <p>
245 * The default implementation simply computes the logarithm of {@code probability(x)}.</p>
246 *
247 * @param x the point at which the PMF is evaluated
248 * @return the logarithm of the value of the probability mass function at {@code x}
249 */
250 public double logProbability(int x) {
251 return FastMath.log(probability(x));
252 }
253}
Note: See TracBrowser for help on using the repository browser.