1/*
2 * Copyright (c) 2013, Oracle and/or its affiliates. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4 *
5 * This code is free software; you can redistribute it and/or modify it
6 * under the terms of the GNU General Public License version 2 only, as
7 * published by the Free Software Foundation.
8 *
9 * This code is distributed in the hope that it will be useful, but WITHOUT
10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
11 * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
12 * version 2 for more details (a copy is included in the LICENSE file that
13 * accompanied this code).
14 *
15 * You should have received a copy of the GNU General Public License version
16 * 2 along with this work; if not, write to the Free Software Foundation,
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
18 *
19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 */
23
24import java.util.*;
25import java.util.function.*;
26import java.util.stream.*;
27
28import static java.lang.Double.*;
29
30/*
31 * @test
32 * @bug 8006572 8030212
33 * @summary Test for use of non-naive summation in stream-related sum and average operations.
34 */
35public class TestDoubleSumAverage {
36    public static void main(String... args) {
37        int failures = 0;
38
39        failures += testZeroAverageOfNonEmptyStream();
40        failures += testForCompenstation();
41        failures += testNonfiniteSum();
42
43        if (failures > 0) {
44            throw new RuntimeException("Found " + failures + " numerical failure(s).");
45        }
46    }
47
48    /**
49     * Test to verify that a non-empty stream with a zero average is non-empty.
50     */
51    private static int testZeroAverageOfNonEmptyStream() {
52        Supplier<DoubleStream> ds = () -> DoubleStream.iterate(0.0, e -> 0.0).limit(10);
53
54        return  compareUlpDifference(0.0, ds.get().average().getAsDouble(), 0);
55    }
56
57    /**
58     * Compute the sum and average of a sequence of double values in
59     * various ways and report an error if naive summation is used.
60     */
61    private static int testForCompenstation() {
62        int failures = 0;
63
64        /*
65         * The exact sum of the test stream is 1 + 1e6*ulp(1.0) but a
66         * naive summation algorithm will return 1.0 since (1.0 +
67         * ulp(1.0)/2) will round to 1.0 again.
68         */
69        double base = 1.0;
70        double increment = Math.ulp(base)/2.0;
71        int count = 1_000_001;
72
73        double expectedSum = base + (increment * (count - 1));
74        double expectedAvg = expectedSum / count;
75
76        // Factory for double a stream of [base, increment, ..., increment] limited to a size of count
77        Supplier<DoubleStream> ds = () -> DoubleStream.iterate(base, e -> increment).limit(count);
78
79        DoubleSummaryStatistics stats = ds.get().collect(DoubleSummaryStatistics::new,
80                                                         DoubleSummaryStatistics::accept,
81                                                         DoubleSummaryStatistics::combine);
82
83        failures += compareUlpDifference(expectedSum, stats.getSum(), 3);
84        failures += compareUlpDifference(expectedAvg, stats.getAverage(), 3);
85
86        failures += compareUlpDifference(expectedSum,
87                                         ds.get().sum(), 3);
88        failures += compareUlpDifference(expectedAvg,
89                                         ds.get().average().getAsDouble(), 3);
90
91        failures += compareUlpDifference(expectedSum,
92                                         ds.get().boxed().collect(Collectors.summingDouble(d -> d)), 3);
93        failures += compareUlpDifference(expectedAvg,
94                                         ds.get().boxed().collect(Collectors.averagingDouble(d -> d)),3);
95        return failures;
96    }
97
98    private static int testNonfiniteSum() {
99        int failures = 0;
100
101        Map<Supplier<DoubleStream>, Double> testCases = new LinkedHashMap<>();
102        testCases.put(() -> DoubleStream.of(MAX_VALUE, MAX_VALUE),   POSITIVE_INFINITY);
103        testCases.put(() -> DoubleStream.of(-MAX_VALUE, -MAX_VALUE), NEGATIVE_INFINITY);
104
105        testCases.put(() -> DoubleStream.of(1.0d, POSITIVE_INFINITY, 1.0d), POSITIVE_INFINITY);
106        testCases.put(() -> DoubleStream.of(POSITIVE_INFINITY),             POSITIVE_INFINITY);
107        testCases.put(() -> DoubleStream.of(POSITIVE_INFINITY, POSITIVE_INFINITY), POSITIVE_INFINITY);
108        testCases.put(() -> DoubleStream.of(POSITIVE_INFINITY, POSITIVE_INFINITY, 0.0), POSITIVE_INFINITY);
109
110        testCases.put(() -> DoubleStream.of(1.0d, NEGATIVE_INFINITY, 1.0d), NEGATIVE_INFINITY);
111        testCases.put(() -> DoubleStream.of(NEGATIVE_INFINITY),             NEGATIVE_INFINITY);
112        testCases.put(() -> DoubleStream.of(NEGATIVE_INFINITY, NEGATIVE_INFINITY), NEGATIVE_INFINITY);
113        testCases.put(() -> DoubleStream.of(NEGATIVE_INFINITY, NEGATIVE_INFINITY, 0.0), NEGATIVE_INFINITY);
114
115        testCases.put(() -> DoubleStream.of(1.0d, NaN, 1.0d),               NaN);
116        testCases.put(() -> DoubleStream.of(NaN),                           NaN);
117        testCases.put(() -> DoubleStream.of(1.0d, NEGATIVE_INFINITY, POSITIVE_INFINITY, 1.0d), NaN);
118        testCases.put(() -> DoubleStream.of(1.0d, POSITIVE_INFINITY, NEGATIVE_INFINITY, 1.0d), NaN);
119        testCases.put(() -> DoubleStream.of(POSITIVE_INFINITY, NaN), NaN);
120        testCases.put(() -> DoubleStream.of(NEGATIVE_INFINITY, NaN), NaN);
121        testCases.put(() -> DoubleStream.of(NaN, POSITIVE_INFINITY), NaN);
122        testCases.put(() -> DoubleStream.of(NaN, NEGATIVE_INFINITY), NaN);
123
124        for(Map.Entry<Supplier<DoubleStream>, Double> testCase : testCases.entrySet()) {
125            Supplier<DoubleStream> ds = testCase.getKey();
126            double expected = testCase.getValue();
127
128            DoubleSummaryStatistics stats = ds.get().collect(DoubleSummaryStatistics::new,
129                                                             DoubleSummaryStatistics::accept,
130                                                             DoubleSummaryStatistics::combine);
131
132            failures += compareUlpDifference(expected, stats.getSum(), 0);
133            failures += compareUlpDifference(expected, stats.getAverage(), 0);
134
135            failures += compareUlpDifference(expected, ds.get().sum(), 0);
136            failures += compareUlpDifference(expected, ds.get().average().getAsDouble(), 0);
137
138            failures += compareUlpDifference(expected, ds.get().boxed().collect(Collectors.summingDouble(d -> d)), 0);
139            failures += compareUlpDifference(expected, ds.get().boxed().collect(Collectors.averagingDouble(d -> d)), 0);
140        }
141
142        return failures;
143    }
144
145    /**
146     * Compute the ulp difference of two double values and compare against an error threshold.
147     */
148    private static int compareUlpDifference(double expected, double computed, double threshold) {
149        if (!Double.isFinite(expected)) {
150            // Handle NaN and infinity cases
151            if (Double.compare(expected, computed) == 0)
152                return 0;
153            else {
154                System.err.printf("Unexpected sum, %g rather than %g.%n",
155                                  computed, expected);
156                return 1;
157            }
158        }
159
160        double ulpDifference = Math.abs(expected - computed) / Math.ulp(expected);
161
162        if (ulpDifference > threshold) {
163            System.err.printf("Numerical summation error too large, %g ulps rather than %g.%n",
164                              ulpDifference, threshold);
165            return 1;
166        } else
167            return 0;
168    }
169}
170