| /third_party/ltp/testcases/realtime/lib/ |
| D | libstats.c | 206 int stats_quantiles_init(stats_quantiles_t * quantiles, int nines) in stats_quantiles_init() argument
211 quantiles->nines = nines; in stats_quantiles_init()
213 quantiles->quantiles = calloc(sizeof(long), (nines - 1)); in stats_quantiles_init()
214 if (!quantiles->quantiles) { in stats_quantiles_init()
220 int stats_quantiles_free(stats_quantiles_t * quantiles) in stats_quantiles_free() argument
222 free(quantiles->quantiles); in stats_quantiles_free()
227 stats_quantiles_t * quantiles) in stats_quantiles_calc() argument
235 (data->index + 1) < (long)exp10(quantiles->nines)) { in stats_quantiles_calc()
242 for (i = 2; i <= quantiles->nines; i++) { in stats_quantiles_calc()
244 quantiles->quantiles[i - 2] = data->records[index].y; in stats_quantiles_calc()
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| /third_party/ltp/testcases/realtime/include/ |
| D | libstats.h | 72 long *quantiles; member
126 int stats_quantiles_init(stats_quantiles_t *quantiles, int nines);
131 int stats_quantiles_free(stats_quantiles_t *quantiles);
137 int stats_quantiles_calc(stats_container_t *data, stats_quantiles_t *quantiles);
142 void stats_quantiles_print(stats_quantiles_t *quantiles);
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| /third_party/ltp/testcases/realtime/func/sched_latency/ |
| D | sched_latency.c | 76 stats_quantiles_t quantiles; variable
234 stats_quantiles_calc(&dat, &quantiles); in periodic_thread()
235 stats_quantiles_print(&quantiles); in periodic_thread()
283 if (stats_quantiles_init(&quantiles, (int)log10(iterations))) { in main()
301 stats_quantiles_free(&quantiles); in main()
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| /third_party/ltp/testcases/realtime/func/periodic_cpu_load/ |
| D | periodic_cpu_load.c | 68 stats_quantiles_t quantiles[THREADS_PER_GROUP * NUM_GROUPS]; variable
199 stats_quantiles_init(&quantiles[i], (int)log10(iterations)); in main()
227 stats_quantiles_calc(&dat[i], &quantiles[i]); in main()
228 stats_quantiles_print(&quantiles[i]); in main()
243 stats_quantiles_free(&quantiles[i]); in main()
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| D | periodic_cpu_load_single.c | 90 stats_quantiles_t quantiles; in periodic_thread() local
102 stats_quantiles_init(&quantiles, (int)log10(iterations)); in periodic_thread()
155 stats_quantiles_calc(&dat, &quantiles); in periodic_thread()
156 stats_quantiles_print(&quantiles); in periodic_thread()
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| /third_party/ltp/testcases/realtime/func/hrtimer-prio/ |
| D | hrtimer-prio.c | 178 stats_quantiles_t quantiles; in main() local
187 if (stats_quantiles_init(&quantiles, (int)log10(iterations))) { in main()
221 stats_quantiles_calc(&dat, &quantiles); in main()
222 stats_quantiles_print(&quantiles); in main()
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| /third_party/ltp/testcases/realtime/func/gtod_latency/ |
| D | gtod_latency.c | 235 stats_quantiles_t quantiles; in main() local
253 stats_quantiles_init(&quantiles, (int)log10(iterations)); in main()
349 stats_quantiles_calc(&dat, &quantiles); in main()
350 stats_quantiles_print(&quantiles); in main()
354 stats_quantiles_free(&quantiles); in main()
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| /third_party/ltp/testcases/realtime/func/pthread_kill_latency/ |
| D | pthread_kill_latency.c | 121 stats_quantiles_t quantiles; in signal_receiving_thread() local
126 stats_quantiles_init(&quantiles, (int)log10(ITERATIONS)); in signal_receiving_thread()
216 stats_quantiles_calc(&dat, &quantiles); in signal_receiving_thread()
217 stats_quantiles_print(&quantiles); in signal_receiving_thread()
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| /third_party/python/Lib/test/ |
| D | test_statistics.py | 2254 quantiles = statistics.quantiles
2271 self.assertEqual(expected, quantiles(data, n=n))
2272 self.assertEqual(len(quantiles(data, n=n)), n - 1)
2275 result = quantiles(map(datatype, data), n=n)
2280 self.assertEqual(quantiles(expected, n=n), expected)
2289 quantiles(data, n=n),
2290 quantiles(padded_data, n=n, method='inclusive'),
2297 act = quantiles(map(f, data), n=n)
2302 q1, q2, q3 = quantiles(data)
2309 quantiles = statistics.quantiles
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| /third_party/boost/libs/math/reporting/performance/ |
| D | test_distributions.cpp | 28 static const double quantiles[19]; member in distribution_tester
52 for(unsigned i = 0; i < sizeof(quantiles) / sizeof(quantiles[0]); ++i) in add_test_case()
54 tests.back().x_values.push_back(sanitize_x(f(quantiles[i]))); in add_test_case()
62 for(unsigned i = 0; i < sizeof(quantiles) / sizeof(quantiles[0]); ++i) in add_test_case()
64 tests.back().x_values.push_back(sanitize_x(f(p1, quantiles[i]))); in add_test_case()
73 for(unsigned i = 0; i < sizeof(quantiles) / sizeof(quantiles[0]); ++i) in add_test_case()
75 tests.back().x_values.push_back(sanitize_x(f(p1, p2, quantiles[i]))); in add_test_case()
85 for(unsigned i = 0; i < sizeof(quantiles) / sizeof(quantiles[0]); ++i) in add_test_case()
87 tests.back().x_values.push_back(sanitize_x(f(p1, p2, p3, quantiles[i]))); in add_test_case()
117 sum += f(tests[i].params, p_value ? quantiles[j] : tests[i].x_values[j]); in run_timed_tests()
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| /third_party/boost/libs/math/doc/policies/ |
| D | policy_tutorial.qbk | 68 /rounded outwards/. That is to say lower quantiles (where the probability is
69 less than 0.5) are rounded downward, and upper quantiles (where the probability
408 * Normally people calculate quantiles so that they can perform
421 The converse applies to upper-quantiles: If the probability is greater than
426 Likewise for two-sided intervals, we would round lower quantiles down,
427 and upper quantiles up. This ensures that we have ['at least the requested
433 for the 0.05 and 0.95 quantiles with the results ['rounded outwards] so that
467 Therefore if U and L are the upper and lower quantiles respectively, then
489 [This is the default policy as described above: lower quantiles
490 are rounded down (probability < 0.5), and upper quantiles
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| D | policy.qbk | 318 quantiles - we can either ignore the discreteness of the distribution and return
323 discrete quantiles work, and how integer results are rounded:
354 * Lower quantiles (where the probability is less than 0.5) are rounded
356 * Upper quantiles (where the probability is greater than 0.5) are rounded up.
379 * Lower quantiles (where the probability is less than 0.5) are rounded
381 * Upper quantiles (where the probability is greater than 0.5) are rounded ['down].
595 Determines how discrete quantiles return their results: either
626 discrete quantiles to return a real-valued result (rather than round to
834 Specifies how discrete quantiles are evaluated, will be an instance
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| /third_party/boost/libs/math/doc/distributions/ |
| D | background.qbk | 56 /rounded outwards/. That is to say lower quantiles (where the probability is
57 less than 0.5) are rounded downward, and upper quantiles (where the probability
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| D | dist_tutorial.qbk | 52 behaviour such as how errors are handled, or how the quantiles
109 /quantiles/ etc for these distributions.
126 And quantiles are just the same:
245 /outwards/: that is to say, lower quantiles - where the probability
246 is less than 0.5 are rounded down, while upper quantiles - where
306 [*Critical values are just quantiles]
308 Some texts talk about quantiles, or percentiles or fractiles,
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| D | hypergeometric.qbk | 87 /rounded outwards/. That is to say lower quantiles (where the probability is
88 less than 0.5) are rounded downward, and upper quantiles (where the probability
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| D | triangular.qbk | 125 except quantiles with arguments nearing the extremes of zero and unity.
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| /third_party/python/Doc/library/ |
| D | statistics.rst | 55 :func:`quantiles` Divide data into intervals with equal probability.
529 .. function:: quantiles(data, *, n=4, method='exclusive')
548 The *method* for computing quantiles can be varied depending on
576 >>> [round(q, 1) for q in quantiles(data, n=10)]
788 .. method:: NormalDist.quantiles(n=4)
863 >>> list(map(round, sat.quantiles()))
865 >>> list(map(round, sat.quantiles(n=10)))
881 >>> quantiles(map(model, X, Y, Z)) # doctest: +SKIP
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| D | random.rst | 517 from statistics import mean, quantiles
538 print('Quartiles:', [round(q, 1) for q in quantiles(waits)])
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| /third_party/python/Lib/ |
| D | statistics.py | 641 def quantiles(data, *, n=4, method='exclusive'): function
1111 def quantiles(self, n=4): member in NormalDist
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| /third_party/boost/libs/math/doc/ |
| D | math.qbk | 402 …d_dis_quant [link math_toolkit.pol_tutorial.understand_dis_quant understanding discrete quantiles]]
510 /rounded outwards/. That is to say lower quantiles (where the probability is
511 less than 0.5) are rounded downward, and upper quantiles (where the probability
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| /third_party/boost/libs/math/doc/overview/ |
| D | faq.qbk | 6 Nearly all are provided, and many more like mean, skewness, quantiles, complements ...
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| D | roadmap.qbk | 469 * [*Breaking change:] Changed discrete quantiles to return an integer result:
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| /third_party/python/Misc/NEWS.d/ |
| D | 3.8.0a4.rst | 477 Add statistics.quantiles()
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| D | 3.9.0a1.rst | 2794 The *dist* argument for statistics.quantiles() is now positional only. The
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| /third_party/python/Doc/whatsnew/ |
| D | 3.8.rst | 163 def quantiles(dist, /, *, n=4, method='exclusive')
1200 Added :func:`statistics.quantiles` that divides data or a distribution
|