| /frameworks/base/apct-tests/perftests/utils/src/android/perftests/utils/ |
| Stats.java | 87 private static long getPercentile(List<Long> values, int percentile) {
88 if (percentile < 0 || percentile > 100) {
90 "invalid percentile " + percentile + ", should be 0-100");
92 int idx = (values.size() - 1) * percentile / 100;
|
| /external/grpc-grpc/tools/run_tests/performance/ |
| massage_qps_stats.py | 327 "core_call_initial_size_50p"] = massage_qps_stats_helpers.percentile(
330 "core_call_initial_size_95p"] = massage_qps_stats_helpers.percentile(
333 "core_call_initial_size_99p"] = massage_qps_stats_helpers.percentile(
342 "core_poll_events_returned_50p"] = massage_qps_stats_helpers.percentile(
345 "core_poll_events_returned_95p"] = massage_qps_stats_helpers.percentile(
348 "core_poll_events_returned_99p"] = massage_qps_stats_helpers.percentile(
356 "core_tcp_write_size_50p"] = massage_qps_stats_helpers.percentile(
359 "core_tcp_write_size_95p"] = massage_qps_stats_helpers.percentile(
362 "core_tcp_write_size_99p"] = massage_qps_stats_helpers.percentile(
371 "core_tcp_write_iov_size_50p"] = massage_qps_stats_helpers.percentile(
[all...] |
| massage_qps_stats_helpers.py | 40 def percentile(buckets, pctl, boundaries): function
|
| /external/autotest/client/bin/ |
| fio_util.py | 59 h_title = { True: 'Percentile', False: 'Time (s)' }
76 def _parse_log_file(cls, file_name, pass_index, pass_count, percentile):
88 @param percentile: flag to use percentile as key instead of timestamp
102 # Sort & calculate percentile
103 if percentile:
120 def _gen_data_col(cls, pass_list, percentile):
128 @param percentile: flag to use percentile as key instead of timestamp
132 if percentile
[all...] |
| /external/chromium-trace/catapult/common/lab/ |
| commits.py | 35 def Percentile(data, percentile):
36 """Find a percentile of a list of values.
40 percentile: The percentile to look up, from 0.0 to 1.0.
43 The percentile.
51 k = (len(data) - 1) * percentile
97 percentile = Percentile(commit_durations, p)
98 print '%3d%% commit duration:' % (p * 100), '%6.1fm' % percentile
[all...] |
| /external/opencensus-java/api/src/main/java/io/opencensus/metrics/export/ |
| Summary.java | 137 * Represents the value at a given percentile of a distribution.
145 * Returns the percentile in this {@code ValueAtPercentile}.
149 * @return the percentile in this {@code ValueAtPercentile}.
165 * @param percentile the percentile in this {@code ValueAtPercentile}.
170 public static ValueAtPercentile create(double percentile, double value) {
172 0 < percentile && percentile <= 100.0,
173 "percentile must be in the interval (0.0, 100.0]");
175 return new AutoValue_Summary_Snapshot_ValueAtPercentile(percentile, value)
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| sinh_arcsinh_test.py | 131 np.percentile(norm_samps, 0.1, axis=0),
132 np.percentile(sasnorm_samps, 0.1, axis=0))
134 np.percentile(sasnorm_samps, 99.9, axis=0),
135 np.percentile(norm_samps, 99.9, axis=0))
139 np.percentile(sasnorm_samps, q, axis=0),
140 np.percentile(norm_samps, q, axis=0),
143 np.percentile(sasnorm_samps, 100 - q, axis=0),
144 np.percentile(norm_samps, 100 - q, axis=0),
173 np.percentile(sasnorm_samps, 0.1, axis=0),
174 np.percentile(norm_samps, 0.1, axis=0)
[all...] |
| vector_sinh_arcsinh_diag_test.py | 123 np.percentile(norm_samps, 0.1, axis=0),
124 np.percentile(sasnorm_samps, 0.1, axis=0))
126 np.percentile(sasnorm_samps, 99.9, axis=0),
127 np.percentile(norm_samps, 99.9, axis=0))
131 np.percentile(sasnorm_samps, q, axis=0),
132 np.percentile(norm_samps, q, axis=0),
135 np.percentile(sasnorm_samps, 100 - q, axis=0),
136 np.percentile(norm_samps, 100 - q, axis=0),
168 np.percentile(sasnorm_samps, 0.1, axis=0),
169 np.percentile(norm_samps, 0.1, axis=0)
[all...] |
| sample_stats_test.py | 272 expected_percentile = np.percentile(
275 pct = sample_stats.percentile(
283 expected_percentile = np.percentile(
286 pct = sample_stats.percentile(x, q=q, interpolation=self._interpolation)
293 expected_percentile = np.percentile(
297 pct_neg_index = sample_stats.percentile(
299 pct_pos_index = sample_stats.percentile(
309 expected_percentile = np.percentile(
312 pct = sample_stats.percentile(
320 expected_percentile = np.percentile(
[all...] |
| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| test_util.cc | 82 int ExponentialRandomPositiveInt(float percentile, int percentile_val,
85 -std::log(1.f - percentile) / static_cast<float>(percentile_val);
94 float ExponentialRandomPositiveFloat(float percentile, float percentile_val,
97 -std::log(1.f - percentile) / static_cast<float>(percentile_val);
|
| test_util.h | 52 int ExponentialRandomPositiveInt(float percentile, int percentile_val,
56 float ExponentialRandomPositiveFloat(float percentile, float percentile_val,
|
| /external/grpc-grpc/src/core/lib/debug/ |
| stats.h | 64 double percentile);
|
| stats.cc | 133 double percentile) {
140 static_cast<double>(count) * percentile / 100.0);
|
| /external/grpc-grpc/test/core/util/ |
| histogram.h | 41 double grpc_histogram_percentile(grpc_histogram* histogram, double percentile);
|
| histogram_test.cc | 28 static void expect_percentile(grpc_histogram* h, double percentile,
30 double got = grpc_histogram_percentile(h, percentile);
31 gpr_log(GPR_INFO, "@%f%%, expect %f <= %f <= %f", percentile, min_expect, got,
|
| histogram.cc | 196 double grpc_histogram_percentile(grpc_histogram* h, double percentile) {
197 return threshold_for_count_below(h, h->count * percentile / 100.0);
|
| /external/caliper/caliper/src/main/java/com/google/caliper/runner/ |
| ConsoleOutput.java | 37 import org.apache.commons.math.stat.descriptive.rank.Percentile;
119 Percentile percentile = new Percentile(); local
120 percentile.setData(weightedValues);
126 descriptiveStatistics.getMin(), percentile.evaluate(25),
127 percentile.evaluate(50), descriptiveStatistics.getMean(),
128 percentile.evaluate(75), descriptiveStatistics.getMax());
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| test_data.py | 33 quantiles = np.percentile(
|
| /external/v8/tools/ |
| eval_gc_nvp.py | 109 for percentile in self.percentiles:
110 index = int(ceil((len(self.values) - 1) * percentile / 100))
111 ret.append(" {0}%: {1}".format(percentile, sorted_values[index]))
191 for percentile in args.percentiles.split(','):
193 percentiles.append(float(percentile))
|
| /external/grpc-grpc/tools/profiling/latency_profile/ |
| profile_analyzer.py | 185 def percentile(N, percent, key=lambda x: x): function
187 Find the percentile of an already sorted list of values.
193 @return - the percentile of the values
214 return '%.1f/%.1f/%.1f' % (1e6 * percentile(values, 0.5),
215 1e6 * percentile(values, 0.9),
216 1e6 * percentile(values, 0.99))
|
| /external/tensorflow/tensorflow/core/kernels/data/experimental/ |
| choose_fastest_dataset_op.cc | 323 // of the completed threads. The input with the best 90th percentile
328 VLOG(2) << "90.0 percentile iteration time:";
329 double best_percentile = histograms_[0].Percentile(kPercentile);
333 double percentile = histograms_[i].Percentile(kPercentile); local
334 VLOG(2) << "Branch " << i << ": " << percentile; local
335 if (percentile <= best_percentile) {
336 best_percentile = percentile;
|
| choose_fastest_branch_dataset_op.cc | 450 // of the completed iterations. The input with the best 90th percentile
455 VLOG(2) << "90.0 percentile iteration time:";
456 double best_percentile = histograms_[0].Percentile(kPercentile);
460 double percentile = histograms_[i].Percentile(kPercentile); local
461 VLOG(2) << "Branch " << i << ": " << percentile; local
462 if (percentile <= best_percentile) {
463 best_percentile = percentile;
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/ |
| StatUtils.java | 28 import org.apache.commons.math.stat.descriptive.rank.Percentile;
66 /** percentile */
67 private static final Percentile PERCENTILE = new Percentile();
498 * Returns an estimate of the <code>p</code>th percentile of the values
511 * See {@link org.apache.commons.math.stat.descriptive.rank.Percentile} for
512 * a description of the percentile estimation algorithm used.</p>
515 * @param p the percentile value to compute
516 * @return the percentile value or Double.NaN if the array is empt
520 public static double percentile(final double[] values, final double p) { method in class:StatUtils
551 public static double percentile(final double[] values, final int begin, method in class:StatUtils
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| sample_stats.py | 37 "percentile",
216 # TODO(langmore) To make equivalent to numpy.percentile:
219 def percentile(x, function
226 """Compute the `q`-th percentile of `x`.
228 Given a vector `x`, the `q`-th percentile of `x` is the value `q / 100` of the
232 `interpolation` parameter will determine the percentile if the normalized
240 # Get 30th percentile with default ('nearest') interpolation.
242 percentile(x, q=30.)
245 # Get 30th percentile with 'lower' interpolation
247 percentile(x, q=30., interpolation='lower'
[all...] |
| /external/syzkaller/vendor/golang.org/x/net/trace/ |
| histogram.go | 120 func (h *histogram) percentileBoundary(percentile float64) int64 {
130 percentOfTotal := round(float64(total) * percentile)
|
