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/aosp_15_r20/external/guava/guava/src/com/google/common/math/
H A DQuantiles.java35 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
63 * 50, 90, and 99, to their corresponding quantile values.
74 * <p>The definition of the kth q-quantile of N values is as follows: define x = k * (N - 1) / q; if
80 * href="http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html">R</a>, and it is
82 * href="http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population">
134 /** Specifies the computation of a median (i.e. the 1st 2-quantile). */
170 checkArgument(scale > 0, "Quantile scale must be positive"); in Scale()
175 * Specifies a single quantile index to be calculated, i.e. the k in the kth q-quantile.
177 * @param index the quantile index, which must be in the inclusive range [0, q] for q-quantiles
184 * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
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/aosp_15_r20/external/guava/android/guava/src/com/google/common/math/
H A DQuantiles.java35 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
63 * 50, 90, and 99, to their corresponding quantile values.
74 * <p>The definition of the kth q-quantile of N values is as follows: define x = k * (N - 1) / q; if
80 * href="http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html">R</a>, and it is
82 * href="http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population">
134 /** Specifies the computation of a median (i.e. the 1st 2-quantile). */
170 checkArgument(scale > 0, "Quantile scale must be positive"); in Scale()
175 * Specifies a single quantile index to be calculated, i.e. the k in the kth q-quantile.
177 * @param index the quantile index, which must be in the inclusive range [0, q] for q-quantiles
184 * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
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/aosp_15_r20/external/apache-commons-math/src/main/java/org/apache/commons/math3/stat/descriptive/rank/
H A DPercentile.java76 * <a href="http://en.wikipedia.org/wiki/Quantile">Quantile page(wikipedia)</a>,
117 * with no quantile argument */
118 private double quantile; field in Percentile
126 * <li>default quantile: 50.0, can be reset with {@link #setQuantile(double)}</li>
141 * Constructs a Percentile with the specific quantile value and the following
147 * @param quantile the quantile
151 public Percentile(final double quantile) throws MathIllegalArgumentException { in Percentile() argument
152 this(quantile, EstimationType.LEGACY, NaNStrategy.REMOVED, in Percentile()
174 setQuantile(original.quantile); in Percentile()
179 * Constructs a Percentile with the specific quantile value,
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H A DPSquarePercentile.java65 * A Default quantile needed in case if user prefers to use default no
89 * The quantile needed should be in range of 0-1. The constructor
93 private final double quantile; field in PSquarePercentile
128 this.quantile = p / 100d;// always set it within (0,1] in PSquarePercentile()
133 * default quantile} needed
147 final double[] toHash = {result, quantile, markersHash, countOfObservations}; in hashCode()
180 * approximate quantile.
198 .get((int) (quantile * (initialFive.size() - 1))); in increment()
202 markers = newMarkers(initialFive, quantile); in increment()
210 * of the quantile and all markers.
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/aosp_15_r20/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/rank/
H A DPercentile.java94 * with no quantile argument */
95 private double quantile = 0.0; field in Percentile
101 * Constructs a Percentile with a default quantile
109 * Constructs a Percentile with the specific quantile value.
110 * @param p the quantile
169 * Calls to this method do not modify the internal <code>quantile</code>
178 * is null or p is not a valid quantile value (p must be greater than 0
197 * Returns an estimate of the <code>quantile</code>th percentile of the
198 * designated values in the <code>values</code> array. The quantile
199 * estimated is determined by the <code>quantile</code> property.
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/aosp_15_r20/external/webrtc/rtc_base/numerics/
H A Dsample_stats.cc27 return Quantile(0.5); in Median()
30 double SampleStats<double>::Quantile(double quantile) { in Quantile() argument
33 return GetPercentile(quantile); in Quantile()
81 return Quantile(0.5); in Median()
84 TimeDelta SampleStats<TimeDelta>::Quantile(double quantile) { in Quantile() argument
85 return TimeDelta::Seconds(stats_.Quantile(quantile)); in Quantile()
129 return Quantile(0.5); in Median()
132 DataRate SampleStats<DataRate>::Quantile(double quantile) { in Quantile() argument
133 return DataRate::BitsPerSec(stats_.Quantile(quantile)); in Quantile()
H A Dsample_stats.h29 double Quantile(double quantile);
46 TimeDelta Quantile(double quantile);
66 DataRate Quantile(double quantile);
/aosp_15_r20/external/pytorch/aten/src/ATen/native/
H A DSorting.cpp202 … "quantile() interpolation must be one of linear, lower, higher, midpoint or nearest, but got ", in get_quantile_interpolation_mode()
208 TORCH_CHECK(self.numel() > 0, "quantile() input tensor must be non-empty"); in quantile_checks()
209 TORCH_CHECK(q.dim() <= 1, "quantile() q must be a scalar or 1D tensor"); in quantile_checks()
212 "quantile() input tensor must be either float or double dtype"); in quantile_checks()
215 "quantile() q tensor must be same dtype as the input tensor"); in quantile_checks()
218 "quantile() q tensor must be on the same device as the input tensor"); in quantile_checks()
261 "quantile() q values must be in the range [0, 1]"); in quantile_compute()
289 "quantile() input tensor is too large"); in quantile_compute()
295 // If all values are nan, set rank to 0 so the quantile computed is nan. in quantile_compute()
308 // For quantile, compute ranks based on reduction size. If there is nan in quantile_compute()
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/aosp_15_r20/external/federated-compute/fcp/secagg/server/
H A Ddistribution_utilities.cc96 double HypergeometricDistribution::FindQuantile(double quantile, in FindQuantile() argument
98 if (quantile > 0.5) { in FindQuantile()
99 quantile = 1 - quantile; in FindQuantile()
103 return sampled_ - FindQuantileImpl(quantile, total_ - marked_) - 1; in FindQuantile()
105 return FindQuantileImpl(quantile, marked_); in FindQuantile()
109 double HypergeometricDistribution::FindQuantileImpl(double quantile, in FindQuantileImpl() argument
115 std::sqrt(-std::log(quantile) / (2 * sampled_))); in FindQuantileImpl()
123 while (current_cdf < quantile && result < sampled_) { in FindQuantileImpl()
H A Ddistribution_utilities.h40 // Finds the value whose cdf is quantile rounded outwards to an integer.
41 // Setting complement to true is equivalent to setting quantile = 1 - quantile
43 double FindQuantile(double quantile, bool complement = false);
57 double FindQuantileImpl(double quantile, int counted);
/aosp_15_r20/external/pytorch/torch/_numpy/
H A D_reductions_impl.py375 def quantile( function
387 # raise NotImplementedError("overwrite_input in quantile not implemented.")
397 # edge case: torch.quantile only supports float32 and float64
408 # FIXME(Mario) Doesn't np.quantile accept a tuple?
409 # torch.quantile does accept a number. If we don't want to implement the tuple behaviour
415 return torch.quantile(a, q, axis=axis, interpolation=method)
434 return quantile(
452 return quantile(
/aosp_15_r20/external/google-cloud-java/java-aiplatform/proto-google-cloud-aiplatform-v1beta1/src/main/java/com/google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/
H A DAutoMlForecastingInputsOrBuilder.java203 * * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
226 * * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
609 * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
611 * the value of optimization_objective is minimize-quantile-loss. Represents
624 * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
626 * the value of optimization_objective is minimize-quantile-loss. Represents
639 * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
641 * the value of optimization_objective is minimize-quantile-loss. Represents
/aosp_15_r20/external/webrtc/modules/audio_coding/neteq/
H A Ddelay_manager.cc50 "quantile", &quantile, // in Config()
63 " quantile=" in Log()
64 << quantile << " forget_factor=" << forget_factor in Log()
76 (1 << 30) * config.quantile, in DelayManager()
/aosp_15_r20/external/pytorch/test/torch_np/numpy_tests/lib/
H A Dtest_function_base.py3412 a = np.quantile(x, 0.45)
3420 assert_equal(np.quantile(x, 0), 0.0)
3421 assert_equal(np.quantile(x, 1), 3.5)
3422 assert_equal(np.quantile(x, 0.5), 1.75)
3424 @xfail # (reason="quantile w/integers or bools")
3427 tf_quant = np.quantile(True, False)
3431 quant_res = np.quantile(a, a)
3437 # fractional input, integral quantile
3439 q = np.quantile(x, 0)
3443 q = np.quantile(x, 1)
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/aosp_15_r20/external/tensorflow/tensorflow/core/api_def/base_api/
H A Dapi_def_BoostedTreesFlushQuantileSummaries.pbtxt11 summary: "Flush the quantile summaries from each quantile stream resource."
13 An op that outputs a list of quantile summaries of a quantile stream resource.
H A Dapi_def_BoostedTreesQuantileStreamResourceAddSummaries.pbtxt17 summary: "Add the quantile summaries to each quantile stream resource."
19 An op that adds a list of quantile summaries to a quantile stream resource. Each
H A Dapi_def_IsBoostedTreesQuantileStreamResourceInitialized.pbtxt7 resource; The reference to quantile stream resource handle.
16 summary: "Checks whether a quantile stream has been initialized."
18 An Op that checks if quantile stream resource is initialized.
H A Dapi_def_BoostedTreesQuantileStreamResourceFlush.pbtxt21 entry is the ith quantile of the input with an approximation error of epsilon.
28 summary: "Flush the summaries for a quantile stream resource."
30 An op that flushes the summaries for a quantile stream resource.
/aosp_15_r20/external/skia/tools/calmbench/
H A Dab.py13 # If range (1/3 quantile, 2/3 quantile) is completely disjoint between A and B,
18 # out benches and only take more measurements for benches whose current quantile
49 FACTOR = 3 # lower/upper quantile factor
378 "%(A)s quantile (ns), %(B)s quantile (ns), " +
/aosp_15_r20/external/google-cloud-java/java-aiplatform/proto-google-cloud-aiplatform-v1beta1/src/main/proto/google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/
H A Dautoml_time_series_forecasting.proto198 // * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
261 // Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
263 // the value of optimization_objective is minimize-quantile-loss. Represents
/aosp_15_r20/external/googleapis/google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/
H A Dautoml_time_series_forecasting.proto198 // * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
261 // Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
263 // the value of optimization_objective is minimize-quantile-loss. Represents
/aosp_15_r20/packages/modules/StatsD/lib/libkll/include/
Dkll.h23 // KLL Quantile - Implementation of Approximate quantiles algorithm based on
106 // When a user queries for a quantile phi, the rank of the returned
110 // with delta probability, at most one out of all possible quantile
/aosp_15_r20/external/tensorflow/tensorflow/python/ops/distributions/
H A Dtransformed_distribution.py517 raise NotImplementedError("quantile is not implemented when overriding "
520 raise NotImplementedError("quantile is not implemented when "
522 # x_q is the "qth quantile" of X iff q = P[X <= x_q]. Now, since X =
524 # implies the qth quantile of Y is g(x_q).
525 inv_cdf = self.distribution.quantile(value)
H A Ddistribution.py990 raise NotImplementedError("quantile is not implemented: {}".format(
999 def quantile(self, value, name="quantile"): member in Distribution
1000 """Quantile function. Aka "inverse cdf" or "percent point function".
1002 Given random variable `X` and `p in [0, 1]`, the `quantile` is:
1005 quantile(p) := x such that P[X <= x] == p
1013 quantile: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
/aosp_15_r20/external/google-cloud-java/java-automl/proto-google-cloud-automl-v1beta1/src/main/java/com/google/cloud/automl/v1beta1/
H A DFloat64StatsOrBuilder.java56 * Ordered from 0 to k k-quantile values of the data series of n values.
71 * Ordered from 0 to k k-quantile values of the data series of n values.
86 * Ordered from 0 to k k-quantile values of the data series of n values.

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