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Export of internal Abseil changes

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44efc1bb0e0a47eabf0569eaab81c66710d5b9c3 by Mark Barolak <mbar@google.com>:

Update "strings::Substitute" to "absl::Substitute" in the absl::Substitute error messages.

PiperOrigin-RevId: 282388042

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9ec7e9385f5469473f76857dc5b067d869bbc65b by Abseil Team <absl-team@google.com>:

Remove deprecated ExponentialBiased::Get()

PiperOrigin-RevId: 282045123
GitOrigin-RevId: 44efc1bb0e0a47eabf0569eaab81c66710d5b9c3
Change-Id: I915bf0ff5fa7ac2bd5f9fb653d1fbd9ece6af9fc
This commit is contained in:
Abseil Team 2019-11-25 10:40:20 -08:00 • committed by Gennadiy Rozental
parent 16d9fd58a5
commit 7f4fe64af8
5 changed files with 18 additions and 58 deletions
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52
absl/base/internal/exponential_biased.cc
View file

@ -27,7 +27,16 @@
namespace absl {
namespace base_internal {
// The algorithm generates a random number between 0 and 1 and applies the
// inverse cumulative distribution function for an exponential. Specifically:
// Let m be the inverse of the sample period, then the probability
// distribution function is m*exp(-mx) so the CDF is
// p = 1 - exp(-mx), so
// q = 1 - p = exp(-mx)
// log_e(q) = -mx
// -log_e(q)/m = x
// log_2(q) * (-log_e(2) * 1/m) = x
// In the code, q is actually in the range 1 to 2**26, hence the -26 below
int64_t ExponentialBiased::GetSkipCount(int64_t mean) {
if (ABSL_PREDICT_FALSE(!initialized_)) {
Initialize();
@ -63,47 +72,6 @@ int64_t ExponentialBiased::GetStride(int64_t mean) {
return GetSkipCount(mean - 1) + 1;
}
// The algorithm generates a random number between 0 and 1 and applies the
// inverse cumulative distribution function for an exponential. Specifically:
// Let m be the inverse of the sample period, then the probability
// distribution function is m*exp(-mx) so the CDF is
// p = 1 - exp(-mx), so
// q = 1 - p = exp(-mx)
// log_e(q) = -mx
// -log_e(q)/m = x
// log_2(q) * (-log_e(2) * 1/m) = x
// In the code, q is actually in the range 1 to 2**26, hence the -26 below
int64_t ExponentialBiased::Get(int64_t mean) {
if (ABSL_PREDICT_FALSE(!initialized_)) {
Initialize();
}
uint64_t rng = NextRandom(rng_);
rng_ = rng;
// Take the top 26 bits as the random number
// (This plus the 1<<58 sampling bound give a max possible step of
// 5194297183973780480 bytes.)
// The uint32_t cast is to prevent a (hard-to-reproduce) NAN
// under piii debug for some binaries.
double q = static_cast<uint32_t>(rng >> (kPrngNumBits - 26)) + 1.0;
// Put the computed p-value through the CDF of a geometric.
double interval = bias_ + (std::log2(q) - 26) * (-std::log(2.0) * mean);
// Very large values of interval overflow int64_t. To avoid that, we will cheat
// and clamp any huge values to (int64_t max)/2. This is a potential source of
// bias, but the mean would need to be such a large value that it's not likely
// to come up. For example, with a mean of 1e18, the probability of hitting
// this condition is about 1/1000. For a mean of 1e17, standard calculators
// claim that this event won't happen.
if (interval > static_cast<double>(std::numeric_limits<int64_t>::max() / 2)) {
// Assume huge values are bias neutral, retain bias for next call.
return std::numeric_limits<int64_t>::max() / 2;
}
int64_t value = std::max<int64_t>(1, std::round(interval));
bias_ = interval - value;
return value;
}
void ExponentialBiased::Initialize() {
// We don't get well distributed numbers from `this` so we call NextRandom() a
// bunch to mush the bits around. We use a global_rand to handle the case

6
absl/base/internal/exponential_biased.h
View file

@ -96,12 +96,6 @@ class ExponentialBiased {
// `GetSkipCount()` depends mostly on what best fits the use case.
int64_t GetStride(int64_t mean);
// Generates a rounded exponentially distributed random variable
// by rounding the value to the nearest integer.
// The result will be in the range [0, int64_t max / 2].
ABSL_DEPRECATED("Use GetSkipCount() or GetStride() instead")
int64_t Get(int64_t mean);
// Computes a random number in the range [0, 1<<(kPrngNumBits+1) - 1]
//
// This is public to enable testing.