Revert of Avoid precision loss in TrendlineEstimator from int64_t -> double conversion (patchset #7 id:120001 of https://codereview.webrtc.org/2577463002/ )
Reason for revert: Multiple definitions of TestEstimator Original issue's description: > Avoid precision loss in TrendlineEstimator by passing the arrival time as an int64_t instead of a double. > > BUG=webrtc:6884 > > Committed: https://crrev.com/c12cbaf9dd0729dd45f3fc45a1938d1b3455e40a > Cr-Commit-Position: refs/heads/master@{#15631} TBR=stefan@webrtc.org,brandtr@webrtc.org # Skipping CQ checks because original CL landed less than 1 days ago. NOPRESUBMIT=true NOTREECHECKS=true NOTRY=true BUG=webrtc:6884 Review-Url: https://codereview.webrtc.org/2582513002 Cr-Commit-Position: refs/heads/master@{#15636}
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@ -13,14 +13,12 @@
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#include <algorithm>
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#include "webrtc/base/checks.h"
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#include "webrtc/base/optional.h"
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#include "webrtc/modules/remote_bitrate_estimator/test/bwe_test_logging.h"
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namespace webrtc {
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namespace {
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rtc::Optional<double> LinearFitSlope(
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const std::list<std::pair<double, double>> points) {
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double LinearFitSlope(const std::list<std::pair<double, double>> points) {
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RTC_DCHECK(points.size() >= 2);
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// Compute the "center of mass".
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double sum_x = 0;
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@ -38,9 +36,7 @@ rtc::Optional<double> LinearFitSlope(
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numerator += (point.first - x_avg) * (point.second - y_avg);
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denominator += (point.first - x_avg) * (point.first - x_avg);
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}
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if (denominator == 0)
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return rtc::Optional<double>();
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return rtc::Optional<double>(numerator / denominator);
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return numerator / denominator;
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}
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} // namespace
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@ -53,7 +49,6 @@ TrendlineEstimator::TrendlineEstimator(size_t window_size,
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smoothing_coef_(smoothing_coef),
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threshold_gain_(threshold_gain),
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num_of_deltas_(0),
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first_arrival_time_ms(-1),
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accumulated_delay_(0),
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smoothed_delay_(0),
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delay_hist_(),
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@ -63,35 +58,30 @@ TrendlineEstimator::~TrendlineEstimator() {}
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void TrendlineEstimator::Update(double recv_delta_ms,
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double send_delta_ms,
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int64_t arrival_time_ms) {
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double now_ms) {
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const double delta_ms = recv_delta_ms - send_delta_ms;
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++num_of_deltas_;
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if (num_of_deltas_ > kDeltaCounterMax)
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if (num_of_deltas_ > kDeltaCounterMax) {
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num_of_deltas_ = kDeltaCounterMax;
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if (first_arrival_time_ms == -1)
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first_arrival_time_ms = arrival_time_ms;
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}
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// Exponential backoff filter.
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accumulated_delay_ += delta_ms;
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BWE_TEST_LOGGING_PLOT(1, "accumulated_delay_ms", arrival_time_ms,
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accumulated_delay_);
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BWE_TEST_LOGGING_PLOT(1, "accumulated_delay_ms", now_ms, accumulated_delay_);
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smoothed_delay_ = smoothing_coef_ * smoothed_delay_ +
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(1 - smoothing_coef_) * accumulated_delay_;
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BWE_TEST_LOGGING_PLOT(1, "smoothed_delay_ms", arrival_time_ms,
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smoothed_delay_);
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BWE_TEST_LOGGING_PLOT(1, "smoothed_delay_ms", now_ms, smoothed_delay_);
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// Simple linear regression.
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delay_hist_.push_back(std::make_pair(
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static_cast<double>(arrival_time_ms - first_arrival_time_ms),
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smoothed_delay_));
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if (delay_hist_.size() > window_size_)
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delay_hist_.push_back(std::make_pair(now_ms, smoothed_delay_));
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if (delay_hist_.size() > window_size_) {
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delay_hist_.pop_front();
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}
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if (delay_hist_.size() == window_size_) {
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// Only update trendline_ if it is possible to fit a line to the data.
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trendline_ = LinearFitSlope(delay_hist_).value_or(trendline_);
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trendline_ = LinearFitSlope(delay_hist_);
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}
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BWE_TEST_LOGGING_PLOT(1, "trendline_slope", arrival_time_ms, trendline_);
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BWE_TEST_LOGGING_PLOT(1, "trendline_slope", now_ms, trendline_);
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}
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} // namespace webrtc
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@ -10,13 +10,11 @@
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#ifndef WEBRTC_MODULES_CONGESTION_CONTROLLER_TRENDLINE_ESTIMATOR_H_
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#define WEBRTC_MODULES_CONGESTION_CONTROLLER_TRENDLINE_ESTIMATOR_H_
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#include <stddef.h>
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#include <stdint.h>
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#include <list>
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#include <utility>
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#include "webrtc/base/constructormagic.h"
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#include "webrtc/common_types.h"
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namespace webrtc {
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@ -35,9 +33,7 @@ class TrendlineEstimator {
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// Update the estimator with a new sample. The deltas should represent deltas
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// between timestamp groups as defined by the InterArrival class.
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void Update(double recv_delta_ms,
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double send_delta_ms,
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int64_t arrival_time_ms);
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void Update(double recv_delta_ms, double send_delta_ms, double now_ms);
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// Returns the estimated trend k multiplied by some gain.
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// 0 < k < 1 -> the delay increases, queues are filling up
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@ -55,8 +51,6 @@ class TrendlineEstimator {
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const double threshold_gain_;
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// Used by the existing threshold.
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unsigned int num_of_deltas_;
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// Keep the arrival times small by using the change from the first packet.
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int64_t first_arrival_time_ms;
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// Exponential backoff filtering.
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double accumulated_delay_;
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double smoothed_delay_;
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@ -15,59 +15,100 @@
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namespace webrtc {
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namespace {
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constexpr size_t kWindowSize = 20;
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constexpr size_t kWindowSize = 15;
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constexpr double kSmoothing = 0.0;
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constexpr double kGain = 1;
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constexpr int64_t kAvgTimeBetweenPackets = 10;
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constexpr size_t kPacketCount = 2 * kWindowSize + 1;
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} // namespace
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void TestEstimator(double slope, double jitter_stddev, double tolerance) {
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TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random random(0x1234567);
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int64_t send_times[kPacketCount];
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int64_t recv_times[kPacketCount];
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int64_t send_start_time = random.Rand(1000000);
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int64_t recv_start_time = random.Rand(1000000);
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for (size_t i = 0; i < kPacketCount; ++i) {
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send_times[i] = send_start_time + i * kAvgTimeBetweenPackets;
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double latency = i * kAvgTimeBetweenPackets / (1 - slope);
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double jitter = random.Gaussian(0, jitter_stddev);
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recv_times[i] = recv_start_time + latency + jitter;
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}
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for (size_t i = 1; i < kPacketCount; ++i) {
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double recv_delta = recv_times[i] - recv_times[i - 1];
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double send_delta = send_times[i] - send_times[i - 1];
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estimator.Update(recv_delta, send_delta, recv_times[i]);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = 2 * send_delta;
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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if (i < kWindowSize)
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
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else
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EXPECT_NEAR(estimator.trendline_slope(), slope, tolerance);
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EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.001);
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}
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}
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TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
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TestEstimator(0.5, 0, 0.001);
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}
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TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
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TestEstimator(-1, 0, 0.001);
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = 0.5 * send_delta;
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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if (i < kWindowSize)
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
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else
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EXPECT_NEAR(estimator.trendline_slope(), -1, 0.001);
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}
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}
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TEST(TrendlineEstimator, PerfectLineSlopeZero) {
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TestEstimator(0, 0, 0.001);
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = send_delta;
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
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}
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}
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TEST(TrendlineEstimator, JitteryLineSlopeOneHalf) {
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TestEstimator(0.5, kAvgTimeBetweenPackets / 3.0, 0.01);
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = 2 * send_delta + rand.Gaussian(0, send_delta / 3);
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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if (i < kWindowSize)
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
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else
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EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.1);
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}
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}
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TEST(TrendlineEstimator, JitteryLineSlopeMinusOne) {
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TestEstimator(-1, kAvgTimeBetweenPackets / 3.0, 0.075);
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = 0.5 * send_delta + rand.Gaussian(0, send_delta / 25);
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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if (i < kWindowSize)
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
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else
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EXPECT_NEAR(estimator.trendline_slope(), -1, 0.1);
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}
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}
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TEST(TrendlineEstimator, JitteryLineSlopeZero) {
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TestEstimator(0, kAvgTimeBetweenPackets / 3.0, 0.02);
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TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
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Random rand(0x1234567);
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double now_ms = rand.Rand<double>() * 10000;
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for (size_t i = 1; i < 2 * kWindowSize; i++) {
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double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
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double recv_delta = send_delta + rand.Gaussian(0, send_delta / 8);
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now_ms += recv_delta;
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estimator.Update(recv_delta, send_delta, now_ms);
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EXPECT_NEAR(estimator.trendline_slope(), 0, 0.1);
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}
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}
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} // namespace webrtc
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