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5b8c0a2a1e
Changes places where we explicitly construct an Optional to instead use nullopt or the requisite value type only. This CL was uploaded by git cl split. Bug: None Change-Id: If2a98dc714d1755f07af1f70248cf41e4a9db750 Reviewed-on: https://webrtc-review.googlesource.com/23612 Reviewed-by: Björn Terelius <terelius@webrtc.org> Commit-Queue: Oskar Sundbom <ossu@webrtc.org> Cr-Commit-Position: refs/heads/master@{#20887}
97 lines
3.2 KiB
C++
97 lines
3.2 KiB
C++
/*
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* Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "modules/congestion_controller/trendline_estimator.h"
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#include <algorithm>
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#include "api/optional.h"
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#include "modules/remote_bitrate_estimator/test/bwe_test_logging.h"
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#include "rtc_base/checks.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::deque<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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double sum_y = 0;
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for (const auto& point : points) {
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sum_x += point.first;
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sum_y += point.second;
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}
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double x_avg = sum_x / points.size();
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double y_avg = sum_y / points.size();
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// Compute the slope k = \sum (x_i-x_avg)(y_i-y_avg) / \sum (x_i-x_avg)^2
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double numerator = 0;
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double denominator = 0;
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for (const auto& point : points) {
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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::nullopt;
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return numerator / denominator;
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}
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} // namespace
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enum { kDeltaCounterMax = 1000 };
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TrendlineEstimator::TrendlineEstimator(size_t window_size,
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double smoothing_coef,
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double threshold_gain)
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: window_size_(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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trendline_(0) {}
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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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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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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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// 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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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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// 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_.pop_front();
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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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}
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BWE_TEST_LOGGING_PLOT(1, "trendline_slope", arrival_time_ms, trendline_);
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}
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} // namespace webrtc
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