afaef8bbeb
Parse the estimation parameters from the field trial string. BUG=webrtc:6690 Review-Url: https://codereview.webrtc.org/2489323002 Cr-Commit-Position: refs/heads/master@{#15126}
115 lines
4.1 KiB
C++
115 lines
4.1 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 "webrtc/test/gtest.h"
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#include "webrtc/base/random.h"
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#include "webrtc/modules/congestion_controller/trendline_estimator.h"
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namespace webrtc {
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namespace {
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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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} // namespace
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TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
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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;
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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, 0.001);
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}
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}
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TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
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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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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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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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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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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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