Revert of Avoid precision loss in TrendlineEstimator from int64_t -> double conversion

webrtc/modules/congestion_controller/trendline_estimator_unittest.cc

 /*
  *  Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
  *
  *  Use of this source code is governed by a BSD-style license
  *  that can be found in the LICENSE file in the root of the source
  *  tree. An additional intellectual property rights grant can be found
  *  in the file PATENTS.  All contributing project authors may
  *  be found in the AUTHORS file in the root of the source tree.
  */
 
 #include "webrtc/test/gtest.h"
 #include "webrtc/base/random.h"
 #include "webrtc/modules/congestion_controller/trendline_estimator.h"
 
 namespace webrtc {
 
 namespace {
-constexpr size_t kWindowSize = 20;
+constexpr size_t kWindowSize = 15;
 constexpr double kSmoothing = 0.0;
 constexpr double kGain = 1;
 constexpr int64_t kAvgTimeBetweenPackets = 10;
-constexpr size_t kPacketCount = 2 * kWindowSize + 1;
 }  // namespace
 
-void TestEstimator(double slope, double jitter_stddev, double tolerance) {
+TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random random(0x1234567);
-  int64_t send_times[kPacketCount];
-  int64_t recv_times[kPacketCount];
-  int64_t send_start_time = random.Rand(1000000);
-  int64_t recv_start_time = random.Rand(1000000);
-  for (size_t i = 0; i < kPacketCount; ++i) {
-    send_times[i] = send_start_time + i * kAvgTimeBetweenPackets;
-    double latency = i * kAvgTimeBetweenPackets / (1 - slope);
-    double jitter = random.Gaussian(0, jitter_stddev);
-    recv_times[i] = recv_start_time + latency + jitter;
-  }
-  for (size_t i = 1; i < kPacketCount; ++i) {
-    double recv_delta = recv_times[i] - recv_times[i - 1];
-    double send_delta = send_times[i] - send_times[i - 1];
-    estimator.Update(recv_delta, send_delta, recv_times[i]);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = 2 * send_delta;
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
     if (i < kWindowSize)
       EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
     else
-      EXPECT_NEAR(estimator.trendline_slope(), slope, tolerance);
+      EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.001);
   }
 }
 
-TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
-  TestEstimator(0.5, 0, 0.001);
-}
-
 TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
-  TestEstimator(-1, 0, 0.001);
+  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = 0.5 * send_delta;
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
+    if (i < kWindowSize)
+      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
+    else
+      EXPECT_NEAR(estimator.trendline_slope(), -1, 0.001);
+  }
 }
 
 TEST(TrendlineEstimator, PerfectLineSlopeZero) {
-  TestEstimator(0, 0, 0.001);
+  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = send_delta;
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
+    EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
+  }
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeOneHalf) {
-  TestEstimator(0.5, kAvgTimeBetweenPackets / 3.0, 0.01);
+  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = 2 * send_delta + rand.Gaussian(0, send_delta / 3);
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
+    if (i < kWindowSize)
+      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
+    else
+      EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.1);
+  }
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeMinusOne) {
-  TestEstimator(-1, kAvgTimeBetweenPackets / 3.0, 0.075);
+  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = 0.5 * send_delta + rand.Gaussian(0, send_delta / 25);
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
+    if (i < kWindowSize)
+      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
+    else
+      EXPECT_NEAR(estimator.trendline_slope(), -1, 0.1);
+  }
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeZero) {
-  TestEstimator(0, kAvgTimeBetweenPackets / 3.0, 0.02);
+  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
+  Random rand(0x1234567);
+  double now_ms = rand.Rand<double>() * 10000;
+  for (size_t i = 1; i < 2 * kWindowSize; i++) {
+    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
+    double recv_delta = send_delta + rand.Gaussian(0, send_delta / 8);
+    now_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, now_ms);
+    EXPECT_NEAR(estimator.trendline_slope(), 0, 0.1);
+  }
 }
 
 }  // namespace webrtc