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 = 15;
 constexpr double kSmoothing = 0.0;
 constexpr double kGain = 1;
 constexpr int64_t kAvgTimeBetweenPackets = 10;
 }  // namespace
 
 TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1, 1000000);
   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);
+    int64_t send_delta = rand.Rand(2 * kAvgTimeBetweenPackets);
+    int64_t recv_delta = 2 * send_delta;
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_ms);
     if (i < kWindowSize)
       EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
     else
       EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.001);
   }
 }
 
 TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1, 1000000);
   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);
+    int64_t send_delta = 2 * rand.Rand(kAvgTimeBetweenPackets);
+    int64_t recv_delta = send_delta / 2;  // This division is always exact.
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_ms);
     if (i < kWindowSize)
       EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
     else
       EXPECT_NEAR(estimator.trendline_slope(), -1, 0.001);
   }
 }
 
 TEST(TrendlineEstimator, PerfectLineSlopeZero) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1, 1000000);
   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);
+    int64_t send_delta = rand.Rand(2 * kAvgTimeBetweenPackets);
+    int64_t recv_delta = send_delta;
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_ms);
     EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
   }
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeOneHalf) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1, 1000000);
   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);
+    int64_t send_delta = rand.Rand(2 * kAvgTimeBetweenPackets);
+    int64_t recv_delta = 2 * send_delta + rand.Gaussian(0, send_delta / 3);
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_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) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1000000);
   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);
+    int64_t send_delta = 2 * rand.Rand(kAvgTimeBetweenPackets);
+    int64_t recv_delta = send_delta / 2 + rand.Gaussian(0, send_delta / 25);
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_ms);
     if (i < kWindowSize)
       EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
     else
       EXPECT_NEAR(estimator.trendline_slope(), -1, 0.1);
   }
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeZero) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
   Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
+  int64_t arrival_time_ms = rand.Rand(1, 1000000);
   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);
+    int64_t send_delta = rand.Rand(2 * kAvgTimeBetweenPackets);
+    int64_t recv_delta = send_delta + rand.Gaussian(0, send_delta / 8);
+    arrival_time_ms += recv_delta;
+    estimator.Update(recv_delta, send_delta, arrival_time_ms);
     EXPECT_NEAR(estimator.trendline_slope(), 0, 0.1);
   }
 }
 
 }  // namespace webrtc