Avoid precision loss in TrendlineEstimator from int64_t -> double conversion

webrtc/modules/congestion_controller/trendline_estimator_unittest.cc

Index: webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
diff --git a/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc b/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
index 51778e6cf37f2b099b0c518b070b363ba4473027..04f30f0c71a02807850487548af1a1d5815b46b6 100644
--- a/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
+++ b/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
@@ -24,12 +24,12 @@ constexpr int64_t kAvgTimeBetweenPackets = 10;
 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
@@ -40,12 +40,12 @@ TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
 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
@@ -56,12 +56,12 @@ TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
 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);
   }
 }
@@ -69,12 +69,12 @@ TEST(TrendlineEstimator, PerfectLineSlopeZero) {
 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
@@ -85,12 +85,12 @@ TEST(TrendlineEstimator, JitteryLineSlopeOneHalf) {
 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
@@ -101,12 +101,12 @@ TEST(TrendlineEstimator, JitteryLineSlopeMinusOne) {
 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);
   }
 }