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/*
* libjingle
* Copyright 2011, Google Inc.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
* 3. The name of the author may not be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
* EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
* OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "talk/base/gunit.h"
#include "talk/base/rollingaccumulator.h"
namespace talk_base {
namespace {
const double kLearningRate = 0.5;
} // namespace
TEST(RollingAccumulatorTest, ZeroSamples) {
RollingAccumulator<int> accum(10);
EXPECT_EQ(0U, accum.count());
EXPECT_EQ(0, accum.ComputeMean());
EXPECT_EQ(0, accum.ComputeVariance());
}
TEST(RollingAccumulatorTest, SomeSamples) {
RollingAccumulator<int> accum(10);
for (int i = 0; i < 4; ++i) {
accum.AddSample(i);
}
EXPECT_EQ(4U, accum.count());
EXPECT_EQ(6, accum.ComputeSum());
EXPECT_EQ(1, accum.ComputeMean());
EXPECT_EQ(2, accum.ComputeWeightedMean(kLearningRate));
EXPECT_EQ(1, accum.ComputeVariance());
}
TEST(RollingAccumulatorTest, RollingSamples) {
RollingAccumulator<int> accum(10);
for (int i = 0; i < 12; ++i) {
accum.AddSample(i);
}
EXPECT_EQ(10U, accum.count());
EXPECT_EQ(65, accum.ComputeSum());
EXPECT_EQ(6, accum.ComputeMean());
EXPECT_EQ(10, accum.ComputeWeightedMean(kLearningRate));
EXPECT_NEAR(9, accum.ComputeVariance(), 1);
}
TEST(RollingAccumulatorTest, RollingSamplesDouble) {
RollingAccumulator<double> accum(10);
for (int i = 0; i < 23; ++i) {
accum.AddSample(5 * i);
}
EXPECT_EQ(10u, accum.count());
EXPECT_DOUBLE_EQ(875.0, accum.ComputeSum());
EXPECT_DOUBLE_EQ(87.5, accum.ComputeMean());
EXPECT_NEAR(105.049, accum.ComputeWeightedMean(kLearningRate), 0.1);
EXPECT_NEAR(229.166667, accum.ComputeVariance(), 25);
}
TEST(RollingAccumulatorTest, ComputeWeightedMeanCornerCases) {
RollingAccumulator<int> accum(10);
EXPECT_EQ(0, accum.ComputeWeightedMean(kLearningRate));
EXPECT_EQ(0, accum.ComputeWeightedMean(0.0));
EXPECT_EQ(0, accum.ComputeWeightedMean(1.1));
for (int i = 0; i < 8; ++i) {
accum.AddSample(i);
}
EXPECT_EQ(3, accum.ComputeMean());
EXPECT_EQ(3, accum.ComputeWeightedMean(0));
EXPECT_EQ(3, accum.ComputeWeightedMean(1.1));
EXPECT_EQ(6, accum.ComputeWeightedMean(kLearningRate));
}
} // namespace talk_base