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// Copyright 2018 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "ui/base/prediction/least_squares_predictor.h"
#include "testing/gtest/include/gtest/gtest.h"
#include "ui/base/prediction/input_predictor_unittest_helpers.h"
namespace ui {
namespace test {
class LSQPredictorTest : public InputPredictorTest {
public:
explicit LSQPredictorTest() {}
LSQPredictorTest(const LSQPredictorTest&) = delete;
LSQPredictorTest& operator=(const LSQPredictorTest&) = delete;
void SetUp() override {
predictor_ = std::make_unique<LeastSquaresPredictor>();
}
};
TEST_F(LSQPredictorTest, ShouldHasPrediction) {
LeastSquaresPredictor predictor;
for (size_t i = 0; i < LeastSquaresPredictor::kSize; i++) {
// First |kSize| point do not have prediction available.
EXPECT_FALSE(predictor.HasPrediction());
InputPredictor::InputData data = {gfx::PointF(1, 1),
FromMilliseconds(8 * i)};
predictor.Update(data);
}
EXPECT_TRUE(predictor.HasPrediction());
}
// Tests the lest squares filter behavior.
// The data set is generated by a "known to work" quadratic fit.
TEST_F(LSQPredictorTest, PredictedValue) {
std::vector<double> x = {22, 58, 102, 108.094};
std::vector<double> y = {100, 100, 100, 100};
std::vector<double> t = {13, 21, 37, 42};
ValidatePredictor(x, y, t);
x = {100, 100, 101, 104.126};
y = {120, 280, 600, 1364.93};
t = {101, 126, 148, 180};
ValidatePredictor(x, y, t);
}
// Tests the LSQ predictor predict constant velocity.
TEST_F(LSQPredictorTest, PredictLinearValue) {
std::vector<double> x = {0, 4, 10, 15, 20, 28, 30, 38};
std::vector<double> y = {30, 34, 40, 45, 50, 58, 60, 68};
std::vector<double> t = {0, 4, 10, 15, 20, 28, 30, 38};
ValidatePredictor(x, y, t);
}
// Tests the LSQ predictor predict quadratic value correctly.
TEST_F(LSQPredictorTest, PredictQuadraticValue) {
std::vector<double> x = {2, 8, 18, 32, 50};
std::vector<double> y = {100, 400, 900, 1600, 2500};
std::vector<double> t = {8, 16, 24, 32, 40};
ValidatePredictor(x, y, t);
}
// Tests that lsq predictor will not crash when given constant time stamp.
TEST_F(LSQPredictorTest, ConstantTimeStampNotCrash) {
std::vector<double> x = {100, 101, 102};
std::vector<double> y = {101, 102, 103};
std::vector<double> t = {0, 0, 0};
for (size_t i = 0; i < t.size(); i++) {
InputPredictor::InputData data = {gfx::PointF(x[i], y[i]),
FromMilliseconds(t[i])};
predictor_->Update(data);
}
// Expect false because the matrix is singular
// and the predictor cannot compute a prediction
EXPECT_FALSE(predictor_->GeneratePrediction(FromMilliseconds(42)));
x = {100, 100, 100};
y = {100, 100, 100};
t = {100, 100, 100};
for (size_t i = 0; i < t.size(); i++) {
InputPredictor::InputData data = {gfx::PointF(x[i], y[i]),
FromMilliseconds(t[i])};
predictor_->Update(data);
}
EXPECT_TRUE(predictor_->GeneratePrediction(FromMilliseconds(142)));
}
// Tests the LSQ predictor produce the time interval correctly.
TEST_F(LSQPredictorTest, TimeInterval) {
EXPECT_EQ(predictor_->TimeInterval(), kExpectedDefaultTimeInterval);
std::vector<double> x = {0, 4, 10};
std::vector<double> y = {30, 34, 40};
std::vector<double> t = {0, 4, 10};
for (size_t i = 0; i < t.size(); i++) {
InputPredictor::InputData data = {gfx::PointF(x[i], y[i]),
FromMilliseconds(t[i])};
predictor_->Update(data);
}
EXPECT_EQ(predictor_->TimeInterval(), base::Milliseconds((t[2] - t[0]) / 2));
}
} // namespace test
} // namespace ui
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