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// Copyright 2022 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ink_stroke_modeler/internal/prediction/kalman_filter/axis_predictor.h"
#include <vector>
#include "gtest/gtest.h"
namespace ink {
namespace stroke_model {
namespace {
constexpr int kStableIterNum = 4;
constexpr double kProcessNoise = 0.01;
constexpr double kMeasurementNoise = 1.0;
} // namespace
struct DataSet {
double initial_observation;
std::vector<double> observation;
std::vector<double> position;
std::vector<double> velocity;
std::vector<double> acceleration;
std::vector<double> jerk;
};
void ValidateAxisPredictor(AxisPredictor* predictor, const DataSet& data) {
predictor->Reset();
predictor->Update(data.initial_observation);
for (int i = 0; i < data.observation.size(); i++) {
predictor->Update(data.observation[i]);
EXPECT_NEAR(data.position[i], predictor->GetPosition(), 0.0001);
EXPECT_NEAR(data.velocity[i], predictor->GetVelocity(), 0.0001);
EXPECT_NEAR(data.acceleration[i], predictor->GetAcceleration(), 0.0001);
EXPECT_NEAR(data.jerk[i], predictor->GetJerk(), 0.0001);
}
}
// Test that the predictor will stable.
TEST(AxisPredictorTest, ShouldStable) {
AxisPredictor predictor(kProcessNoise, kMeasurementNoise, kStableIterNum);
for (int i = 0; i < kStableIterNum; i++) {
EXPECT_FALSE(predictor.Stable());
predictor.Update(1);
}
EXPECT_TRUE(predictor.Stable());
}
// Test the kalman filter behavior. The data set is generated by a "known to
// work" kalman filter.
TEST(AxisPredictorTest, PredictedValue) {
AxisPredictor predictor(kProcessNoise, kMeasurementNoise, kStableIterNum);
DataSet data;
data.initial_observation = 0;
data.observation = {1, 2, 3, 4, 5, 6};
data.position = {0.6949411066858742, 1.8880162111305765, 3.0596776689233476,
4.080666568886563, 5.039574058758894, 5.990101744132957};
data.velocity = {0.48326413015846115, 1.349212968908908, 1.5150757723942188,
1.2449353797925855, 0.9823147273054352, 0.831418084705206};
data.acceleration = {0.20388102703160751, 0.6602537865634062,
0.46392675203046707, 0.0691864035645362,
-0.1571001901104591, -0.2303438651979314};
data.jerk = {0.051351580374544535, 0.17805019978769315,
0.06592110190532013, -0.06063794909774803,
-0.10198612906906362, -0.09541445938944032};
ValidateAxisPredictor(&predictor, data);
data.initial_observation = 0;
data.observation = {1, 2, 4, 8, 16, 32};
data.position = {0.6949411066858742, 1.8880162111305765, 3.9597202826804603,
7.9052737853848285, 15.720340533540115, 31.24662046486774};
data.velocity = {0.48326413015846115, 1.349212968908908, 2.492271225870179,
4.610844489557212, 8.828231877380588, 16.987494416071463};
data.acceleration = {0.20388102703160751, 0.6602537865634062,
1.090991623810185, 1.885675547541351,
3.4586206593783526, 6.34082285106952};
data.jerk = {0.051351580374544535, 0.17805019978769315, 0.25373225050247916,
0.4023497012294069, 0.6945464157568688, 1.1947316519015612};
ValidateAxisPredictor(&predictor, data);
}
} // namespace stroke_model
} // namespace ink
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