forked from Archive/PX4-Autopilot
261 lines
7.5 KiB
C++
261 lines
7.5 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2019 ECL Development Team. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name PX4 nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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****************************************************************************/
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/**
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* @file test_AlphaFilter.cpp
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*
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* @brief Unit tests for the alpha filter class
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*/
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#include <gtest/gtest.h>
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#include <cmath>
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#include <matrix/math.hpp>
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#include "AlphaFilter/AlphaFilter.hpp"
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using matrix::Vector3f;
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TEST(AlphaFilterTest, initializeToZero)
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{
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AlphaFilter<float> filter_float{};
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ASSERT_EQ(filter_float.getState(), 0.f);
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}
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TEST(AlphaFilterTest, resetToValue)
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{
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AlphaFilter<float> filter_float{};
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const float reset_value = 42.42f;
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filter_float.reset(reset_value);
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ASSERT_EQ(filter_float.getState(), reset_value);
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}
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TEST(AlphaFilterTest, runZero)
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{
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AlphaFilter<float> filter_float{};
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const float input = 0.f;
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for (int i = 0; i < 10; i++) {
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filter_float.update(input);
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}
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ASSERT_EQ(filter_float.getState(), input);
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}
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TEST(AlphaFilterTest, runPositive)
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{
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// GIVEN an input of 1 in a filter with a default time constant of 9 (alpha = 0.9)
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AlphaFilter<float> filter_float{};
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const float input = 1.f;
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filter_float.setAlpha(.1f);
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// WHEN we run the filter 9 times
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for (int i = 0; i < 9; i++) {
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filter_float.update(input);
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}
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// THEN the state of the filter should have reached 63%
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ASSERT_NEAR(filter_float.getState(), 0.63f, 0.02);
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}
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TEST(AlphaFilterTest, runNegative)
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{
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// GIVEN an input of 1 in a filter with a default time constant of 9 (alpha = 0.9)
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AlphaFilter<float> filter_float{};
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const float input = -1.f;
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filter_float.setAlpha(.1f);
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// WHEN we run the filter 9 times
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for (int i = 0; i < 9; i++) {
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filter_float.update(input);
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}
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// THEN the state of the filter should have reached 63%
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ASSERT_NEAR(filter_float.getState(), -0.63f, 0.02);
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}
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TEST(AlphaFilterTest, riseTime)
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{
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// GIVEN an input of 1 in a filter with a default time constant of 9 (alpha = 0.9)
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AlphaFilter<float> filter_float{};
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const float input = 1.f;
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filter_float.setAlpha(.1f);
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// WHEN we run the filter 27 times (3 * time constant)
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for (int i = 0; i < 3 * 9; i++) {
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filter_float.update(input);
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}
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// THEN the state of the filter should have reached 95%
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ASSERT_NEAR(filter_float.getState(), 0.95f, 0.02f);
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}
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TEST(AlphaFilterTest, convergence)
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{
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// GIVEN an input of 1 in a filter with a default time constant of 9 (alpha = 0.9)
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AlphaFilter<float> filter_float{};
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const float input = 1.f;
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filter_float.setAlpha(.1f);
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// WHEN we run the filter 45 times (5 * time constant)
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for (int i = 0; i < 5 * 9; i++) {
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filter_float.update(input);
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}
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// THEN the state of the filter should have converged to the input
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ASSERT_NEAR(filter_float.getState(), 1.f, 0.01f);
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}
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TEST(AlphaFilterTest, convergenceVector3f)
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{
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// GIVEN an Vector3f input in a filter with a default time constant of 9 (alpha = 0.9)
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AlphaFilter<Vector3f> filter_v3{};
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const Vector3f input = {3.f, 7.f, -11.f};
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filter_v3.setAlpha(.1f);
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// WHEN we run the filter 45 times (5 * time constant)
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for (int i = 0; i < 5 * 9; i++) {
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filter_v3.update(input);
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}
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// THEN the state of the filter should have converged to the input (1% error allowed)
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Vector3f output = filter_v3.getState();
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for (int i = 0; i < 3; i++) {
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ASSERT_NEAR(output(i), input(i), fabsf(0.01f * input(i)));
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}
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}
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TEST(AlphaFilterTest, convergenceVector3fAlpha)
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{
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// GIVEN a Vector3f input in a filter with a defined time constant and the default sampling time
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AlphaFilter<Vector3f> filter_v3{};
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const Vector3f input = {3.f, 7.f, -11.f};
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const float tau = 18.f;
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const float dt = 1.f;
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filter_v3.setParameters(dt, tau);
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// WHEN we run the filter 18 times (1 * time constant)
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for (int i = 0; i < 18; i++) {
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filter_v3.update(input); // dt is assumed equal to 1
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}
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// THEN the state of the filter should have reached 65% (2% error allowed)
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Vector3f output = filter_v3.getState();
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for (int i = 0; i < 3; i++) {
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ASSERT_NEAR(output(i), 0.63f * input(i), fabsf(0.02f * input(i)));
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}
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}
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TEST(AlphaFilterTest, convergenceVector3fTauDt)
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{
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// GIVEN a Vector3f input in a filter with a defined time constant and sampling time
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AlphaFilter<Vector3f> filter_v3{};
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const Vector3f input = {51.f, 7.f, -11.f};
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const float tau = 2.f;
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const float dt = 0.1f;
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filter_v3.setParameters(dt, tau);
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// WHEN we run the filter (1 * time constant)
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const float n = tau / dt;
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for (int i = 0; i < n; i++) {
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filter_v3.update(input);
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}
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// THEN the state of the filter should have reached 65% (2% error allowed)
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Vector3f output = filter_v3.getState();
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for (int i = 0; i < 3; i++) {
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ASSERT_NEAR(output(i), 0.63f * input(i), fabsf(0.02f * input(i)));
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}
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// ALSO when the filter is reset to a specified value
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const Vector3f reset_vector = {-1.f, 71.f, -42.f};
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filter_v3.reset(reset_vector);
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output = filter_v3.getState();
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// THEN the filter should exactly contain those values
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for (int i = 0; i < 3; i++) {
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ASSERT_EQ(output(i), reset_vector(i));
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}
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}
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TEST(AlphaFilterTest, AllZeroTest)
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{
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AlphaFilter<float> _alpha_filter;
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_alpha_filter.update(0.f);
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EXPECT_FLOAT_EQ(_alpha_filter.getState(), 0.f);
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}
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TEST(AlphaFilterTest, AlphaOneTest)
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{
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AlphaFilter<float> _alpha_filter;
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_alpha_filter.setParameters(1e-5f, 1e5f);
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for (int i = 0; i < 100; i++) {
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_alpha_filter.update(1.f);
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EXPECT_NEAR(_alpha_filter.getState(), 0.f, 1e-4f);
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}
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}
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TEST(AlphaFilterTest, AlphaZeroTest)
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{
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AlphaFilter<float> _alpha_filter;
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_alpha_filter.setParameters(.1f, 0.f);
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for (int i = 0; i < 100; i++) {
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const float new_smaple = static_cast<float>(i);
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_alpha_filter.update(new_smaple);
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EXPECT_FLOAT_EQ(_alpha_filter.getState(), new_smaple);
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}
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}
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TEST(AlphaFilterTest, ConvergenceTest)
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{
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AlphaFilter<float> _alpha_filter;
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_alpha_filter.setParameters(.1f, 1.f);
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float last_value{0.f};
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for (int i = 0; i < 100; i++) {
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_alpha_filter.update(1.f);
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EXPECT_GE(_alpha_filter.getState(), last_value);
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last_value = _alpha_filter.getState();
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}
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EXPECT_NEAR(last_value, 1.f, 1e-4f);
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for (int i = 0; i < 1000; i++) {
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_alpha_filter.update(-100.f);
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EXPECT_LE(_alpha_filter.getState(), last_value);
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last_value = _alpha_filter.getState();
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}
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EXPECT_NEAR(last_value, -100.f, 1e-4f);
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}
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