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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* 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 "itkKalmanLinearEstimator.h"
#include <iostream>
/**
* This program test one instantiation of the itk::KalmanLinearEstimator class
*
* The test is done by providing a Linear Equation in 6D for which the
* coefficients are known. A population of samples is generated and
* passed to the KalmanLinearEstimator.
*
*/
int
itkKalmanLinearEstimatorTest(int, char *[])
{
using KalmanFilterType = itk::KalmanLinearEstimator<double, 6>;
using VectorType = KalmanFilterType::VectorType;
using ValueType = KalmanFilterType::ValueType;
KalmanFilterType filter;
filter.ClearEstimation();
filter.SetVariance(1.0);
ValueType measure;
VectorType predictor;
VectorType planeEquation;
planeEquation(0) = 9.0;
planeEquation(1) = 6.0;
planeEquation(2) = 7.0;
planeEquation(3) = 9.0;
planeEquation(4) = 4.0;
planeEquation(5) = 6.0;
constexpr unsigned int N = 10;
predictor(5) = 1.0;
for (unsigned int ax = 0; ax < N; ++ax)
{
predictor(0) = ax;
for (unsigned int bx = 0; bx < N; ++bx)
{
predictor(1) = bx;
for (unsigned int cx = 0; cx < N; ++cx)
{
predictor(2) = cx;
for (unsigned int dx = 0; dx < N; ++dx)
{
predictor(3) = dx;
for (unsigned int ex = 0; ex < N; ++ex)
{
predictor(4) = ex;
measure = dot_product(predictor, planeEquation);
filter.UpdateWithNewMeasure(measure, predictor);
}
}
}
}
}
VectorType estimation = filter.GetEstimator();
std::cout << std::endl << "The Right answer should be : " << std::endl;
std::cout << planeEquation;
std::cout << std::endl << "The Estimation is : " << std::endl;
std::cout << estimation;
VectorType error = estimation - planeEquation;
ValueType errorMagnitude = dot_product(error, error);
std::cout << std::endl << "Errors : " << std::endl;
std::cout << error;
std::cout << std::endl << "Error Magnitude : " << std::endl;
std::cout << errorMagnitude;
std::cout << std::endl << "Variance : " << std::endl;
std::cout << filter.GetVariance();
std::cout << std::endl << std::endl;
bool pass = true;
const float tolerance = 1e-4;
if (errorMagnitude > tolerance)
{
pass = false;
}
if (!pass)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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