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The discussed changes to Hyperplane, the ParametrizedLine class, and the

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API update in Regression...
This commit is contained in:
Benoit Jacob
2008-08-31 04:25:30 +00:00
parent 5c8c09e021
commit 5c34d8e20a
8 changed files with 255 additions and 119 deletions
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13
Eigen/src/Regression/Regression.h
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@@ -155,21 +155,20 @@ void linearRegression(int numPoints,
*
* \sa linearRegression()
*/
template<typename VectorType, typename BigVectorType>
template<typename VectorType, typename HyperplaneType>
void fitHyperplane(int numPoints,
VectorType **points,
BigVectorType *result,
HyperplaneType *result,
typename NumTraits<typename VectorType::Scalar>::Real* soundness = 0)
{
typedef typename VectorType::Scalar Scalar;
typedef Matrix<Scalar,VectorType::SizeAtCompileTime,VectorType::SizeAtCompileTime> CovMatrixType;
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(BigVectorType)
ei_assert(numPoints >= 1);
int size = points[0]->size();
ei_assert(size+1 == result->size());
ei_assert(size+1 == result->coeffs().size());
// compue the mean of the data
// compute the mean of the data
VectorType mean = VectorType::Zero(size);
for(int i = 0; i < numPoints; i++)
mean += *(points[i]);
@@ -186,13 +185,13 @@ void fitHyperplane(int numPoints,
// now we just have to pick the eigen vector with smallest eigen value
SelfAdjointEigenSolver<CovMatrixType> eig(covMat);
result->start(size) = eig.eigenvectors().col(0);
result->normal() = eig.eigenvectors().col(0);
if (soundness)
*soundness = eig.eigenvalues().coeff(0)/eig.eigenvalues().coeff(1);
// let's compute the constant coefficient such that the
// plane pass trough the mean point:
result->coeffRef(size) = - (result->start(size).cwise()* mean).sum();
result->offset() = - (result->normal().cwise()* mean).sum();
}