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University of Murcia, Spain ![]() |
StatisticsStatistics, regression and model fitting related functionality.
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Classes | |
class | QVRANSAC< Element, Model > |
Implementation of RANSAC, a robust statistical model fitting algorithm. More... | |
class | QVPROSAC< Element, Model > |
Implementation of PROSAC, an extension to RANSAC (see QVRANSAC). More... | |
Functions | |
double | BhattacharyyaDistance (const QVVector &m1, const QVMatrix &S1, const QVVector &m2, const QVMatrix &S2) |
Obtains the Bhattacharyya distance of two gaussian distributions. | |
QVVector | qvLinearRegularizedRegression (const QVMatrix &A, const QVVector &b, const QVMatrix &Gamma=QVMatrix()) |
Estimates linear regression using Tikhonov regularization. |
double BhattacharyyaDistance | ( | const QVVector & | m1, | |
const QVMatrix & | S1, | |||
const QVVector & | m2, | |||
const QVMatrix & | S2 | |||
) |
Obtains the Bhattacharyya distance of two gaussian distributions.
Obtains the Bhattacharyya distance between two Gaussian distributions, given by their mean vectors and covariance matrices.
m1 | first mean. | |
S1 | first covariance matrix. | |
m2 | second mean. | |
S2 | second covariance matrix. |
Definition at line 27 of file qvstatistics.cpp.
QVVector qvLinearRegularizedRegression | ( | const QVMatrix & | A, | |
const QVVector & | b, | |||
const QVMatrix & | Gamma = QVMatrix() | |||
) |
Estimates linear regression using Tikhonov regularization.
This function solves an overdetermined system of linear equations, given as:
avoiding ill conditioned cases by minimizing the following regularized expression:
Where the matrix is called the Tikhonov matrix. In many cases, it is convenient to use the identity matrix as the
matrix.
A | Coefficients matrix. | |
b | Objective values vector. | |
Gamma | Tikhonov Matrix. If no value is provided, an identity matrix with adequate dimentions will be used in the regularized expression. |
Definition at line 36 of file qvstatistics.cpp.