PARP Research Group  Universidad de Murcia 
Statistics

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  randomNormalValue (const double mean, const double variance) 
Generate a normally distributed random number. 
Statistics, regression and model fitting related functionality.
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 28 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 dimensions will be used in the regularized expression. 
Definition at line 37 of file qvstatistics.cpp.
double randomNormalValue  (  const double  mean,  
const double  variance  
) 
Generate a normally distributed random number.
This function uses the BoxMuller transform to generate independent samples of a normal distribution, provided its mean and variance parameters.
mean  Mean of the normal distribution.  
variance  Variance of the normal distribution. 
Definition at line 44 of file qvstatistics.cpp.