PLearn 0.1
Namespaces | Functions
distr_maths.h File Reference
#include "TMat.h"
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Namespaces

namespace  PLearn
 

< for swap


Functions

real PLearn::log_of_normal_density (Vec x, Vec mu, real sigma2)
real PLearn::normal_density (Vec x, Vec mu, real sigma2)
real PLearn::log_rbf (Vec x, Vec mu, real sigma2)
real PLearn::rbf (Vec x, Vec mu, real sigma2)
real PLearn::log_of_normal_density (Vec x, Vec mu, Vec sigma2)
real PLearn::normal_density (Vec x, Vec mu, Vec sigma2)
real PLearn::log_of_normal_density (Vec x, Vec mu, Mat evectors, Vec evalues, real remainder_evalue)
real PLearn::normal_density (Vec x, Vec mu, Mat evectors, Vec evalues, real remainder_evalue=0)
real PLearn::log_fullGaussianRBF (Vec x, Vec mu, Mat evectors, Vec evalues, real remainder_evalue)
real PLearn::fullGaussianRBF (Vec x, Vec mu, Mat evectors, Vec evalues, real remainder_evalue=0)
real PLearn::logOfNormal (const Vec &x, const Vec &mu, const Mat &C)
real PLearn::logPFittedGaussian (const Vec &x, const Mat &X, real lambda)
 Fits a gaussian to the points in X (computing its mean and covariance matrix, and adding lambda to the diagonal of that covariance matrix) Then calls logOfNormal to return log(p(x | the_gaussian))
real PLearn::logOfCompactGaussian (const Vec &x, const Vec &mu, const Vec &eigenvalues, const Mat &eigenvectors, real gamma, bool add_gamma_to_eigenval)
 Computes and returns log( Normal(x; mu,C) ) where mu is the normal's mean and C its covariance matrix.
real PLearn::beta_density (real x, real alpha, real beta)
 Returns the density of a proportion x under a Beta(alpha,beta) distribution, equal to x^{alpha-1} (1-x}^{beta-1} / Beta(a,b) where Beta(a,b) = Gamma(a)Gamma(b)/Gamma(a+b)
real PLearn::log_beta_density (real x, real alpha, real beta)
 Log of the beta_density.
void PLearn::addEigenMatrices (Mat A_evec, Vec A_eval, Mat B_evec, Vec B_eval, Mat C_evec, Vec C_eval, bool inverses)
void PLearn::sums2Gaussian (real sum_w, Vec sum_wx, Mat sum_wx2, Vec mu, Mat cov_evectors, Vec cov_evalues, real min_variance)
 Given weighted statistics of order 0, 1 and 2, compute first and second moments of a Gaussian.

Detailed Description

Definition in file distr_maths.h.

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