PLearn 0.1
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#include "TMat.h"
Go to the source code of this file.
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. |
Definition in file distr_maths.h.