代码搜索：multivariate
找到约 564 项符合「multivariate」的源代码
代码结果 564
www.eeworm.com/read/103816/15719772
imp gpoly.imp
//
// $Source: /home/gambit/CVS/gambit/sources/poly/gpoly.imp,v $
// $Date: 2002/08/27 17:29:46 $
// $Revision: 1.3 $
//
// DESCRIPTION:
// Implementation of multivariate polynomial type
//
// This fi
www.eeworm.com/read/103816/15719778
h gpoly.h
//
// $Source: /home/gambit/CVS/gambit/sources/poly/gpoly.h,v $
// $Date: 2002/08/27 17:29:46 $
// $Revision: 1.2 $
//
// DESCRIPTION:
// Declaration of multivariate polynomial type
//
// This file is
www.eeworm.com/read/431675/8661721
m gauss.m
%GAUSS Generation of multivariate Gaussian dataset.
%
% A = gauss(n,U,G)
%
% Generation of n k-dimensional Gaussian distributed vectors with
% covariance matrices G (size k*k*c) and with means, la
www.eeworm.com/read/373627/9446082
r ch11.r
#-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 11 Exploratory Multivariate Analysis
library(MASS)
postscript(file="ch11.ps", width=8, height=6, pointsize
www.eeworm.com/read/362500/9995978
m pcr.m
function [b,ssq,t,p,eigs] = pcr(x,y,pc,out)
%PCR Principal components regression for multivariate y.
% Inputs are the matrix of predictor variables (x), vector
% or matrix of predicted variable (
www.eeworm.com/read/422591/10626979
rd kda.kde.rd
\name{kda.kde}
\alias{kda.kde}
\title{Kernel density estimate for kernel discriminant analysis for multivariate data}
\description{
Kernel density estimate for kernel discriminant analysis for 1- t
www.eeworm.com/read/418695/10935186
m gauss.m
%GAUSS Generation of multivariate Gaussian dataset.
%
% A = gauss(n,U,G)
%
% Generation of n k-dimensional Gaussian distributed vectors with
% covariance matrices G (size k*k*c) and with means, la
www.eeworm.com/read/236873/7119029
m gausprod.m
function [g,u,k]=gausprod(m,c)
%GAUSPROD calculates a product of gaussians [G,U,K]=(M,C)
% calculates the product of n d-dimensional multivariate gaussians
% this product is itself a gaussian
% In
www.eeworm.com/read/446117/7585299
c sample_ghmm.c
/* sample_ghmm.c
Returns samples of a Multivariate Gaussian process driven by HMM.
Usage
-------
[Z , X] = sample_ghmm(K , PI , A , M , S , [v1] , ... , [vp] );
Inputs
www.eeworm.com/read/397102/8067992
m gauss.m
%GAUSS Generation of multivariate Gaussian dataset.
%
% A = gauss(n,U,G)
%
% Generation of n k-dimensional Gaussian distributed vectors with
% covariance matrices G (size k*k*c) and with means, la