代码搜索：Hyper
找到约 912 项符合「Hyper」的源代码
代码结果 912
www.eeworm.com/read/239553/7071647
txt hyper_vol_1_300.txt
static double hyper_vol[300]=
{
2 ,
3.141592654 ,
4.188790204 ,
4.934802202 ,
5.263789015 ,
5.167712783 ,
4.724765972 ,
4.058712129 ,
3.298508904 ,
2.550164042 ,
1.884103881 ,
1.33526277 ,
0.91062
www.eeworm.com/read/478033/6718314
pas hyper1f1.pas
www.eeworm.com/read/478033/6718394
pas hyper2f1.pas
www.eeworm.com/read/392345/8349387
cpp hyper1f1.cpp
/*************************************************************************
Cephes Math Library Release 2.8: June, 2000
Copyright by Stephen L. Moshier
Contributors:
* Sergey Bochkanov (ALGL
www.eeworm.com/read/392345/8349389
h hyper1f1.h
/*************************************************************************
Cephes Math Library Release 2.8: June, 2000
Copyright by Stephen L. Moshier
Contributors:
* Sergey Bochkanov (ALGL
www.eeworm.com/read/392330/8349557
cpp hyper1f1.cpp
/*************************************************************************
Cephes Math Library Release 2.8: June, 2000
Copyright by Stephen L. Moshier
Contributors:
* Sergey Bochkanov (ALGL
www.eeworm.com/read/392330/8349559
h hyper1f1.h
/*************************************************************************
Cephes Math Library Release 2.8: June, 2000
Copyright by Stephen L. Moshier
Contributors:
* Sergey Bochkanov (ALGL
www.eeworm.com/read/469123/6977806
m binaryepgp.m
function varargout = binaryEPGP(hyper, covfunc, varargin)
% binaryEPGP - The Expectation Propagation approximation for binary Gaussian
% process classification. Two modes are possible: training or te
www.eeworm.com/read/469123/6977827
m binarygp.m
function [out1, out2, out3, out4, alpha, sW, L] = binaryGP(hyper, approx, covfunc, lik, x, y, xstar)
% Approximate binary Gaussian Process classification. Two modes are possible:
% training or testin
www.eeworm.com/read/469123/6977828
m binarylaplacegp.m
function varargout = binaryLaplaceGP(hyper, covfunc, lik, varargin)
% binaryLaplaceGP - Laplace's approximation for binary Gaussian process
% classification. Two modes are possible: training or testi