代码搜索：Generalized
找到约 2,645 项符合「Generalized」的源代码
代码结果 2,645
www.eeworm.com/read/299459/7850269
m pandr.m
function varargout = pandr(model,distrib)
% PANDR Visualizes solution of the Generalized Anderson's task.
%
% Synopsis:
% h = pandr(model)
%
% Description:
% It vizualizes solution of the Gen
www.eeworm.com/read/198546/7928886
m gedrnd.m
function random = gedrnd(n,nu)
% PURPOSE:
% Generates Deviates from the Generalized Error Distribution
% This is the same as the Exponential Power Distn with the single exception
%
%
www.eeworm.com/read/296909/8073033
m inv.m
function r = inv(x)
% GPOSYNOMIAL/INV Inverse function is only allowed for generalized posynomials
% that are actually monomials.
%
if ismonomial(x)
x = eval( x, {'' []} );
r = in
www.eeworm.com/read/143706/12849811
m glminit.m
function net = glminit(net, prior)
%GLMINIT Initialise the weights in a generalized linear model.
%
% Description
%
% NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and
% sets the weig
www.eeworm.com/read/143706/12850013
m demglm2.m
%DEMGLM2 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. The da
www.eeworm.com/read/140851/13059161
m glminit.m
function net = glminit(net, prior)
%GLMINIT Initialise the weights in a generalized linear model.
%
% Description
%
% NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and
% sets th
www.eeworm.com/read/140851/13059398
m demglm2.m
%DEMGLM2 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. Th
www.eeworm.com/read/138798/13212245
m glminit.m
function net = glminit(net, prior)
%GLMINIT Initialise the weights in a generalized linear model.
%
% Description
%
% NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and
% sets th
www.eeworm.com/read/138798/13212445
m demglm2.m
%DEMGLM2 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. Th
www.eeworm.com/read/319478/13450868
res rgrun.res
Sample data set for generalized runs test with N = 9, CFORM = 4 and G = 2.
GENERALIZED RUNS PERMUTATION PROCEDURES (RGRUN)
I