代码搜索：Fitting
找到约 695 项符合「Fitting」的源代码
代码结果 695
www.eeworm.com/read/257015/4366723
m test1.m
% Demonstration of different neural network training algorithms used
% for curve fitting
close all
%---------- Generate training and test set ----------
clc
PHI=0:0.25:6;
Y=sin(PHI);
PHI1 = P
www.eeworm.com/read/170936/9779320
m demmdn1.m
%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by
www.eeworm.com/read/415313/11076590
m demmdn1.m
%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by
www.eeworm.com/read/413912/11137292
m demmdn1.m
%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by
www.eeworm.com/read/200130/15440756
m curvefit.m
Function CurveFit(aData, sTarget1, sTarget2, sTarget3)
'MATLAB regression and curve fitting macro
MLPutMatrix "data", aData
MLEvalString "y = data(:,3)"
MLEvalString "n = len
www.eeworm.com/read/191902/8417310
m loglikelihood.m
function ll = loglikelihood(theta, features, h, center_point, cp_target)
% Used by the polynomial fitting algorithm
[c,r] = size(features);
features = center_point * ones(1,r) - features;
www.eeworm.com/read/191475/8429213
readme
There are basic fitting routines in this package:
* Turbo Pascal source codes:
MARQFITP.PAS (includes also demo)
DEFFLOAT.PAS (some definitions of float)
* C++ source codes:
MARQFITP.H (hea
www.eeworm.com/read/286662/8751902
m loglikelihood.m
function ll = loglikelihood(theta, patterns, h, center_point, cp_target)
% Used by the polynomial fitting algorithm
[c,r] = size(patterns);
patterns = center_point * ones(1,r) - patterns;
www.eeworm.com/read/177129/9468934
m loglikelihood.m
function ll = loglikelihood(theta, features, h, center_point, cp_target)
% Used by the polynomial fitting algorithm
[c,r] = size(features);
features = center_point * ones(1,r) - features;
www.eeworm.com/read/372113/9521280
m loglikelihood.m
function ll = loglikelihood(theta, patterns, h, center_point, cp_target)
% Used by the polynomial fitting algorithm
[c,r] = size(patterns);
patterns = center_point * ones(1,r) - patterns;