代码搜索：evaluate
找到约 3,619 项符合「evaluate」的源代码
代码结果 3,619
www.eeworm.com/read/170249/9813359
m ioleval.m
function [Yhat,PI]=ioleval(NetDeff,NetDefg,NN,W1f,W2f,W1g,W2g,Y,U)
% NNIOL
% -----
% Evaluate a neural network trained by 'nniol'.
%
% The function produces the following plots:
%
www.eeworm.com/read/161485/10404567
extra entries.extra
D/external///
/compute_quadrant_selection.m///
/compute_spline_filter.m///
/mirror_filter.m///
/test_pyramidal.m///
/test_laplacian_do.m///
/evaluate_nbr_bits_wavelets.m///
/mirdwt.dll///
/mrd
www.eeworm.com/read/443410/7633419
m petest_main_dist.m
%Invoke petest to compute theoretical BER vs SNR.
% set K and l values below to evaluate BER
%note that L must be smaller than K
clear all; close all;
begtime=datestr(now)
K=4;
L=3;
SNR = [0 1
www.eeworm.com/read/437034/7756459
deck polarity.deck
Polarity of voltages and currents
*
* This circuit contains a set of gain blocks to evaluate
* the polarity of voltages and currents on code models
*
.tran 1e-5 1e-3
*
v1 1 0 0.0 sin(0 1 1k)
www.eeworm.com/read/141300/5772630
deck polarity.deck
Polarity of voltages and currents
*
* This circuit contains a set of gain blocks to evaluate
* the polarity of voltages and currents on code models
*
.tran 1e-5 1e-3
*
v1 1 0 0.0 sin(0 1 1k)
www.eeworm.com/read/111672/6154027
m ioleval.m
function [Yhat,PI]=ioleval(NetDeff,NetDefg,NN,W1f,W2f,W1g,W2g,Y,U)
% NNIOL
% -----
% Evaluate a neural network trained by 'nniol'.
%
% The function produces the following plots:
%
www.eeworm.com/read/170690/6326707
m gaussian_prob.m
function p=gaussian_prob(x, m, C, use_log)
% p=gaussian(x,m,C);
%
% Evaluate the multi-variate density with mean vector m and covariance
% matrix C for the input vector x.
% Vectorized version: Here X
www.eeworm.com/read/257015/4366728
m ioleval.m
function [Yhat,PI]=ioleval(NetDeff,NetDefg,NN,W1f,W2f,W1g,W2g,Y,U)
% NNIOL
% -----
% Evaluate a neural network trained by 'nniol'.
%
% The function produces the following plots:
%
www.eeworm.com/read/474492/6810869
c interpolating polynomial.c
// Write a computer program using a Lagrange interpolating polynomial of the 10th order to evaluate f(x)=lnx where [a, b]=[1,2], h=0.1, xi = 1+ih, i=0, 1, …, 10, and find the approximations of ln1.54
www.eeworm.com/read/248729/12544074
m gaussian_prob.m
function p=gaussian_prob(x, m, C, use_log)
% p=gaussian(x,m,C);
%
% Evaluate the multi-variate density with mean vector m and covariance
% matrix C for the input vector x.
% Vectorized version: Here X