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/111603/15509370
m evaluate.m
function K = evaluate(ker, x1, x2)
% EVALUATE
%
% Evaluate a polynomial kernel, for example
%
% K = evaluate(kernel, x1, x2);
%
% where x1 and x2 are matrices containing input patterns, wh
www.eeworm.com/read/111603/15509373
m evaluate.m
function K = evaluate(ker, x1, x2)
% EVALUATE
%
% Evaluate a linear kernel, for example
%
% K = evaluate(ker, x1, x2);
%
% where x1 and x2 are matrices containing input patterns, where ea
www.eeworm.com/read/383097/8973731
m evaluate_objective.m
function f = evaluate_objective(x,problem)
% Function to evaluate the objective functions for the given input vector
% x. x has the decision variables
switch problem
case 1
f = []
www.eeworm.com/read/185363/9041917
m evaluate_objective.m
function f = evaluate_objective(x, M, V)
%% function f = evaluate_objective(x, M, V)
% Function to evaluate the objective functions for the given input vector
% x. x is an array of decision varia
www.eeworm.com/read/381172/9106529
m evaluate_objective.m
function f = evaluate_objective(x,problem)
% Function to evaluate the objective functions for the given input vector
% x. x has the decision variables
switch problem
case 1
f = []
www.eeworm.com/read/184270/9113719
m nco_evaluate.m
%================================================================
% nco_evaluate.m is a program that evaluate rom sin output using FFT
% Copyright :blutea 2004_10_20
% vionsion: 1.0
%==========
www.eeworm.com/read/170114/9818737
sh grade_evaluate.sh
#!/bin/sh
# Usage: ./grade_evaluate grade
# grade should be between 0 and 100, including 0 and 100
if [ '$1' –lt '0' ] ; then
echo "Error: invalid grade"
elif [ '$1' –lt '60' ] ; then
echo 'no
www.eeworm.com/read/365862/9842981
m gmm_evaluate.m
function w = gmm_evaluate(g,x)
%
% assumes weights are not log-likelihood
% TODO: implement the fast gauss transform (FGT)
N = size(x, 2);
w = zeros(1, N);
for i=1:size(g.x, 2)
dx = x -
www.eeworm.com/read/365862/9843044
m kernel_evaluate.m
function w = kernel_evaluate(g,x)
%
% assumes weights are not log-likelihood
N = size(x, 2);
w = zeros(1, N);
for i=1:size(g.x, 2)
dx = x - repcol(g.x(:,i), N);
w = w + g.w(i)*gaus