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/369845/9631893
js dw.js
var DW={doXpath:!!document.evaluate,internalSiteRe:/(^|\.)(3qit\.com|atlarge\.com|bnet\.com|builderau\.com\.au|businessmobile\.fr|buying\.com|chow\.com|chowhound\.com|cnetasia\.com|cnet\.com|cnet\.co
www.eeworm.com/read/432455/8604574
m eval_ar_perf.m
function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model)
% Evaluate the performance of an AR model.
%
% Inputs
% coef(:,:,k,m) - coef. matrix to use for k steps back, model m
% C(:,:,m) - cov
www.eeworm.com/read/432455/8604595
m eval_ar_perf.m
function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model)
% Evaluate the performance of an AR model.
%
% Inputs
% coef(:,:,k,m) - coef. matrix to use for k steps back, model m
% C(:,:,m) - cov
www.eeworm.com/read/386253/8759892
m alg027.m
% HORNER'S ALGORITHM 2.7
%
% To evaluate the polynomial
% p(x) = a(n) * x^n + a(n-1) * x^(n-1) + ... + a(1) * x + a(0)
% and its derivative p'(x) at x = x0;
%
% INPUT: degree n; co
www.eeworm.com/read/386253/8760079
m alg027.m
% HORNER'S ALGORITHM 2.7
%
% To evaluate the polynomial
% p(x) = a(n) * x^n + a(n-1) * x^(n-1) + ... + a(1) * x + a(0)
% and its derivative p'(x) at x = x0;
%
% INPUT: degree n; co
www.eeworm.com/read/385990/8772784
s sumsq_sse2_assist.s
# SSE2 assist routines for sumsq
# Copyright 2001 Phil Karn, KA9Q
# May be used under the terms of the GNU Public License (GPL)
.text
# Evaluate sum of squares of signed 16-bit input samples
# long
www.eeworm.com/read/429558/8803012
m gaussian.m
function p=gaussian(x,m,C);
% p=gaussian(x,m,C);
%
% Evaluate the multi-variate density with mean vector m and covariance
% matrix C for the input vector x.
%
% p=gaussian(X,m,C);
%
% Vectorized ver
www.eeworm.com/read/285038/8874632
m eval_ar_perf.m
function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model)
% Evaluate the performance of an AR model.
%
% Inputs
% coef(:,:,k,m) - coef. matrix to use for k steps back, model m
% C(:,:,m) - cov
www.eeworm.com/read/427909/8913063
m student_t_logprob.m
function L = log_student_pdf(X, mu, lambda, alpha)
% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point
% L = log_student_pdf(X, mu, lambda, alpha)
%
% Each col
www.eeworm.com/read/427909/8913764
m eval_ar_perf.m
function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model)
% Evaluate the performance of an AR model.
%
% Inputs
% coef(:,:,k,m) - coef. matrix to use for k steps back, model m
% C(:,:,m) - cov