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/465793/7046123
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/449132/7517914
m predprey2.m
function [out] = PredPrey2(x, y,flag)
%
% Evaluate the right hand side function for the
% Predator Prey model
%
% y_1(x) * y_2(x)
% y_1'(x) = 1.2 * y_1(x)
www.eeworm.com/read/434746/7802310
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/299736/7836238
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/296717/8080516
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/140851/13058419
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/140697/13066787
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/140697/13066965
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/140162/13101617
c cceval.c
/* CCEVAL.C - Evaluate and optimize expression parse trees
**
** (c) Copyright Ken Harrenstien 1989
** All changes after v.24, 11-Jan-1988 including CCFOLD merge
** All CCFOLD changes after v.11
www.eeworm.com/read/138798/13211443
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