代码搜索：deterministic
找到约 440 项符合「deterministic」的源代码
代码结果 440
www.eeworm.com/read/393163/2488021
m deterministic_cpd.m
function CPD = deterministic_CPD(bnet, self, fname, pfail)
% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function
%
% CPD = deterministic_CPD(bnet, self, fname)
% This ca
www.eeworm.com/read/386597/2570104
m deterministic_boltzmann.m
function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% train_pattern
www.eeworm.com/read/386597/2570204
m deterministic_sa.m
function [patterns, targets] = Deterministic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic simulated annealing algorithm
%Inputs:
%
www.eeworm.com/read/386597/2570207
m deterministic_annealing.m
function [patterns, targets] = deterministic_annealing(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic annealing algorithm
%Inputs:
% tra
www.eeworm.com/read/160391/5571300
m deterministic_cpd.m
function CPD = deterministic_CPD(bnet, self, fname, pfail)
% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function
%
% CPD = deterministic_CPD(bnet, self, fname)
% Thi
www.eeworm.com/read/474600/6813419
m deterministic_boltzmann.m
function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% train_pattern
www.eeworm.com/read/474600/6813550
m deterministic_sa.m
function [patterns, targets] = Deterministic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic simulated annealing algorithm
%Inputs:
%
www.eeworm.com/read/474600/6813553
m deterministic_annealing.m
function [patterns, targets] = deterministic_annealing(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic annealing algorithm
%Inputs:
% tra
www.eeworm.com/read/415311/11077034
m deterministic_boltzmann.m
function D = Deterministic_Boltzmann(train_features, train_targets, params, region);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% features - Train features
% targets - Tra
www.eeworm.com/read/415311/11077248
m deterministic_sa.m
function [features, targets] = Deterministic_SA(train_features, train_targets, params, region, plot_on)
%Reduce the number of data points using the deterministic simulated annealing algorithm
%Inp