代码搜索：Simulated
找到约 1,823 项符合「Simulated」的源代码
代码结果 1,823
www.eeworm.com/read/454376/7392845
m tsp.m
function out = tsp(loc)
% TSP Traveling salesman problem (TSP) using SA (simulated annealing).
% TSP by itself will generate 20 cities within a unit cube and
% then use SA to slove this problem.
www.eeworm.com/read/453187/7424923
cpp newencryptionanddecryption.cpp
//Description : The following C code can encrypt any sort of file be it be a sound,image,video or etc
/* A simulated C program to encrypt or decrypt a file with L=26, K=3 and
Buffer Stream Divisi
www.eeworm.com/read/438025/7737551
txt readme.txt
simcdma.m allows a set of CDMA parameters to be simulated, such
as BER verse number of users, etc. All the graphs used in the
thesis were generated using this script. The output from this script
is
www.eeworm.com/read/297233/8039183
h eabilesim.h
/* Support for GCC on simulated PowerPC systems targeted to embedded ELF
systems.
Copyright (C) 1995 Free Software Foundation, Inc.
Contributed by Cygnus Support.
This file is part of GNU CC
www.eeworm.com/read/143498/12870364
m tsp.m
function out = tsp(loc)
% TSP Traveling salesman problem (TSP) using SA (simulated annealing).
% TSP by itself will generate 20 cities within a unit cube and
% then use SA to slove this problem.
www.eeworm.com/read/317470/13504368
txt readme.txt
simcdma.m allows a set of CDMA parameters to be simulated, such
as BER verse number of users, etc. All the graphs used in the
thesis were generated using this script. The output from this script
is
www.eeworm.com/read/316604/13520427
m stochastic_sa.m
function [features, targets] = Stochastic_SA(train_features, train_targets, params, region, plot_on)
%Reduce the number of data points using the stochastic simulated annealing algorithm
%Inputs:
www.eeworm.com/read/316604/13520508
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
www.eeworm.com/read/359185/6352520
m stochastic_sa.m
function [features, targets] = Stochastic_SA(train_features, train_targets, params, region, plot_on)
%Reduce the number of data points using the stochastic simulated annealing algorithm
%Inputs:
www.eeworm.com/read/359185/6352575
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