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@psc_readme_5721_3.txt
Title: bellman.cpp
Description: Find the single source shortest path using Bellman Ford algorithm.
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alg14.c
#include
#include
#include
template
void print_elements( Type elem ) { cout
ch12.4.02.c
#include
#include
#include
// #include
/**
**
stanl@john:d.12 338 : xsh32 ch12.4.02.c
stanl@john:d.12 339 : a.out
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0 1 1 2
vit_dec.m
function decod = vit_dec(u,constlen,codegen, puncpat) %
% hardvit.m -- Hard decision decoding Viterbi algorithm.
% Default Rate = k/n without function
[next_state,output] = trellisgen(constlen,cod
pidc.m
function [u,s,e0]=pid0(v,s,e0,u0)
kp1=3;
ti1=600;
td1=0.000;
t=1;
kp2=8;
ti2=1000;
td2=0.0;
% pid1 algorithm
E1=s(1)-v(3);
e0(3)=E1;
u(1)=u0(1)+kp1*[e0(3)-e0(2)]+kp1*t/ti1*e0(3)+kp1*td1/t
aes.cpp
/**\addtogroup AES Advanced Encryption Standard******************************
*
* Advanced Encryption Standard (Rijndael) algorithm implementation.
*
* @author Serge Trusov
*
* @ver
predict_performance.m
function a = predict_performance(algorithm, algorithm_params, patterns, targets)
% Predict the final performance of an algorithm from the learning curves
% Inputs:
% algorithm - The a
ica.m
function [patterns, targets, W] = ICA(patterns, targets, params)
%Reshape the data points using the independent component analysis algorithm
%Inputs:
% train_patterns - Input patterns
% train_ta
contents.m
% HMM's
% HMM_Backward - HMM backward algorithm
% HMM_Boltzmann - Find the transition matrices of an HMM using Boltzmann networks
% HMM_Decoding - Find probable states fro
rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns