jameslyons

#include <stdlib.h>
#include <stdio.h>

void railfence_encipher(int key, const char *plaintext, char *ciphertext);
void railfence_decipher(int key, const char *ciphertext, char *plaintext);

int main(int argc, char *argv[]){
    /* the string to encipher / decipher */
    char *plaintext = "defendtheeastwallofthecastle";
    /* allocate some space for results of enciphering/deciphering */

jameslyons

'''
implements porta cipher
Author: James Lyons 
Created: 2012-06-27
'''
from .base import Cipher

####################################################################################
class Porta(Cipher):
    def __init__(self,key='CODE'):

jameslyons

function arburg(x,p)
    N = length(x)

    P = zeros(1,p+1)
    f = zeros(p+1,N)
    b = zeros(p+1,N)
    a = zeros(p,p)

    # Initialisation
    f[1,:] = x

jameslyons

from itertools import product

key = "EPSDUCVWYM ZLKXNBTFGORIJHAQ"

IND2L = dict(zip(list(product((1,2,3),repeat=3)),key))
L2IND = dict(zip(key,list(product((1,2,3),repeat=3))))

ptext = 'DEFEND THE EAST WALL OF THE CASTLE'
ctext = ""
for c in ptext:

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SNR=40; % the SNR of white noise to apply to each file
path = '/home/james/databases/speech_db/timit_xxl_white/db/timit_full';
[status,result]=system(['find ',path,' -name "*.wav" | grep -v snr > ',path,'/timit_list.txt']);

listfilename = [path,'/timit_list.txt'];

filelist = fopen(listfilename);
wavname = fgetl(filelist);
count = 1;
while ischar(wavname),

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[sig fs] = read_NIST_file(filename); % read the input file, assume filename is supplied
NFFT = 1024;
WINLEN = 0.025; %frame length in ms
WINSTEP = 0.01;
frames = frame_sig(sig, WINLEN*fs, WINSTEP*fs, @hamming);
cspec = fft(frames,NFFT,2); % complex spectrum
pspec = abs(cspec).^2; % power spectrum of noisy signal
phase = angle(cspec);
 
% do spectral subtraction, produce modified_pspec

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import sys

if len(sys.argv) <= 1:
    print 'usage: python pdb2fasta.py file.pdb > file.fasta'
    exit()
    
input_file = open(sys.argv[1])

letters = {'ALA':'A','ARG':'R','ASN':'N','ASP':'D','CYS':'C','GLU':'E','GLN':'Q','GLY':'G','HIS':'H',
           'ILE':'I','LEU':'L','LYS':'K','MET':'M','PHE':'F','PRO':'P','SER':'S','THR':'T','TRP':'W',

jameslyons

XUKEXWSLZJUAXUNKIGWFSOZRAWURORKXAOSLHROBXBTKCMUWDVPTFBLMKEFVWMUXTVTWUIDDJVZKBRMCWOIWYDXMLUFPVSHAGSVWUFWORCWUIDUJCNVTTBERTUNOJUZHVTWKORSVRZSVVFSQXOCMUWPYTRLGBMCYPOJCLRIYTVFCCMUWUFPOXCNMCIWMSKPXEDLYIQKDJWIWCJUMVRCJUMVRKXWURKPSEEIWZVXULEIOETOOFWKBIUXPXUGOWLFPWUSCH
XRFMTOUWLUKVLBCWCEKXWUEMUJIEAMFRUFUOXOUPTWPMEWSXVUPOVTSLTSNYXROLHVHOTCCOZRTAJRNJFJOWGULMUWUBUSZGCMKAXIVHBIVBXBDWVMWRIUTDUTCMKUNKJFWYSXXKCVWKPKWPIMZOOOPUXGUKRRJXRUWWBCSCEKGFDRWVLDPOSVMURRLSWOPCIZIYELZTWDSYIEFRFOUVTQIPABIJVVKWWWLWCFFDZUUMYNSCJVSRKDVQCWXCOEXTXMIUH
HCSUWPFLWOGUXPXUIBKWFOOTEOIELUXVZWIEESPKRUWXKRVMUJCRVMUJCWIWJDKQIYLDEXPKSMWICMNCXOPFUWUMCCFVTYIRLCJOPYCMBGLRTYPWUMCOXQSFVVSZRVSROKWTVHZUJONUTREBTTVNCJUDIUWCROWFUWVSGAHSVPFULMXDYWIOWCMRBKZVJDDIUWTVTXUMWVFEKMLBFTPVDWUMCKTBXBORHLSOAXKRORUWARZOSFWGIKNUXAUJZLSWXEKUX
HUIMXTXEOCXWCQVDKRSVJCSNYMUUZDFFCWLWWWKVVJIBAPIQTVUOFRFEIYSDWTZLEYIZICPOWSLRRUMVSOPDLVWRDFGKECSCBWWURXJRRKUGXUPOOOZMIPWKPKWVCKXXSYWFJKNUKMCTUDTUIRWMVWDBXBVIBHVIXAKMCGZSUBUWUMLUGWOJFJNRJATRZOCCTOHVHLORXYNSTLSTVOPUVXSWEMPWTPUOXOUFURFMAEIJUM

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% load the dataset, this is built in to matlab
load fisheriris

% the data is ordered, we want to randomly select 100 points for train, 50
% for test. This part just generates a random list of array indices.
indices = randperm(150);
train_indices = indices(1:100);
test_indices = indices(101:end);

% meas contains the features, we use our random indices to select a subset

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name: "iris"
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include: {
    phase: TRAIN
  }
  hdf5_data_param {