KarlHerler

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KarlHerler

##
# STATUS BAR CUSTOMIZATION
##
set -g status-utf8 on
set -g status-keys vi
set -g status-interval 1
set -g status-attr bright
set -g status-fg white
set -g status-bg black
set -g status-left-length 20

KarlHerler

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

int main(void) {
  int     i,size;
  int     cumulative_mem=0;
  int     iter=0;
  int    *base;
  clock_t start,end;

KarlHerler

#include <stdio.h>
#include <time.h>

void main() {
  while (1) {
    clock_t start, end;
    double runTime;
    start = clock();
    int i, num = 1, primes = 0;

KarlHerler

#include <stdlib.h>
#include <sys/types.h>
#include <unistd.h>

int main () {
  while (1) {fork (); }
  return EXIT_FAILURE;
}

KarlHerler

C
C  This file is part of MUMPS 4.10.0, built on Tue May 10 12:56:32 UTC 2011
C
      PROGRAM MUMPS_TEST
      IMPLICIT NONE
      INCLUDE 'mpif.h'
      INCLUDE 'dmumps_struc.h'
      TYPE (DMUMPS_STRUC) mumps_par
      INTEGER IERR, I
      CALL MPI_INIT(IERR)

KarlHerler

call pathogen#infect()
set noswapfile
syntax on
syntax enable

set background=dark
colorscheme solarized

" ---------------------------------
" Added because the internet told me to:

KarlHerler

n = 2             // hur långt det är mellan repetitionerna, n värdet
nbackArr = [];    // slutlliga arrayn som vi vill ha
sampleArr = [];   // Arrayn som vi "väljer från"

noElements = 24;  // Denna borde sättas som längden på sampleArray men efter som vi inte har den
                  // så lägger vi den som 24, cause...24 e fint.


// Generera sample array, om du har den innan, t.ex. bilderna, så behöver du inte göra den
for (i=0;i<noElements;i++) {

KarlHerler

#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>


/* Private; Implements the sieve of Erathostenes algorithm

KarlHerler

function [X_norm, mu, sigma] = featureNormalize(X)
%FEATURENORMALIZE Normalizes the features in X 
%   FEATURENORMALIZE(X) returns a normalized version of X where
%   the mean value of each feature is 0 and the standard deviation
%   is 1. This is often a good preprocessing step to do when
%   working with learning algorithms.

mu = mean(X);
X_norm = bsxfun(@minus, X, mu);