start new:
tmux
start new with session name:
tmux new -s myname
#include <regex> | |
#include <string> | |
#include <vector> | |
std::vector<std::string> Split(const std::string& str, const std::string& regex) | |
{ | |
return {std::sregex_token_iterator(str.begin(), str.end(), std::regex(regex), -1), std::sregex_token_iterator()}; | |
} |
#include <Python.h> | |
#include <stdio.h> | |
/* | |
* gcc embpython.c -I/usr/include/python2.7 -lpython | |
**/ | |
void loadModule() | |
{ | |
/* run objects with low-level calls */ | |
char *arg1="sir", *arg2="robin", *cstr; | |
printf("Load Module err!\n"); |
This text now lives at https://github.com/MarcDiethelm/contributing/blob/master/README.md. I turned it into a Github repo so you can, you know, contribute to it by making pull requests.
If you want to contribute to a project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request.
# _______ __ __ _______ ______ _______ _______ _______ ______ # | |
#| || | | || || | | _ || || || | # | |
#| _ || | | ||_ _|| _ || |_| ||_ _|| ___|| _ |# | |
#| | | || |_| | | | | | | || | | | | |___ | | | |# | |
#| |_| || | | | | |_| || | | | | ___|| |_| |# | |
#| || | | | | || _ | | | | |___ | |# | |
#|_______||_______| |___| |______| |__| |__| |___| |_______||______| # | |
# # | |
# Modern CMake practices and importing the QT scripts by adding it to # | |
# your module path makes things a lot better than it used to be # |
/* Example of embedding Python in another program */ | |
// to compile run: | |
// gcc -o test $(python-config --cflags) test.c $(python-config --ldflags) && ./test | |
#include<stdio.h> | |
#include "Python.h" | |
void initxyzzy(void); /* Forward */ | |
main(int argc, char **argv) |
#!/usr/bin/python | |
# -*- coding: utf-8 -*- | |
# Author: Axel Angel, copyright 2015, license GPLv3. | |
import sys | |
import caffe | |
import numpy as np | |
import lmdb | |
import argparse |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
from ctypes import * | |
from ctypes.wintypes import * | |
INVALID_HANDLE_VALUE = -1 | |
CREATE_UNICODE_ENVIRONMENT = 0x00000400 |
With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.
The following section is divided in to two parts. Caffe's documentation suggest