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package enum_example

import (
	"bytes"
	"encoding/json"
)

// TaskState represents the state of task, moving through Created, Running then Finished or Errorred
type TaskState int

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#!/bin/bash

name=$RANDOM
url='http://localhost:9093/api/v1/alerts'

echo "firing up alert $name" 

# change url o
curl -XPOST $url -d "[{ 
	\"status\": \"firing\",

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Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

---

General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

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#
# Some constants
#
aws_profile = "your_profile"
aws_region = "your_region"
s3_bucket = "your_bucket"

# 
# Reading environment variables from aws credential file 
#

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"""
Patched version to support PostgreSQL 
(original version: https://github.com/pydata/pandas/blob/v0.13.1/pandas/io/sql.py)

Adapted functions are:
- added _write_postgresql
- updated table_exist
- updated get_sqltype
- updated get_schema

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#Compilng You need g++ 4.9 to compile this code. Follow these steps to install g++-4.9

After installing run the following command to compile

/usr/bin/g++-4.9 -std=c++11 lambda.cpp

#Running

./a.out

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# !/bin/bash

# Step 1. Install pyenv

git clone https://github.com/pyenv/pyenv.git ~/.pyenv
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi' >> ~/.bashrc

# Step 2. Install missing headers for all the Python modules to be built

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""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

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#include <Python.h> // Must be first
#include <vector>
#include <stdexcept>
#include "PyUtils.h"
using namespace std;

// =====
// LISTS
// =====

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from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext

ext_modules = [
        Extension("test_um",
            ["test_um.pyx"],
            include_dirs=["/usr/local/include/boost"],
            library_dirs=["/usr/local/lib"],
            language="c++")