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# Torndb is a very thin wrapper around MySQLdb that makes it even easier to use MySQL.
# Because it is very light, one can just go through the one-file python source
# to learn how to use it.
# Installation: pip install torndb
# Official doc: http://torndb.readthedocs.org/en/latest/
# Source: https://github.com/bdarnell/torndb/blob/master/torndb.py
from torndb import Connection
@micaleel
micaleel / forms.html
Last active August 29, 2015 14:06 — forked from gnunicorn/forms.html
{%- macro form_field_label(field) -%}
<label for="{{ field.id }}">{{ field.label.text }}
{%- if field.flags.required -%}
<abbr title="Diese Feld muss angegeben werden">*</abbr>
{%- endif %}</label>
{% endmacro %}
{%- macro form_field_description(field) -%}
{% if field.description %}
<span class="descr">{{ field.description }}</span>
{% macro form_field(field) -%}
{% set with_label = kwargs.pop('with_label', False) %}
{% set placeholder = '' %}
{% if not with_label %}
{% set placeholder = field.label.text %}
{% endif %}
<div class="control-group {% if field.errors %}error{% endif %}">
{% if with_label %}
<label for="{{ field.id }}" class="control-label">
{{ field.label.text }}{% if field.flags.required %} *{% endif %}:
<html>
<title>
SQLite Example
</title>
<body>
<FORM ACTION="/create" METHOD=POST>
Name: <input type="text" name="name">
Marks: <input type="text" name="marks">
import string
import collections
from nltk import word_tokenize
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from pprint import pprint
@micaleel
micaleel / afinn.py
Created August 25, 2014 11:26 — forked from fnielsen/afinn.py
#!/usr/bin/python
#
# (originally entered at https://gist.github.com/1035399)
#
# License: GPLv3
#
# To download the AFINN word list do:
# wget http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip
# unzip imm6010.zip
#
# -*- coding: utf-8 -*-
import unicodedata
""" Normalise (normalize) unicode data in Python to remove umlauts, accents etc. """
data = u'naïve café'
normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')
print normal
#-*- coding: utf-8 -*-
import re
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk import bigrams, trigrams
import math
stopwords = nltk.corpus.stopwords.words('portuguese')
auth = OAuthHandler(CLIENT_ID, CLIENT_SECRET, CALLBACK)
auth.set_access_token(ACCESS_TOKEN)
api = API(auth)
venue = api.venues(id='4bd47eeb5631c9b69672a230')
stopwords = nltk.corpus.stopwords.words('portuguese')
tokenizer = RegexpTokenizer("[\w’]+", flags=re.UNICODE)
public class Maze_Best {
public int counter = 0;
private final static int MAX_VALUE = 1000;
int best_solution = MAX_VALUE;
public char[][] maze =
{{'#', '#', '#', '#', '#', '#', '#', '#', '#', '#'},
{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'},