alvinj

#!/bin/bash

#------------------------------------------------------------------------------
# Name:    sbtmkdirs
# Version: 1.5
# Purpose: Create an SBT project directory structure with a few simple options.
# Author:  Alvin Alexander, http://alvinalexander.com
# License: Creative Commons Attribution-ShareAlike 2.5 Generic
#          http://creativecommons.org/licenses/by-sa/2.5/
#------------------------------------------------------------------------------

miguelgrinberg

#!flask/bin/python
from flask import Flask, jsonify, abort, request, make_response, url_for
from flask_httpauth import HTTPBasicAuth

app = Flask(__name__, static_url_path = "")
auth = HTTPBasicAuth()

@auth.get_password
def get_password(username):
    if username == 'miguel':

kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-Clause
# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
# Visualization based on code from Gael Varoquaux gael.varoquaux@normalesup.org
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

QuantumGhost

@startuml
' uncomment the line below if you're using computer with a retina display
' skinparam dpi 300
!define Table(name,desc) class name as "desc" << (T,#FFAAAA) >>
' we use bold for primary key
' green color for unique
' and underscore for not_null
!define primary_key(x) <b>x</b>
!define unique(x) <color:green>x</color>
!define not_null(x) <u>x</u>

vgiri2015

#Create a Method to handle the Non Ascii to Ascii conversion
def nonasciitoascii(unicodestring):
  return unicodestring.encode("ascii","ignore")

#Create a Sample Dataframe
from pyspark.sql.window import Window
from pyspark.sql.functions import count, col
from pyspark.sql import Row
d=[ Row(coltype='regular', value="Happy Coding"),
    Row(coltype='non ascii', value="hello aåbäcö"),

MaxHalford

import os

from keras import backend as K
from keras import callbacks
from keras import layers
from keras import models
from keras.wrappers.scikit_learn import KerasClassifier
import pandas as pd
import tensorflow as tf
from sklearn import metrics