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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(5, 8))


def update(i):
    im_normed = np.random.random((64, 64))
    ax.imshow(im_normed)

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The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

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# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

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import folium
import pandas as pd

# Map init.
map_osm = folium.Map()

data = pd.read_csv('data.csv')
data_ = data[['LATITUDE (HIGH ACCURACY)','LONGITUDE (HIGH ACCURACY)']].astype(float)

for index, row in data_.iterrows():

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import cv2
import os
import numpy as np 
import pandas as pd
from sys import exit
from collections import Counter
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from keras.utils.np_utils import to_categorical
from keras.models import Sequential

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from sys import exit
import time
import os.path
from sys import exit
from subprocess import call
import datetime
import urllib

# years
years  = [2016]

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import os
os.environ["THEANO_FLAGS"] = "device=gpu,floatX=float32"   
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

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installation guide for spot GPU instances :

* MUSTS :
- sudo apt-get update
- sudo apt-get -y dist-upgrade  

- sudo apt-get install -y gcc g++ gfortran build-essential git wget linux-image-generic libopenblas-dev python-dev python-pip python-nose python-numpy python-scipy
- sudo apt-get install -y liblapack-dev
- sudo apt-get install -y libblas-dev

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