towshif

startTime = time.time()

AD = i['AD']
ADOSC = i['ADOSC']
ADX = i['ADX']
ADX_HIST = i['ADX_HIST']
ADXR = i['ADXR']
KVO = i['KVO']
KVOSignal = i['KVOSignal']

towshif

# Ignore all
*

# Unignore all with extensions
!*.*

# Unignore all dirs
!*/

### Above combination will ignore all files without extension ###

towshif

#!/bin/sh 

pip install mkdocs pymdown-extensions mkdocs-material pygments --user

# Start HTTP Server 
# python -m SimpleHTTPServer 


# Start mkdoc markdown engine 
# python -m mkdocs serve

towshif

# bash-aliases@https://gist.github.com/towshif/
# Add this to .bashrc .zshrc # Or run script to append to .bashrc #check manually after run 
# $ curl https://gist.githubusercontent.com/towshif/bba124a507d1f7513118977c7dd591ae/raw/22f26f731f0349c2b0fcd170dc33eca93e3c561e/bash-aliases.sh >> ~/.bashrc

# 
alias df="df -Tha --total"
alias du="du -ach | sort -h"
alias free="free -mht"
alias ps="ps auxf"

towshif

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential