| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ |
| import pandas as pd | |
| from random import random | |
| flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000 | |
| pdata = pd.DataFrame({"a":flow, "b":flow}) | |
| pdata.b = pdata.b.shift(9) | |
| data = pdata.iloc[10:] * random() # some noise | |
| import numpy as np |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # This is a simplified implementation of the LSTM language model (by Graham Neubig) | |
| # | |
| # LSTM Neural Networks for Language Modeling | |
| # Martin Sundermeyer, Ralf Schlüter, Hermann Ney | |
| # InterSpeech 2012 | |
| # | |
| # The structure of the model is extremely simple. At every time step we |
| Type: Shiny | |
| Title: actionButton demo | |
| License: MIT | |
| Author: Winston Chang <[email protected]> | |
| AuthorUrl: http://www.rstudio.com/ | |
| Tags: actionbutton | |
| DisplayMode: Showcase |
| # ~/.bash_profile | |
| [[ -s ~/.bashrc ]] && source ~/.bashrc | |
| export CLICOLOR=1 | |
| export LSCOLORS=GxFxCxDxBxegedabagaced | |
| alias subl='/Applications/Sublime\ Text\ 2.app/Contents/SharedSupport/bin/subl' | |
| alias ls='ls -GFh' | |
| alias ll='ls -l' |
People
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😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
d3js: Dynamic Scatter plot based on Select input.
