A ZSH theme optimized for people who use:
- Solarized
- Git
- Unicode-compatible fonts and terminals (I use iTerm2 + Menlo)
For Mac users, I highly recommend iTerm 2 + Solarized Dark
#!/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/ | |
#------------------------------------------------------------------------------ |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("sna", "ggplot2", "Hmisc", "reshape2") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Empty ggplot2 theme | |
new_theme_empty <- theme_bw() | |
new_theme_empty$line <- element_blank() | |
new_theme_empty$rect <- element_blank() | |
new_theme_empty$strip.text <- element_blank() |
{ | |
"metadata": { | |
"name": "PyMADlib" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
from pyspark import SparkContext | |
import numpy as np | |
from sklearn.cross_validation import train_test_split, Bootstrap | |
from sklearn.datasets import make_classification | |
from sklearn.metrics import accuracy_score | |
from sklearn.tree import DecisionTreeClassifier | |
def run(sc): |
Based on D3.JS and Dimple, ChartFactory provide the ability to build quickly D3.JS charts without coding any lines of javascript. Just define your dashboard in a JSON and voila !
charts: [
{id:'chart1',
width:800,height:250,
xAxis:{type:'Category',field: "Month",orderRule:'Date'},
from __future__ import print_function | |
from keras.datasets import cifar10 | |
from keras.layers import merge, Input | |
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D | |
from keras.layers.core import Dense, Activation, Flatten | |
from keras.layers.normalization import BatchNormalization | |
from keras.models import Model | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.utils import np_utils |