NoRaincheck

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

NoRaincheck

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense

train_data_dir = 'data'
img_width, img_height = 150, 150

train_datagen = ImageDataGenerator(

NoRaincheck

Topic list:

• Zen of Python
• dir
• list vs tuple vs dict
• iterating over things (continue, enumerate)
• list comprehension (in)
• dict comprehension (get)
• **kwargs, *args
• itertools and reading the recipes (e.g. list(itertools.chain.from_iterable(a)))

NoRaincheck

mu <- 2.5
tau <- 0.5
xn <- rnorm(50, mu, 1/tau)

# test for convergence
library(purrr)

mu_i = 0
alpha = 0.01

NoRaincheck

# stolen from stackoverflow, based on how james and i share scripts:

import Tkinter, tkSimpleDialog 
root = Tkinter.Tk() # dialog needs a root window, or will create an "ugly" one for you
root.withdraw() # hide the root window
password = tkSimpleDialog.askstring("Password", "Enter password:", show='*', parent=root)
root.destroy() # clean up after yourself!

NoRaincheck

library(tidyverse)

data(iris)

func <- list(mean=mean, sd=sd, max=max, min=min)
fields <- c("Sepal.Length", "Sepal.Width")

# usage for one field
invoke_map(func, x=unlist(select_(iris, "Sepal.Length"))) %>% data.frame

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# -*- coding: utf-8 -*-
"""
Created on Sun Oct  9 12:57:49 2016

@author: chapm
"""

import numpy as np

b = np.sqrt(2)

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#!/usr/bin/env bash

# Copyright 2015 TappingStone, Inc.
#
# This script will install PredictionIO onto your computer!
#
# Documentation: http://docs.prediction.io
#
# License: http://www.apache.org/licenses/LICENSE-2.0

NoRaincheck

curl -i -XGET 'http://localhost:9200/_count?pretty' -d '
{
    "query": {
        "match_all": {}
    }
}
'

# placing documents in {index:megacorp, type:employee, id:1}
curl -i -XPUT 'http://localhost:9200/megacorp/employee/1' -d '

NoRaincheck

sim <- function(nx, ny, tailorder=c(3,2), rdist=rnorm) {
  xsample <- rdist(nx)
  ysample <- rdist(ny)

  xsample <- rev(xsample[order(xsample)])
  ysample <- rev(ysample[order(ysample)])

  return(xsample[tailorder[1]] > ysample[tailorder[2]])
}