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import numpy as np

def linear_part(x, w):
    return x * w

def non_linear(x, p=0.001):
    multi = (x > 0).astype(np.float64)
    multi[multi == 0] = 0.001
    return x * multi

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pareto_principle <- function(x){
  return (x ^ (log(0.2)/log(0.8)))
}

sprintf("%.2f of the effort comes from %.2f of the causes", 0.99, pareto_principle(0.99))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.95, pareto_principle(0.95))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.8, pareto_principle(0.8))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.5, pareto_principle(0.5))

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FROM jupyter/r-notebook

# docker build --rm -t jupyter/extended-notebook .
# docker run --rm -it -p 8888:8888 jupyter/extended-notebook


RUN conda install --quiet --yes -c r rstudio=1.1*
RUN conda install --quiet --yes -c anaconda pandas scipy gensim scikit-learn

# install nbrsessionproxy

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#

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Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

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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(

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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)))

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

# test for convergence
library(purrr)

mu_i = 0
alpha = 0.01

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# 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!

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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