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"""
An implementation of the `Local reparameterization trick`
from Kingma & Wellings and 
Bayesian RNN 
from Fortunato, Blundell & Vinyals
"""
import os
import time
import copy
from os.path import join as pjoin

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Elasticsearch CLI

Experimental CLI interface for the helpers in the `python library`_.

Main purpose is to expose the bulk functionality to enable rapid loading of data into an elasticsearch cluster. Combined with the scan command it can also be used to reindex data from elasticsearch into a different index or cluster.

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data {
    int<lower=0> J; // number of schools
    real y[J]; // estimated treatment effects
    real<lower=0> sigma[J]; // s.e. of effect estimates
}
parameters {
    real mu;
    real<lower=0> tau;
    real eta[J];
}

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#!/usr/bin/python

# This work is licensed under the Creative Commons Attribution 3.0 United 
# States License. To view a copy of this license, visit 
# http://creativecommons.org/licenses/by/3.0/us/ or send a letter to Creative
# Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.

# from http://oranlooney.com/make-css-sprites-python-image-library/ 
# Orignial Author Oran Looney <[email protected]>

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package com.databricks.spark.jira

import scala.io.Source

import org.apache.spark.rdd.RDD

import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.sources.{TableScan, BaseRelation, RelationProvider}

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import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.conf.Configuration 

  case class S3File(path: String, isDir: Boolean, size: Long) {
    def children = listFiles(path)
  }

  def listFiles(path: String): Seq[S3File] = {
    val fs = FileSystem.get(new java.net.URI(path), new Configuration())
    fs.listStatus(new Path(path)).map(s => S3File(s.getPath.toString, s.isDir, s.getLen))

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class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        with tf.variable_scope(scope):
            #Input and visual encoding layers
            self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)
            self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1])
            self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
                inputs=self.imageIn,num_outputs=16,
                kernel_size=[8,8],stride=[4,4],padding='VALID')
            self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,

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Advanced Functional Programming with Scala - Notes

Copyright © 2017 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

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from __future__ import print_function

import sys
from math import sqrt
import argparse
from collections import defaultdict
from random import randint

from pyspark import SparkContext