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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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#!/bin/bash

ROS_DISTRO=${ROS_DISTRO:-kinetic}


/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

brew update
brew install cmake

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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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def sigmoid(x: Double) = 1.0 / (1.0 + math.exp(-x))

def f1measure(TP: Double, TN: Double, FP: Double, FN: Double, alpha: Double = 1) = {
    val P = precision(TP, FP)
    val R = recall(TP, FN)
    (2.0 * P * R) / (P + R)
}

def precision(TP: Double, FP: Double) = TP / (FP + TP)

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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