aniquetahir

set nocompatible              " required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()

" alternatively, pass a path where Vundle should install plugins
"call vundle#begin('~/some/path/here')

aniquetahir

aniquetahir

NUM_LAYERS = 3
HIDDEN_DIM = 128
DROPOUT = 0.25
NUM_EPOCHS = 1000

key = jax.random.PRNGKey(6)

def loss_fn(params, key, x, y, is_training=True):
    logits = mlp_apply(params, key, x, is_training)
    loss = optax.softmax_cross_entropy_with_integer_labels(logits, y)

aniquetahir

! 7/15/2020 https://colab.research.google.com
colab.research.google.com##.parameter-hints-widget.visible > style:style(right: 20px; min-width: 200px; left: unset !important;)

! 8/1/2020 https://www.reddit.com
www.reddit.com###tooltip708392
www.reddit.com##.email-verification-tooltip

! google sneaky tracking
google.com##+js(remove-attr.js, onmousedown, .g a[href])

aniquetahir

function upvoteAll(){
	let all_elems = document.querySelectorAll('a.vote-up');
	let unvoted = [];
	all_elems.forEach(e=>unvoted.push(e));
	unvoted = unvoted.filter(e=>e.className.indexOf('upvoted')==-1);
	if(unvoted.length>0){
		unvoted[0].click();
	}
}
setInterval(upvoteAll, 1000);

aniquetahir

cd ~/

# Set up project
mkdir projects
cd projects
git clone https://github.com/aniquetahir/yellowtaxi.git
git clone https://github.com/aniquetahir/datapolygamyutils.git
export POLYUTILS=$(pwd)/datapolygamyutils
sudo apt install maven axel pypy
cd yellowtaxi

aniquetahir

#!/anaconda/bin/python
import sys

def GetIntersection(source_data, check_data):
    num_intersection = 0
    for row in check_data:
        if row in source_data:
            num_intersection+=1

    return num_intersection

aniquetahir

    /*---------------------------- Start an example Spatial Join Query using Cartesian Product algorithm ----------------------------*/
    /* Please see http://www.public.asu.edu/~jiayu2/geospark/javadoc/latest/ for function documentation */
    val objectRDD = new PointRDD(sc, "/home/SparkUser/Downloads/GeoSpark/src/test/resources/arealm.csv", 0, FileDataSplitter.CSV, false, 10); /* The O means spatial attribute starts at Column 0 and the 10 means 10 RDD partitions */
    val rectangleRDD = new RectangleRDD(sc, "/home/SparkUser/Downloads/GeoSpark/src/test/resources/zcta510.csv", 0, FileDataSplitter.CSV, false); /* The O means spatial attribute starts at Column 0. You might need to "collect" all rectangles into a list and do the Carteian Product join. */
    val resultSize = JoinQuery.SpatialJoinQuery(objectRDD, rectangleRDD, true).count();
    /*---------------------------- End an example Spatial Join Query using Cartesian Product algorithm ----------------------------*/
    

    CSE 512 Naive Spatial Join

aniquetahir

1.Create a PointRDD objectRDD;
2.Create a RectangleRDD queryWindowRDD;
3.Collect rectangles from queryWindowRDD to one Java List L;
4. For each rectangle R in L
    do RangeQuery.SpatialRangeQuery(objectRDD, queryEnvelope, 0, true);
   End;
5.Collect all results; //"Collect" is a standard function under SparkContext.
6.Parallelize the results to generate a RDD in this format: JavaPairRDD<Envelope, HashSet<Point>>.;//"Parallelize" is a standard function under SparkContext.
7.Return the result RDD;

aniquetahir

import sys

AUTHORS_FILE_NAME = ""
AFFILIATIONS_FILE_NAME = ""
AUTHOR_AFFILIATION_FILE_NAME = ""

authors_file = open(AUTHORS_FILE_NAME)
affiliations_file = open(AFFILIATIONS_FILE_NAME)
author_affiliation_file = open(AUTHOR_AFFILIATION_FILE_NAME)