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#@title Prompts

prompts = {
    0:"unfortunate failed crayon drawing sketched while drunk looks awful, should have thrown it away, badly drawn crayon sketch of bill murray scribbled by a child while having a stroke seizure, wouldnt even put this on my fridge, by five year old could've drawn this",
    50:"unfortunate failed crayon drawing sketched while drunk looks awful, should have thrown it away, badly drawn crayon sketch CARICATURE of bill murray scribbled by a child while having a stroke seizure, wouldnt even put this on my fridge, by five year old could've drawn this",
    150:"crayon drawing sketched while drunk looks awful, should have thrown it away, badly drawn crayon sketch of bill murray scribbled by a child while having a stroke seizure, wouldnt even put this on my fridge, by five year old could've drawn this",
    200:"crayon drawing sketched while drunk, hastily drawn crayon sketch of bill murray scribbled by a child while having a stroke seizure, wouldnt even put this on my fridge, by five ye

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"""

It looks like the Scene and Storyboard classes are intended to be used for creating animations by specifying keyframed curves for various parameters. The Scene class appears to allow users to specify the length of the scene and set keyframed curves for various attributes of the scene. The Storyboard class then allows users to create a sequence of Scene objects and specify default values for attributes that are not explicitly set in a given scene.

It looks like the KeyframedObject class is a subclass of the Keyframed class from the keyframed module, which provides some basic functionality for working with keyframed curves. The KeyframedObject class adds a weight attribute to the Keyframed class, which specifies the weight of the object when it is used in a storyboard.

The to_keyframed function provides a convenient way to convert various types of input into Keyframed objects, which can then be used as attributes in a Scene object. This function allows users to specify keyframed curves using strings, d

dmarx

#install.packages("rjson","RCurl")
library(RCurl)
library(rjson)

get_User_subs_page = function(user, after=NULL, cache=c()){
  
  baseurl = "http://www.reddit.com/user/"
  params = "limit=100"
  if(!is.null(after)){    
    params = paste(params, "&after=",after, sep="")

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""" 
This script was written to illustrate libraries that could be used to improve upon
the efficiency of a script posted to /r/learnprogramming:
Original script:
  https://github.com/aesptux/download-reddit-saved-images/blob/master/script.py
Reddit post
  http://www.reddit.com/r/learnprogramming/comments/14dojd 
  /pythoncode_review_script_to_download_saved_images/
"""
from urllib2 import Request, urlopen

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import praw # simple interface to the reddit API, also handles rate limiting of requests
import re
from collections import deque 
from time import sleep

USERNAME  = "Your username here"
PASSWORD  = "Your password here"
USERAGENT = "Your useragent string here. It should include your /u/username as a courtesy to reddit"

r = praw.Reddit(USERAGENT)

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partial_reconstruction = function(svd, n){
  # Takes an SVD object, reconstructs the matrix using
  # the top 'n' singular values
  j = nrow(svd$u)
  k = nrow(svd$v)
  with(svd, u[,1:n] %*% (d[1:n]*diag(n)) %*% t(v[,1:n]) )
}

SVD_K_Test = function(mat, svd_ob=NULL, k=NULL){
  # takes an SVD object and a vector of integers k. Let j be

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To Read:

Publication Date	Article	Notes
2016	End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures	Cited in multi-task sciERC (2018, below)
2018-10-11	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
	Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction	Probably a lot of useful citations in here, not sure we need the coreference stuff. * SciERC datasets: http://nlp.cs.washington.edu/sciIE/ * Code: https://bitbucket.org/luanyi/scierc/src/master/ * Pretrained (best) models: NER, Coref, Relation
2017-08-08	[Structural

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install.packages('caret')
install.packages('ccd')
install.packages('d3Network')
install.packages('data.table')
install.packages('dplyr')
install.packages('DMwR')
install.packages('e1071')
install.packages('ergm')
install.packages('ff')
install.packages('foreach')

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library(MASS)
library(rpart)

random_pos_def_matrix = function(n){
	# https://math.stackexchange.com/a/358092/109246
	A = matrix(runif(n*n), n)
	A = 0.5*(A+t(A))
	A + n*diag(n)
}