xuwang xuwangyin

## eval_cifar10_autoattack.py
import os
import argparse
from pathlib import Path
import warnings

import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torch.utils.data as data
import torchvision.transforms as transforms

## gist:d472ac6a06ed3d7f0f542de294b4706d
from contextlib import contextmanager
import matplotlib.pyplot as plt
import base64
from io import BytesIO
from IPython.display import HTML, display

@contextmanager
def dp(width=600, dpi=300):
    def display_figure():
        # Save the current figure as a PNG image in memory

## eval_imagenet_resnet50_autoattack.py
import os
import argparse
from pathlib import Path
import warnings

import torch
import torchvision
import torch.nn as nn
import torchvision.datasets as datasets
import torch.utils.data as data

## affinity_propagation_clustering.py
# coding: utf-8

from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
import scipy
import scipy.stats
import numpy as np
from collections import Counter

## kl_similarity.py
import scipy
import numpy as np
from collections import Counter

# Kullback–Leibler divergence
# https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
# http://scipy.github.io/devdocs/generated/scipy.stats.entropy.html
def kl(p, q):

    # compute common elements

## render_guardian_json_files.php
<?php
error_reporting(E_ERROR | E_PARSE);
function get_content($body) {
  $doc = new DOMDocument;
  $doc->preserveWhiteSpace = false;
  $content = "";
  if ($doc->loadXML($body)) {
    foreach ($doc->getElementsByTagName("body") as $t) {
      foreach ($t->getElementsByTagName("paragraph") as $p) {
        $content = $content . '<p>' . $p->textContent . '</p>';

## render_json_files.php
<?php
error_reporting(E_ERROR | E_PARSE);
function get_content($body) {
  $doc = new DOMDocument;
  $doc->preserveWhiteSpace = false;
  if (!$doc->loadXML($body))
    return $body;
  $content = "";
  foreach ($doc->getElementsByTagName("body") as $t) {
    foreach ($t->getElementsByTagName("paragraph") as $p) {
	import os
	import argparse
	from pathlib import Path
	import warnings

	import torch
	import torch.nn as nn
	import torchvision.datasets as datasets
	import torch.utils.data as data
	import torchvision.transforms as transforms
	from contextlib import contextmanager
	import matplotlib.pyplot as plt
	import base64
	from io import BytesIO
	from IPython.display import HTML, display

	@contextmanager
	def dp(width=600, dpi=300):
	def display_figure():
	# Save the current figure as a PNG image in memory
	# coding: utf-8

	from sklearn.cluster import AffinityPropagation
	from sklearn import metrics
	from sklearn.datasets.samples_generator import make_blobs
	import scipy
	import scipy.stats
	import numpy as np
	from collections import Counter
	<?php
	error_reporting(E_ERROR \| E_PARSE);
	function get_content($body) {
	$doc = new DOMDocument;
	$doc->preserveWhiteSpace = false;
	$content = "";
	if ($doc->loadXML($body)) {
	foreach ($doc->getElementsByTagName("body") as $t) {
	foreach ($t->getElementsByTagName("paragraph") as $p) {
	$content = $content . '<p>' . $p->textContent . '</p>';