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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import math
import numpy as np

# Hardcoded variables for hyperfan init

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Section "InputClass"
    Identifier "touchpad"
    Driver "libinput"
    MatchIsTouchpad "on"
    Option "Tapping" "on"
    Option "TappingButtonMap" "lmr"
EndSection

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# If under a valid institutional IP address, the followng command will download an IEEE hosted paper of a specific <ID-NUMBER> 
and saved it as paper.pdf

wget "http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&isnumber=&arnumber=<ID-NUMBER>" -O paper.pdf

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Welcome to WordGrinder

Important note for Windows users

WordGrinder is a port of a Unix program, and a few things don’t map well onto the way Windows works. There are some things you need to know.

• the mouse is ignored. WordGrinder is keyboard driven.
• the close button at the top right hand corner of the window won’t work. Instead, to quit, type CTRL+Q (or press ESC to open the menu and pick File_→_Quit).

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# Drawn from https://gist.github.com/rocknrollnerd/06bfed6b9d1bce612fd6 (in theano)
# This is implemented in PyTorch
# Author : Anirudh Vemula
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from scipy.stats import norm
import matplotlib.pyplot as plt

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# Drawn from https://gist.github.com/rocknrollnerd/c5af642cf217971d93f499e8f70fcb72 (in Theano)
# This is implemented in PyTorch
# Author : Anirudh Vemula

import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np

from sklearn.datasets import fetch_mldata

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def sample_gumbel(shape, eps=1e-20): 
  """Sample from Gumbel(0, 1)"""
  U = tf.random_uniform(shape,minval=0,maxval=1)
  return -tf.log(-tf.log(U + eps) + eps)

def gumbel_softmax_sample(logits, temperature): 
  """ Draw a sample from the Gumbel-Softmax distribution"""
  y = logits + sample_gumbel(tf.shape(logits))
  return tf.nn.softmax( y / temperature)

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import pdb
class Config(pdb.DefaultConfig):
    sticky_by_default = True

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function top = nms(boxes, overlap)
% top = nms_fast(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
% but an inner loop has been eliminated to significantly speed it
% up in the case of a large number of boxes
% Tomasz Malisiewicz (tomasz@cmu.edu)