billhhh

Install VMWare Workstation PRO 17 (Read it right. PRO!)
Also, these keys might also work with VMWare Fusion 13 PRO. Just tested it.

Sub to me on youtube pls - PurpleVibe32
if you want more keys - call my bot on telegram. @purector_bot (THE BOT WONT REPLY ANYMORE) - Or: https://cdn.discordapp.com/attachments/1040615179894935645/1074016373228978277/keys.zip - the password in the zip is 102me.
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This gist can get off at any time.
PLEASE, DONT COPY THIS. IF YOU FORK IT, DONT EDIT IT.
*If you have a problem comment and people will try to help you!
*No virus

billhhh

I've used this in the past to grab offline copies of some of the Google API docs.

$ wget --no-parent --recursive --page-requisites --html-extension --convert-links "http://code.google.com/apis/gears/"

# --no-parent - Don't fetch anything in the parent directory of the URL specified
# --page-requisites - Download everything required for the page to display correctly
# --recursive - Recursively download (use default depth of 5)
# --html-extension - Add .html to downloaded html files (this isn't necessarily required for the google code example but might be useful on other sites)

--convert-links - Update links in the downloaded document to point to, either the downloaded copy of a file or a full URL

billhhh

deb http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse

deb http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse

deb http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse

deb http://archive.ubuntu.com/ubuntu/ focal-backports main restricted universe multiverse

billhhh

I recently happened upon an implementation of popen() (different API, same idea) using clone(2), and so I opened an issue requesting use of vfork(2) or posix_spawn() for portability. It turns out that on Linux there's an important advantage to using clone(2). I think I should capture the things I wrote there in a better place. A gist, a blog, whatever.

So here goes.

Long ago, I, like many Unix fans, thought that fork(2) and the fork-exec process spawning model were the greatest thing, and the Windows sucked for only having [exec*()](http://pubs.opengroup.org/onlinepubs/9699919

billhhh

#sudo wget https://github.com/shiftkey/desktop/releases/download/release-2.1.0-linux1/GitHubDesktop-linux-2.1.0-linux1.deb
#sudo gdebi GitHubDesktop-linux-2.1.0-linux1.deb

# UPDATE (2021-03-05): Thanks to PaoloRanzi81's comment, the updated code is as follows https://gist.github.com/PaoloRanzi81

sudo wget https://github.com/shiftkey/desktop/releases/download/release-2.6.3-linux1/GitHubDesktop-linux-2.6.3-linux1.deb
### Uncomment below line if you have not installed gdebi-core before
# sudo apt-get install gdebi-core 
sudo gdebi GitHubDesktop-linux-2.6.3-linux1.deb

billhhh

import nibabel as nib
import numpy as np

data = np.ones((32, 32, 15, 100), dtype=np.int16) # dummy data in numpy matrix
img = nib.Nifti1Image(data, np.eye(4))  # Save axis for data (just identity)

img.header.get_xyzt_units()
img.to_filename(os.path.join('build','test4d.nii.gz'))  # Save as NiBabel file

billhhh

import numpy as np
from scipy.sparse import csc_matrix

def pageRank(G, s = .85, maxerr = .001):
    """
    Computes the pagerank for each of the n states.

    Used in webpage ranking and text summarization using unweighted
    or weighted transitions respectively.

billhhh

def contextual_loss(x, y, h=0.5):
    """Computes contextual loss between x and y.
    
    Args:
      x: features of shape (N, C, H, W).
      y: features of shape (N, C, H, W).
      
    Returns:
      cx_loss = contextual loss between x and y (Eq (1) in the paper)
    """

billhhh

#import random, math

outputdebug = False 

def debug(msg):
    if outputdebug:
        print msg

class Node():
    def __init__(self, key):

billhhh

# Imports
from sklearn.datasets import fetch_olivetti_faces
import numpy as np

# Download Olivetti faces dataset
olivetti = fetch_olivetti_faces()
x = olivetti.images
y = olivetti.target

# Print info on shapes and reshape where necessary