justinabrahms

#!/usr/bin/env python
# Ported to Python from http://www.vim.org/scripts/script.php?script_id=1349

print "Color indexes should be drawn in bold text of the same color."
print

colored = [0] + [0x5f + 40 * n for n in range(0, 5)]
colored_palette = [
	"%02x/%02x/%02x" % (r, g, b) 
	for r in colored

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

jpsim

1. Plain Strings (207): foo
2. Anchors (208): k$
3. Ranges (202): ^[a-f]*$
4. Backrefs (201): (...).*\1
5. Abba (169): ^(.(?!(ll|ss|mm|rr|tt|ff|cc|bb)))*$|^n|ef
6. A man, a plan (177): ^(.)[^p].*\1$
7. Prime (286): ^(?!(..+)\1+$)
8. Four (199): (.)(.\1){3}
9. Order (198): ^[^o].....?$
10. Triples (507): (^39|^44)|(^([0369]|([147][0369]*[258])|(([258]|[147][0369]*[147])([0369]*|[258][0369]*[147])([147]|[258][0369]*[258])))*$)

jetsonhacks

#!/bin/sh
# Fresh install for CUDA 6.5 on Jetson TK1 for Linux for Tegra (L4T) 21.1
# CUDA 6.5 REQUIRES L4T 21.1 !!!
sudo apt-add-repository universe
sudo apt-get update
# This is for L4T r21.1 ; Update for your L4T i.e. r21.3
wget http://developer.download.nvidia.com/compute/cuda/6_5/rel/installers/cuda-repo-l4t-r21.1-6-5-prod_6.5-14_armhf.deb

# Install the CUDA repo metadata that you downloaded 
# This is for L4T 21.1 ; Update for your L4T i.e. 21.3

Zulko

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# vispy: gallery 2
# Copyright (c) 2014, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.

#
# Modified for animation with MoviePy by Zulko
# See result here: http://i.imgur.com/sSCBkFd.gif
#

huqi

Beyond Compare 4
Licensed to:    ASIO Allsoftinone
Quantity:       1 user
Serial number:  1822-9597
License type:   Pro Edition for Windows

--- BEGIN LICENSE KEY ---
H1bJTd2SauPv5Garuaq0Ig43uqq5NJOEw94wxdZTpU-pFB9GmyPk677gJ
vC1Ro6sbAvKR4pVwtxdCfuoZDb6hJ5bVQKqlfihJfSYZt-xVrVU27+0Ja
hFbqTmYskatMTgPyjvv99CF2Te8ec+Ys2SPxyZAF0YwOCNOWmsyqN5y9t

soumith

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

matthewfeickert

import json
import argparse

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams['text.usetex'] = True

# Inspiration came from https://stackoverflow.com/q/3609852/8931942