Iver Jordal iver56

## docker-clean.sh
#!/bin/sh

echo -e "-- Removing exited containers --\n"
docker ps --all --quiet --filter="status=exited" | xargs --no-run-if-empty docker rm --volumes

echo -e "\n\n-- Removing untagged images --\n"
docker rmi --force $(docker images | awk '/^<none>/ { print $3 }')

echo -e "\n\n-- Removing volume directories --\n"
docker volume rm $(docker volume ls --quiet --filter="dangling=true")

## overlaps.py
from __future__ import division, print_function, absolute_import
import numpy as np
import scipy
from spectral import check_COLA  # https://github.com/scipy/scipy/pull/6058

windows = np.unique(scipy.signal.windows._win_equiv.values())
windows = sorted(windows, key=lambda x: x.__name__)

for window in windows:
    try:

## getusermedia_picture.html
<html>
<body>
	<video id="v" width="300" height="300"></video>
	<input id="b" type="button" disabled="true" value="Take Picture"></input>
	<canvas id="c" style="display:none;" width="300" height="300"></canvas>
</body>
<script>
	navigator.getUserMedia({video: true}, function(stream) {
		var video = document.getElementById("v");
		var canvas = document.getElementById("c");

## resample_benchmark.ipynb

      
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                mogwai
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              January 11, 2023 03:42
            
              
                Resampling Benchmarks
              
          
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## xor.py
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

model = Sequential()
model.add(Dense(8, input_dim=2))

## pyximport_numpy.py
import pyximport
import numpy as np
pyximport.install(setup_args={'include_dirs': np.get_include()})

## elastic_transform.py
import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.

## wiener_deconvolution_example.py
#!/usr/bin/env python

# Simple example of Wiener deconvolution in Python.
# We use a fixed SNR across all frequencies in this example.
#
# Written 2015 by Dan Stowell. Public domain.

import numpy as np
from numpy.fft import fft, ifft, ifftshift

## base64ArrayBuffer.js
// Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then
// use window.btoa' step. According to my tests, this appears to be a faster approach:
// http://jsperf.com/encoding-xhr-image-data/5

/*
MIT LICENSE

Copyright 2011 Jon Leighton

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

## aes-256-cbc.js
'use strict';

const crypto = require('crypto');
const ENC_KEY = "bf3c199c2470cb477d907b1e0917c17b"; // set random encryption key
const IV = "5183666c72eec9e4"; // set random initialisation vector
// ENC_KEY and IV can be generated as crypto.randomBytes(32).toString('hex');

const phrase = "who let the dogs out";

var encrypt = ((val) => {
	#!/bin/sh

	echo -e "-- Removing exited containers --\n"
	docker ps --all --quiet --filter="status=exited" \| xargs --no-run-if-empty docker rm --volumes

	echo -e "\n\n-- Removing untagged images --\n"
	docker rmi --force $(docker images \| awk '/^<none>/ { print $3 }')

	echo -e "\n\n-- Removing volume directories --\n"
	docker volume rm $(docker volume ls --quiet --filter="dangling=true")
	from __future__ import division, print_function, absolute_import
	import numpy as np
	import scipy
	from spectral import check_COLA # https://github.com/scipy/scipy/pull/6058

	windows = np.unique(scipy.signal.windows._win_equiv.values())
	windows = sorted(windows, key=lambda x: x.__name__)

	for window in windows:
	try:
	<html>
	<body>
	<video id="v" width="300" height="300"></video>
	<input id="b" type="button" disabled="true" value="Take Picture"></input>
	<canvas id="c" style="display:none;" width="300" height="300"></canvas>
	</body>
	<script>
	navigator.getUserMedia({video: true}, function(stream) {
	var video = document.getElementById("v");
	var canvas = document.getElementById("c");
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation
	from keras.optimizers import SGD
	import numpy as np

	X = np.array([[0,0],[0,1],[1,0],[1,1]])
	y = np.array([[0],[1],[1],[0]])

	model = Sequential()
	model.add(Dense(8, input_dim=2))
	import pyximport
	import numpy as np
	pyximport.install(setup_args={'include_dirs': np.get_include()})
	import numpy as np
	from scipy.ndimage.interpolation import map_coordinates
	from scipy.ndimage.filters import gaussian_filter

	def elastic_transform(image, alpha, sigma, random_state=None):
	"""Elastic deformation of images as described in [Simard2003]_.
	.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
	Convolutional Neural Networks applied to Visual Document Analysis", in
	Proc. of the International Conference on Document Analysis and
	Recognition, 2003.
	#!/usr/bin/env python

	# Simple example of Wiener deconvolution in Python.
	# We use a fixed SNR across all frequencies in this example.
	#
	# Written 2015 by Dan Stowell. Public domain.

	import numpy as np
	from numpy.fft import fft, ifft, ifftshift
	// Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then
	// use window.btoa' step. According to my tests, this appears to be a faster approach:
	// http://jsperf.com/encoding-xhr-image-data/5

	/*
	MIT LICENSE

	Copyright 2011 Jon Leighton

	Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
	'use strict';

	const crypto = require('crypto');
	const ENC_KEY = "bf3c199c2470cb477d907b1e0917c17b"; // set random encryption key
	const IV = "5183666c72eec9e4"; // set random initialisation vector
	// ENC_KEY and IV can be generated as crypto.randomBytes(32).toString('hex');

	const phrase = "who let the dogs out";

	var encrypt = ((val) => {