| J=1;exec'''z="";b=-1.6;exec' | |
| x=y=0;exec"x,y=2*x*y+J,y*y-x | |
| *x+b;"*20;z+=" O"[x*x+y*y<2] | |
| ;b+=.040;'*60;print z;J-=.10 | |
| ;'''.replace('\n','')*20#Jay |
| import sys,os;M=[0]*30000;A=[] | |
| p=k=x=0;S=open(sys.argv[1]).read() | |
| while x<len(S):exec"""M[p]+=1 | |
| M[p]-=1 M[p]=ord(os.read(0,1)or"\\0") | |
| os.write(1,chr(M[p])) p-=p>0 p+=1 | |
| k=A.append(x)if+M[p]else+1 x=A.pop()-1 | |
| k+=1 0 k-=1 0""".split()['+-,.<>[][+]+' | |
| .find(S[x]+'+'*(k>0))];M[p]&=255;x+=1 |
| ''' | |
| Non-parametric computation of entropy and mutual-information | |
| Adapted by G Varoquaux for code created by R Brette, itself | |
| from several papers (see in the code). | |
| These computations rely on nearest-neighbor statistics | |
| ''' | |
| import numpy as np |
| # Full example for my blog post at: | |
| # https://danijar.com/building-variational-auto-encoders-in-tensorflow/ | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| tfd = tf.contrib.distributions |
| # VSCode Nautilus Extension | |
| # | |
| # Place me in ~/.local/share/nautilus-python/extensions/, restrart Nautilus, and enjoy :) | |
| # mkdir -p ~/.local/share/nautilus-python/extensions && cp -f VSCodeExtension.py ~/.local/share/nautilus-python/extensions/VSCodeExtension.py && nautilus -q | |
| # | |
| # This script was written by cra0zy and is released to the public domain | |
| from gi import require_version | |
| require_version('Gtk', '3.0') | |
| require_version('Nautilus', '3.0') |
| #coding: utf-8 | |
| from bottle import route, error, post, get, run, static_file, abort, redirect, response, request, template | |
| @route('/') | |
| @route('/index.html') | |
| def index(): | |
| return '<a href="/hello">Go to Hello World page</a>' | |
| @route('/hello') | |
| def hello(): |
| #include <Python.h> | |
| #include <numpy/arrayobject.h> | |
| #include "chi2.h" | |
| /* Docstrings */ | |
| static char module_docstring[] = | |
| "This module provides an interface for calculating chi-squared using C."; | |
| static char chi2_docstring[] = | |
| "Calculate the chi-squared of some data given a model."; |
| #include <Python.h> | |
| #include <numpy/arrayobject.h> | |
| #include "chi2.h" | |
| /* Docstrings */ | |
| static char module_docstring[] = | |
| "This module provides an interface for calculating chi-squared using C."; | |
| static char chi2_docstring[] = | |
| "Calculate the chi-squared of some data given a model."; |
This is an unfinished list of remarks on how to write good pseudocode.
Pseudocode is a loosely defined way of transmitting the concept of an algorithm from a writer to a reader. Central is the efficiency of this communication, not the interpretability of the code by an automated program (e.g., a parser).
| \documentclass{standalone} | |
| %% for compilation with htlatex (to produce svg image), | |
| %% uncomment the line below: | |
| % \def\pgfsysdriver{pgfsys-tex4ht.def} | |
| \usepackage{tikz} | |
| \tikzstyle{tensor}=[rectangle,draw=blue!50,fill=blue!20,thick] |
