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| Here's a block of code in some imaginary assembly language: | |
| jmp A | |
| block1 | |
| jmp A | |
| block2 | |
| jmp A | |
| block3 | |
| .A ... |
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| import csv | |
| import math | |
| import matplotlib.pyplot as plt | |
| import locale | |
| def frac(x): | |
| return x - math.floor(x) | |
| def first_digit(x): | |
| return int(math.floor(math.pow(10, frac(math.log10(x))) + 0.0001)) |
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| # Python 3 | |
| import collections | |
| Write = collections.namedtuple("Write", ["written"]) | |
| def hello_world(): | |
| yield Write("Hello, world!") | |
| yield Write("Hello, world!") | |
| return 1 | |
| def identity(gen): |
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| /* | |
| * This is a literate quine. That means that | |
| * 1. the comments will tell you a little about how it works and | |
| * 2. if you compile and run it its output will be identical to its source | |
| * code even though it doesn't look at its original source. It literally | |
| * contains within itself a complete recipe for how to display itself. | |
| * | |
| * Quines are ten a penny. This one is unusual because | |
| * 1. its main loop consists solely of a loop to print characters | |
| * generated by a function called programChar() and |
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| A short exact sequence is a sequence of maps: | |
| f g h k | |
| 0 --> A --> B --> C --> 0 | |
| s.t. | |
| Im f = Ker g | |
| Im g = Ker h | |
| Im h = Ker k | |
| Example: |
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| `RUN` to assemble | |
| `GOTO s` to start it |
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| <html> | |
| <body> | |
| <button type="button" id="start"> | |
| Switch me on to start | |
| </button> | |
| <HR> | |
| <div> |
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| from __future__ import print_function | |
| import sys | |
| # If you don't know jax, check it out | |
| # https://github.com/google/jax | |
| import jax.numpy as np | |
| import jax.scipy as scipy | |
| from jax import jit, random, value_and_grad |
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| > scale :: Num a => a -> [a] -> [a] | |
| > scale a bs = map (a *) bs | |
| Invert an infinite upper triangular matrix with 1 on diagonal | |
| [ | |
| [1, b₀₁, b₀₂, b₀₃, …], | |
| [1, b₁₂, b₁₃, …], | |
| [1, b₂₃, …], | |
| … | |
| ] |
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| # Trying to get a handle on "Deep Image Prior" | |
| # at https://dmitryulyanov.github.io/deep_image_prior | |
| # This is a toy version with a single purely linear | |
| # convolution layer | |
| # The goal is to start with an image with high res detail, | |
| # corrupt a few bits, and then | |
| # repair the corrupt bits using an a priori model | |
| # that simply says "we can make the image from a lower resolution |