epignatelli

for file in $(find ./ -type f -name "*.py"); echo "$(cat ./COPYRIGHT; cat $file)" > $file

epignatelli

import decimal
import math
from typing import List


def is_prime(n: int) -> bool:
    if n == 0:
        return False
    if n == 1:
        return False

epignatelli

# activate shell
source $HOME/anaconda3/etc/profile.d/conda.sh
./anaconda3/bin/conda init

# download privacy notebook
git clone https://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@github.com/astanziola/privacy-exercise
conda env create -f privacy-exercise/env/environment.yml

# download computer vision notebook
git clone https://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@github.com/epignatelli/scalable-recognition-with-a-vocabulary-tree

epignatelli

A computer program can be seen simultaneously as a set of computer instructions or as concrete poetry formed by the indentations in the text of the program. Nicholas Negroponte, Being Digital, 1995.

Here lies a lake of data that will never be found.
In a variable called start is an int that will never be parsed.
In a variable called stop is a string that will never be trimmed.
In a variable called step is a string that will never be concatenated.
Here lies a lake of data that will never be found.
109 111 110 103 111 100 98 43 115 114 118 58 47 47 112 108 97 121 101 114 58 111 109 65 115 105 108 117 120 99 65 75 55 105 77 104 70 64 99 108 117 115 116 101 114 48 46 48 114 121 117 119 46 109 111 110 103 111 100 98 46 110 101 116

epignatelli

import os
from boxsdk import Client, OAuth2
from tqdm import tqdm
import keyring


CLIENT_ID = os.environ["BOX_CLIENT_ID"]
CLIENT_SECRET = os.environ["BOX_CLIENT_SECRET"]

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// gmail.svg

<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 24" width="64" height="64"><path d="M29.986 27.715H2.008C.915 27.715 0 26.85 0 25.733V6.376A2.01 2.01 0 0 1 2.008 4.37h27.978c1.093 0 2.008.9 2.008 2.008v19.33c-.025 1.144-.915 2.008-2.008 2.008z" fill="#f2f2f2"/><path d="M4 27.715l11.97-8.76.076-.508L3.7 9.578l-.025 17.705z" opacity=".1" fill="#221f1f"/><g fill="#d44c3d"><path d="M2.008 27.715C.9 27.715 0 26.85 0 25.733V6.35c0-1.118.9-1.32 2.008-1.32s2.008.23 2.008 1.32v21.364z"/><path d="M2.008 5.334c1.423 0 1.703.432 1.703 1.016v21.084H2.008c-.94 0-1.703-.762-1.703-1.703V6.35c-.025-.6.28-1.016 1.703-1.016zm0-.28C.9 5.055 0 5.283 0 6.35v19.356a1.98 1.98 0 0 0 2.008 2.008h2.008V6.35C4 5.258 3.126 5.055 2.008 5.055zm27.978.28c1.296 0 1.703.254 1.703.966v19.458c0 .94-.762 1.703-1.703 1.703h-1.703V6.3c-.025-.737.407-.966 1.703-.966zm0-.28c-1.118 0-2.008.152-2.008 1.245v21.44h2.008c1.118 0 2.008-.9 2.008-2.008V6.274c-.025-1.093-.915-1.22-2.008-1.22z"/><path d="M29.986 27.715h-2.008V6.3c0-1.118

epignatelli

// google-scholar.svg
<svg height="2500" width="2500" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512">
  <g fill="none" fill-rule="evenodd"><path d="M256 411.12L0 202.667 256 0z" fill="#4285f4"/>
    <path d="M256 411.12l256-208.453L256 0z" fill="#356ac3"/>
    <circle cx="256" cy="362.667" fill="#a0c3ff" r="149.333"/>
    <path d="M121.037 298.667c23.968-50.453 75.392-85.334 134.963-85.334s110.995 34.881 134.963 85.334H121.037z" fill="#76a7fa"/>
  </g>
</svg>

// style.css

epignatelli

import jax
import jax.numpy as jnp


@jax.jit
def compute_loss(y_hat: jnp.ndarray, y: jnp.ndarray):
    raise NotImplementedError


@partial(jax.jit, static_argnums=0)

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from typing import Tuple, NamedTuple, Callable, Any
import functools
import jax.numpy as jnp


Params = Any
RNGKey = jnp.ndarray
Shape = Tuple[int]

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"""
This gist is an extract from:
https://github.com/epignatelli/reinforcement-learning-an-introduction
"""

def policy_evaluation(env, policy=None, steps=1, discount=1., in_place=False):
    """
    Args:
        policy (numpy.array): a numpy 3-D numpy array, where the first two dimensions identify a state and the third dimension identifies the actions.
                              The array stores the probability of taking each action.