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from sympy import simplify, Pow
from sympy.polys import Poly, domains, polyroots
from sympy.assumptions import assuming, ask, Q

import signal
from functools import wraps

# Exceptions #

class GaloisOverflow(Exception):

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# Valid
for i in range(1):
  for j in range(2) if i else range(1,3):
    print(i,j)

# Syntax error :(
[(i,j) for i in range(1) for j in range(2) if i else range(1,3)]

javipus

__pycache__/*

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import time
import os

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

"""
Understanding upper bounds for Brier scores through random predictors.
"""

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"""
I never cared about my machine's epsilon until I did.
"""

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

# Machine precision - in bits
prec = -int(np.log2(np.finfo(float).eps))

javipus

Teaching a neural network to use a calculator

INTRODUCTION

The goal is to solve two types of probability problems about sampling strings/multi-sets without replacement. The main differences between the two is permutation invariance. One example of each:

• Problem type # 1: What is the probability of obtaining the sequence 'abb' when picking 3 letters without replacement from {a: 2, b: 3, z: 1}
• Problem type # 2: What is the probability of picking 1 'd', 1 's' and 3 'g' when picking five letters without replacement from 'gggggggsggdgggg'?

In the naive setting, a transformer is used to map the question to the answer, e.g.

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% Prolog translation of the key functions in
%  https://github.com/nearai/program_synthesis/blob/master/program_synthesis/algolisp/dataset/code_lisp.py

:- use_module(library(lists)).
:- use_module(library(dicts)).

% if ???
% if(context, cond, then, else).

% get Index-th element in list - alias of nth0 from lists library

javipus

0x995A18F41dcBE5186feFDF8c6aB2852519b9391A

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0x443140A42AC33de1a38ED9B853B8137542641960

javipus