| from typing import TypeVar, Generic, Callable | |
| from dataclasses import dataclass | |
| from argparse import Namespace | |
| T = TypeVar('T') | |
| S = TypeVar('S') | |
| @dataclass | |
| class ListMap(Generic[S, T]): | |
| f: Callable[[T], S] |
| """ | |
| "algebraic data types" in python | |
| """ | |
| from __future__ import annotations | |
| from dataclasses import dataclass | |
| @dataclass | |
| class Value: | |
| value: int |
| class Lstm(Layer): | |
| def __init__(self, input_dim: int, hidden_dim: int) -> None: | |
| self.input_dim = input_dim | |
| self.hidden_dim = hidden_dim | |
| # Forget-gate weights | |
| self.w_f = random_tensor(hidden_dim, input_dim, variance=2/(hidden_dim + input_dim)) | |
| self.u_f = random_tensor(hidden_dim, hidden_dim, variance=1/hidden_dim) | |
| self.b_f = random_tensor(hidden_dim) |
| """ | |
| I am trying to figure out asyncio. | |
| I have no idea if this code is good or not, but it works. | |
| I also have no idea how to type annotate asyncio code. | |
| """ | |
| from typing import List, NamedTuple | |
| import random | |
| import asyncio |
| { | |
| "dataset_reader": { | |
| "type": "conll2003", | |
| "token_indexers": { | |
| "tokens": { | |
| "type": "single_id", | |
| "lowercase_tokens": true | |
| }, | |
| "token_characters": { | |
| "type": "characters" |
| mpg | cylinders | displacement | horsepower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 18 | 8 | 307 | 130 | 3504 | 12 | 70 | 1 | chevrolet chevelle malibu | |
| 15 | 8 | 350 | 165 | 3693 | 11.5 | 70 | 1 | buick skylark 320 | |
| 18 | 8 | 318 | 150 | 3436 | 11 | 70 | 1 | plymouth satellite | |
| 16 | 8 | 304 | 150 | 3433 | 12 | 70 | 1 | amc rebel sst | |
| 17 | 8 | 302 | 140 | 3449 | 10.5 | 70 | 1 | ford torino | |
| 15 | 8 | 429 | 198 | 4341 | 10 | 70 | 1 | ford galaxie 500 | |
| 14 | 8 | 454 | 220 | 4354 | 9 | 70 | 1 | chevrolet impala | |
| 14 | 8 | 440 | 215 | 4312 | 8.5 | 70 | 1 | plymouth fury iii | |
| 14 | 8 | 455 | 225 | 4425 | 10 | 70 | 1 | pontiac catalina |
This is not guaranteed to work, but it might be worth trying. (If it doesn't work, it's doubtful I can help you any further than this.)
conda install cython
| """ | |
| a response to https://gist.github.com/TomAugspurger/f96d41ae7a40d41fe9053d0adbceb4f1 | |
| """ | |
| from collections import deque | |
| import datetime | |
| import itertools | |
| import random | |
| import string | |
| from typing import NamedTuple, Tuple, Iterable, List |
| """ | |
| Run-length encoding is a fast and simple method of encoding strings. | |
| The basic idea is to represent repeated successive characters | |
| as a single count and character. For example, the string | |
| "AAAABBBCCDAA" would be encoded as "4A3B2C1D2A". | |
| Implement run-length encoding and decoding. You can assume | |
| the string to be encoded has no digits and consists solely of alphabetic | |
| characters. You can assume the string to be decoded is valid. | |
| """ |
| # c.f. https://xkcd.com/287/ | |
| from typing import Sequence | |
| target = 1505 | |
| weights = [215, 275, 335, 355, 420, 580] | |
| def knapsack(goal: int, # what we're trying to add up to | |
| weights: Sequence[int], # the weights we can use | |
| start: int=0, # only add weights starting here (prevents duplicate work / answers) |