nagataka

def construct_index(docs):
    term_dict = {}
    postings = {}
    for id, doc in docs.items():

        terms = list(set(doc.lower().split()))
        for term in terms:
            term_count = term_dict.get(term, 0)
            term_posting = postings.get(term, None)

nagataka

Object detection で得られた結果を利用する際に、「少し（例えば5%）実際のbboxよりも大きな領域をcropしてcontext情報を含めてやると良い」いうことを慣例としてやっていたのだけど、それってどこかで示されているのだっけ？それとも経験的にやっているだけなのだっけ？とふと疑問に思ったので調べてみた。これとかが参考になるか？ An Empirical Study of Context in Object Detection

AWS

aws login コマンドを初めて使ってみたが、これであればアクセスキーを作らなくていいので良さそうだなと思った。 https://dev.classmethod.jp/articles/aws-cli-aws-login/

SAM (Segment Anything Model)

nagataka

たまに色々と忘れるので便利な資料をメモ

• How to Read Figures, Mathematical Expressions and Equations, and Glossary
• 英語による数学表現

nagataka

• ブログなど
  • 日本語なら 今更聞けないLSTMの基本 がとても参考になる。
  • In English: Understanding LSTM Networks
• Implementation
  • Docs > torch.nn > LSTM
  • SEQUENCE MODELS AND LONG-SHORT TERM MEMORY NETWORKS

その他、未読だけれど参考になりそうなリンク (随時読んだら上へ)

• The Unreasonable Effectiveness of Recurrent Neural Networks

nagataka

# OpenAI gym custom environment mimicking Blocking Maze
# See Sutton and Barto "Reinforcement Learning an Introduction"
#     Example 8.2: Blocking Maze
from enum import Enum
import sys
import copy

import gym
from gym import error, spaces, utils
from gym.utils import seeding

nagataka

{
    "python.formatting.provider": "black",
    "python.linting.pylintEnabled": false,
    "python.linting.flake8Enabled": true,
    "python.linting.flake8Args": [
        "--ignore=E501,W503"
    ],
    "python.sortImports.args": [
        "-m 3"
    ],

nagataka

import random
import numpy as np
np.random.seed(0)

def kerri(p, b):
    """https://en.wikipedia.org/wiki/Kelly_criterion
    """
    return (p*(b+1)-1 )/b

N = 300

nagataka

import gym
import ray
from ray.rllib.agents.ppo import PPOTrainer, DEFAULT_CONFIG

import pprint as pp

#tune.run(PPOTrainer, config={"env": "Breakout-v0", "use_pytorch": True})
ray.init(num_gpus=1, ignore_reinit_error=True, log_to_driver=False)

# https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/ppo.py#L15

nagataka

#!/bin/bash

set -eu

### Incoming WebHooks URL
WEBHOOKURL="https://hooks.slack.com/services/FILL_YOUR_WEBHOOKURL"

### channel
CHANNEL=${CHANNEL:-"#notifications"}

nagataka

The repository is organized as follows:

• src : Contains the source codes for all .... The source code is written in Python and it takes advantage of Numpy and Matplotlib. In order to run a simulation you have to use the file run_xxxx.py.

• tools: In this folder you can find some tools for.... With yyy.py you can reproduce the figures found in ().

• data: Here are saved all the results once you run a simulation.

• params: Here you can find all the configuration files containing all the parameters (for each experiments).