From da64e945c145d20ccbbc5d6f0c5a475c8fa7d493 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Sun, 27 Aug 2023 18:30:43 +0900 Subject: [PATCH 01/10] add chat function --- cloud/sam/scripts/chat_function/Dockerfile | 10 + cloud/sam/scripts/chat_function/__init__.py | 0 cloud/sam/scripts/chat_function/app.py | 141 +++++++ cloud/sam/scripts/chat_function/data/FAQ.md | 170 +++++++++ .../scripts/chat_function/data/GameStatus.md | 218 +++++++++++ cloud/sam/scripts/chat_function/data/ai.md | 207 +++++++++++ cloud/sam/scripts/chat_function/data/art.md | 228 ++++++++++++ .../chat_function/data/block_controller.md | 143 +++++++ .../data/block_controller_sample.md | 78 ++++ .../chat_function/data/board_manager.md | 250 +++++++++++++ .../sam/scripts/chat_function/data/docker.md | 75 ++++ .../chat_function/data/game_manager.md | 131 +++++++ .../scripts/chat_function/data/install_mac.md | 39 ++ .../chat_function/data/install_ubuntu.md | 25 ++ .../chat_function/data/install_windows.md | 39 ++ .../data/install_windows_powershell.md | 205 ++++++++++ .../chat_function/data/install_windows_wsl.md | 81 ++++ .../chat_function/data/reference_score.md | 13 + .../sam/scripts/chat_function/data/tetris.md | 350 ++++++++++++++++++ .../chat_function/events/event_delete.json | 6 + .../chat_function/events/event_get.json | 6 + .../chat_function/events/event_post.json | 6 + .../chat_function/events/event_post_func.json | 7 + .../scripts/chat_function/events/test.bash | 1 + cloud/sam/scripts/chat_function/local.py | 37 ++ .../scripts/chat_function/requirements.txt | 6 + .../scripts/chat_function/role/role_tetris.py | 68 ++++ .../chat_function/storage/docstore.json | 1 + .../chat_function/storage/graph_store.json | 1 + .../chat_function/storage/index_store.json | 1 + .../chat_function/storage/vector_store.json | 1 + .../sam/scripts/chat_function/tools/config.py | 5 + .../scripts/chat_function/tools/response.py | 28 ++ .../sam/scripts/chat_function/tools/utils.py | 137 +++++++ cloud/sam/template.yaml | 43 ++- 35 files changed, 2756 insertions(+), 1 deletion(-) create mode 100644 cloud/sam/scripts/chat_function/Dockerfile create mode 100644 cloud/sam/scripts/chat_function/__init__.py create mode 100644 cloud/sam/scripts/chat_function/app.py create mode 100644 cloud/sam/scripts/chat_function/data/FAQ.md create mode 100644 cloud/sam/scripts/chat_function/data/GameStatus.md create mode 100644 cloud/sam/scripts/chat_function/data/ai.md create mode 100644 cloud/sam/scripts/chat_function/data/art.md create mode 100644 cloud/sam/scripts/chat_function/data/block_controller.md create mode 100644 cloud/sam/scripts/chat_function/data/block_controller_sample.md create mode 100644 cloud/sam/scripts/chat_function/data/board_manager.md create mode 100644 cloud/sam/scripts/chat_function/data/docker.md create mode 100644 cloud/sam/scripts/chat_function/data/game_manager.md create mode 100644 cloud/sam/scripts/chat_function/data/install_mac.md create mode 100644 cloud/sam/scripts/chat_function/data/install_ubuntu.md create mode 100644 cloud/sam/scripts/chat_function/data/install_windows.md create mode 100644 cloud/sam/scripts/chat_function/data/install_windows_powershell.md create mode 100644 cloud/sam/scripts/chat_function/data/install_windows_wsl.md create mode 100644 cloud/sam/scripts/chat_function/data/reference_score.md create mode 100644 cloud/sam/scripts/chat_function/data/tetris.md create mode 100644 cloud/sam/scripts/chat_function/events/event_delete.json create mode 100644 cloud/sam/scripts/chat_function/events/event_get.json create mode 100644 cloud/sam/scripts/chat_function/events/event_post.json create mode 100644 cloud/sam/scripts/chat_function/events/event_post_func.json create mode 100644 cloud/sam/scripts/chat_function/events/test.bash create mode 100644 cloud/sam/scripts/chat_function/local.py create mode 100644 cloud/sam/scripts/chat_function/requirements.txt create mode 100644 cloud/sam/scripts/chat_function/role/role_tetris.py create mode 100644 cloud/sam/scripts/chat_function/storage/docstore.json create mode 100644 cloud/sam/scripts/chat_function/storage/graph_store.json create mode 100644 cloud/sam/scripts/chat_function/storage/index_store.json create mode 100644 cloud/sam/scripts/chat_function/storage/vector_store.json create mode 100644 cloud/sam/scripts/chat_function/tools/config.py create mode 100644 cloud/sam/scripts/chat_function/tools/response.py create mode 100644 cloud/sam/scripts/chat_function/tools/utils.py diff --git a/cloud/sam/scripts/chat_function/Dockerfile b/cloud/sam/scripts/chat_function/Dockerfile new file mode 100644 index 00000000..b82c23cb --- /dev/null +++ b/cloud/sam/scripts/chat_function/Dockerfile @@ -0,0 +1,10 @@ +FROM public.ecr.aws/lambda/python:3.9 + +COPY requirements.txt ./ +RUN python3.9 -m pip install --upgrade pip +RUN python3.9 -m pip install -r requirements.txt -t . + +COPY . . + +# Command can be overwritten by providing a different command in the template directly. +CMD ["app.lambda_handler"] diff --git a/cloud/sam/scripts/chat_function/__init__.py b/cloud/sam/scripts/chat_function/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cloud/sam/scripts/chat_function/app.py b/cloud/sam/scripts/chat_function/app.py new file mode 100644 index 00000000..d72a874d --- /dev/null +++ b/cloud/sam/scripts/chat_function/app.py @@ -0,0 +1,141 @@ +import json +from tools.utils import DynamoDBManager, OpenAIManager +from tools.response import HttpResponse +from role.role_tetris import TetrisAssistant +import logging + +# ロギングの設定 +logger = logging.getLogger() +logger.setLevel(logging.INFO) + +class ChatHandler: + + def __init__(self, event): + self.event = event + self.db_manager = DynamoDBManager() + self.openai_manager = OpenAIManager() + self.http_response = HttpResponse() + self.tetris_assistant = TetrisAssistant() + + def get_message_from_event(self): + body_content = self.event.get('body', None) + if not body_content: + raise ValueError("The 'body' field in the event is missing or empty.") + try: + return json.loads(body_content)['input_text'] + except KeyError: + raise ValueError("Invalid input. 'input_text' key is required.") + + def handle_get_request(self): + return self.http_response.success('hello world') + + def handle_delete_request(self): + # RESTfulな設計では、DELETEはbodyを持たせるべきではないが、他に方法が分からなかった。 + try: + data = json.loads(self.event["body"]) + user_id = data['identity_id'] + char_name = data['character_name'] + try: + self.db_manager.delete_items_with_secondary_index(user_id, char_name) + return self.http_response.success('delete success') + + except Exception as e: + print(f"An error occurred: {e}") + return self.http_response.server_error(f'Error during delete operation: {e}') + except KeyError as e: + print(f"An error occurred: {e}") + return self.http_response.client_error(f'Error during delete operation: {e}') + + + def gpt_function_call(self, response_data, messages, functions, user_id, char_name, max_order_id, input_text): + print(f"function call defined\n") + # gptが定義した関数を実行し、結果を取得する + func_name, args, function_response, function_args = self.openai_manager.execute_function_call(response_data) + + # 2回目のAPI実行のための関数の引数を作成 + function_args = self.openai_manager.create_function_args(func_name, args) + #print(f"function_args: {function_args}") + messages.append({"role": "assistant", "content": None, "function_call": function_args}) + messages.append({"role": "function", "content": function_response, "name": func_name}) + + response_2nd = self.openai_manager.get_chat_response_func(messages, functions) + response_content = response_2nd.choices[0]["message"]["content"] + + # DynamoDBにトーク履歴を記録 + self.db_manager.store_conversation(user_id, char_name, max_order_id + 0, "user", input_text) + self.db_manager.store_conversation(user_id, char_name, max_order_id + 1, "assistant", None, name=None, function_call=function_args) + self.db_manager.store_conversation(user_id, char_name, max_order_id + 2, "function", function_response, name=func_name, function_call=None) + self.db_manager.store_conversation(user_id, char_name, max_order_id + 3, "assistant", response_content) + + return response_content + + + def gpt_simple_response(self, response_data, user_id, char_name, max_order_id, input_text): + print(f"function call undefined\n") + response_content = response_data["message"]["content"] + self.db_manager.store_conversation(user_id, char_name, max_order_id + 0, "user", input_text) + self.db_manager.store_conversation(user_id, char_name, max_order_id + 1, "assistant", response_content) + return response_content + + + def call_openai_api(self, messages, functions, user_id, char_name, input_text, max_order_id): + response_1st = self.openai_manager.get_chat_response_func(messages, functions) + response_data = response_1st["choices"][0] + if response_data["finish_reason"] == "function_call": + if response_data["message"]["function_call"]["name"]: + return self.gpt_function_call(response_data, messages, functions, user_id, char_name, max_order_id, input_text) + else: + return self.gpt_simple_response(response_data, user_id, char_name, max_order_id, input_text) + + + def handle_post_request(self): + self.openai_manager.get_secret() + + # メッセージを取得 + try: + data = json.loads(self.event["body"]) + logger.info('Event: %s', json.dumps(data)) + user_id = data['identity_id'] + char_name = data['character_name'] + input_text = data['input_text'] + except ValueError as e: + return self.http_response.client_error(f'Error during post operation: {e}') + + # 過去の応答を取得 + try: + messages = self.tetris_assistant.get_chat_messages() + functions = self.tetris_assistant.get_chat_functions() + max_order_id,items = self.db_manager.get_max_conversation_id(user_id, char_name) + + #今までの対話をmessagesに並べる + messages.extend([ + { + "role": item["role"], + "content": item["content"], + **({"name": item["name"]} if "name" in item else {}), + **({"function_call": item["function_call"]} if "function_call" in item else {}) + } + for item in items + ]) + messages.append({"role": "user", "content": data['input_text']}) + + #openAIのAPIを叩く + response_content = self.call_openai_api(messages, functions, user_id, char_name, input_text, max_order_id) + return self.http_response.success(response_content) + + except Exception as e: + return self.http_response.server_error(f'Error during post operation: {e}') + + def handle(self): + http_method = self.event.get('httpMethod', '') + if http_method == 'GET': + return self.handle_get_request() + elif http_method == 'DELETE': + return self.handle_delete_request() + elif http_method == 'POST': + return self.handle_post_request() + + +def lambda_handler(event, context): + handler = ChatHandler(event) + return handler.handle() diff --git a/cloud/sam/scripts/chat_function/data/FAQ.md b/cloud/sam/scripts/chat_function/data/FAQ.md new file mode 100644 index 00000000..3877223a --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/FAQ.md @@ -0,0 +1,170 @@ +# FAQ + +## level1ではブロックの順番は固定とのことだが、具体的にはどんな順番になっているのか +`level1（ブロックの順番固定）`の場合は、[ブロックのindex値](https://github.com/seigot/tetris/blob/master/doc/files/block_controller.md#ブロック情報)が`1→2→3→4→5→6→7→1→...`の順番に出現します。 + +## python実行環境について + +| 環境 | 環境構築手順 | +| ---- | ---- | +| ubuntu18.04,20.04 | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_ubuntu.md) | +| Mac | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_mac.md) | +| Windows+powershell | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_windows_powershell.md) | +| Windows+Docker | [こちら](https://github.com/seigot/tetris/blob/master/docker/README.md) | +| JetsonNano | （動作未確認だがおそらく動くはず） | +| RaspberryPi | （動作未確認だがおそらく動くはず） | +| Windows+GoogleChrome+ubuntu-free-online-linux | [chrome webstore URL](https://chrome.google.com/webstore/detail/ubuntu-free-online-linux/pmaonbjcobmgkemldgcedmpbmmncpbgi?hl=ja) | +| AWS | EC2 4CPU 8GBメモリ 20GBストレージ、GPU環境で動作確認済（課金に注意） | + +## `Windows+GoogleChrome+ubuntu-free-online-linux`の環境構築について + +>google chrome上でubuntu serverを実行する
+・[google chrome用のubuntu online server拡張プラグイン](https://chrome.google.com/webstore/detail/ubuntu-free-online-linux/pmaonbjcobmgkemldgcedmpbmmncpbgi)をインストール
+・選択肢のうち、"Xubuntu"を選択（ubuntu18.04かつ軽量なものが良さそう）
+・serverにログインして以下を実施
+　・Desktop上で右クリックし"Open Terminal Here"を選択
+　・terminal上で以下コマンドを実行
+``` +sudo apt install −y git +git clone http://github.com/seigot/tetris +cd tetris game +bash doc/files/install_ubuntu.sh +``` + +--> installが成功すればOK + +``` +python start.py +``` + +--> テトリスが表示されればOK + +## `sudo apt install`時に`E: ロック /var/lib/dpkg/lock-frontend が取得できませんでした - open (11: リソースが一時的に利用できません)`のエラーが出る + +[こちらのサイト](https://marginalia.hatenablog.com/entry/2019/07/03/133854)参照
+以下で解決するはず + +``` +$ sudo rm /var/lib/apt/lists/lock +$ sudo rm /var/lib/dpkg/lock +$ sudo rm /var/lib/dpkg/lock-frontend +``` + +## サンプルプログラム（`python start.py -s y`で動くやつ）の中身はどうなってるのか +[こちら](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)で解説 + +## スコアアタック時の動作PC環境について + +2021/7時点で以下を想定しています。
+PC: [ZBOX Magnux en52060](https://www.zotac.com/jp/product/mini_pcs/magnus-en52060v#spec)
+ +``` +- OS : ubuntu18.04 +- CPU: Intel Core i5 +- Memory: 16GB +- NVIDIA GeForce RTX 2060 +``` + +``` +- pythonバージョンは以下 +$ python3 --version +Python 3.6.9 +$ python3 -c 'import torch; print(torch.__version__) ' +1.4.0 +``` + +ソフト環境は以下スクリプトで構築しています。(散らかっててすみません)
+[auto_setup_for_Linux.sh](https://github.com/seigot/tetris/blob/master/docker/auto_setup_for_Linux.sh)
+ +## Windows Docker install時のトラブルシューティング + +``` +①BIOSのCPU関連設定 +　Docker for Windowsをインストールして起動すると、 +　「An error occurred Hardware assisted virtualization and data execution protection +　 must be enabled in the BIOS.」と出てくる。 +　⇒ 以下サイトの「1. BIOS設定の確認」をすることでエラー解消 +　　 https://qiita.com/LemonmanNo39/items/b1b104e7fb609464727b + +②WSLのインストール +　Dockerを起動したところ、①は解消されたが以下のメッセージが表示される。 +　「WSL2 installation is incomplete」 +　⇒ 以下を参考にしてWSL2をインストールする。 +　　 https://docs.microsoft.com/ja-jp/windows/wsl/install-win10 +　　 ※ Docker for Windowsをインストールするときに一緒にWSLも入れられたっぽい + +③ Dockerイメージの取得途中で死ぬ +自宅のネットワーク回線が貧弱なのか、下記コマンドでイメージを持ってくる途中で止まる。 +docker run -p 6080:80 --shm-size=512m seigott/tetris_docker + +⇒ Docker for Windowsを起動して、ウィンドウ上部の"歯車(設定)ボタン"、"Docker Engine"をクリック。 +　 configファイルに、「"max-concurrent-download":1」を追記。 +　※これでデフォルトの3並列ダウンロードが直列になる + +④ Dockerイメージの取得途中で、「docker: unauthorized authentication required」と出て死ぬ +⇒ windows power shellを管理者権限で実行してコマンドを叩くといけたっぽい。 +``` + +## pytorch v1.4 インストール方法 + +ubuntu18.04環境では、以下のようにしてインストールできることを確認済
+[pytorch v1.4 インストール済のDocker環境（お試し版）](https://github.com/seigot/tetris/blob/master/docker/README.md)を作成しました。追加で必要なものがあればDockerfileを更新して下さい。 + +``` +function install_torch(){ + ### pytorch from pip image (v1.4) + sudo apt-get install -y libopenblas-base libopenmpi-dev + sudo apt-get -y install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev + + #python -m pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp27-cp27mu-linux_x86_64.whl + python -m pip install torchvision==0.2.2 + pip3 install torch==1.4.0 torchvision==0.2.2 + pip install 'pillow<7' +} +``` +## Dockerはコンテナ終了の度にデータが消えて手間が掛かるので何とかしたい +[WSL(Windows Subsystem for Linux)を使う場合](https://github.com/seigot/tetris/blob/master/doc/files/install_windows_wsl.md)の手順を用意しました。
+（kyadさんありがとうございます）
+
+追記：cygwin環境構築手順
+[isshy-youさんによる`Cygwin Install for tetris`構築手順](https://github.com/isshy-you/tetris_game/wiki/Cygwin-Install-for-tetris_game)
+（isshy-youさんありがとうございます） + +## pythonコマンドが見つからない旨のエラーが出る場合 + +ubuntu20.04等では、`python3`があるにも関わらず`python`コマンドがない + +``` +$ python start.py +CompletedProcess(args='python --version', returncode=127, stderr='/bin/sh: 1: python: not found\n') +/bin/sh: 1: python: not found +error: subprocess failed. +``` + +``` +$ python --version + +Command 'python' not found, did you mean: + + command 'python3' from deb python3 + command 'python' from deb python-is-python3 +``` + +ログ記載の通り`python-is-python3`をインストールすればOK + +``` +sudo apt install -y python-is-python3 +``` + +## スコア評価用サーバについて + +以下を作成してみました。 + +[https://github.com/seigot/tetris_score_server](https://github.com/seigot/tetris_score_server) + +## `python start.py`実行時に`TypeError: arguments did not match any overloaded call:`エラーが出る + +以下issueで対策中
+> [`python start.py`実行時に`TypeError: arguments did not match any overloaded call:`エラーが出る](https://github.com/seigot/tetris/issues/5) + +## 以下、順次追記 diff --git a/cloud/sam/scripts/chat_function/data/GameStatus.md b/cloud/sam/scripts/chat_function/data/GameStatus.md new file mode 100644 index 00000000..5023a8cf --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/GameStatus.md @@ -0,0 +1,218 @@ +## `GameStatus`データ構造 + +[`block_controller.py`](https://github.com/seigot/tetris/blob/master/block_controller.py)内の`def GetNextMove(self, nextMove, GameStatus)`で扱う`GameStatus`は辞書型データです。
+ブロックの動きを決定するための参考データとして以下を格納しています。 + +``` +* field_info : フィールド情報 +* block_info : ブロック情報 +* judge_info : 審判情報 +* debug_info : デバッグ情報 +``` + +以下、各情報の詳細について記載します。 + +## field_info + +以下を格納しています。 + +``` +* 'backboard': フィールドのデータ +* 'height': フィールドの高さ +* 'width' : フィールドの幅 +* 'withblock' : フィールド+ブロックの位置を合わせたデータ +``` + +具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 + +``` +'field_info': {'backboard': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + 'height': 22, + 'width': 10, + 'withblock': [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, +``` + +## block_info + +以下を格納しています。 + +``` +'currentDirection': 現在のブロックが初期形状から何回回転した形状か +'currentShape': { + 'class': 現在のブロックを管理しているクラスのアドレス + 'direction_range': 現在のブロックが回転できる回数 + 'index': 現在のブロックのindex値 +}, +'currentX': 現在のブロック操作の基準点(x座標) +'currentY': 現在のブロック操作の基準点(y座標) +'nextShape': { + 'class': 次のブロックを管理しているクラスのアドレス + 'direction_range': 次のブロックが回転できる回数 + 'index' : 次のブロックのindex値 +} +``` + +具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 + +``` +'block_info': {'currentDirection': 0, + 'currentShape': {'class': , + 'direction_range': (0, 1), + 'index': 1}, + 'currentX': 5, + 'currentY': 1, + 'nextShape': {'class': , + 'direction_range': (0, 1, 2, + 3), + 'index': 2}}, +``` + +## debug_info + +以下を格納しています。
+デバック用に準備している情報であり、必ずしも使う必要はないかもしれないです。 + +``` +'dropdownscore': 落下により獲得したスコアの合計 +'line_score': { lineを消した時などに獲得できるスコア + 'line1': 1line消した時の獲得スコア + 'line2': 2line消した時の獲得スコア + 'line3': 3line消した時の獲得スコア + 'line4': 4line消した時の獲得スコア + 'gameover': game overになった時の獲得スコア +}, +'line_score_stat': 消したlineの統計データ +'linescore': lineを消して獲得したスコアの合計 +'shape_info': { + 'shapeI': { + 'color': ShapeIの色 + 'index': ShapeIのindex + }, + 'shapeJ': { + 'color': ShapeJの色 + 'index': ShapeJのindex + }, + 'shapeL': { + 'color': ShapeLの色 + 'index': ShapeLのindex + }, + 'shapeNone': { + 'color': ShapeNone(ブロックがない状態)の色 + 'index': ShapeNone(ブロックがない状態)のindex + }, + 'shapeO': { + 'color': ShapeOの色 + 'index': ShapeOのindex + }, + 'shapeS': { + 'color': ShapeSの色 + 'index': ShapeSのindex + }, + 'shapeT': { + 'color': ShapeTの色 + 'index': ShapeTのindex + }, + 'shapeZ': { + 'color': ShapeZの色 + 'index': ShapeZのindex + } +}, +'shape_info_stat': ゲーム開始時から現時点までに出現したブロックの統計情報 +``` + +具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 + +``` + 'debug_info': {'dropdownscore': 0, + 'line_score': {'line1': 100, + 'line2': 300, + 'line3': 700, + 'line4': 1300, + 'gameover': -500}, + 'line_score_stat': [0, 0, 0, 0], + 'linescore': 0, + 'shape_info': {'shapeI': {'color': 'red', + 'index': 1}, + 'shapeJ': {'color': 'purple', + 'index': 3}, + 'shapeL': {'color': 'green', + 'index': 2}, + 'shapeNone': {'color': 'none', + 'index': 0}, + 'shapeO': {'color': 'pink', + 'index': 5}, + 'shapeS': {'color': 'blue', + 'index': 6}, + 'shapeT': {'color': 'gold', + 'index': 4}, + 'shapeZ': {'color': 'yellow', + 'index': 7}}, + 'shape_info_stat': [0, 1, 0, 0, 0, 0, 0, 0]}, +``` + +## judge_info + +以下を格納しています。 + +``` +'block_index': 現在のブロックがゲーム開始時から何番目に登場したブロックか +'elapsed_time': ゲーム開始時からの経過時間(s) +'game_time': 制限時間(s) +'gameover_count': ゲーム開始時から現在までにゲームオーバーになった回数 +'line': ゲーム開始時から現在までに消したラインの数 +'score': ゲーム開始時から現在までに獲得した合計スコア +``` + +具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 + +``` + 'judge_info': {'block_index': 1, + 'elapsed_time': 1.083, + 'game_time': 180, + 'gameover_count': 0, + 'line': 0, + 'score': 0}} + ``` + diff --git a/cloud/sam/scripts/chat_function/data/ai.md b/cloud/sam/scripts/chat_function/data/ai.md new file mode 100644 index 00000000..4ceca12c --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/ai.md @@ -0,0 +1,207 @@ +# AIについて（準備中） + +# 1.環境準備 +- sample のコードでは [pytorch](https://pytorch.org/get-started/locally/) を使ってAIのニューラルネットワークを構築します。 +- pytorch　については[こちら](https://pytorch.org/get-started/locally/)を参考にし、環境に合わせてインストールしてください。 + +### 例) WindowsのCPU動作用をインストールする場合 +``` +pip3 install torch torchvision torchaudio +``` +### 例) MacのCPU実行用をインストールする場合 +``` +pip3 install torch torchvision torchaudio +``` + +# 2.学習と推論 + +* サンプルとして下記の２つのニューラルネットワークが +[こちら](../../game_manager/machine_learning/model/deepqnet.py) +のコードに定義されています。 + + * sample: DQN(Deep Q Network)を使った学習・推論 + * sample2: MLP (Multilayer perceptron)を使った学習・推論 + + +## 2.1学習の実行 +- DQN(Deep Q Network)を使う場合 + +``` +python start.py -m train_sample -d 1 -l 2 -t -1 +``` + +- MLP (Multilayer perceptron)を使う場合 +``` +python start.py -m train_sample2 -d 1 -l 2 -t -1 +``` + +## 2.2推論の実行 + +- DQN(Deep Q Network)を使う場合 +``` +python start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt +``` + +- MLP (Multilayer perceptron)を使う場合 +``` +python start.py -m predict_sample2 -l 2 --predict_weight weight/MLP/sample_weight.pt +``` + +## 2.3 再学習/追加学習（Fine Tuning) の実行 +- 学習済みの重みを初期状態として学習する場合は下記の手順で　configのyamlファイルを設定する。 +1. ft_weight に学習済み重みファイルのパスを設定 +2. finetune を　False → True に変更 +3. initial_epsilon を小さくする（0より大きい１以下の値） + +- initial_epsilon は ε-greedy法というアルゴリズムのパラメータになり、学習初期ににどの程度ランダムな行動を取るかを設定します。追加学習の場合は、小さい値を設定することで、学習済みのモデルを有効活用できます。 +- ε-greedy法については下記のサイトなどが参考になります。 + - [https://horomary.hatenablog.com/entry/2021/01/26/233351](https://horomary.hatenablog.com/entry/2021/01/26/233351) + +## 2.4 -m オプションについて + - 学習/推論の切り替えおよびサンプル/本番コードの切り替には　"-m"　オプションを使用します。 + +**※ sampleコードは基本的に変更せず、[本番用コード](../../game_manager/machine_learning/block_controller_train.py )で開発してください** + +- "-m" オプションに対して下記の引数を渡すことで書くモードを切り替えられる。 + +|オプション名|説明| +| ---- | ---- | +| train_sample| [サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py)を用いてDQNによる学習を行う| +| train_sample2| [サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py)を用いてMLPによる学習を行う| +| train| [本番用コード](../../game_manager/machine_learning/block_controller_train.py ) を用いて学習を行う| +| predict_sample| [サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py)を用いてDQNによる推論を行う。
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| +| predict_sample2| [サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py)を用いてMLPによる学習を行う　
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| +| predict| [本番用コード](../../game_manager/machine_learning/block_controller_train.py ) を用いて学習を行う　
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| + + +## 2.5 AI関連のその他オプションについて +- AI学習関連のオプションは下記 + +|オプション名|引数|説明| train or predict| +| ---- | ---- | ---- | ---- | +| *--train_yaml* | 学習に用いるyamlファイルのパス|学習に用いるyamlファイルを選択する。|train| +| *--predict_weight* | 推論に用いるweightファイルのパス|推論に用いるweightファイルを選択する|predict| + + +# 3. 強化学習について +[サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py) 　および　[サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py) では強化学習と呼ばれる方法でテトリスをプレイするための最適なパラメータを学習します。 + +![Screenshot](../../doc/pics/rainforcement_learning.png) + + +# 4. パラメータについて +## 4.1 yamlファイルについて +- サンプルコードではyaml ファイルによってハイパーパラメータ（パラメータを学習するためのパラメータ）を設定する。 +- サンプル用yaml ファイルのフォーマットは下記 + +
共通 + +|パラメータ名|説明|型|train or predict|default| +| ---- | ---- | ---- | ---- | ---- | +|**common** |- |- |- |-| +|ft_weight|ファインチューニング(初期値として学習済み
の重みを用いる学習)に用いる重みファイルのパス|str|train|None| +|log_path| tensorboardのログ出力フォルダ名|str|共通|tensorboard| +
+ +
model関連 + +|パラメータ名|説明|型|train or predict|default| +| ---- | ---- | ---- | ---- | ---- | +|**model** |- |- |- |-| +|name|使用するAIモデル名|str|共通|DQN| +|finetune|ファインチューニングするかどうかのフラグ|bool|train|False| +|**state** |- |- |- |-| +|dim|状態の次元数|int|共通|4| +
+ +
学習 + +|パラメータ名|説明|型|train or predict|default| +| ---- | ---- | ---- | ---- | ---- | +|**train** |- |- |- |-| +|optimizer|最適化関数(サンプルではAdamまたはSGDが使用可能)|str|train|Adam| +|lr|学習率|float|train|1.0e-3| +|lr_gamma|更新率(optimizerがSGDの時のみ使用)|float|train|0.1| +|lr_momentum|モーメンタム(optimizerがSGDの時のみ使用)|float|train|0.99| +|lr_step_size|ステップサイズ(optimizerがSGDの時のみ使用)|int|train|1000| +|num_epoch|エポック数(学習するイテレーション数)|int|train|5000| +|num_decay_epochs|ε-greedyのεを最小にするエポック数
(ここからランダム性を下げる。)|int|train|3000| +|initial_epsilon|ε-greedyのεの初期値|float|train|1.0| +|final_epsilon|ε-greedyのεの最小値|float|train|1.0e-3| +|batch_size|バッチサイズ|int|train|512| +|gamma|割引率|float|train|0.99| +|max_penalty|ペナルティの最大値|float|train|-1| +|target_net|TargetNetworkの使用フラグ|bool|train|True| +|target_copy_intarval|TargetNetworkにコピーするインターバル|int|train|500| +|replay_memory_size|Experience replayのreplay bufferのサイズ|int|train|30000| +|double_dqn|Double DQNの使用フラグ|bool|train|True| +|reward_clipping|Reward clippingの使用フラグ|bool|train|True| +|prioritized_replay|Prioritized Experience Replayの使用フラグ|bool|train|True| +|multi_step_learning|Multi step learningの使用フラグ|bool|train|True| +|multi_step_num|Multi step learningのステップ数|int|train|0,100,300,700,1300,-1300
　左から　0列崩し,1列崩し,2列崩し,3列崩し,4列崩し,ゲームオーバー)| +|reward_list|点数に応じた報酬の設定値
（0列崩し,1列崩し,2列崩し,3列崩し,4列崩し,ゲームオーバーの順）|int|train|3| +|reward_weight|点数以外で与えられるペナルティの重み
（隣接する行との高さ差分,最大の高さ,穴の数 の順)
0以上の値が必須|float|train|0.01,0.0,0.01
（左から　隣接する行との高さ差分,最大の高さ,穴の数 ）| +
+ +## 4.2 パラメータ調整例（得たい効果とパラメータの関係例） +- 下記にパラメータ調整の方針例を記載する +- それぞれの効果が常に得られるとは限らないので実験を繰り返しながら、最適な値を探してみてください。 +1. ブロックを積み上げる + - reward_listの３列崩し、４列崩しの値を大きくする + - reward_weightの max_heightを小さくする + +2. ゲームオーバの発生を防ぐ（ブロックを積み上げ量を減らす） + - reward_listの３列崩し、４列崩しの値を小さくする + - reward_weightの max_heightを大きくする + +3. 過去の盤面からの影響を小さくする + - gamma(割引率)を大きくする。 + +4. 学習を安定させる + - reward_clipping を Trueにする。 + - target_net を Trueにする + - double_DQN を Trueにする。 + +5. 学習を早くする。（学習するモデルのパラメータを減らす） + - モデルのレイヤ数、カーネルサイズを減らす + - MLPを使用する + +6. 学習を早くする。（効果の高いデータを効率的に学習に使用する） + - prioritized_replay を Trueにする。 + - lr（学習率）を大きくする。（大きすぎると収束しなくなるので注意） + - batch_size　を大きくする （学習に使用するメモリ量が大きくなるので注意） + - replay_memory_size　（学習に使用するメモリ量が大きくなるので注意） + + +# 5.チュートリアルの実行 +- サンプル用のコードを試すための[チュートリアル用Jupyter-notebook]()../../game_manager/machine_learning/) +を用意しているので、コード理解のためにお使いください。 + +- Jupyter-notebookか下記の手順で使用できます. +- 1. Jupyter-notebookのインストール +``` +pip install jupyter +``` + +- 2. Jupyter-notebookの起動 +``` +jupyter notebook +``` +下記のような画面が、ブラウザで開かれるので +game_manager > machine_learning > model > tutorial +の準備進み、***.ipynb ファイルを開いてください。 +![Screenshot](../../doc/pics/jupyter-sample.png) + +- Jupyter-notebook のセルは　Shit＋Enter key で実行できます + +- Jupyter-notebook の使い方は下記のサイトなどが参考になります。 +[https://qiita.com/takuyanin/items/8bf396e7b6b051670147](https://qiita.com/takuyanin/items/8bf396e7b6b051670147) + +- venv などの仮想環境を用いている場合は下記のサイトなどが参考になります。 +　　 +[https://qiita.com/smiler5617/items/e0d9b3034d79457cc253](https://qiita.com/smiler5617/items/e0d9b3034d79457cc253) + +# 6. さらなる強化に向けて +- 第3回での cookie4869 の強化資料を下記で共有いたします。 + +![AI強化資料](../pics/20220920_Tetris_AI.pdf) diff --git a/cloud/sam/scripts/chat_function/data/art.md b/cloud/sam/scripts/chat_function/data/art.md new file mode 100644 index 00000000..7b5004ca --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/art.md @@ -0,0 +1,228 @@ +# artについて + +本ドキュメントではテトリスアートの取り組みについて記載する + +# 概要 + +通常、テトリスは上から落ちてくるブロック（テトリミノ）を操作して、横列を埋めて消していくようにして遊ぶ。 +一方、テトリスアートはブロックでフィールドに模様を描くことを目的とする。（ブロックを消すことを必ずしも目的としない） +詳しくは"テトリスアート"でgoogle検索をしてみて下さい。 + +# 取り組み方 + +1.まず、ブロックでフィールドに描きたい模様をイメージする。 +2.次に、模様がテトリス上で実現可能かどうかを検討する。（作図などがお勧めです） +3.実現可能であればブロックを操作して模様を作成する。 + +3.によりブロックを操作する場合、後述するconfigファイルを使い各ブロックの色や出現順序(index)などを調整できるようにしている。 + +# サンプルコード + +`python start.py`実行時に以下オプションを指定するとサンプルコードが実行される +[youtube-link: tetris art sample](https://www.youtube.com/watch?v=Seh6g9_nL6o) + +`1:onigiri` + +``` +python start.py -l1 -m art --art_config_filepath config/art/art_config_sample1.json +``` + +![Screenshot](../pics/art_sample_onigiri.png) + +`2:manji` + +``` +python start.py -l1 -m art --art_config_filepath config/art/art_config_sample2.json +``` + +![Screenshot](../pics/art_sample_manji.png) + +`3:cartoon charactor` + +``` +python start.py -l1 -m art --art_config_filepath config/art/art_config_sample3.json +``` + +![Screenshot](../pics/art_sample_cartoon.png) + +other sample + +| name | --art_config_filepath | note | +| ---- | ---- | ---- | +| 4:heart | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample4.json | - | +| 5:hamburger_shop | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample5.json | - | +| 6:parking | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample6.json | - | +| 7:team rocket | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample7.json | - | +| 8:happy_new_year_2023 | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample8.json | - | +| 9:taka | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample9.json | - | +| ... | - | - | + +contribution + +| name | --art_config_filepath | thanks | +| ---- | ---- | ---- | +| mario | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample_tanaka_2.json | @tanaken | +| ... | - | - | + +# configファイルの説明 + +テトリスアートを作成し易くするために各ブロックの色や出現順序(index)などをconfigファイルで調整できるようにしている。 +以下はconfigファイルの説明である。 + +art用configファイルサンプル +[config/art/art_config_sample_default.json](https://github.com/seigot/tetris/blob/master/config/art/art_config_sample_default.json) + +``` +{ + // 各ブロックの色を調整する。色についてはカラーコードを参照下さい。 + "color": { + "shapeI": "0xCC6666", + "shapeL": "0x66CC66", + "shapeJ": "0x6666CC", + "shapeT": "0xCCCC66", + "shapeO": "0xCC66CC", + "shapeS": "0x66CCCC", + "shapeZ": "0xDAAA00" + }, + // 各ブロックの出現順序(index)/direction/x/yを調整する。 + // 行を追加することが可能であり、追加すると記載に応じたブロックが出現する。 + "block_order": [ + [1,0,0,1], + [2,0,0,1], + [3,0,0,1], + [4,0,0,1], + [5,0,0,1], + [6,0,0,1], + [7,0,0,1] + ] +} +``` + +ブロックの色については以下のカラーコードを参考にしてください。 + +> [色の名前とカラーコードが一目でわかるWEB色見本 原色大辞典 - HTMLカラーコード](https://www.colordic.org/) + +各ブロックの出現順序(index)/direction/x/yについては以下を参考にしてください。 + +> [ブロック操作用プログラムについて](./block_controller.md) + +# 実行方法 + +以下のように`-m art`,`--art_config_filepath`によりモード及びconfigファイルを指定する。 + +``` +# "art_config_sample.json"を指定して実行 +python start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json + +# 落下速度を早くする場合は"-d500"を加えて実行 +python start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json -d500 +``` + +自作したart用configファイル(`xxx.json`)を作成する場合は以下のようにファイルコピーして使用してください。 + +``` +# 事前にサンプルをコピーしておく(art_config_sample.json --> xxx.json) +cp config/art/art_config_sample.json config/art/xxx.json +# 実行 +python start.py -l1 -m art --art_config_filepath config/art/xxx.json +``` + + +# 作成アルゴリズムの例 +#### ◆2ドットプリンター方式 +横10マスのフィールドにテトリスミノを置いた後に残るブロックの個数は 4n-10m 個です。 +そのためテトリスアートを 4n-10m 個のドットの組み合わせに分解することで楽に作ることができます。 +汎用性が高いのは2ドット残し（32ブロック：n=8, m=3 や 52ブロック：n=13, m=5 など）。 +アートには1行あたり最大9個のドットを置けます。 +そのため基本的に各行を 2\*4 + 1ドットに分解し、余った1同士で2ドットを構成することを目指します。 +``` +2ドットを同じ行に残す場合、その組み合わせは恐らく2205通り（x座標10*9÷2、shape7*7）。 +2ドットを違う行に残す場合、その組み合わせは恐らく4900通り（x座標10*10、shape7*7）。 +アートを構成する2ドットを残せる出現順序 [index,direction,x,y] をあらかじめ用意すれば、config作成の自動化が可能になります。 +``` + + +**Step1：任意の2ドットを残す出現順序をまとめる** +`art_lib.py`： +> [https://github.com/mattshamrock/tetris/blob/art/art_lib.py](https://github.com/mattshamrock/tetris/blob/art/art_lib.py) +xs_0000 ← 同じ行に残す場合。例えば 'xs_5294'なら、x=5にshapeindex=2（Lミノ）、x=9にshapeindex=4（Tミノ）が残る置き方。 +xs_00_00 ← 違う行に残す場合。前半が1行目のx座標とshapeindex、後半が2行目のx座標とshapeindex。 +未発見の場合は False と記載 + +``` +art_lib.pyの現状： +【2023/3/E時点】 + ・'xs_0000'のうち32ブロック使用で実現可能なもの：全探索済み; 後述 + ・'xs_0000'のうち52ブロック以上使用で実現可能なもの：手作業。不完全 + ・'xs_00_00'のうち見つけたもの：手作業。とても不完全 +``` + +`art_lib.py`用の探索アルゴリズム： +> [https://github.com/mattshamrock/tetris/blob/art/artlib_search_32.py](https://github.com/mattshamrock/tetris/blob/art/artlib_search_32.py) +各xs_0000について、32ドット（8ミノ）の出現順序をシミュレートし、指定の2ドットのみが残るかを判定します。 +枝切りをすることで探索時間を減らそうとしています。手元PCでは36時間で半分の約1200通りが出力できます。 +後述の「反転」と組み合わせると約36時間で完了します。 + +`art_lib.py`用の反転アルゴリズム： +> [https://github.com/mattshamrock/tetris/blob/art/artlib_reverse.py](https://github.com/mattshamrock/tetris/blob/art/artlib_reverse.py) +横10マスのテトリスはすべての配置を左右反転させても成り立ちます。 +探索でart_lib.pyを埋めていく際、結果が出た出現順序を反転させれば探索のみで埋める約半分の時間で完了できるはずです。 +注意…基準点ズレの調整が必要な組み合わせが存在します。（例：shape:Sの形状:1 の反転とshape:Zの形状:1 は基準点が1マス違う） + + + + +**Step2：ドットアートを描く** +現状は手作業です +　　 + + +**Step3：ドットアートを数値に変換する** +ブロックのindex値に変換します。 +Step4の入力に合わせて文字数=10のstrに変換しています。 +空白=0です。 +現状はexcelで作業しています +　　 + +**Step4：art_lib.pyにある実現可能な2ドットの組み合わせを探索する** +`art_lib.py`の組み合わせ探索アルゴリズム： +> [https://github.com/mattshamrock/tetris/blob/art/create_art.py](https://github.com/mattshamrock/tetris/blob/art/create_art.py) +10個のindex値を2ドットの組み合わせに分解します。 +art_lib.pyでは'xs_0000'の出現順序が未発見の場合にFalseを返すことを利用して、実現可能な2ドットの組み合わせを探索できます。 +1行に奇数個のドット（空白を除く）がある場合、余ったドットのx座標とindex値も返します。これが'xs_00_00'の前半か後半のいずれかになります。 + +　 + +**Step5：Step4の結果を調整する** +・'xs_00_00' は余りとして返された情報を基に自力で発見し、`art_lib.py`に追加する必要があります。 +・余りとして返るドットのみで'xs_00_00'が構成されることを前提にしています。そのため各行のドット数（空白を除く）の偶奇によっては成立しません。 +・'xs_0000' において、すぐ下のマスが空白の場合に、そこにハマる形で意図しない結果になる場合があります。左右のマスにブロックがあれば引っ掛かるため回避可能な可能性が高く、その場合は順番を変更することで解決できます。 +　　 + +**Step6：2ドットの組み合わせをconfigファイルの出現順序に変換する** +Step4-5で得られた 'xs_0000' と 'xs_00_00' の羅列を`art_lib.py`の`artdict`のキーとして利用し、出力します。 +現状は余計な二重括弧　[[]] が入ってきてしまいます。置換等で取り除きます。 +各出力の最後のカンマ , が不足しています。二重括弧と合わせて置換で対応します。 +　　 + +**Step7：configファイルに転記する** +転記すればOK。 +色の調整は手作業です。 +　　 + +**今後に向けて：** +・`art_lib.py`の抜けている部分を埋める。52ブロック（13手）以上を効率的に探索する。 +・`art_lib.py`の抜けている部分を埋める。2行に分かれるブロックを効率的に探索する。32マスを8つに分割した結果がテトリスミノの形になるか判定する、という探し方が良い気がする。 +・Step5の調整を自動化する。 +・Step2を自動化する（画像生成AI的な） + + + + + + + +# 参考 +[「テトリス」の斬新すぎる遊び方が話題に。積み上げたブロックでマリオやルイージを再現!?](https://nlab.itmedia.co.jp/nl/articles/1109/13/news025.html) +[色の名前とカラーコードが一目でわかるWEB色見本 原色大辞典 - HTMLカラーコード](https://www.colordic.org/) +[youtube-link: tetris art sample](https://www.youtube.com/watch?v=Seh6g9_nL6o) diff --git a/cloud/sam/scripts/chat_function/data/block_controller.md b/cloud/sam/scripts/chat_function/data/block_controller.md new file mode 100644 index 00000000..e815bf0f --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/block_controller.md @@ -0,0 +1,143 @@ +>本ページでは、[ブロック操作用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)について説明を追記頂ける方を募集しています。
+>説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
+ +# ブロック操作用プログラムについて + +[ブロック操作用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)では、ボード情報からブロックの次の動作を決定します。
+このファイルの`def GetNextMove`関数にて、ボード情報`GameStatus`を入力として受け取り、次の動作決定用のデータ`nextMove`を返す事でブロックが動く仕様になっています。
+
+以下は、ボード情報`GameStatus`、次の動作決定用のデータ`nextMove`について記載します。
+ +# ボード情報 +ボード情報には以下が含まれます。
+詳細は、[`GameStatus`データ構造](https://github.com/seigot/tetris/blob/master/doc/files/GameStatus.md)や、[サンプルコード](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)のようにGameStatusをログ出力することで確認可能です。 + +``` +* field_info : フィールド情報 +* block_info : ブロック情報 +* judge_info : 審判情報 +* debug_info : デバッグ情報 +``` + +# フィールド情報 + +## フィールドの定義 +フィールドは横幅10×高さ22ブロック分の大きさです。
+座標系は、左上を原点(0,0)として、x軸は右側、y軸は下側を正の方向として定義しています。
+ +![Screenshot](../pics/coordinate_difinition.PNG) + +フィールドのデータは、一次元配列として定義しています。
+座標(x, y)のデータは、一次元配列の[x + y * 横幅]番目にアクセスすることで取得可能です。
+
+| | フィールド | 一次元配列 | +| --- | --- | --- | +| データ | ![Screenshot](../pics/field_data.png) | ![Screenshot](../pics/matrix_data.png) | + +## フィールド情報に格納されているデータ + +上記の一次元配列の各座標にブロックが有るか無いかを管理しています。 + +``` +* `0以外` : ブロック有り +* `0` : ブロック無し +``` + +ブロックが有る場合は、各ブロックのIndex値を格納しています。
+Index値はブロック情報を参照下さい。 + +# ブロック情報 + +## ブロックの定義 +ブロックは７種類あり、Index値と初期形状を以下の通り定義しています。
+横移動と回転が可能です。
+回転は時計回りに回転します。ブロックによって１周の回転に必要な回数が異なります。
+ +| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | +| --- | --- | --- | --- | --- | --- | --- | --- | +| index値 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | +| 初期形状 | ![Screenshot](../pics/ShapeI.png) | ![Screenshot](../pics/ShapeL.png) | ![Screenshot](../pics/ShapeJ.png) | ![Screenshot](../pics/ShapeT.png) | ![Screenshot](../pics/ShapeO.png) | ![Screenshot](../pics/ShapeS.png) | ![Screenshot](../pics/ShapeZ.png) | +| １周の回転に必要な回数 | 2 | 4 | 4 | 4 | 1 | 2 | 2 | + +## ブロック操作の基準点 +各ブロックには操作の基準となる点（以下、ブロック操作の基準点）があります。
+また、ブロック操作の基準点以外の座標も取得可能です。基準点(x,y)とその周囲の座標は以下のように表します。
+ +![Screenshot](../pics/block_coordinate_difinition2.PNG) + +ブロック毎に操作の基準点(x,y)は異なります。各ブロックの形状毎の基準点とそれ以外の座標は以下の通り。
+ +| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | +| --- | --- | --- | --- | --- | --- | --- | --- | +| 形状0における操作の基準点(x,y) | ![Screenshot](../pics/I11.PNG) | ![Screenshot](../pics/L11.PNG) | ![Screenshot](../pics/J11.PNG) | ![Screenshot](../pics/T11.PNG) | ![Screenshot](../pics/O11.PNG) | ![Screenshot](../pics/S11.PNG) | ![Screenshot](../pics/Z11.PNG) | +| 基準点とそれ以外の座標 | (x,y-1),(x,y),(x,y+1),(x,y+2) | (x,y-1),(x,y),(x,y+1),(x+1,y+1) | (x,y-1),(x,y),(x,y+1),(x-1,y+1) | (x,y-1),(x,y),(x+1,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x+1,y) | (x,y-1),(x+1,y-1),(x-1,y),(x,y) | (x-1,y-1),(x,y-1),(x,y),(x+1,y) | +| 形状1における操作の基準点(x,y) | ![Screenshot](../pics/I22.PNG) | ![Screenshot](../pics/L22.PNG) | ![Screenshot](../pics/J22.PNG) | ![Screenshot](../pics/T22.PNG) | --- | ![Screenshot](../pics/S22.PNG) | ![Screenshot](../pics/Z22.PNG) | +| 基準点とそれ以外の座標 | (x-2,y),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x-1,y+1) | (x-1,y-1),(x-1,y),(x,y),(x+1,y) | (x,y+1),(x-1,y),(x,y),(x+1,y) | --- | (x,y-1),(x,y),(x+1,y),(x+1,y+1) | (x+1,y-1),(x,y),(x+1,y),(x,y+1) | +| 形状2における操作の基準点(x,y) | --- | ![Screenshot](../pics/L33.PNG) | ![Screenshot](../pics/J33.PNG) | ![Screenshot](../pics/T33.PNG) | --- | --- | --- | +| 基準点とそれ以外の座標 | --- | (x-1,y-1),(x,y-1),(x,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x,y+1) | (x,y-1),(x-1,y),(x,y),(x,y+1) | --- | --- | --- | +| 形状3における操作の基準点(x,y) | --- | ![Screenshot](../pics/L44.PNG) | ![Screenshot](../pics/J44.PNG) | ![Screenshot](../pics/T44.PNG) | --- | --- | --- | +| 基準点とそれ以外の座標 | --- | (x+1,y-1),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x+1,y+1) | (x,y-1),(x-1,y),(x,y),(x+1,y) | --- | --- | --- | + +尚、基準点とそれ以外の座標については現在のブロック情報（メソッド`Shape_class.getCoords()`）から取得可能です。
+また、各形状におけるx,y方向の最大長は、現在のブロック情報（メソッド`Shape_class.getBoundingOffsets()`)から取得可能です。
+取得の具体的な実装方法は、[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)を参照ください。 + +## ブロック出現時の初期位置 +ブロックは、ブロック操作の基準点が座標(5,1)になるように出現します。 + +![Screenshot](../pics/init_block_location3.png) + +尚、ブロックの出現時、既に基準点(x, y)にブロックが存在していると`game over`となりフィールドがリセットされます。 + +## ブロック情報に格納されているデータ + +主に以下を格納しています。 + +* `現在のブロック操作の基準点(x,y)` +* `現在のブロックのIndex値` +* `次のブロックのIndex値` + +# 審判情報 + +以下を格納しています。 + +* `経過時間[s]` : ゲーム開始時からの経過時間 +* `スコア[点]` : ゲーム開始時から現在までに獲得した合計スコア +* `ライン数` : ゲーム開始時から現在までに消したラインの数 +* `ゲームオーバー回数` : ゲーム開始時から現在までにゲームオーバーになった回数 + +# デバッグ情報 + +以下等を格納しています。 + +* `スコア内訳` : 合計スコア（消したライン、落下ボーナス）の内訳 + +その他、詳細はコード上のGameStatusを参照下さい。 + +# 次の動作決定用のデータ + +以下のデータを、次の動作決定用のデータ`nextMove`に与えることで次の動作を決定します。
+`nextMove`は辞書型のデータであり、`nextMove["strategy"]`以下の各値に応じてブロックが動きます。
+ +* `direction` : 現在の形状から時計回りに何回回転するか（範囲：0〜(1周の回転に必要な回数-1) ） +* `x` : x座標のどの位置に移動するか（範囲：0-9、x座標の絶対値を指定する） +* `y_operation` : y座標方向の操作（1:落下、0:移動） +* `y_moveblocknum` : y座標方向に移動する場合、y座標に何ブロック分移動するか（範囲：1-21） + +![Screenshot](../pics/nextMove.PNG) +
+（今後ルール変更がある場合は、ルールに応じて`nextMove`もアップデートしていく予定です。） + +# HOLD機能 + +HOLD機能とは、ブロックを保持する機能です。この機能を使うと現在のブロックがHOLD扱いになります。
+その後、もし保持しているブロックがない場合は次のブロックが落ちてきます。
+もし保持しているブロックが既にある場合はそのブロックが再び落ちてきます。
+ +プログラムから使用する場合は`nextMove["strategy"]`の以下の値を調整してください。
+* `use_hold_function` : HOLD機能を使うかどうか（"y":使う、"n":使わない）
+ +![Screenshot](../pics/holdfunction.PNG) + +# 使用例 +[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)を参照ください。 diff --git a/cloud/sam/scripts/chat_function/data/block_controller_sample.md b/cloud/sam/scripts/chat_function/data/block_controller_sample.md new file mode 100644 index 00000000..f0af3569 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/block_controller_sample.md @@ -0,0 +1,78 @@ +>本ページでは、[ブロック操作用サンプルプログラム(block_controller_sample.py)](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)について説明を追記頂ける方を募集しています。
+>説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
+ +# ブロック操作用サンプルプログラム + +実行方法 + +``` +python start.py -m sample +``` + +一般的に、テトリスは他ゲーム同様に組合せ最適化問題と捉える事が可能であり、過去に様々なアルゴリズムが提案されている。（例えば下記）
+- [ニューラルネットワークと遺伝的アルゴリズムを用いた テトリスコントローラの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjACegQIAxAD&url=https%3A%2F%2Fipsj.ixsq.nii.ac.jp%2Fej%2F%3Faction%3Drepository_action_common_download%26item_id%3D109968%26item_no%3D1%26attribute_id%3D1%26file_no%3D1&usg=AOvVaw0ic6uDC29wGYWl8KKIL8P3) +- [テトリスにおけるAIの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjAEegQIDhAD&url=https%3A%2F%2Fwww.info.kindai.ac.jp%2F~takasi-i%2Fthesis%2F2016_13-1-037-0113_S_Kawahara.ppt&usg=AOvVaw1tik6miknSKem4wE5bmpFP) +- [Tetris is Hard, Even to Approximate](https://arxiv.org/pdf/cs/0210020.pdf) +- ... + +本リポジトリはプログラミング練習と共に、探索アルゴリズムの仮説検証にも利用できるようにしている。
+サンプルプログラムでは、一般的な探索アルゴリズムでの基本部分となる、現在ブロックの探索方法、盤面評価を簡潔に実装している。
+ +## step1.探索方法 + +[ブロック操作用サンプルプログラム](block_controller_sample.py)の`def GetNextMove(self, nextMove, GameStatus):`関数の、
+`for direction0 in CurrentShapeDirectionRange:` +`for x0 in range(x0Min, x0Max):`部分が該当する。
+ここでは現在のブロックの情報（以下）を使って全探索を行っている。
+- 現在のブロックが回転できる回数
+- 現在のブロックが移動できるx軸方向の移動量
+ +![Screenshot](../pics/search_method_sample.PNG) + +一般的に、考慮すべき組み合わせが多くなると[ゲーム木](https://ja.wikipedia.org/wiki/ゲーム木)を利用した探索範囲の効率化が行われる。
+今回も１秒（１フレーム更新頻度）以内に次の手を決める必要があり、状況によってはゲーム木の採用が必要と思われる。
+ただ、サンプルコードでは簡潔に理解できることを優先し、現在のブロックを用いた全探索を採用している。
+ +## step2.盤面評価 + +[ブロック操作用サンプルプログラム](block_controller_sample.py)の`def GetNextMove(self, nextMove, GameStatus):`関数の、
+` EvalValue = self.calcEvaluationValueSample(board)`部分が該当する。
+
+ここでは以下を考えた盤面評価を行っている。
+- 仮に現在のブロックを探索候補箇所に置いた時、消せるブロックが多いほど良い。
+- 同様に、穴ぼこや孤立したブロックが少ないほど良い。
+- 同様に、高さにばらつきが少ないほど良い。
+ +![Screenshot](../pics/board_evaluate_method_sample2.PNG) + + +`EvalValue = self.calcEvaluationValueSample(board)`内では以下のような変数を扱っている。
+[block_controller_sample.py#L140](https://github.com/seigot/tetris/blob/c7f1c540f7d0cd1aa6daeb1e5917d18be35895f5/game_manager/block_controller_sample.py#L140) + +``` +BlockMaxY：あるX座標のブロックの高さが入る +holeCandidates：あるX座標の穴候補の数 +holeConfirm：あるX座標の確定した穴の数（下から"穴","穴",”ブロック”,"穴","穴",”ブロック”,"なし","なし",”なし”・・・の場合は４） +nIsolatedBlocks：下がブロックなしのブロックの総数 +fullLines：あるY座標の横一列全部にブロックがあったら１が加算される（最大値は４） +nHoles += abs(x)：各X座標の穴の数の合計値 +absDy += abd(x)：でこぼこ具合を表現している。となりの列との高さの差分を合計して、値が大きい＝ブロックの高さが凸凹してる +``` + +しかし、サンプルプログラムの方法では盤面に穴が残る事がまだまだ多い。
+ +# サンプルプログラムの課題 + +上記の通りサンプルプログラムは最小限の機能しかなく、いくつか課題が存在する。 + +- 数手先読み探索 + - サンプルプログラムでは探索時に現在のブロックしか考慮できていない。次のブロックや以降を考慮する方が良いはず。 +- 盤面評価の改良 + - サンプルプログラムでは目先の１ブロック消す事を優先している。４ブロック消すための手を選択できていない。 + - サンプルプログラムの手法では盤面に穴が残る事がまだまだ多い。 +- 最適解を人が考える必要がある。 + - AIのように自ら学習する方が楽に最適解を得られる可能性がある。 + +上記を解決するとより高いスコアが獲得できると思われる。
+以下の先行文献によるとサンプルプログラムで採用しているパラメータ以外も利用しており、改良の余地がある事は間違いない。
+[ニューラルネットワークと遺伝的アルゴリズムを用いた テトリスコントローラの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjACegQIAxAD&url=https%3A%2F%2Fipsj.ixsq.nii.ac.jp%2Fej%2F%3Faction%3Drepository_action_common_download%26item_id%3D109968%26item_no%3D1%26attribute_id%3D1%26file_no%3D1&usg=AOvVaw0ic6uDC29wGYWl8KKIL8P3) diff --git a/cloud/sam/scripts/chat_function/data/board_manager.md b/cloud/sam/scripts/chat_function/data/board_manager.md new file mode 100644 index 00000000..45db3a04 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/board_manager.md @@ -0,0 +1,250 @@ +>本ページでは、[ボード管理用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/board_manager.py)について説明を追記頂ける方を募集しています。
+>説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
+ +# ボード管理用プログラムについて + +ここでは [board_manager.py](../../game_manager/board_manager.py) の解説を行う。 + +このファイルにはテトリミノの 形状、回転方向を担う Shape Class と、画面ボードの各種処理を行う BoardData Class がある。 +block_controller.py 側からもよく使うメソッドが多いので解説を書いておく。 + +## Shape Class + +ここでは、[board_manager.py](../../game_manager/board_manager.py) の Shape Class について解説する。 +Shape Class は下記テトリミノの形状について保持しているクラスである。 + +| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | +| --- | --- | --- | --- | --- | --- | --- | --- | +| index値 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | +| 初期形状 | ![Screenshot](../pics/ShapeI.png) | ![Screenshot](../pics/ShapeL.png) | ![Screenshot](../pics/ShapeJ.png) | ![Screenshot](../pics/ShapeT.png) | ![Screenshot](../pics/ShapeO.png) | ![Screenshot](../pics/ShapeS.png) | ![Screenshot](../pics/ShapeZ.png) | +| １周の回転に必要な回数 | 2 | 4 | 4 | 4 | 1 | 2 | 2 | + +詳細は、[ブロック情報](block_controller.md#ブロック情報) に詳しく記載がある。 + +### getRotatedOffsets method + +getRotatedOffsets メソッドは テトリミノ形状を回転した基準座標からの相対座標を返します。 +引数として direction テトリミノ回転方向を指定する。 + +[ブロック操作の基準点](block_controller.md#ブロック操作の基準点)の形状番号を direction で指定すると、ここで書かれている相対座標が返される。 + +### getCoords method + +getCoords メソッドは さらに x,y を引数に持ち、x,y を基準点に置いたテトリミノの絶対座標を返す。 + +### getBoundingOffsets method + +getBoundingOffsets メソッドはテトリミノの基準点からの最大 x, y 最小x, y を取得する。 +direction で回転方向を指定し、minX, maxX, minY, maxY が返ってくる。 + + + + + + +## BoardData Class + +ここでは、[board_manager.py](../../game_manager/board_manager.py) の BoardData Class について解説する。 +BoardData Class は画面ボードに配置されているテトリミノ情報が格納されており、画面ボードの処理はこの Class において行う。 + +### 画面ボード情報取得系メソッド + +#### getData method + +現状の画面ボードデータを返す。一次元配列である。 + + + +### テトリミノ情報取得関係メソッド + +#### getDataWithCurrentBlock method + +画面ボードデータコピーし動いているテトリミノデータを付加し、その画面ボードデータを返す。 + + +#### getValue method + +引数として x,y を指定し画面ボード上のその座標のテトリミノの有無(およびその種類)を返す。 +返り値は Shape Class の index値。何もないなら 0。 + + + +#### getCurrentShapeCoord method + +direction (回転状態)のテトリミノ座標配列を取得し、それをx,yに配置した場合の座標配列を返す + + + +#### getShapeListLength method + +予告テトリミノ配列の長さを返す + + + +#### getShapeDataFromShapeClass method + +テトリミノクラスデータ, テトリミノ座標配列、テトリミノ回転種類を返す + +引数 + + ShapeClass ... Shape のオブジェクト + +返値 + + ShapeClass ... Shape のオブジェクト + ShapeIdx ... Shape Class の index値 + ShapeRange ... Shape Class の回転方向配列。Oミノなら (0,) Lミノなら (0,1,2,3)。 + + + +#### getShapeData method + +テトリミノの index 番号から shape オブジェクトを返す + +引数 + + ShapeNumber ... テトリミノの index + +返値 + + ShapeClass ... Shape のオブジェクト + + + + + +### HOLD 関係 + +#### getholdShapeData method + +ホールドしている Shape オブジェクトを返す + + + + + +### ART 関係 + +#### getcolorTable method + +colorTableを返す + + + +#### getnextShapeIndexListDXY method + +index を指定して、nextShapeIndexList の d, x, y を返す + +引数 + + index + +返値 + + d + x + y + + + + + + +### ゲームターン進行関係メソッド + + +#### mergePiece method + +画面ボードに固着したテトリミノを書き込む + + +#### getNewShapeIndex method + +次のテトリミノの index 取得 + +#### createNewPiece method + +画面ボード上に新しいテトリミノの配置。配置できなかったら False を返す。 + + +#### tryMoveNext method + +direction 方向で x,y へ2回配置して動かせなかったら Reset + + +#### exchangeholdShape method + +HOLD 入れ替え + +#### removeFullLines method + +画面ボードの消去できるラインを探して消去し、画面ボードを更新、そして消した Line を返す + + + + + + + + +### テトリミノ配置確認関係メソッド + +#### tryMoveCurrent method + +テトリミノを direction 方向で x,y に動かせるかどうか確認する +動かせない場合 tryMove メソッドを使い False を返す + + +#### tryMove method + +direction (回転状態)のテトリミノ座標配列を取得し、それをx,yに配置可能か判定する +配置できない場合 False を返す + +#### moveDown method + +テノリミノを1つ落とし消去ラインとテトリミノ落下数を返す + +#### dropDown method + +テトリミノを一番下まで落とし消去ラインとテトリミノ落下数を返す + +#### moveLeft method + +左へテトリミノを1つ動かす +失敗したら False を返す + +#### moveRight method + +右へテトリミノを1つ動かす +失敗したら False を返す + +#### rotateRight method + +右回転させる +失敗したら False を返す + + +#### rotateLeft method + +左回転させる +失敗したら False を返す + + + + + + + +### 初期配置関係メソッド + + +#### clear method + +画面ボードと現テトリミノ情報をクリア + +#### addobstacle method + +初期障害物配置。Level3 Level4 用。 + + + diff --git a/cloud/sam/scripts/chat_function/data/docker.md b/cloud/sam/scripts/chat_function/data/docker.md new file mode 100644 index 00000000..8e30c67e --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/docker.md @@ -0,0 +1,75 @@ +# Tetris Game docker + +## step0. docker をインストールする + +[Install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu)
+[Install Docker Desktop on Mac](https://docs.docker.com/docker-for-mac/install/)
+[Install Docker Desktop on Windows](https://docs.docker.com/docker-for-windows/install/)
+ +## step1. docker コンテナを起動する + +tetris ディレクトリで以下を実行する。 + +``` +docker-compose up +``` + +`pytorch`インストール済コンテナを使いたい場合、上記の代わりに以下を実行する。
+ +``` +docker-compose -f docker-compose.pytorch.yaml up +``` + +## step2. docker コンテナにアクセスする + +コンテナ内に入ってターミナルを起動 + +``` +docker exec -it tetris-container bash +``` + +## step3. 起動ホスト OS 側で GUI 起動のための準備 + +docker コンテナ内でテトリスプログラムを実行する場合、GUI を表示させるためにはホスト OS 側で[x サーバ](https://qiita.com/kakkie/items/c6ccce13ce0beaefaad1)を起動する必要があります。 +x サーバの起動方法は以下を参照してください。 + +- Windows→[こちら](./README.xserver.md#Windows-の場合) +- Linux→ + +## step3'. GUI なしでテトリスプログラムを実行 + +GUI の起動が必要ない場合には先程立ち上げたコンソールかたコンテナ内の環境変数`QT_QPA_PLATFORM`を`offscreen`に設定します。 + +``` +export QT_QPA_PLATFORM=offscreen +``` + +## step4. テトリスプログラムの実行 + +`/tetris`にいることを確認し、以下を実行 + +``` +python start.py +``` + +テトリスの画面が表示されれば無事完了です。 + +## コンテナの停止 + +ホスト OS 側の tetris ディレクトリから以下を実行する。 + +``` +docker-compose stop +``` + +この場合には、コンテナは削除されずに残ります。 + +## コンテナの停止、削除 + +ホスト OS 側の tetris ディレクトリから以下を実行する。 + +``` +docker-compose down +``` + +この場合には、コンテナは削除されます。 diff --git a/cloud/sam/scripts/chat_function/data/game_manager.md b/cloud/sam/scripts/chat_function/data/game_manager.md new file mode 100644 index 00000000..ecd6ba99 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/game_manager.md @@ -0,0 +1,131 @@ +>本ページでは、[ゲーム管理用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/game_manager.py)について説明を追記頂ける方を募集しています。
+>説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
+ +# ゲーム管理用プログラムについて + +## [start.py](../../start.py) の Option 設定 + +ここでは [start.py](../../start.py) で指定する option について解説する。 + +#### -m --mode モード + default : ルールベース + train : AI学習 + predict : AI 推論（予測実行) + predict_sample : AI MLP 推論（予測実行) + predict_sample2 : AI DQN 推論（予測実行) + keyboard : キーボード操作 + gamepad : ゲームパッド操作 +#### -l --game_levelレベル + 1 : Level 1 固定テトリミノ + 2 : Level 2 ランダムテトリミノ + 3 : Level 3 初期ブロックありテトリミノ + 4 : Level 4 初期ブロックありテトリミノ 0.001秒更新 +#### -d --drop_interval 更新間隔 + default => 1000 (=1秒) + ms 指定 + ※ テトリミノを DROP すれば この時間待てば次のテトリミノが出るが、降下だけさせた場合は次の操作ができるまでこの時間分停止する。 + ※ Level 4指定した場合は無効 +#### -t --game_time ゲーム時間 + default => 180 + 秒指定 + -1 で制限なし 学習時は -1 指定推奨。 +#### -r --random_seed 乱数のタネ + 整数指定。特別に必要なければ指定しない。 +#### -f --resultlogjson + default => result.json + 結果の json ファイル指定 +#### --train_yaml + default => config/default.yaml + AI 学習推論時 (train, predict)の設定ファイル指定 +#### --predict_weight + default => outputs/latest/best_weight.pt + AI 推論時 (predict) の学習結果 weight ファイル指定 + +## Game_Manager Class + +ここでは [game_manager.py](../../game_manager/game_manager.py) の Game_Manager Class を解説する。 + +### 概要 + +最初に start.py から呼び出される。このゲームの基本的な動作はこのクラスから各クラス、メソッドを実行することによって行われる。 + +### 初期化 + +\_\_init\_\_ にて Option や初期設定値を各インスタンス変数に格納する。 +\_\_init\_\_ にある initUI メソッドにて、Window 生成、位置設定表示が行われる。ここで SidePanel Class, Board Class のインスタンスも生成される。 +initUI の中の start メソッドで、スコア、画面ボード、予告テトリミノ、ステータスバーがクリアされる。また、この中で timerEvent も生成される。timerEvent の間隔は上記の drop_interval となる。これにより次の操作が可能となる。 + +### timerEvent method + +上記の初期化により timerEvent が drop_interval オプションにて指定された間隔で実行される。 +この Event によりゲームが進行していく。 +なお、timerEvent メソッド実行中に drop_interval 指定の時間を超えても、次の timerEvent は発生しない。 +よって、drop_interval が非常に短い (1ms など = Level 4) の場合は、timerEvent のロスが多発する。 +そういったルールの場合はいかに早く timerEvent を終了させるかが肝となる。 + +timerEvent において各 mode に対応する [block_controller.py](block_controller.md) (or block_controller_train*.py) の GetNextMove が呼び出される。 +ここで、[GameStatus](GameStatus.md) が GetNextMove に引き渡され、各自のプログラムで指示した操作結果が nextMove 変数に格納される。 + +この結果をもって、テトリミノのホールド、横移動、回転、落下、降下処理を行う。 + +ホールドはホールド画面のテトリミノとの入れ替えを行う。ただし、ホールド画面に何もない場合はホールド画面に現テトリミノを入れて timerEvent を終了する。 + +横移動は画面ボードの端やテトリミノにぶつかると停止する。 + +回転は**右回転** (rotateRight)で行い、画面ボードの端やテトリミノにぶつかるとエラーとなりtimerEvent無効となるので要注意である。 + +降下 (move down) 処理の場合は指定数分降下させる。 + +落下 (drop down) 処理が行われると、ただちにテトリミノは一番低いところまで落とされ、テトリミノは固定される。 + +いずれにしてもテトリミノの下側が画面ボード下限かテトリミノに当たっている場合は、消去ライン数計算(moveDown)とスコア計算(UpdateScore)が実施され、次の timerEvent では次のテトリミノが出る。 +なお、ゲームオーバーになればゲームオーバー分のスコアを減らされれ、画面ボードをクリアして次の timerEvent となる。 + + +### keyPressEvent method + +下記キー操作により Event が発生しテトリミノを操作する。 +なお、GetNextMove で指定する場合とことなり**左回転** (rotateLeft) である点に注意が必要である。 + +| 手動操作 | PC操作準拠 | ゲーム機コントローラ準拠 | +| ---- | ---- | ---- | +| 実行コマンド | python start.py -m keyboard | python start.py -m gamepad | +| *up* key | **左回転** | 落下 | +| *left* key | 左に移動 | 左に移動 | +| *right* key | 右に移動 | 右に移動 | +| *m* key | 降下（下に移動） | 降下（下に移動） | +| *space* key | 落下 | **左回転** | +| *P* key | Pause | Pause | +| *c* key | hold | hold | + +## SidePanel Class + +ここでは [game_manager.py](../../game_manager/game_manager.py) の SidePanel Class を解説すｒ。 +横の予告テトリミノ、およびホールドテトリミノ描画画面 Class である。 + + + +### paintEvent method + +ここでは上記のように 4つの予告テトリミノを描画する。 +また、下にホールド画面がありそこでホールドしたテトリミノも表示される。 +timerEvent や keyEvent などにより呼び出され描画される。 + +## Board Class + +ここでは [game_manager.py](../../game_manager/game_manager.py) の Board Class を解説する。 +ゲームの画面ボードおよびステータスバーの Class である。 + + + +### paintEvent method + +ここでは縦22,横10の画面ボードを描画する。 +固定されたテトリミノおよび動作中のテトリミノを描画する。 + +### updateData method + +ステータスバーの文字の更新を行う。 +また、 game_time 経過、もしくはブロック数上限に達したらプログラムを終了し、Result を表示する。 +学習モードで game_time を -1 にしていないと、ここで終了してしまう。 + diff --git a/cloud/sam/scripts/chat_function/data/install_mac.md b/cloud/sam/scripts/chat_function/data/install_mac.md new file mode 100644 index 00000000..5466ff12 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/install_mac.md @@ -0,0 +1,39 @@ +# 実行環境(Macの場合) + +Finder→Application→Utility→Terminalから、ターミナル画面を起動して以下コマンドを実行する。
+ +``` +# install pyqt5 and NumPy +brew install python3 +brew install pyqt5 +brew install numpy +#pip3 install pyqt5 +#pip3 install numpy +# install other packages +brew install git +``` + +Mac(M1)環境等では`pip3 install pyqt5`でエラーになることがある。`brew`を使うことで解決することがある。 +[Mac(M1)にpip3 install pyqt5でpyqt5をインストールしようしようとするとエラーになる（Preparing metadata (pyproject.toml) ... error）](https://qiita.com/seigot/items/c779d187982268cf8b12) + +``` +brew install pyqt5 +``` + +`pytorch`を使う場合は以下のようにすればOK +[MacBook Pro (Apple Silicon, M1 PRO, 2021) でPython開発環境を整える #49](https://github.com/seigot/tetris/issues/49) +[AIについて]([https://github.com/seigot/tetris/blob/master/doc/files/install_mac.md](https://github.com/seigot/tetris/blob/master/doc/files/ai.md) + +``` +# install +pip3 install -U pip +pip3 install torch +pip3 install tensorboardX +python3 -c "import torch" +# 実行例 (詳細は"AIについて"リンク参照) +python3 start.py -m train_sample -d 1 -l 2 -t -1 +python3 start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt +``` + +もし他にも足りないことがあれば`pull request`頂けると助かります + diff --git a/cloud/sam/scripts/chat_function/data/install_ubuntu.md b/cloud/sam/scripts/chat_function/data/install_ubuntu.md new file mode 100644 index 00000000..dcd9a70a --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/install_ubuntu.md @@ -0,0 +1,25 @@ +# 実行環境(Ubuntu/JetsonNanoの場合) + +Need python3, PyQt5 and NumPy to be installed. + +``` +# install pyqt5 and NumPy +sudo apt-get install -y python3-pip +pip3 install --upgrade pip +pip3 install numpy +pip3 install pyqt5 +``` + +その他、パッケージのインストール + +``` +sudo apt-get install -y git +``` + +ubuntu20.04LTS以降の場合は`python`コマンドを使うために以下を実行する + +``` +sudo apt install python-is-python3 +``` + +その後は、実行方法を参照 diff --git a/cloud/sam/scripts/chat_function/data/install_windows.md b/cloud/sam/scripts/chat_function/data/install_windows.md new file mode 100644 index 00000000..e9bc26ba --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/install_windows.md @@ -0,0 +1,39 @@ +# 実行環境(windowsの場合) +docker for windowsを使います。
+ +### step0. DockerDesktopのインストール + +以下を参照する。
+[DockerDeskop](https://docs.docker.com/docker-for-windows/install)
+[Windows10用Windows Subsystem for Linuxのインストール ガイド](https://docs.microsoft.com/ja-jp/windows/wsl/install-win10) + +以下にインストール時のトラブルシューティングをまとめました。
+[FAQ/Windows Docker install時のトラブルシューティング](https://github.com/seigot/tetris/blob/master/doc/files/FAQ.md#windows-docker-install%E6%99%82%E3%81%AE%E3%83%88%E3%83%A9%E3%83%96%E3%83%AB%E3%82%B7%E3%83%A5%E3%83%BC%E3%83%86%E3%82%A3%E3%83%B3%E3%82%B0) + +### step1. パワーシェル上でコンテナを起動する
+ +`Windowsボタン`から`Windows PowerShell`を起動してterminalから以下実行する。
+コンテナのダウンロード〜起動が完了するまで少し待つ。
+ +``` +docker run -p 6080:80 --shm-size=512m seigott/tetris_docker +``` + +### step2. ブラウザからdockerコンテナにアクセスする + +``` +localhost:6080 +``` + +アクセスできたら以下により動作検証する + +左下アイコン --> system tools --> terminator + +Terminalを立ち上げて以下を実行 + +``` +cd ~/tetris +python start.py +``` + +注意）パワーシェルを閉じたり電源OFFするとコンテナも終了します。作成中データが失われないよう注意してください。 diff --git a/cloud/sam/scripts/chat_function/data/install_windows_powershell.md b/cloud/sam/scripts/chat_function/data/install_windows_powershell.md new file mode 100644 index 00000000..82679465 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/install_windows_powershell.md @@ -0,0 +1,205 @@ +# windowsのpowershellを使ってテトリス環境を構築する + +### powershellとは + +windowsで利用できる、コマンドラインインターフェース。 +「Windows」＋「S」で検索ボックス"powershell"と入力すると見つかるはず。 +> [Windows10 - PowerShell(パワーシェル)の開き方](https://www.curict.com/item/f0/f0f6ab0.html) + +## 1. git のインストール + +### Windows Package Manager(winget)がある場合 + +[Windows Package Manager(winget)](https://www.microsoft.com/ja-jp/p/app-installer/9nblggh4nns1?activetab=pivot:overviewtab)があることを確認する。 +powershellを起動して以下を実行すれば確認できる。 + +``` +winget --version +## v1.1.13405 等と出力されれば、Windows Package Manager(winget)がある +``` + +gitをインストールする。 + +``` +winget install Git.Git +## もし y/n と出る場合は、y と入力する。y=yes という意味である。 +``` + +新しくpowershellを起動し、以下を実行して結果が表示されればOK + +``` +git --version +## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） +``` + +### 上記でインストールができない場合 + +上記でインストールができない場合は、公式サイトのインストーラーを使う。以下をダウンロードする。 + +> [git Downloading Git](https://git-scm.com/download/win) +> 64-bit Git for Windows Setup + +以下の「Gitのインストール」部分記載の通り、インストールを進める。以下の記載部分は選択が必要。 + +> Windows 10 と Powershell（WSL含む） で git を利用する +> https://qiita.com/kerobot/items/78372640127771f92ee0#git-%E3%81%AE%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB +> > コマンドラインやPowerShellなどからGitのコマンドを利用するため「Git from the command line and also from 3rd-party software」を選択します。 + +powershell上で以下を実行して結果が表示されればOK + +``` +git --version +## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） +``` + +## 2. python のインストール + +### microsoft storeからインストールする + +powershell上で以下を実行、もしくは`microsoft store`上で`python`と検索し、インストールページからインストールする。 + +``` +python +``` + +![Screenshot](../pics/python3.10.msstore.png) + +新しくpowershellを起動し、以下を実行して結果が表示されればOK + +``` +python --version +## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） +``` + +### 古いバージョンのpythonが表示される場合 + +過去に古いバージョンのpythonをインストールしている場合にそちらが優先される可能性がある。 +「Windows」＋「python」で検索ボックスから古いpythonバージョンをアンインストールして再度新しいpythonをインストールすると解決することがある。 + +### 上記でインストールができない場合 + +上記でインストールができない場合は、公式サイトのインストーラーを使う。 +以下記事を参照し、パッケージのダウンロード～PowerShellの環境設定までを実施する + +> [Windows版Pythonのインストール](https://www.python.jp/install/windows/install.html) + +2023.1時点ではpythonのバージョン3.10.9が利用し易そうである。 +この場合は、以下のダウンロードリンクからインストーラ(`.exe`)を取得するとよい。 + +> [非公式Pythonダウンロードリンク](https://pythonlinks.python.jp/ja/index.html) + +特に以下の点を忘れずに実施する。 + +> > "Add Python 3.x to PATH" をチェックするのを忘れないようにしてください。 +> > もし忘れたら、あわてず落ち着いてもう一度インストールしましょう。何度でも繰り返して大丈夫です。 +> +> > PowerShellの環境設定¶ +> > PowerShellでスクリプトの実行を許可しておきます。 +> > スタートメニューで Windows PowerShell | Windows PowerShell を起動し、次のコマンドを実行します。 +> Set-ExecutionPolicy RemoteSigned -Scope CurrentUser -Force + +powershell上で以下を実行して結果が表示されればOK + +``` +python --version +## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） +``` + +## 3. powershellからtetrisを実行する + +powershellを新たに起動する +(「Windows」＋「S」で検索ボックス"powershell"と入力すると見つかるはず。見つかったらクリックして起動する。) + + +バージョンを確認する +ここでバージョンが何も表示されなければインストールが成功していないため、インストールからやり直す + +``` +git --version +## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） +python --version +## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） +``` + +必要なパッケージをインストールする + +``` +pip install pyqt5 +pip install numpy +``` + +以下の通り実行し、tetrisが表示されればOK + +``` +cd ~ +mkdir work +cd work +git clone https://github.com/seigot/tetris +cd tetris +python start.py # ここでtetrisが表示されればOK +``` + +プログラム内部の説明については以下など参照ください + +[ブロック操作用サンプルプログラムについて](./block_controller_sample.md) +[AIについて](./ai.md) +[tetris artについて](./art.md) +[README.md](https://github.com/seigot/tetris#%E5%AE%9F%E8%A1%8C%E6%96%B9%E6%B3%95) + +### option. エディタのインストール + +最近流行りのエディタである`visual studio code`をインストールしておくと後のコード編集に便利である +powershell上でwingetが使える場合は以下を実行、 + +``` +# powershell上でwingetが使える場合 +winget install vscode +``` + +もしくは`microsoft store`上で`visual studio code`と検索し、インストールページからインストールする。 + +![Screenshot](../pics/vscode.msstore.png) + +powershell上で以下を実行して`vscode`と対象のコードが表示されればOK + +``` +# cd ~/work/tetris 等で事前にcloneした場所に移動しておく +code game_manager/block_controller.py +``` + +### `python start.py`実行後にtetrisが表示されない場合 + +実行環境の違いにより`python start.py`実行後にtetrisが表示されないことがたまにある。 +この場合はwarningやエラーログが出力されるはずなので、以下の実行成功時のログと比較すると解決に近づくと思われる。 + +``` +# （例）実行成功時のログ +$ python start.py +game_level: 1 +game_time: 180 +RANDOM_SEED: 0 +IS_MODE :default +OBSTACLE_HEIGHT: 0 +OBSTACLE_PROBABILITY: 0 +USER_NAME: window_sample +SHAPE_LIST_MAX: 6 +BLOCK_NUM_MAX: -1 +RESULT_LOG_JSON: result.json +TRAIN_YAML: config/default.yaml +PREDICT_WEIGHT: outputs/latest/best_weight.pt +Python 3.7.1 +CompletedProcess(args='python --version', returncode=0, stderr='') +=================================================> +...(以降、tetrisデバッグ情報が表示される) +``` + +## 参考 +[git Downloading Git](https://git-scm.com/download/win) +[Windows 10 と Powershell（WSL含む） で git を利用する](https://qiita.com/kerobot/items/78372640127771f92ee0#git-%E3%81%AE%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB) +[Windows版Pythonのインストール](https://www.python.jp/install/windows/install.html) +[非公式Pythonダウンロードリンク](https://pythonlinks.python.jp/ja/index.html) +[WindowsPowerShellでPythonを利用する](https://bluebirdofoz.hatenablog.com/entry/2019/01/19/141007) +[GNU Emacs for Windows再入門](https://emacs-jp.github.io/tips/emacs-for-windows) +[visual studio codeをinstall](https://azure.microsoft.com/ja-jp/products/visual-studio-code/) +[WindowsにBashをインストールする方法](https://lab.sonicmoov.com/development/windows-bash/) +[Windows Subsystem for Linuxが表示されない](https://qiita.com/taraka/items/0b5919ac8ee02d81f7ff) diff --git a/cloud/sam/scripts/chat_function/data/install_windows_wsl.md b/cloud/sam/scripts/chat_function/data/install_windows_wsl.md new file mode 100644 index 00000000..c8f803f9 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/install_windows_wsl.md @@ -0,0 +1,81 @@ +# 実行環境 (Windows WSLを使う場合) + +Windows WSLを使って開発環境を立ち上げる方法を説明する。 +Dockerを使う方法と比較して、Dockerが不要な点と、Dockerコンテナ終了でデータが失われることを気にする必要が無い特長がある。 + +## Step 1. WSLのインストール + +- 以下を参照してインストールする。 + - [Windows10用Windows Subsystem for Linuxのインストール ガイド](https://docs.microsoft.com/ja-jp/windows/wsl/install-win10) + +### a. WSL1でインストールする場合（簡単） + +- 上記URLの「手動インストールの手順」の「手順 1 - Linux 用 Windows サブシステムを有効にする」「手順 6 - 選択した Linux ディストリビューションをインストールする」のみ実施すれば良い。 +- 「手順 6 - 選択した Linux ディストリビューションをインストールする」では、Ubuntu 18.04 LTS または Ubuntu 20.04 LTS をインストールすれば良い。 + +### b. a以外の場合 + +- WSL1でもWSL2でも良い。 +- Linuxのディストリビューションはどれでも良いはずであるが、Ubuntu 18.04 LTS または　Ubuntu 20.04 LTS をおすすめする。 + +## Step 2. Xサーバのインストール + +GWSLまたはVcXsrvをインストールする。 + +### a. GWSLを使う場合（おすすめ） + +1. スタートメニューからMicrosoft Storeアプリを起動する。 +2. GWSLを検索して、インストールする。 + +### b. VcXsrvを使う場合 + +1. https://sourceforge.net/projects/vcxsrv/ からVcXsrvを入手してインストールする。 + +## Step 3. WSLの起動 + +### ターミナルを開く + +- スタートメニューから、Step 1でインストールしたLinux ディストリビューション(Ubuntu 18.04 LTS など)を起動する。 + +### 必要なパッケージのインストール・設定（初回のみ必要） +- 下記を実行する。 + +``` +sudo apt-get install -y python3-pip +sudo apt-get install -y python3-pyqt5 +pip3 install --upgrade pip +pip3 install numpy +sudo apt-get install -y git + +echo 'export DISPLAY=localhost:0.0' >> ~/.bashrc +source ~/.bashrc +``` + +## Step 4. Xサーバの起動 + +Step 2 でインストールしたXサーバを起動する。 + +### a. GWSLを使う場合 + +1. スタートメニューからGWSLを起動する。 + +### b. VcXsrvを使う場合 + +1. スタートメニューやデスクトップのショートカットからVcXsrvを起動する。 +2. 「次へ」を3回押して、「完了」を押すとXサーバが起動する。 + +## Step 5. tetrisの起動 + +### ソースコードを取得する（初回のみ必要） + +``` +git clone https://github.com/seigot/tetris +``` + +### tetrisを実行する +``` +cd ~/tetris +python start.py +``` + +一度実行環境を立ち上げた後の2回目からは、Step 3以降を実行すれば良い。 diff --git a/cloud/sam/scripts/chat_function/data/reference_score.md b/cloud/sam/scripts/chat_function/data/reference_score.md new file mode 100644 index 00000000..75a01bd4 --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/reference_score.md @@ -0,0 +1,13 @@ +# 参考スコア + +（注）乱数の影響により多少のばらつきあり
+ +| | level1 | level2 | level3 | 自由部門 | +| --- | --- | --- | --- | - | +| random | -916 | -731 | -4948 | - | +| sample(python start.py -m sample) | 6935 | 6259 | 3737 | - | +| [kyadさん作の無限テトリス](https://github.com/kyad/tetris/blob/forever-branch/forever.md) | - | - | - | 20592 | +| 理論値 | 23400+落下ボーナス | - | - | - | + +理論値は「（全てI字の棒が出るなどして）全て4ライン消しをした場合」を想定。
+計算式：(制限時間 180(s))÷(テトリス1回に掛かる時間 10(s))×1300点 = 23400点
diff --git a/cloud/sam/scripts/chat_function/data/tetris.md b/cloud/sam/scripts/chat_function/data/tetris.md new file mode 100644 index 00000000..b6dbc73c --- /dev/null +++ b/cloud/sam/scripts/chat_function/data/tetris.md @@ -0,0 +1,350 @@ + + +**Table of Contents** + +- [Tetris](#tetris) + - [実行環境準備](#%E5%AE%9F%E8%A1%8C%E7%92%B0%E5%A2%83%E6%BA%96%E5%82%99) + - [Mac環境](#mac%E7%92%B0%E5%A2%83) + - [windows環境](#windows%E7%92%B0%E5%A2%83) + - [Ubuntu/JetsonNano環境](#ubuntujetsonnano%E7%92%B0%E5%A2%83) + - [docker環境](#docker%E7%92%B0%E5%A2%83) + - [実行方法](#%E5%AE%9F%E8%A1%8C%E6%96%B9%E6%B3%95) + - [サンプルコード](#%E3%82%B5%E3%83%B3%E3%83%97%E3%83%AB%E3%82%B3%E3%83%BC%E3%83%89) + - [手動操作](#%E6%89%8B%E5%8B%95%E6%93%8D%E4%BD%9C) + - [AI実装](#ai%E5%AE%9F%E8%A3%85) + - [art](#art) + - [自動評価サーバ](#%E8%87%AA%E5%8B%95%E8%A9%95%E4%BE%A1%E3%82%B5%E3%83%BC%E3%83%90) + - [Play rules](#play-rules) + - [Score](#score) + - [game level](#game-level) + - [ファイル構成](#%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB%E6%A7%8B%E6%88%90) + - [ファイル一覧](#%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB%E4%B8%80%E8%A6%A7) + - [詳細](#%E8%A9%B3%E7%B4%B0) + - [コード作成のはじめかた](#%E3%82%B3%E3%83%BC%E3%83%89%E4%BD%9C%E6%88%90%E3%81%AE%E3%81%AF%E3%81%98%E3%82%81%E3%81%8B%E3%81%9F) + - [本リポジトリのfork](#%E6%9C%AC%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AEfork) + - [実行](#%E5%AE%9F%E8%A1%8C) + - [自リポジトリのバイナリを公式リリースする](#%E8%87%AA%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AE%E3%83%90%E3%82%A4%E3%83%8A%E3%83%AA%E3%82%92%E5%85%AC%E5%BC%8F%E3%83%AA%E3%83%AA%E3%83%BC%E3%82%B9%E3%81%99%E3%82%8B) + - [本リポジトリの最新バージョン取り込み](#%E6%9C%AC%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AE%E6%9C%80%E6%96%B0%E3%83%90%E3%83%BC%E3%82%B8%E3%83%A7%E3%83%B3%E5%8F%96%E3%82%8A%E8%BE%BC%E3%81%BF) + - [Pull Requestを送る（Optional）](#pull-request%E3%82%92%E9%80%81%E3%82%8Boptional) + - [FAQ](#faq) + - [参考](#%E5%8F%82%E8%80%83) + - [今後の課題](#%E4%BB%8A%E5%BE%8C%E3%81%AE%E8%AA%B2%E9%A1%8C) + - [次のブロックのランダム性](#%E6%AC%A1%E3%81%AE%E3%83%96%E3%83%AD%E3%83%83%E3%82%AF%E3%81%AE%E3%83%A9%E3%83%B3%E3%83%80%E3%83%A0%E6%80%A7) + - [競争要素の追加](#%E7%AB%B6%E4%BA%89%E8%A6%81%E7%B4%A0%E3%81%AE%E8%BF%BD%E5%8A%A0) + - [学習要素の追加](#%E5%AD%A6%E7%BF%92%E8%A6%81%E7%B4%A0%E3%81%AE%E8%BF%BD%E5%8A%A0) + - [LICENSE](#license) + - [Finnaly](#finnaly) + + + +# Tetris + +プログラミング学習を目的とした、ブロックを操作してスコアを競うゲームです。
+[FAQはこちら。](https://github.com/seigot/tetris/blob/master/doc/files/FAQ.md)
+[tutorialはこちら。](https://github.com/seigot/tetris_game_tutorial)
+[tetris_score_serverはこちら](https://github.com/seigot/tetris_score_server) +[tetris_battle_serverはこちら](https://github.com/seigot/tetris_battle_server) + +## 実行環境準備 + +#### Mac環境 + +Finder→Application→Utility→Terminalから、ターミナルを起動して以下コマンドを実行する。 +install成功後、[実行方法](https://github.com/seigot/tetris/tree/master#実行方法)によりゲーム開始すればOK + +``` +# install pyqt5 and NumPy +brew install python3 +brew install pyqt5 +brew install numpy +# install other packages +brew install git +``` + +[doc/files/install_mac.md](./doc/files/install_mac.md)に手順を記載 + +#### windows環境 + +[windowsのpowershellを使ってテトリス環境を構築する場合](./doc/files/install_windows_powershell.md)の手順
+[WSL(Windows Subsystem for Linux)を使う場合](./doc/files/install_windows_wsl.md)の手順
+[Docker for Windowsを使う場合](./doc/files/install_windows.md)の手順
+ +#### Ubuntu/JetsonNano環境 + +[doc/files/install_ubuntu.md](./doc/files/install_ubuntu.md)に手順を記載 + +#### docker環境 + +[docker/README.md](docker/README.md)に手順を記載 + +## 実行方法 + +本リポジトリを取得 + +```shell +cd $HOME +git clone https://github.com/seigot/tetris +``` + +ゲーム開始用スクリプトを実行 + +```shell +cd tetris +python start.py +``` + +![Screenshot](doc/pics/screenshot_02.png) + +#### サンプルコード + +実行時、以下のようにオプションを与えることで、スコアアタック用サンプルコードの実行が可能です。
+サンプルコードの詳細は、[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)を参照下さい。
+ +```shell +python start.py -m sample +``` + +#### 手動操作 + +実行時、以下のようにオプションを与えることで、手動操作が可能です。 +操作方法は、PC操作準拠とゲーム機コントローラ準拠の2種類を選択できるようにしています。 + +| 手動操作 | PC操作準拠 | ゲーム機コントローラ準拠 | +| ---- | ---- | ---- | +| 実行コマンド | python start.py -m keyboard | python start.py -m gamepad | +| *up* key | 回転 | 落下 | +| *left* key | 左に移動 | 左に移動 | +| *right* key | 右に移動 | 右に移動 | +| *m* key | 下に移動 | 下に移動 | +| *space* key | 落下 | 回転 | +| *P* key | Pause | Pause | +| *c* key | hold | hold | + +`--nextShapeMode hate`オプション付きで実行すると[hateモード(手動操作で遊ぶ用)](https://manabitimes.jp/math/795)になります。 + +``` +python3 start.py -m keyboard --nextShapeMode hate +``` + +#### AI実装 + +実行方法、サンプルコードを用意しています +詳細は[AIについて](doc/files/ai.md)を参照下さい。 + +#### art + +実行方法、サンプルコードを用意しています +詳細は[artについて](doc/files/art.md)を参照下さい。 + +#### 自動評価サーバ + +表題のサーバを有志で用意しています +https://github.com/ChallengeClub/tetris_score_server + +## Play rules + +制限時間内の獲得スコアを評価します。 + +### Score + +加点 + +| 項目 | 得点 | 備考 | +| ---- | ---- | ---- | +| 1ライン消し | + 100点 | - | +| 2ライン消し | + 300点 | - | +| 3ライン消し | + 700点 | - | +| 4ライン消し | + 1300点 | - | +| 落下ボーナス | + 落下したブロック数を得点に加算 | - | +| 全消しボーナス | + 全消し時に得点 | - | + +減点 + +| 項目 | 得点 | 備考 | +| ---- | ---- | ---- | +| gameover | - 500点 | ブロック出現時にフィールドが埋まっていたらgameover + +### game level + +実行時、オプションを与えることで、難易度（レベル）を指定できます。
+ +| | level1 | level2 | level3 | level4 | +| --- | --- | --- | --- | --- | +| 実行方法 | python start.py | python start.py -l2 | python start.py -l3 | python start.py -l4 | +| 制限時間 | 180秒 | 180秒 | 180秒 | 180秒 | +| ブロックの順番 | 固定(1-7まで順番に繰り返し) | ランダム | ランダム | ランダム | +| フィールドの初期ブロック | なし | なし | あり | あり | +| フレーム更新頻度 | 約1秒 | 約1秒 | 約1秒 | 約0.001秒 | +| 全消しボーナス | +0 | +0 | +4000 | +4000 | +| 備考 | - | - | - | - | + +[各レベルの参考スコア](doc/files/reference_score.md) + +## ファイル構成 + +#### ファイル一覧 + +* `game_manager/game_manager.py` : ゲーム管理用プログラム +* `game_manager/board_manager.py` : ボード管理用プログラム +* `game_manager/block_controller.py` : ブロック操作用プログラム（ブロックの操作は、このファイルを更新して下さい。） +* `start.py` : ゲーム開始用コマンド + +#### 詳細 + +以下のような構成になっています。
+ブロック操作用プログラムは、管理プログラムから定期的に呼び出されるので、ボード情報から次の動作を決定して下さい。
+ +```mermaid + graph TB + + subgraph ゲーム管理用プログラム + B1["game_manager.py"] + C1["board_manager.py"] + D1["block_controller.py
ここで現在のブロックの動作を決定する"] + B1 --update--> C1 + B1 --getNextMove--> D1 + D1 --NextMove--> B1 + subgraph ボード管理用プログラム + C1 + end + subgraph ブロック操作用プログラム + D1 + end + end + + + subgraph ゲーム開始用コマンド + A1[start.py] --> B1 + end +style ブロック操作用プログラム fill:#fef +``` + +詳細 +- [ブロック操作用プログラムについての説明](doc/files/block_controller.md)
+- [ボード管理用プログラムについての説明](doc/files/board_manager.md)
+- [ゲーム管理用プログラムについての説明](doc/files/game_manager.md)
+ +## コード作成のはじめかた + +### 本リポジトリのfork + +まず、Githubアカウントを取得して本リポジトリを自リポジトリにforkして下さい。 + +> リポジトリのフォークの例
+> +> 0. GitHubアカウントを作成/ログインする。
+> 1. GitHub で、[https://github.com/seigot/tetris](https://github.com/seigot/tetris)リポジトリに移動します
+> 2. ページの右上にある [Fork] をクリックします。
+> 参考：[リポジトリをフォークする](https://docs.github.com/ja/get-started/quickstart/fork-a-repo#forking-a-repository)
+ +その後、自リポジトリにforkした`tetris`をローカルマシンに取得して下さい。 + +``` +cd ~ +git clone https://github.com//tetris # ""さん（yourname=自分のアカウント名に読みかえて下さい）のリポジトリを取得する場合 +git clone https://github.com/seigot/tetris # このリポジトリを取得する場合 +``` + +既に`tetris`が存在しており、これを削除したい場合は`rm -f`を実行して下さい。 + +``` +# ubuntu/mac等の場合 +sudo rm -rf tetris + +# windows powershellの場合 +Remove-Item -Recurse -Force tetris +``` + +取得後はソースコード変更、変更リポジトリに反映する等してアップデートを進めて下さい。 + +### 実行 + +`実行方法`を参考に実行環境の構築をして下さい。
+環境構築の完了後、ブロック操作用プログラム[`block_controller.py`](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)を更新していってください。
+ +### 自リポジトリのバイナリを公式リリースする + +提出時、自リポジトリのバイナリを公式リリースする場合は、Githubリリースの機能を使うと簡単なのでお勧めです。 + +> 自リポジトリのコードを提出（バイナリリリース）する場合の手順参考
+> [リポジトリのリリースを管理する](https://docs.github.com/ja/free-pro-team@latest/github/administering-a-repository/managing-releases-in-a-repository)
+> 7.オプションで、コンパイルされたプログラムなどのバイナリファイルをリリースに含めるには、ドラッグアンドドロップするかバイナリボックスで手動で選択します。
+ +### 本リポジトリの最新バージョン取り込み + +今後、本リポジトリもバージョンアップしていく予定です。
+本リポジトリのバージョンアップを取り込む場合は、forkしたリポジトリにて以下を実行して下さい。
+ +※追記 2021/5より、Github UI上から操作可能になったようです。
+[GitHub新機能「Fetch upstream」使ってみた！　1クリックで親リポジトリに追従（同期）](https://note.com/llminatoll/n/n423296287697)
+ +``` +git checkout master # ローカルのmainブランチに移動 +git remote add upstream https://github.com/seigot/tetris # fork元のリポジトリをupstream という名前でリモートリポジトリに登録（名前はなんでもいい。登録済みならスキップ） +git fetch upstream # upstream から最新のコードをfetch +git merge upstream/master # upstream/main を ローカルのmaster にmerge +git push # 変更を反映 +``` + +参考：[github で fork したリポジトリで本家に追従する](https://please-sleep.cou929.nu/track-original-at-forked-repo.html) + +### Pull Requestを送る（Optional） + +本リポジトリへ修正リクエストを送ることが可能です。詳しくは参考をご参照下さい。
+
+※追記　Pull Request練習用リポジトリを作成しました。
+[test_pull_request](https://github.com/seigot/test_pull_request)
+
+解説図:
+ +![Git Commentary](doc/pics/20230115_Git_Commentary.png) + +参考：
+[GitHub-プルリクエストの作成方法](https://docs.github.com/ja/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)
+[[実践] はじめてのPull Requestをやってみよう](https://qiita.com/wataryooou/items/8dce6b6d5f54ab2cef04)
+[【GitHub】Pull Requestの手順](https://qiita.com/aipacommander/items/d61d21988a36a4d0e58b)
+ +### FAQ + +[doc/files/FAQ.md](doc/files/FAQ.md)を参照下さい。 + +## 参考 + +[https://github.com/LoveDaisy/tetris_game](https://github.com/LoveDaisy/tetris_game)
+[https://github.com/seigot/tetris_game (2021.12時点まで使用)](https://github.com/seigot/tetris_game)
+[http://zetcode.com/gui/pyqt5/tetris/](http://zetcode.com/gui/pyqt5/tetris/)
+[テトリスの歴史を「ブロックが落ちるルール」の進化から学ぶ](https://gigazine.net/news/20191116-tetris-algorithm/)
+[『テトリスでPythonを学ぼうv4』優勝コード解説](https://qiita.com/mozziemagnet/private/b6839c9b7b438cbdb1b1)
+ + +## 今後の課題 + +### 次のブロックのランダム性 + +次のブロックのランダム性は、現在はrandom関数の出力に依存しています。
+しかし、[こちらの記事](https://gigazine.net/news/20191116-tetris-algorithm/)によると選択方式が色々ありそうです。
+有識者の方からアドバイス頂けると助かります。
+ +* 参考：次のブロック選択処理 [game_manager.py](game_manager/game_manager.py) + +``` +nextShapeIndex = np_randomShape.random.randint(1, 8) +``` + +### 競争要素の追加 + +例えば、AI学習を目的としたルールのアップデート、火力ボーナス、などが該当する +[issues](https://github.com/seigot/tetris/issues/67) + +### 学習要素の追加 + +例えば、文法、アルゴリズム、AI要素、などが該当する +[issues#120](https://github.com/seigot/tetris/issues/120) + +## LICENSE + +[MIT LICENSE](LICENSE) + +## Finnaly + +~ HAVE FUN ~ diff --git a/cloud/sam/scripts/chat_function/events/event_delete.json b/cloud/sam/scripts/chat_function/events/event_delete.json new file mode 100644 index 00000000..ac576e79 --- /dev/null +++ b/cloud/sam/scripts/chat_function/events/event_delete.json @@ -0,0 +1,6 @@ +{ + "httpMethod": "DELETE", + "body": "{\"identity_id\": \"id_hoge\", \"character_name\": \"test_user\"}", + "resource": "/ask", + "path": "/ask" +} diff --git a/cloud/sam/scripts/chat_function/events/event_get.json b/cloud/sam/scripts/chat_function/events/event_get.json new file mode 100644 index 00000000..f087d87d --- /dev/null +++ b/cloud/sam/scripts/chat_function/events/event_get.json @@ -0,0 +1,6 @@ +{ + "httpMethod": "GET", + "resource": "/ask", + "path": "/ask" +} + \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/events/event_post.json b/cloud/sam/scripts/chat_function/events/event_post.json new file mode 100644 index 00000000..8a877d84 --- /dev/null +++ b/cloud/sam/scripts/chat_function/events/event_post.json @@ -0,0 +1,6 @@ +{ + "httpMethod": "POST", + "body": "{\"identity_id\": \"id_hoge\", \"input_text\": \"Rustはどんな言語ですか？\", \"character_name\": \"test_user\"}", + "resource": "/ask", + "path": "/ask" +} diff --git a/cloud/sam/scripts/chat_function/events/event_post_func.json b/cloud/sam/scripts/chat_function/events/event_post_func.json new file mode 100644 index 00000000..8f1c1c67 --- /dev/null +++ b/cloud/sam/scripts/chat_function/events/event_post_func.json @@ -0,0 +1,7 @@ +{ + "httpMethod": "POST", + "body": "{\"identity_id\": \"id_hoge\", \"input_text\": \"ミノを消した時の点数は？\", \"character_name\": \"test_user\"}", + "resource": "/ask", + "path": "/ask" + } + \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/events/test.bash b/cloud/sam/scripts/chat_function/events/test.bash new file mode 100644 index 00000000..59773e50 --- /dev/null +++ b/cloud/sam/scripts/chat_function/events/test.bash @@ -0,0 +1 @@ +sam local invoke "ChatFunction" -e scripts/chat_function/events/event_get.json \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/local.py b/cloud/sam/scripts/chat_function/local.py new file mode 100644 index 00000000..2249533a --- /dev/null +++ b/cloud/sam/scripts/chat_function/local.py @@ -0,0 +1,37 @@ +import os +import openai + +from dotenv import load_dotenv +from llama_index import VectorStoreIndex, SimpleDirectoryReader +from llama_index import StorageContext, load_index_from_storage + +# ローカルで辞書を作成し、storageに保存する +def main(): + # APIkeyの設定 + load_dotenv() + try: + OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] + except KeyError: + print("OPENAI_API_KEY environment variable not found. Please make sure it is set.") + openai.api_key = OPENAI_API_KEY + + # モデルの読み込み + if(1): + documents = SimpleDirectoryReader(input_dir="./data").load_data() + print("documents: ", documents) + index = VectorStoreIndex.from_documents(documents) + # 保存 + index.storage_context.persist() + else: + # rebuild storage context + storage_context = StorageContext.from_defaults(persist_dir='storage') + # load index + index = load_index_from_storage(storage_context) + + # クエリの実行 + query_engine = index.as_query_engine() + response = query_engine.query("Dockerイメージの取得途中で止まる原因は?") + print("response: ", response) + +if __name__ == "__main__": + main() diff --git a/cloud/sam/scripts/chat_function/requirements.txt b/cloud/sam/scripts/chat_function/requirements.txt new file mode 100644 index 00000000..ab2a8275 --- /dev/null +++ b/cloud/sam/scripts/chat_function/requirements.txt @@ -0,0 +1,6 @@ +requests +urllib3==1.26 +langchain==0.0.234 +llama-index==0.7.9 +openai==0.27.8 +python-dotenv \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/role/role_tetris.py b/cloud/sam/scripts/chat_function/role/role_tetris.py new file mode 100644 index 00000000..03be0dbf --- /dev/null +++ b/cloud/sam/scripts/chat_function/role/role_tetris.py @@ -0,0 +1,68 @@ +import json +from langchain import OpenAI +from llama_index import VectorStoreIndex, SimpleDirectoryReader +from llama_index import StorageContext, load_index_from_storage + +class TetrisAssistant: + @staticmethod + def get_chat_messages(): + return [ + { + "role": "system", + "content": +""" +質問には100文字以内で回答する。 +必要に応じて"function"を実行し、その応答を用いて、"user"の言語に合わせた言語で100文字以内で回答する。 +""" + }, + { + "role": "user", + "content":"テトリスのルールを教えて。" + }, + { + "role": "assistant", + "content": "テトリスは、異なる形のブロックを落としてきて、行を埋めるゲームです。行を完全に埋めると、その行は消えます。ブロックが画面上部に積み上げられるとゲームオーバーです。" + } + ] + + @staticmethod + def get_chat_functions(): + return [ + { + "name": "search_tetris_index", + "description": +""" +テトリスをpythonで操作することを通してプログラミングを学ぶ時に用いる資料を検索する。 +この資料は以下の内容を含む。 +・テトリスのルール（点数,ルール,ボード情報,フィールド情報） +・プログラムによるテトリスの操作方法（実行コマンド,ミノの説明,コマンド、各種ファイル,アートの作り方） +・環境構築（AI,docker,Git,Windows(WSL,PowerShell),Linux,Mac） +""", + "parameters": { + "type": "object", + "properties": { + "SearchContent": { + "type": "string", + "description": "テトリス対戦に関する情報を検索 e.g.ミノを消した時の点数は？" + } + }, + "required": ["SearchContent"] + } + } + ] + +class TetrisIndexSearch: + def __init__(self): + # indexの読み込み + storage_context = StorageContext.from_defaults(persist_dir='./storage') + self.index = load_index_from_storage(storage_context) + + def search_tetris_index(self, SearchContent): + # クエリの実行 + query_engine = self.index.as_query_engine() + response = query_engine.query(SearchContent) + + result = { + "response": response.response + } + return json.dumps(result) diff --git a/cloud/sam/scripts/chat_function/storage/docstore.json b/cloud/sam/scripts/chat_function/storage/docstore.json new file mode 100644 index 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"2ce78b70-9965-4bef-afed-8005e9808f4a"}, "e6aed9e4-13ac-46ae-b02e-d6fd2d2cb428": {"doc_hash": "a657c202ee96c62cb40d7011d84c339d3bba282759574c10cd025bb900bb221a", "ref_doc_id": "9b9a4941-3530-464c-bc90-233826c569de"}}, "docstore/data": {"ea8f2a24-783c-4441-87e9-3d6a524e88d4": {"__data__": {"id_": "ea8f2a24-783c-4441-87e9-3d6a524e88d4", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ce79c7e0-2737-497f-b28b-129a092972f6", "node_type": null, "metadata": {}, "hash": "948a9a8b9ae5c4df51d5287a7f6ccd35396be203d297dd6bc8a7e4c98bb1af7d"}}, "hash": "f375989fb7cb098656fd92ec76edb8a6988b562e8611c4cc24231a9ee2a45483", "text": "FAQ", "start_char_idx": 0, "end_char_idx": 3, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9dd5ef61-505f-4cf8-8a57-775a5a4a6d9e": {"__data__": {"id_": "9dd5ef61-505f-4cf8-8a57-775a5a4a6d9e", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d9c805d3-b4c7-4edd-979f-5e9e9ddbf844", "node_type": null, "metadata": {}, "hash": "ec4555c82c10b6a2432de6a2206083fccd3d865e170ee3fef1f85039d9494acc"}}, "hash": "bc5c6fb817cf903b1231a7e4186490ea92c64bb44e82b11754ff231d802eca7f", "text": "level1\u3067\u306f\u30d6\u30ed\u30c3\u30af\u306e\u9806\u756a\u306f\u56fa\u5b9a\u3068\u306e\u3053\u3068\u3060\u304c\u3001\u5177\u4f53\u7684\u306b\u306f\u3069\u3093\u306a\u9806\u756a\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u304b\n`level1\uff08\u30d6\u30ed\u30c3\u30af\u306e\u9806\u756a\u56fa\u5b9a\uff09`\u306e\u5834\u5408\u306f\u3001\u30d6\u30ed\u30c3\u30af\u306eindex\u5024\u304c`1\u21922\u21923\u21924\u21925\u21926\u21927\u21921\u2192...`\u306e\u9806\u756a\u306b\u51fa\u73fe\u3057\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 111, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a5dd4ed2-ffad-4506-a1f2-e25c12569798": {"__data__": {"id_": "a5dd4ed2-ffad-4506-a1f2-e25c12569798", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "068cf630-c9b1-45ad-ad7a-42114cb0f760", "node_type": null, "metadata": {}, "hash": "9018fbc49df1e1fed233679a220f7d9f5374fe2c6066940f5d2c54101fb87987"}}, "hash": "6297f83b572f6e2e8102c1d2629d3792d35e1a5d0bcbe70207999ba810e8086e", "text": "python\u5b9f\u884c\u74b0\u5883\u306b\u3064\u3044\u3066\n\n| \u74b0\u5883 | \u74b0\u5883\u69cb\u7bc9\u624b\u9806 |\n| ---- | ---- |\n| ubuntu18.04,20.04 | \u3053\u3061\u3089 |\n| Mac | \u3053\u3061\u3089 |\n| Windows+powershell | \u3053\u3061\u3089 |\n| Windows+Docker | \u3053\u3061\u3089 |\n| JetsonNano | \uff08\u52d5\u4f5c\u672a\u78ba\u8a8d\u3060\u304c\u304a\u305d\u3089\u304f\u52d5\u304f\u306f\u305a\uff09 |\n| RaspberryPi | \uff08\u52d5\u4f5c\u672a\u78ba\u8a8d\u3060\u304c\u304a\u305d\u3089\u304f\u52d5\u304f\u306f\u305a\uff09 |\n| Windows+GoogleChrome+ubuntu-free-online-linux | chrome webstore URL |\n| AWS | EC2 4CPU 8GB\u30e1\u30e2\u30ea 20GB\u30b9\u30c8\u30ec\u30fc\u30b8\u3001GPU\u74b0\u5883\u3067\u52d5\u4f5c\u78ba\u8a8d\u6e08\uff08\u8ab2\u91d1\u306b\u6ce8\u610f\uff09 |", "start_char_idx": 0, "end_char_idx": 377, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "83a1dda7-4b27-46a2-90b7-42ee0797ec3f": {"__data__": {"id_": "83a1dda7-4b27-46a2-90b7-42ee0797ec3f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "88c52465-0591-4e30-b3bb-80b593e67e63", "node_type": null, "metadata": {}, "hash": "417cc94d31493896d4278f7c7611ee243727fea9ac423e8e8b474e90a6a3cfc0"}}, "hash": "3f344eafb68aeb714deaca9505252830e4cd38d123e739e45727701e9f5f7eb8", "text": "`Windows+GoogleChrome+ubuntu-free-online-linux`\u306e\u74b0\u5883\u69cb\u7bc9\u306b\u3064\u3044\u3066\n\n>google chrome\u4e0a\u3067ubuntu server\u3092\u5b9f\u884c\u3059\u308b\n\u30fbgoogle chrome\u7528\u306eubuntu online server\u62e1\u5f35\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\u30fb\u9078\u629e\u80a2\u306e\u3046\u3061\u3001\"Xubuntu\"\u3092\u9078\u629e\uff08ubuntu18.04\u304b\u3064\u8efd\u91cf\u306a\u3082\u306e\u304c\u826f\u3055\u305d\u3046\uff09\n\u30fbserver\u306b\u30ed\u30b0\u30a4\u30f3\u3057\u3066\u4ee5\u4e0b\u3092\u5b9f\u65bd\n\u3000\u30fbDesktop\u4e0a\u3067\u53f3\u30af\u30ea\u30c3\u30af\u3057\"Open Terminal Here\"\u3092\u9078\u629e\n\u3000\u30fbterminal\u4e0a\u3067\u4ee5\u4e0b\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\n```\nsudo apt install \u2212y git\ngit clone http://github.com/seigot/tetris\ncd tetris game\nbash doc/files/install_ubuntu.sh\n```\n \n--> install\u304c\u6210\u529f\u3059\u308c\u3070OK\n\n```\npython start.py\n```\n\n--> \u30c6\u30c8\u30ea\u30b9\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK", "start_char_idx": 0, "end_char_idx": 461, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "06a505d0-5a9c-48de-aa71-92c9d2e1598c": {"__data__": {"id_": "06a505d0-5a9c-48de-aa71-92c9d2e1598c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9d1aa5a7-32ce-4db8-90f5-b80c3590d2a8", "node_type": null, "metadata": {}, "hash": "0203d09081c04a97f4c166c46860b4862eeefd7812f0464daf35438c8bc6cd8a"}}, "hash": "5c6a03d957946224e42a33e6f543c6645ac923e98611d19044edf61d7a78c22b", "text": "`sudo apt install`\u6642\u306b`E: \u30ed\u30c3\u30af /var/lib/dpkg/lock-frontend \u304c\u53d6\u5f97\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f - open (11: \u30ea\u30bd\u30fc\u30b9\u304c\u4e00\u6642\u7684\u306b\u5229\u7528\u3067\u304d\u307e\u305b\u3093)`\u306e\u30a8\u30e9\u30fc\u304c\u51fa\u308b\n\n\u3053\u3061\u3089\u306e\u30b5\u30a4\u30c8\u53c2\u7167\n\u4ee5\u4e0b\u3067\u89e3\u6c7a\u3059\u308b\u306f\u305a\n\n```\n$ sudo rm /var/lib/apt/lists/lock\n$ sudo rm /var/lib/dpkg/lock\n$ sudo rm /var/lib/dpkg/lock-frontend\n```", "start_char_idx": 0, "end_char_idx": 236, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a64c9c75-bf7f-49b2-b815-51798474fc5b": {"__data__": {"id_": "a64c9c75-bf7f-49b2-b815-51798474fc5b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ae939c3e-1ba7-45a9-ab77-a57971c19e70", "node_type": null, "metadata": {}, "hash": "5b667b04545504678e10b114fdf67d6de2f920ec39e06536193b6340ad8b9b09"}}, "hash": "1fa69491710ecc093d2108b602e1f752569c1869c813d394c8b9870590c608a0", "text": "\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\uff08`python start.py -s y`\u3067\u52d5\u304f\u3084\u3064\uff09\u306e\u4e2d\u8eab\u306f\u3069\u3046\u306a\u3063\u3066\u308b\u306e\u304b\n\u3053\u3061\u3089\u3067\u89e3\u8aac", "start_char_idx": 0, "end_char_idx": 57, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "4250c708-0eaa-454d-a61e-ca6949f4cab8": {"__data__": {"id_": "4250c708-0eaa-454d-a61e-ca6949f4cab8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3e7b8570-c846-456b-a2da-99e9e00b152e", "node_type": null, "metadata": {}, "hash": "8c00b975d5e616a2e5361b5a3fc169ef3b57a1bc812873a738758f4308da3d45"}}, "hash": "f54c4d73b17d4e6acfbd9108d2a7ba9ed788fdeb6c074f6d75b6a3d2913207f5", "text": "\u30b9\u30b3\u30a2\u30a2\u30bf\u30c3\u30af\u6642\u306e\u52d5\u4f5cPC\u74b0\u5883\u306b\u3064\u3044\u3066\n\n2021/7\u6642\u70b9\u3067\u4ee5\u4e0b\u3092\u60f3\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\nPC: ZBOX Magnux en52060\n\n```\n- OS : ubuntu18.04\n- CPU: Intel Core i5\n- Memory: 16GB\n- NVIDIA GeForce RTX 2060\n```\n\n```\n- python\u30d0\u30fc\u30b8\u30e7\u30f3\u306f\u4ee5\u4e0b\n$ python3 --version \nPython 3.6.9 \n$ python3 -c 'import torch; print(torch.__version__) ' \n1.4.0\n```\n\n\u30bd\u30d5\u30c8\u74b0\u5883\u306f\u4ee5\u4e0b\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u69cb\u7bc9\u3057\u3066\u3044\u307e\u3059\u3002(\u6563\u3089\u304b\u3063\u3066\u3066\u3059\u307f\u307e\u305b\u3093)\nauto_setup_for_Linux.sh", "start_char_idx": 0, "end_char_idx": 339, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f5dff58d-3f9f-438b-aec4-ad9d405dd0d0": {"__data__": {"id_": "f5dff58d-3f9f-438b-aec4-ad9d405dd0d0", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "987b823a-a32f-4e0a-be51-321a464fa024", "node_type": null, "metadata": {}, "hash": "544481584faf4d286aa1720e3a9dc49c333cf43dcaaaafa4303003e2593e8087"}}, "hash": "24e096266776cfc23be49f77cf23fa1835baff1b63bd0e146ec6464d9d197580", "text": "Windows Docker install\u6642\u306e\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\n\n```\n\u2460BIOS\u306eCPU\u95a2\u9023\u8a2d\u5b9a\n\u3000Docker for Windows\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u8d77\u52d5\u3059\u308b\u3068\u3001\n\u3000\u300cAn error occurred Hardware assisted virtualization and data execution protection\n\u3000 must be enabled in the BIOS.\u300d\u3068\u51fa\u3066\u304f\u308b\u3002\n\u3000\u21d2 \u4ee5\u4e0b\u30b5\u30a4\u30c8\u306e\u300c1. BIOS\u8a2d\u5b9a\u306e\u78ba\u8a8d\u300d\u3092\u3059\u308b\u3053\u3068\u3067\u30a8\u30e9\u30fc\u89e3\u6d88\n\u3000\u3000 https://qiita.com/LemonmanNo39/items/b1b104e7fb609464727b\n\n\u2461WSL\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\u3000Docker\u3092\u8d77\u52d5\u3057\u305f\u3068\u3053\u308d\u3001\u2460\u306f\u89e3\u6d88\u3055\u308c\u305f\u304c\u4ee5\u4e0b\u306e\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u8868\u793a\u3055\u308c\u308b\u3002\n\u3000\u300cWSL2 installation is incomplete\u300d\n\u3000\u21d2 \u4ee5\u4e0b\u3092\u53c2\u8003\u306b\u3057\u3066WSL2\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\n\u3000\u3000 https://docs.microsoft.com/ja-jp/windows/wsl/install-win10\n\u3000\u3000 \u203b Docker for Windows\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3068\u304d\u306b\u4e00\u7dd2\u306bWSL\u3082\u5165\u308c\u3089\u308c\u305f\u3063\u307d\u3044\n \n\u2462 Docker\u30a4\u30e1\u30fc\u30b8\u306e\u53d6\u5f97\u9014\u4e2d\u3067\u6b7b\u306c\n\u81ea\u5b85\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u56de\u7dda\u304c\u8ca7\u5f31\u306a\u306e\u304b\u3001\u4e0b\u8a18\u30b3\u30de\u30f3\u30c9\u3067\u30a4\u30e1\u30fc\u30b8\u3092\u6301\u3063\u3066\u304f\u308b\u9014\u4e2d\u3067\u6b62\u307e\u308b\u3002\ndocker run -p 6080:80 --shm-size=512m seigott/tetris_docker\n\n\u21d2 Docker for Windows\u3092\u8d77\u52d5\u3057\u3066\u3001\u30a6\u30a3\u30f3\u30c9\u30a6\u4e0a\u90e8\u306e\"\u6b6f\u8eca(\u8a2d\u5b9a)\u30dc\u30bf\u30f3\"\u3001\"Docker Engine\"\u3092\u30af\u30ea\u30c3\u30af\u3002\n\u3000 config\u30d5\u30a1\u30a4\u30eb\u306b\u3001\u300c\"max-concurrent-download\":1\u300d\u3092\u8ffd\u8a18\u3002\n\u3000\u203b\u3053\u308c\u3067\u30c7\u30d5\u30a9\u30eb\u30c8\u306e3\u4e26\u5217\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u304c\u76f4\u5217\u306b\u306a\u308b\n \n\u2463 Docker\u30a4\u30e1\u30fc\u30b8\u306e\u53d6\u5f97\u9014\u4e2d\u3067\u3001\u300cdocker: unauthorized authentication required\u300d\u3068\u51fa\u3066\u6b7b\u306c\n\u21d2 windows power shell\u3092\u7ba1\u7406\u8005\u6a29\u9650\u3067\u5b9f\u884c\u3057\u3066\u30b3\u30de\u30f3\u30c9\u3092\u53e9\u304f\u3068\u3044\u3051\u305f\u3063\u307d\u3044\u3002\n```", "start_char_idx": 0, "end_char_idx": 929, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "71c6d102-1b98-40e2-975b-a0bca03bf152": {"__data__": {"id_": "71c6d102-1b98-40e2-975b-a0bca03bf152", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "68654e11-e95e-4198-b977-342ffea414d1", "node_type": null, "metadata": {}, "hash": "feacc1ccd770a0b86724729ba25520f86651fed878347ac5841a6555dd40f968"}}, "hash": "d4dbf46a058ef642f70d18b1cf22e16df356d3dd9e238622e2ded663cf985fce", "text": "pytorch v1.4 \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\n\nubuntu18.04\u74b0\u5883\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u308b\u3053\u3068\u3092\u78ba\u8a8d\u6e08\npytorch v1.4 \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6e08\u306eDocker\u74b0\u5883\uff08\u304a\u8a66\u3057\u7248\uff09\u3092\u4f5c\u6210\u3057\u307e\u3057\u305f\u3002\u8ffd\u52a0\u3067\u5fc5\u8981\u306a\u3082\u306e\u304c\u3042\u308c\u3070Dockerfile\u3092\u66f4\u65b0\u3057\u3066\u4e0b\u3055\u3044\u3002\n\n```\nfunction install_torch(){\n ### pytorch from pip image (v1.4)\n sudo apt-get install -y libopenblas-base libopenmpi-dev\n sudo apt-get -y install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev\n \n #python -m pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp27-cp27mu-linux_x86_64.whl\n python -m pip install torchvision==0.2.2\n pip3 install torch==1.4.0 torchvision==0.2.2\n pip install 'pillow<7'\n}\n```", "start_char_idx": 0, "end_char_idx": 617, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7b56d953-5d3d-4c26-b263-0d7d273eecf3": {"__data__": {"id_": "7b56d953-5d3d-4c26-b263-0d7d273eecf3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4058c3ac-5fc2-44f1-be9b-caf4592c1975", "node_type": null, "metadata": {}, "hash": "b56e5b69867041d1779ccae638325010546b6735f12a538c5fb1f35bd0d446d6"}}, "hash": "0a648ef2385f55a986464f0d1034abcc94fb17c33e221b0c952bf965748f2505", "text": "Docker\u306f\u30b3\u30f3\u30c6\u30ca\u7d42\u4e86\u306e\u5ea6\u306b\u30c7\u30fc\u30bf\u304c\u6d88\u3048\u3066\u624b\u9593\u304c\u639b\u304b\u308b\u306e\u3067\u4f55\u3068\u304b\u3057\u305f\u3044\nWSL(Windows Subsystem for Linux)\u3092\u4f7f\u3046\u5834\u5408\u306e\u624b\u9806\u3092\u7528\u610f\u3057\u307e\u3057\u305f\u3002\n\uff08kyad\u3055\u3093\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\uff09\n\n\u8ffd\u8a18\uff1acygwin\u74b0\u5883\u69cb\u7bc9\u624b\u9806\nisshy-you\u3055\u3093\u306b\u3088\u308b`Cygwin Install for tetris`\u69cb\u7bc9\u624b\u9806\n\uff08isshy-you\u3055\u3093\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3059\uff09", "start_char_idx": 0, "end_char_idx": 192, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2206a4d2-1112-4287-adcd-0bd9dc77978c": {"__data__": {"id_": "2206a4d2-1112-4287-adcd-0bd9dc77978c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c2ae3914-16bb-4bcd-98cc-2bc1b8c64211", "node_type": null, "metadata": {}, "hash": "4f0cce590024bef35ec4f32c471d3690938ee896d0dbf32cff08dd241adb554a"}}, "hash": "33b3ad95fa1d783ff739fefb402d4ed472fda2954f3b894aa98a2379a13857d5", "text": "python\u30b3\u30de\u30f3\u30c9\u304c\u898b\u3064\u304b\u3089\u306a\u3044\u65e8\u306e\u30a8\u30e9\u30fc\u304c\u51fa\u308b\u5834\u5408\n\nubuntu20.04\u7b49\u3067\u306f\u3001`python3`\u304c\u3042\u308b\u306b\u3082\u95a2\u308f\u3089\u305a`python`\u30b3\u30de\u30f3\u30c9\u304c\u306a\u3044\n\n```\n$ python start.py\nCompletedProcess(args='python --version', returncode=127, stderr='/bin/sh: 1: python: not found\\n')\n/bin/sh: 1: python: not found\nerror: subprocess failed.\n```\n\n```\n$ python --version\n\nCommand 'python' not found, did you mean:\n\n command 'python3' from deb python3\n command 'python' from deb python-is-python3\n```\n\n\u30ed\u30b0\u8a18\u8f09\u306e\u901a\u308a`python-is-python3`\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308c\u3070OK\n\n```\nsudo apt install -y python-is-python3\n```", "start_char_idx": 0, "end_char_idx": 502, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c64c4674-00e9-4ba9-a68d-c40cbcba032b": {"__data__": {"id_": "c64c4674-00e9-4ba9-a68d-c40cbcba032b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cbb01147-887b-4402-bc1f-e0d680de53b5", "node_type": null, "metadata": {}, "hash": "082549f5da274e2bf81e2a4fbc9ff02c337435d92924f2da8ff3ae9b9fe1a74c"}}, "hash": "881871480d10c68a5e1be49cbb8063234e1e23129919d72c2764e776ad3d4700", "text": "\u30b9\u30b3\u30a2\u8a55\u4fa1\u7528\u30b5\u30fc\u30d0\u306b\u3064\u3044\u3066\n\n\u4ee5\u4e0b\u3092\u4f5c\u6210\u3057\u3066\u307f\u307e\u3057\u305f\u3002\n\nhttps://github.com/seigot/tetris_score_server", "start_char_idx": 0, "end_char_idx": 74, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1d8fe66d-74de-42b6-b42c-602120f097f1": {"__data__": {"id_": "1d8fe66d-74de-42b6-b42c-602120f097f1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e196e0eb-1d62-4e56-b3c7-860b8b9cf82f", "node_type": null, "metadata": {}, "hash": "8bd7e0ac54e23a8ea6a0889153c24b2df3eba0b69297c2b5d3fffc1e8e7c4edf"}}, "hash": "9ca6a17e42a0e4b1531505d05ca55c6e2a3c1b8e5c571732016eebdc29f7b7dd", "text": "`python start.py`\u5b9f\u884c\u6642\u306b`TypeError: arguments did not match any overloaded call:`\u30a8\u30e9\u30fc\u304c\u51fa\u308b\n\n\u4ee5\u4e0bissue\u3067\u5bfe\u7b56\u4e2d\n> `python start.py`\u5b9f\u884c\u6642\u306b`TypeError: arguments did not match any overloaded call:`\u30a8\u30e9\u30fc\u304c\u51fa\u308b", "start_char_idx": 0, "end_char_idx": 184, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "4354a668-d85d-4c5b-bda7-a24d7f204a75": {"__data__": {"id_": "4354a668-d85d-4c5b-bda7-a24d7f204a75", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ea29a5cc-7be0-4c1c-9d82-19092d34e2d3", "node_type": null, "metadata": {}, "hash": "79b75d493f960bfa76c3950cd5a228d2a4e38026549ac88a64f17348253e64a8"}}, "hash": "7ef9294516f77094a3862772a5324cef87d75fa222de3a4ba131a2ee1df283e4", "text": "\u4ee5\u4e0b\u3001\u9806\u6b21\u8ffd\u8a18", "start_char_idx": 0, "end_char_idx": 7, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cd26aa1c-a6d6-43dc-9074-a568ef49975e": {"__data__": {"id_": "cd26aa1c-a6d6-43dc-9074-a568ef49975e", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "fe785f87-4fa6-4363-8b94-e20f73ac4c69", "node_type": null, "metadata": {}, "hash": "0c0d559e6096b03dd79986b13dcc56c9d4a027a8e74c5f4083efee4e5766fe05"}}, "hash": "b8336924f1327f09b4ae9c889f78d26b2eaf74f11919776dca513b255b0da0eb", "text": "`GameStatus`\u30c7\u30fc\u30bf\u69cb\u9020\n\n`block_controller.py`\u5185\u306e`def GetNextMove(self, nextMove, GameStatus)`\u3067\u6271\u3046`GameStatus`\u306f\u8f9e\u66f8\u578b\u30c7\u30fc\u30bf\u3067\u3059\u3002\n\u30d6\u30ed\u30c3\u30af\u306e\u52d5\u304d\u3092\u6c7a\u5b9a\u3059\u308b\u305f\u3081\u306e\u53c2\u8003\u30c7\u30fc\u30bf\u3068\u3057\u3066\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n```\n* field_info : \u30d5\u30a3\u30fc\u30eb\u30c9\u60c5\u5831\n* block_info : \u30d6\u30ed\u30c3\u30af\u60c5\u5831\n* judge_info : \u5be9\u5224\u60c5\u5831\n* debug_info : \u30c7\u30d0\u30c3\u30b0\u60c5\u5831\n```\n\n\u4ee5\u4e0b\u3001\u5404\u60c5\u5831\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u8a18\u8f09\u3057\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 264, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "37740239-6453-42e5-9485-42937afd3209": {"__data__": {"id_": "37740239-6453-42e5-9485-42937afd3209", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d3e5dd0c-ff5c-464d-91da-666b1a98ecc0", "node_type": null, "metadata": {}, "hash": "2d83e2c9932b690e55747e770794330fecab9f47177e9c63507ec8f6a316a2eb"}, "3": {"node_id": "9c58f533-fead-4e94-978a-70b0be81b612", "node_type": null, "metadata": {}, "hash": "1e99046ac5735ddf55cdceb820d4dec2eea37bcff89035447376cce2ebc2b914"}}, "hash": "dc9337248cdd1ba876295f92e575d04d4396cf7d0fe3b2c43c3bc88042e5248c", "text": "field_info\n\n\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n```\n* 'backboard': \u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u30c7\u30fc\u30bf\n* 'height': \u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u9ad8\u3055\n* 'width' : \u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u5e45\n* 'withblock' : \u30d5\u30a3\u30fc\u30eb\u30c9+\u30d6\u30ed\u30c3\u30af\u306e\u4f4d\u7f6e\u3092\u5408\u308f\u305b\u305f\u30c7\u30fc\u30bf\n```\n\n\u5177\u4f53\u7684\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u5b9f\u969b\u306b\u51fa\u529b\u3057\u3066\u307f\u308b\u3068\u5206\u304b\u308a\u6613\u3044\u3068\u601d\u3044\u307e\u3059\u3002\n\n```\n'field_info': {'backboard': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0,", "start_char_idx": 0, "end_char_idx": 1237, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9c58f533-fead-4e94-978a-70b0be81b612": {"__data__": {"id_": "9c58f533-fead-4e94-978a-70b0be81b612", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d3e5dd0c-ff5c-464d-91da-666b1a98ecc0", "node_type": null, "metadata": {}, "hash": "2d83e2c9932b690e55747e770794330fecab9f47177e9c63507ec8f6a316a2eb"}, "2": {"node_id": "37740239-6453-42e5-9485-42937afd3209", "node_type": null, "metadata": {}, "hash": "dc9337248cdd1ba876295f92e575d04d4396cf7d0fe3b2c43c3bc88042e5248c"}, "3": {"node_id": "e14c01b1-2eb3-482a-9383-fde15faf6a52", "node_type": null, "metadata": {}, "hash": "ebda4db9fbb9a4708030740448ad6b62cbf946ac231829bbaa22e946e4d964bd"}}, "hash": "1e99046ac5735ddf55cdceb820d4dec2eea37bcff89035447376cce2ebc2b914", "text": " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n 'height': 22,\n 'width': 10,\n 'withblock': [0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0,", "start_char_idx": 1226, "end_char_idx": 2472, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e14c01b1-2eb3-482a-9383-fde15faf6a52": {"__data__": {"id_": "e14c01b1-2eb3-482a-9383-fde15faf6a52", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d3e5dd0c-ff5c-464d-91da-666b1a98ecc0", "node_type": null, "metadata": {}, "hash": "2d83e2c9932b690e55747e770794330fecab9f47177e9c63507ec8f6a316a2eb"}, "2": {"node_id": "9c58f533-fead-4e94-978a-70b0be81b612", "node_type": null, "metadata": {}, "hash": "1e99046ac5735ddf55cdceb820d4dec2eea37bcff89035447376cce2ebc2b914"}}, "hash": "ebda4db9fbb9a4708030740448ad6b62cbf946ac231829bbaa22e946e4d964bd", "text": " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},\n```", "start_char_idx": 2466, "end_char_idx": 2871, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2014263b-21f8-4d9f-ac1f-1ecb67ba4b67": {"__data__": {"id_": "2014263b-21f8-4d9f-ac1f-1ecb67ba4b67", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ad902677-e961-42cb-9ffd-5474262f813d", "node_type": null, "metadata": {}, "hash": "dca15028ef5d667950c5447fd249ebd515d01176e375af4f0aaaf3801dc95a42"}}, "hash": "0ead6f3e62631ad523e2706b309edaaabe6a366f90714511135f866f000995bb", "text": "block_info\n\n\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n```\n'currentDirection': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304c\u521d\u671f\u5f62\u72b6\u304b\u3089\u4f55\u56de\u56de\u8ee2\u3057\u305f\u5f62\u72b6\u304b\n'currentShape': {\n 'class': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u3092\u7ba1\u7406\u3057\u3066\u3044\u308b\u30af\u30e9\u30b9\u306e\u30a2\u30c9\u30ec\u30b9\n 'direction_range': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304c\u56de\u8ee2\u3067\u304d\u308b\u56de\u6570\n 'index': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u306eindex\u5024\n},\n'currentX': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x\u5ea7\u6a19)\n'currentY': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(y\u5ea7\u6a19)\n'nextShape': {\n 'class': \u6b21\u306e\u30d6\u30ed\u30c3\u30af\u3092\u7ba1\u7406\u3057\u3066\u3044\u308b\u30af\u30e9\u30b9\u306e\u30a2\u30c9\u30ec\u30b9\n 'direction_range': \u6b21\u306e\u30d6\u30ed\u30c3\u30af\u304c\u56de\u8ee2\u3067\u304d\u308b\u56de\u6570\n 'index' : \u6b21\u306e\u30d6\u30ed\u30c3\u30af\u306eindex\u5024\n}\n```\n\n\u5177\u4f53\u7684\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u5b9f\u969b\u306b\u51fa\u529b\u3057\u3066\u307f\u308b\u3068\u5206\u304b\u308a\u6613\u3044\u3068\u601d\u3044\u307e\u3059\u3002\n\n```\n'block_info': {'currentDirection': 0,\n 'currentShape': {'class': ,\n 'direction_range': (0, 1),\n 'index': 1},\n 'currentX': 5,\n 'currentY': 1,\n 'nextShape': {'class': ,\n 'direction_range': (0, 1, 2,\n 3),\n 'index': 2}},\n```", "start_char_idx": 0, "end_char_idx": 982, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f90d1ee3-a44d-4025-8de9-ca2eff283765": {"__data__": {"id_": "f90d1ee3-a44d-4025-8de9-ca2eff283765", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "28d917d5-327e-41a3-9f41-7e786ec0ff9e", "node_type": null, "metadata": {}, "hash": "ebec340f1ce217aba0831c7527ea1f63f97f245eda05f362f11d030451ad0b31"}, "3": {"node_id": "cc5a89fa-f97d-4b34-a08d-7c8f2380f647", "node_type": null, "metadata": {}, "hash": "e195c387759a2f897538058a906056e446bfec0460090e954ec9ea7730dd1458"}}, "hash": "1a3594c2491c00e1e31d36057bc76c374c9a8f8af8aa81747854ff0f5ffdc1dc", "text": "debug_info\n\n\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\u30c7\u30d0\u30c3\u30af\u7528\u306b\u6e96\u5099\u3057\u3066\u3044\u308b\u60c5\u5831\u3067\u3042\u308a\u3001\u5fc5\u305a\u3057\u3082\u4f7f\u3046\u5fc5\u8981\u306f\u306a\u3044\u304b\u3082\u3057\u308c\u306a\u3044\u3067\u3059\u3002\n\n```\n'dropdownscore': \u843d\u4e0b\u306b\u3088\u308a\u7372\u5f97\u3057\u305f\u30b9\u30b3\u30a2\u306e\u5408\u8a08\n'line_score': { line\u3092\u6d88\u3057\u305f\u6642\u306a\u3069\u306b\u7372\u5f97\u3067\u304d\u308b\u30b9\u30b3\u30a2\n 'line1': 1line\u6d88\u3057\u305f\u6642\u306e\u7372\u5f97\u30b9\u30b3\u30a2\n 'line2': 2line\u6d88\u3057\u305f\u6642\u306e\u7372\u5f97\u30b9\u30b3\u30a2\n 'line3': 3line\u6d88\u3057\u305f\u6642\u306e\u7372\u5f97\u30b9\u30b3\u30a2\n 'line4': 4line\u6d88\u3057\u305f\u6642\u306e\u7372\u5f97\u30b9\u30b3\u30a2\n 'gameover': game over\u306b\u306a\u3063\u305f\u6642\u306e\u7372\u5f97\u30b9\u30b3\u30a2\n},\n'line_score_stat': \u6d88\u3057\u305fline\u306e\u7d71\u8a08\u30c7\u30fc\u30bf\n'linescore': line\u3092\u6d88\u3057\u3066\u7372\u5f97\u3057\u305f\u30b9\u30b3\u30a2\u306e\u5408\u8a08\n'shape_info': {\n 'shapeI': {\n 'color': ShapeI\u306e\u8272\n 'index': ShapeI\u306eindex\n },\n 'shapeJ': {\n 'color': ShapeJ\u306e\u8272\n 'index': ShapeJ\u306eindex\n },\n 'shapeL': {\n 'color': ShapeL\u306e\u8272\n 'index': ShapeL\u306eindex\n },\n 'shapeNone': {\n 'color': ShapeNone(\u30d6\u30ed\u30c3\u30af\u304c\u306a\u3044\u72b6\u614b)\u306e\u8272\n 'index': ShapeNone(\u30d6\u30ed\u30c3\u30af\u304c\u306a\u3044\u72b6\u614b)\u306eindex\n },\n 'shapeO': {\n 'color': ShapeO\u306e\u8272\n 'index': ShapeO\u306eindex\n },\n 'shapeS': {\n 'color': ShapeS\u306e\u8272\n 'index': ShapeS\u306eindex\n },\n 'shapeT': {\n 'color': ShapeT\u306e\u8272\n 'index': ShapeT\u306eindex\n },\n 'shapeZ': {\n 'color': ShapeZ\u306e\u8272\n 'index': ShapeZ\u306eindex\n }\n},\n'shape_info_stat': \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u6642\u70b9\u307e\u3067\u306b\u51fa\u73fe\u3057\u305f\u30d6\u30ed\u30c3\u30af\u306e\u7d71\u8a08\u60c5\u5831\n```\n\n\u5177\u4f53\u7684\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u5b9f\u969b\u306b\u51fa\u529b\u3057\u3066\u307f\u308b\u3068\u5206\u304b\u308a\u6613\u3044\u3068\u601d\u3044\u307e\u3059\u3002\n\n```\n 'debug_info': {'dropdownscore': 0,\n ", "start_char_idx": 0, "end_char_idx": 1344, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cc5a89fa-f97d-4b34-a08d-7c8f2380f647": {"__data__": {"id_": "cc5a89fa-f97d-4b34-a08d-7c8f2380f647", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "28d917d5-327e-41a3-9f41-7e786ec0ff9e", "node_type": null, "metadata": {}, "hash": "ebec340f1ce217aba0831c7527ea1f63f97f245eda05f362f11d030451ad0b31"}, "2": {"node_id": "f90d1ee3-a44d-4025-8de9-ca2eff283765", "node_type": null, "metadata": {}, "hash": "1a3594c2491c00e1e31d36057bc76c374c9a8f8af8aa81747854ff0f5ffdc1dc"}}, "hash": "e195c387759a2f897538058a906056e446bfec0460090e954ec9ea7730dd1458", "text": "0,\n 'line_score': {'line1': 100,\n 'line2': 300,\n 'line3': 700,\n 'line4': 1300,\n 'gameover': -500},\n 'line_score_stat': [0, 0, 0, 0],\n 'linescore': 0,\n 'shape_info': {'shapeI': {'color': 'red',\n 'index': 1},\n 'shapeJ': {'color': 'purple',\n 'index': 3},\n 'shapeL': {'color': 'green',\n 'index': 2},\n 'shapeNone': {'color': 'none',\n 'index': 0},\n 'shapeO': {'color': 'pink',\n 'index': 5},\n 'shapeS': {'color': 'blue',\n 'index': 6},\n 'shapeT': {'color': 'gold',\n 'index': 4},\n 'shapeZ': {'color': 'yellow',\n 'index': 7}},\n 'shape_info_stat': [0, 1, 0, 0, 0, 0, 0, 0]},\n```", "start_char_idx": 1328, "end_char_idx": 2631, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "67fa80df-74b0-495b-ac54-b805b10d55be": {"__data__": {"id_": "67fa80df-74b0-495b-ac54-b805b10d55be", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "210f7abe-762f-4d0c-beeb-219591370b19", "node_type": null, "metadata": {}, "hash": "becfe11337598c2147dfe14b45ad48a5ff9234a4611cc5b2f99fce94aba998db"}}, "hash": "e50d961f6c82901c2a9e3b4687fb8d4d030b0ac5952cb7e13c2d72d56b69c46f", "text": "judge_info\n\n\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n```\n'block_index': \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304c\u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u4f55\u756a\u76ee\u306b\u767b\u5834\u3057\u305f\u30d6\u30ed\u30c3\u30af\u304b\n'elapsed_time': \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u306e\u7d4c\u904e\u6642\u9593(s)\n'game_time': \u5236\u9650\u6642\u9593(s)\n'gameover_count': \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u306b\u306a\u3063\u305f\u56de\u6570\n'line': \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u6d88\u3057\u305f\u30e9\u30a4\u30f3\u306e\u6570\n'score': \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u7372\u5f97\u3057\u305f\u5408\u8a08\u30b9\u30b3\u30a2\n```\n\n\u5177\u4f53\u7684\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u5b9f\u969b\u306b\u51fa\u529b\u3057\u3066\u307f\u308b\u3068\u5206\u304b\u308a\u6613\u3044\u3068\u601d\u3044\u307e\u3059\u3002\n\n```\n 'judge_info': {'block_index': 1,\n 'elapsed_time': 1.083,\n 'game_time': 180,\n 'gameover_count': 0,\n 'line': 0,\n 'score': 0}}\n ```", "start_char_idx": 0, "end_char_idx": 540, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a7dec113-d4ad-42b3-8c12-068e790f36fe": {"__data__": {"id_": "a7dec113-d4ad-42b3-8c12-068e790f36fe", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ce577892-aa46-44ec-a3c3-ad82a8847f5d", "node_type": null, "metadata": {}, "hash": "d1d3759bc16167441244a6503fc709abc61ae5421468aab576a2f694a8633283"}}, "hash": "68c4abe4056e733e78c06369a56183076d08e791efcdb55c053971b8ef679a31", "text": "AI\u306b\u3064\u3044\u3066\uff08\u6e96\u5099\u4e2d\uff09", "start_char_idx": 0, "end_char_idx": 11, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ce9b0860-1fd5-4916-bc02-f7003e48000c": {"__data__": {"id_": "ce9b0860-1fd5-4916-bc02-f7003e48000c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4303e09f-8bce-4681-943e-06eac590d9d9", "node_type": null, "metadata": {}, "hash": "05c610a391652d5213d00eaa025202170f366b306dfe00bdedcc9e307afa539f"}}, "hash": "2a2967329848b86917f950d1d0302994900a3af01dab4dbb7ad4fa8619372066", "text": "1.\u74b0\u5883\u6e96\u5099\n- sample \u306e\u30b3\u30fc\u30c9\u3067\u306f pytorch \u3092\u4f7f\u3063\u3066AI\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002 \n- pytorch\u3000\u306b\u3064\u3044\u3066\u306f\u3053\u3061\u3089\u3092\u53c2\u8003\u306b\u3057\u3001\u74b0\u5883\u306b\u5408\u308f\u305b\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 103, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "fab83493-3635-42a2-9e4c-3f9c9811b13f": {"__data__": {"id_": "fab83493-3635-42a2-9e4c-3f9c9811b13f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "32c12c5a-b937-4e78-a5e9-0995028d8043", "node_type": null, "metadata": {}, "hash": "16387e538cb23b0dce282df6d59dac62fa8e2282701ce17d7e4f746ded1b40af"}}, "hash": "d4b84eec23e11bf904eb075ee74377346a1a0f56180d23d99b1085078c2e9ea9", "text": "\u4f8b) Windows\u306eCPU\u52d5\u4f5c\u7528\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\n```\npip3 install torch torchvision torchaudio\n```", "start_char_idx": 0, "end_char_idx": 78, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1b0215d1-5988-4f5c-89a9-25cdeeb163ab": {"__data__": {"id_": "1b0215d1-5988-4f5c-89a9-25cdeeb163ab", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a7c6cd42-a821-42ce-82da-45c1a7653f6b", "node_type": null, "metadata": {}, "hash": "5f9ad89b794df55b75c0a3146f841011c470edd965451322b20fbc126bc33ad2"}}, "hash": "a35ac1d9e755316d7ba9f8f4815331b53bf40f8c4c974fab1fc9e045987f75ba", "text": "\u4f8b) Mac\u306eCPU\u5b9f\u884c\u7528\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\n```\npip3 install torch torchvision torchaudio\n```", "start_char_idx": 0, "end_char_idx": 74, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3948ac0d-cd21-41a2-b82d-23463eda3c94": {"__data__": {"id_": "3948ac0d-cd21-41a2-b82d-23463eda3c94", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "968e52ba-8d2d-41cb-9ecd-02f41e519527", "node_type": null, "metadata": {}, "hash": "e882379d244a4592602686fcba052ccfb221341621fa8eef75804915e215d31a"}}, "hash": "66b6e8ebd2b77a80e2f87e3a9df3ec7701601033fd14bdca4c48f5264dcccbe8", "text": "2.\u5b66\u7fd2\u3068\u63a8\u8ad6\n\n* \u30b5\u30f3\u30d7\u30eb\u3068\u3057\u3066\u4e0b\u8a18\u306e\uff12\u3064\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c \n\u3053\u3061\u3089\n\u306e\u30b3\u30fc\u30c9\u306b\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\n\n * sample: DQN(Deep Q Network)\u3092\u4f7f\u3063\u305f\u5b66\u7fd2\u30fb\u63a8\u8ad6\n * sample2: MLP (Multilayer perceptron)\u3092\u4f7f\u3063\u305f\u5b66\u7fd2\u30fb\u63a8\u8ad6", "start_char_idx": 0, "end_char_idx": 152, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "260d1dd4-dfbf-4c84-b924-99494034f8d5": {"__data__": {"id_": "260d1dd4-dfbf-4c84-b924-99494034f8d5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "37a6860d-b01a-424b-a69a-0371ffab694a", "node_type": null, "metadata": {}, "hash": "2233e4692e3bb6e07cfbd77640eacb0bb508f634931fa23b356ed6de3d1f4996"}}, "hash": "2e619fdeff44a61c43f4e84ad1e270437095dc4fcb3d1ab49c6934575c80fafc", "text": "2.1\u5b66\u7fd2\u306e\u5b9f\u884c\n- DQN(Deep Q Network)\u3092\u4f7f\u3046\u5834\u5408\n\n```\npython start.py -m train_sample -d 1 -l 2 -t -1\n```\n\n- MLP (Multilayer perceptron)\u3092\u4f7f\u3046\u5834\u5408\n```\npython start.py -m train_sample2 -d 1 -l 2 -t -1\n```", "start_char_idx": 0, "end_char_idx": 186, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "dbf80e15-499d-45af-ba7c-ecca12f0bcf5": {"__data__": {"id_": "dbf80e15-499d-45af-ba7c-ecca12f0bcf5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8d31b5c2-c82d-4436-97fa-9038886e7a58", "node_type": null, "metadata": {}, "hash": "9288f6b82d90f7c4d3f248ee3ea2ad5eaef79dc5708dac9c4cc3dbc4d58e94e3"}}, "hash": "fcfe945850dc3854666dfcc3261c0fd9df8a61e5cdd0090a401abfe50d0a6362", "text": "2.2\u63a8\u8ad6\u306e\u5b9f\u884c\n\n- DQN(Deep Q Network)\u3092\u4f7f\u3046\u5834\u5408\n```\npython start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt\n```\n\n- MLP (Multilayer perceptron)\u3092\u4f7f\u3046\u5834\u5408\n```\npython start.py -m predict_sample2 -l 2 --predict_weight weight/MLP/sample_weight.pt\n```", "start_char_idx": 0, "end_char_idx": 258, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "084f7f0a-335a-4332-8fba-ea8af93adb07": {"__data__": {"id_": "084f7f0a-335a-4332-8fba-ea8af93adb07", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ec004f6b-f580-4214-a0ed-5ef66f0b423b", "node_type": null, "metadata": {}, "hash": "152bbfb7da5490eeb205e4d7aae6aed893f253ea01387b82a3e2f09c11f20898"}}, "hash": "94c50a04454c134030264388783239fcdc46f26207f1c2c83bf3f8eec3f99b2f", "text": "2.3 \u518d\u5b66\u7fd2/\u8ffd\u52a0\u5b66\u7fd2\uff08Fine Tuning) \u306e\u5b9f\u884c\n- \u5b66\u7fd2\u6e08\u307f\u306e\u91cd\u307f\u3092\u521d\u671f\u72b6\u614b\u3068\u3057\u3066\u5b66\u7fd2\u3059\u308b\u5834\u5408\u306f\u4e0b\u8a18\u306e\u624b\u9806\u3067\u3000config\u306eyaml\u30d5\u30a1\u30a4\u30eb\u3092\u8a2d\u5b9a\u3059\u308b\u3002\n1. ft_weight \u306b\u5b66\u7fd2\u6e08\u307f\u91cd\u307f\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9\u3092\u8a2d\u5b9a\n2. finetune \u3092\u3000False \u2192 True \u306b\u5909\u66f4\n3. initial_epsilon \u3092\u5c0f\u3055\u304f\u3059\u308b\uff080\u3088\u308a\u5927\u304d\u3044\uff11\u4ee5\u4e0b\u306e\u5024\uff09\n\n- initial_epsilon \u306f \u03b5-greedy\u6cd5\u3068\u3044\u3046\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u306a\u308a\u3001\u5b66\u7fd2\u521d\u671f\u306b\u306b\u3069\u306e\u7a0b\u5ea6\u30e9\u30f3\u30c0\u30e0\u306a\u884c\u52d5\u3092\u53d6\u308b\u304b\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u8ffd\u52a0\u5b66\u7fd2\u306e\u5834\u5408\u306f\u3001\u5c0f\u3055\u3044\u5024\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u5b66\u7fd2\u6e08\u307f\u306e\u30e2\u30c7\u30eb\u3092\u6709\u52b9\u6d3b\u7528\u3067\u304d\u307e\u3059\u3002\n- \u03b5-greedy\u6cd5\u306b\u3064\u3044\u3066\u306f\u4e0b\u8a18\u306e\u30b5\u30a4\u30c8\u306a\u3069\u304c\u53c2\u8003\u306b\u306a\u308a\u307e\u3059\u3002\n - https://horomary.hatenablog.com/entry/2021/01/26/233351", "start_char_idx": 0, "end_char_idx": 397, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e02f1412-7ee8-4e47-8fab-65ec2e609219": {"__data__": {"id_": "e02f1412-7ee8-4e47-8fab-65ec2e609219", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f292052-e685-484c-8a88-ced6af9dee2b", "node_type": null, "metadata": {}, "hash": "bc395ff0aac8b81d248f5b6fc6047b20ce841fe9bd137e1198cc369684ffc9cb"}}, "hash": "b16e8e9908c8bc7898ac3778d79c8323b059d5f8fc2fd3721e4199aeba082e59", "text": "2.4 -m \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3064\u3044\u3066\n - \u5b66\u7fd2/\u63a8\u8ad6\u306e\u5207\u308a\u66ff\u3048\u304a\u3088\u3073\u30b5\u30f3\u30d7\u30eb/\u672c\u756a\u30b3\u30fc\u30c9\u306e\u5207\u308a\u66ff\u306b\u306f\u3000\"-m\"\u3000\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\n\n**\u203b sample\u30b3\u30fc\u30c9\u306f\u57fa\u672c\u7684\u306b\u5909\u66f4\u305b\u305a\u3001\u672c\u756a\u7528\u30b3\u30fc\u30c9\u3067\u958b\u767a\u3057\u3066\u304f\u3060\u3055\u3044**\n\n- \"-m\" \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u5bfe\u3057\u3066\u4e0b\u8a18\u306e\u5f15\u6570\u3092\u6e21\u3059\u3053\u3068\u3067\u66f8\u304f\u30e2\u30fc\u30c9\u3092\u5207\u308a\u66ff\u3048\u3089\u308c\u308b\u3002\n\n|\u30aa\u30d7\u30b7\u30e7\u30f3\u540d|\u8aac\u660e|\n| ---- | ---- |\n| train_sample| \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c91\u3092\u7528\u3044\u3066DQN\u306b\u3088\u308b\u5b66\u7fd2\u3092\u884c\u3046|\n| train_sample2| \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c92\u3092\u7528\u3044\u3066MLP\u306b\u3088\u308b\u5b66\u7fd2\u3092\u884c\u3046|\n| train| \u672c\u756a\u7528\u30b3\u30fc\u30c9 \u3092\u7528\u3044\u3066\u5b66\u7fd2\u3092\u884c\u3046|\n| predict_sample| \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c91\u3092\u7528\u3044\u3066DQN\u306b\u3088\u308b\u63a8\u8ad6\u3092\u884c\u3046\u3002 \u5b9f\u884c\u6642\u306b--predict_weight \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3088\u308a\u91cd\u307f\u306e\u30d1\u30b9\u3092\u9078\u629e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002|\n| predict_sample2| \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c92\u3092\u7528\u3044\u3066MLP\u306b\u3088\u308b\u5b66\u7fd2\u3092\u884c\u3046\u3000 \u5b9f\u884c\u6642\u306b--predict_weight \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3088\u308a\u91cd\u307f\u306e\u30d1\u30b9\u3092\u9078\u629e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002|\n| predict| \u672c\u756a\u7528\u30b3\u30fc\u30c9 \u3092\u7528\u3044\u3066\u5b66\u7fd2\u3092\u884c\u3046\u3000 \u5b9f\u884c\u6642\u306b--predict_weight \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3088\u308a\u91cd\u307f\u306e\u30d1\u30b9\u3092\u9078\u629e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002|", "start_char_idx": 0, "end_char_idx": 554, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2fa953dc-0470-4f3f-9486-03d37d36846c": {"__data__": {"id_": "2fa953dc-0470-4f3f-9486-03d37d36846c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e29949ae-1997-4278-aba6-381cbe396c43", "node_type": null, "metadata": {}, "hash": "3dd00af02cd94fe89e09213b0c2acac05f42cb434ba4e22d18a6c1253b0cd5da"}}, "hash": "889656e98521a12bb9345c316a4ad2d0ed815e364c0cdf04cf9354bf5392daea", "text": "2.5 AI\u95a2\u9023\u306e\u305d\u306e\u4ed6\u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3064\u3044\u3066\n- AI\u5b66\u7fd2\u95a2\u9023\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u306f\u4e0b\u8a18\n\n|\u30aa\u30d7\u30b7\u30e7\u30f3\u540d|\u5f15\u6570|\u8aac\u660e| train or predict|\n| ---- | ---- | ---- | ---- |\n| *--train_yaml* | \u5b66\u7fd2\u306b\u7528\u3044\u308byaml\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9|\u5b66\u7fd2\u306b\u7528\u3044\u308byaml\u30d5\u30a1\u30a4\u30eb\u3092\u9078\u629e\u3059\u308b\u3002|train|\n| *--predict_weight* | \u63a8\u8ad6\u306b\u7528\u3044\u308bweight\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9|\u63a8\u8ad6\u306b\u7528\u3044\u308bweight\u30d5\u30a1\u30a4\u30eb\u3092\u9078\u629e\u3059\u308b|predict|", "start_char_idx": 0, "end_char_idx": 242, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "06ef4019-abd1-439c-bb45-44f3e4971932": {"__data__": {"id_": "06ef4019-abd1-439c-bb45-44f3e4971932", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5eadb649-7007-4e92-9b26-dc8ca221b390", "node_type": null, "metadata": {}, "hash": "7dbfd019eaecc9e540d2bf366795bbb5d51e25be93a8b57f1f1a7d6ec3d60115"}}, "hash": "f923be6c28fcd222cd2742a4ca18823851a2fd4afb5ec91ea10cf3c3a9aa90eb", "text": "3. \u5f37\u5316\u5b66\u7fd2\u306b\u3064\u3044\u3066\n\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c91 \u3000\u304a\u3088\u3073\u3000\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c92 \u3067\u306f\u5f37\u5316\u5b66\u7fd2\u3068\u547c\u3070\u308c\u308b\u65b9\u6cd5\u3067\u30c6\u30c8\u30ea\u30b9\u3092\u30d7\u30ec\u30a4\u3059\u308b\u305f\u3081\u306e\u6700\u9069\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5b66\u7fd2\u3057\u307e\u3059\u3002 \n\n!Screenshot", "start_char_idx": 0, "end_char_idx": 92, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c84d20ec-e28f-4f37-bd24-4c09ae107433": {"__data__": {"id_": "c84d20ec-e28f-4f37-bd24-4c09ae107433", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d26b7075-c33d-4ca5-89e8-dbaeb3ef92e2", "node_type": null, "metadata": {}, "hash": "b488d257a98fc6488eeb03ec9fd3d4541bff065567d5ff17339abf312ba15bbd"}, "3": {"node_id": "533efdc1-0693-470c-b252-baa900561f46", "node_type": null, "metadata": {}, "hash": "14f692b6c1dfa4215e9c16f3809a75f5ddd9bc9c9e8cf5e01c27815a773b6b11"}}, "hash": "55807e90ddf1a4863cbb334bf954f26d34abb8a2f0184d4c98840d935ed712f4", "text": "4. \u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u3064\u3044\u3066\n- \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u306fyaml \u30d5\u30a1\u30a4\u30eb\u306b\u3088\u3063\u3066\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\uff08\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5b66\u7fd2\u3059\u308b\u305f\u3081\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\uff09\u3092\u8a2d\u5b9a\u3059\u308b\u3002\n- \u30b5\u30f3\u30d7\u30eb\u7528yaml \u30d5\u30a1\u30a4\u30eb\u306e\u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u306f\u4e0b\u8a18\n\n\u5171\u901a\n\n|\u30d1\u30e9\u30e1\u30fc\u30bf\u540d|\u8aac\u660e|\u578b|train or predict|default|\n| ---- | ---- | ---- | ---- | ---- |\n|**common** |- |- |- |-|\n|ft_weight|\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0(\u521d\u671f\u5024\u3068\u3057\u3066\u5b66\u7fd2\u6e08\u307f\u306e\u91cd\u307f\u3092\u7528\u3044\u308b\u5b66\u7fd2)\u306b\u7528\u3044\u308b\u91cd\u307f\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9|str|train|None|\n|log_path| tensorboard\u306e\u30ed\u30b0\u51fa\u529b\u30d5\u30a9\u30eb\u30c0\u540d|str|\u5171\u901a|tensorboard|\n\n\nmodel\u95a2\u9023\n\n|\u30d1\u30e9\u30e1\u30fc\u30bf\u540d|\u8aac\u660e|\u578b|train or predict|default|\n| ---- | ---- | ---- | ---- | ---- |\n|**model** |- |- |- |-|\n|name|\u4f7f\u7528\u3059\u308bAI\u30e2\u30c7\u30eb\u540d|str|\u5171\u901a|DQN|\n|finetune|\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u304b\u3069\u3046\u304b\u306e\u30d5\u30e9\u30b0|bool|train|False|\n|**state** |- |- |- |-|\n|dim|\u72b6\u614b\u306e\u6b21\u5143\u6570|int|\u5171\u901a|4|\n\n\n\u5b66\u7fd2\n\n|\u30d1\u30e9\u30e1\u30fc\u30bf\u540d|\u8aac\u660e|\u578b|train or predict|default|\n| ---- | ---- | ---- | ---- | ---- |\n|**train** |- |- |- |-|\n|optimizer|\u6700\u9069\u5316\u95a2\u6570(\u30b5\u30f3\u30d7\u30eb\u3067\u306fAdam\u307e\u305f\u306fSGD\u304c\u4f7f\u7528\u53ef\u80fd)|str|train|Adam|\n|lr|\u5b66\u7fd2\u7387|float|train|1.0e-3|\n|lr_gamma|\u66f4\u65b0\u7387(optimizer\u304cSGD\u306e\u6642\u306e\u307f\u4f7f\u7528)|float|train|0.1|\n|lr_momentum|\u30e2\u30fc\u30e1\u30f3\u30bf\u30e0(optimizer\u304cSGD\u306e\u6642\u306e\u307f\u4f7f\u7528)|float|train|0.99|\n|lr_step_size|\u30b9\u30c6\u30c3\u30d7\u30b5\u30a4\u30ba(optimizer\u304cSGD\u306e\u6642\u306e\u307f\u4f7f\u7528)|int|train|1000|\n|num_epoch|\u30a8\u30dd\u30c3\u30af\u6570(\u5b66\u7fd2\u3059\u308b\u30a4\u30c6\u30ec\u30fc\u30b7\u30e7\u30f3\u6570)|int|train|5000|\n|num_decay_epochs|\u03b5-greedy\u306e\u03b5\u3092\u6700\u5c0f\u306b\u3059\u308b\u30a8\u30dd\u30c3\u30af\u6570(\u3053\u3053\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u6027\u3092\u4e0b\u3052\u308b\u3002)|int|train|3000|\n|initial_epsilon|\u03b5-greedy\u306e\u03b5\u306e\u521d\u671f\u5024|float|train|1.0|\n|final_epsilon|\u03b5-greedy\u306e\u03b5\u306e\u6700\u5c0f\u5024|float|train|1.0e-3|\n|batch_size|\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba|int|train|512|\n|gamma|\u5272\u5f15\u7387|float|train|0.99|\n|max_penalty|\u30da\u30ca\u30eb\u30c6\u30a3\u306e\u6700\u5927\u5024|float|train|-1|\n|target_net|TargetNetwork\u306e\u4f7f\u7528\u30d5\u30e9\u30b0|bool|train|True|\n|target_copy_intarval|TargetNetwork\u306b\u30b3\u30d4\u30fc\u3059\u308b\u30a4\u30f3\u30bf\u30fc\u30d0\u30eb|int|train|500|\n|replay_memory_size|Experience replay\u306ereplay buffer\u306e\u30b5\u30a4\u30ba|int|train|30000|\n|double_dqn|Double DQN\u306e\u4f7f\u7528\u30d5\u30e9\u30b0|bool|train|True|\n|reward_clipping|Reward", "start_char_idx": 0, "end_char_idx": 1500, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "533efdc1-0693-470c-b252-baa900561f46": {"__data__": {"id_": "533efdc1-0693-470c-b252-baa900561f46", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d26b7075-c33d-4ca5-89e8-dbaeb3ef92e2", "node_type": null, "metadata": {}, "hash": "b488d257a98fc6488eeb03ec9fd3d4541bff065567d5ff17339abf312ba15bbd"}, "2": {"node_id": "c84d20ec-e28f-4f37-bd24-4c09ae107433", "node_type": null, "metadata": {}, "hash": "55807e90ddf1a4863cbb334bf954f26d34abb8a2f0184d4c98840d935ed712f4"}}, "hash": "14f692b6c1dfa4215e9c16f3809a75f5ddd9bc9c9e8cf5e01c27815a773b6b11", "text": "clipping\u306e\u4f7f\u7528\u30d5\u30e9\u30b0|bool|train|True|\n|prioritized_replay|Prioritized Experience Replay\u306e\u4f7f\u7528\u30d5\u30e9\u30b0|bool|train|True|\n|multi_step_learning|Multi step learning\u306e\u4f7f\u7528\u30d5\u30e9\u30b0|bool|train|True|\n|multi_step_num|Multi step learning\u306e\u30b9\u30c6\u30c3\u30d7\u6570|int|train|0,100,300,700,1300,-1300 \u3000\u5de6\u304b\u3089\u30000\u5217\u5d29\u3057,1\u5217\u5d29\u3057,2\u5217\u5d29\u3057,3\u5217\u5d29\u3057,4\u5217\u5d29\u3057,\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc)|\n|reward_list|\u70b9\u6570\u306b\u5fdc\u3058\u305f\u5831\u916c\u306e\u8a2d\u5b9a\u5024\uff080\u5217\u5d29\u3057,1\u5217\u5d29\u3057,2\u5217\u5d29\u3057,3\u5217\u5d29\u3057,4\u5217\u5d29\u3057,\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u306e\u9806\uff09|int|train|3|\n|reward_weight|\u70b9\u6570\u4ee5\u5916\u3067\u4e0e\u3048\u3089\u308c\u308b\u30da\u30ca\u30eb\u30c6\u30a3\u306e\u91cd\u307f\uff08\u96a3\u63a5\u3059\u308b\u884c\u3068\u306e\u9ad8\u3055\u5dee\u5206,\u6700\u5927\u306e\u9ad8\u3055,\u7a74\u306e\u6570 \u306e\u9806) 0\u4ee5\u4e0a\u306e\u5024\u304c\u5fc5\u9808|float|train|0.01,0.0,0.01 \uff08\u5de6\u304b\u3089\u3000\u96a3\u63a5\u3059\u308b\u884c\u3068\u306e\u9ad8\u3055\u5dee\u5206,\u6700\u5927\u306e\u9ad8\u3055,\u7a74\u306e\u6570 \uff09|", "start_char_idx": 1501, "end_char_idx": 1986, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3ede1386-094b-460e-b22d-c32b7e2e60b1": {"__data__": {"id_": "3ede1386-094b-460e-b22d-c32b7e2e60b1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f7935bb2-2cad-4d9f-98a5-1058e64a88a9", "node_type": null, "metadata": {}, "hash": "8bfdc6dca10afd0eb6be5444d1e0487f4c43761843f991eac5597619aad93dfc"}}, "hash": "1937692a4cc6ea8e8610b27207fb884709f8055ad3b0a5e1bffa982e446cba16", "text": "4.2 \u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u4f8b\uff08\u5f97\u305f\u3044\u52b9\u679c\u3068\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u95a2\u4fc2\u4f8b\uff09\n- \u4e0b\u8a18\u306b\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u306e\u65b9\u91dd\u4f8b\u3092\u8a18\u8f09\u3059\u308b\n- \u305d\u308c\u305e\u308c\u306e\u52b9\u679c\u304c\u5e38\u306b\u5f97\u3089\u308c\u308b\u3068\u306f\u9650\u3089\u306a\u3044\u306e\u3067\u5b9f\u9a13\u3092\u7e70\u308a\u8fd4\u3057\u306a\u304c\u3089\u3001\u6700\u9069\u306a\u5024\u3092\u63a2\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\n1. \u30d6\u30ed\u30c3\u30af\u3092\u7a4d\u307f\u4e0a\u3052\u308b\n - reward_list\u306e\uff13\u5217\u5d29\u3057\u3001\uff14\u5217\u5d29\u3057\u306e\u5024\u3092\u5927\u304d\u304f\u3059\u308b\n - reward_weight\u306e max_height\u3092\u5c0f\u3055\u304f\u3059\u308b\n\n2. \u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u306e\u767a\u751f\u3092\u9632\u3050\uff08\u30d6\u30ed\u30c3\u30af\u3092\u7a4d\u307f\u4e0a\u3052\u91cf\u3092\u6e1b\u3089\u3059\uff09\n - reward_list\u306e\uff13\u5217\u5d29\u3057\u3001\uff14\u5217\u5d29\u3057\u306e\u5024\u3092\u5c0f\u3055\u304f\u3059\u308b\n - reward_weight\u306e max_height\u3092\u5927\u304d\u304f\u3059\u308b\n\n3. \u904e\u53bb\u306e\u76e4\u9762\u304b\u3089\u306e\u5f71\u97ff\u3092\u5c0f\u3055\u304f\u3059\u308b\n - gamma(\u5272\u5f15\u7387)\u3092\u5927\u304d\u304f\u3059\u308b\u3002\n\n4. \u5b66\u7fd2\u3092\u5b89\u5b9a\u3055\u305b\u308b\n - reward_clipping \u3092 True\u306b\u3059\u308b\u3002\n - target_net \u3092 True\u306b\u3059\u308b\n - double_DQN \u3092 True\u306b\u3059\u308b\u3002\n\n5. \u5b66\u7fd2\u3092\u65e9\u304f\u3059\u308b\u3002\uff08\u5b66\u7fd2\u3059\u308b\u30e2\u30c7\u30eb\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6e1b\u3089\u3059\uff09\n - \u30e2\u30c7\u30eb\u306e\u30ec\u30a4\u30e4\u6570\u3001\u30ab\u30fc\u30cd\u30eb\u30b5\u30a4\u30ba\u3092\u6e1b\u3089\u3059\n - MLP\u3092\u4f7f\u7528\u3059\u308b\n\n6. \u5b66\u7fd2\u3092\u65e9\u304f\u3059\u308b\u3002\uff08\u52b9\u679c\u306e\u9ad8\u3044\u30c7\u30fc\u30bf\u3092\u52b9\u7387\u7684\u306b\u5b66\u7fd2\u306b\u4f7f\u7528\u3059\u308b\uff09\n - prioritized_replay \u3092 True\u306b\u3059\u308b\u3002\n - lr\uff08\u5b66\u7fd2\u7387\uff09\u3092\u5927\u304d\u304f\u3059\u308b\u3002\uff08\u5927\u304d\u3059\u304e\u308b\u3068\u53ce\u675f\u3057\u306a\u304f\u306a\u308b\u306e\u3067\u6ce8\u610f\uff09\n - batch_size\u3000\u3092\u5927\u304d\u304f\u3059\u308b \uff08\u5b66\u7fd2\u306b\u4f7f\u7528\u3059\u308b\u30e1\u30e2\u30ea\u91cf\u304c\u5927\u304d\u304f\u306a\u308b\u306e\u3067\u6ce8\u610f\uff09\n - replay_memory_size\u3000\uff08\u5b66\u7fd2\u306b\u4f7f\u7528\u3059\u308b\u30e1\u30e2\u30ea\u91cf\u304c\u5927\u304d\u304f\u306a\u308b\u306e\u3067\u6ce8\u610f\uff09", "start_char_idx": 0, "end_char_idx": 725, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f8e78a3b-853d-4dd6-96a8-8c757663e864": {"__data__": {"id_": "f8e78a3b-853d-4dd6-96a8-8c757663e864", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6e42d55e-f0a1-4b3d-96e7-26bfcf75a41e", "node_type": null, "metadata": {}, "hash": "881dc8b1ce168e0ee3da7e7a1df14810148c1639c436edfcc924adb927eb6603"}}, "hash": "c371d81356f9428d3bb3bc80d878b1930ad39b12c7e395bfd6624707aafc9f67", "text": "5.\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u5b9f\u884c\n- \u30b5\u30f3\u30d7\u30eb\u7528\u306e\u30b3\u30fc\u30c9\u3092\u8a66\u3059\u305f\u3081\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u7528Jupyter-notebook../../game_manager/machine_learning/)\n\u3092\u7528\u610f\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u30b3\u30fc\u30c9\u7406\u89e3\u306e\u305f\u3081\u306b\u304a\u4f7f\u3044\u304f\u3060\u3055\u3044\u3002\n\n- Jupyter-notebook\u304b\u4e0b\u8a18\u306e\u624b\u9806\u3067\u4f7f\u7528\u3067\u304d\u307e\u3059.\n- 1. Jupyter-notebook\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n```\npip install jupyter\n```\n\n- 2. Jupyter-notebook\u306e\u8d77\u52d5\n```\njupyter notebook\n```\n\u4e0b\u8a18\u306e\u3088\u3046\u306a\u753b\u9762\u304c\u3001\u30d6\u30e9\u30a6\u30b6\u3067\u958b\u304b\u308c\u308b\u306e\u3067\ngame_manager > machine_learning > model > tutorial \n\u306e\u6e96\u5099\u9032\u307f\u3001***.ipynb \u30d5\u30a1\u30a4\u30eb\u3092\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\n!Screenshot\n\n- Jupyter-notebook \u306e\u30bb\u30eb\u306f\u3000Shit\uff0bEnter key \u3067\u5b9f\u884c\u3067\u304d\u307e\u3059\n\n- Jupyter-notebook \u306e\u4f7f\u3044\u65b9\u306f\u4e0b\u8a18\u306e\u30b5\u30a4\u30c8\u306a\u3069\u304c\u53c2\u8003\u306b\u306a\u308a\u307e\u3059\u3002\nhttps://qiita.com/takuyanin/items/8bf396e7b6b051670147\n\n- venv \u306a\u3069\u306e\u4eee\u60f3\u74b0\u5883\u3092\u7528\u3044\u3066\u3044\u308b\u5834\u5408\u306f\u4e0b\u8a18\u306e\u30b5\u30a4\u30c8\u306a\u3069\u304c\u53c2\u8003\u306b\u306a\u308a\u307e\u3059\u3002\n\u3000\u3000\nhttps://qiita.com/smiler5617/items/e0d9b3034d79457cc253", "start_char_idx": 0, "end_char_idx": 624, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "70a996da-13cc-4b16-816a-ef5e21359e11": {"__data__": {"id_": "70a996da-13cc-4b16-816a-ef5e21359e11", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "115941f4-c39b-44ad-b53e-075bc067bf4a", "node_type": null, "metadata": {}, "hash": "6330d76bcbbc9aea89f9fdaf9c3600a505e0969043ff9619a667fdf72374a396"}}, "hash": "b896a14ac1feebc1713e128fff1bcbc52d18bb1f88802732fc56893510a68d70", "text": "6. \u3055\u3089\u306a\u308b\u5f37\u5316\u306b\u5411\u3051\u3066\n- \u7b2c3\u56de\u3067\u306e cookie4869 \u306e\u5f37\u5316\u8cc7\u6599\u3092\u4e0b\u8a18\u3067\u5171\u6709\u3044\u305f\u3057\u307e\u3059\u3002\n\n!AI\u5f37\u5316\u8cc7\u6599", "start_char_idx": 0, "end_char_idx": 59, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "742d11db-0a2b-4ecf-bb72-3544da7c3f52": {"__data__": {"id_": "742d11db-0a2b-4ecf-bb72-3544da7c3f52", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d080c1cd-af48-43f0-a9cf-364a2a1452a4", "node_type": null, "metadata": {}, "hash": "012f650c07c965c7128682b5f7c789148ae57f8ade22096a396dcac570edcc07"}}, "hash": "e60dc689939ee222cffc47ff77c508528d1dd22614ba7811bb5a9e1412a5c28a", "text": "art\u306b\u3064\u3044\u3066\n\n\u672c\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u30c6\u30c8\u30ea\u30b9\u30a2\u30fc\u30c8\u306e\u53d6\u308a\u7d44\u307f\u306b\u3064\u3044\u3066\u8a18\u8f09\u3059\u308b", "start_char_idx": 0, "end_char_idx": 38, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "342e8fc2-89cb-4b43-b0c9-5f505ec39829": {"__data__": {"id_": "342e8fc2-89cb-4b43-b0c9-5f505ec39829", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3a73aaa3-4282-4375-98f5-5566e21358a0", "node_type": null, "metadata": {}, "hash": "7ac5a2e1c5879b6c2bd74d8ad0581a41bf95b1256a31ce7a7c9565d3b111d9a2"}}, "hash": "28449c21a2b2c1e9e9fbe7738122a0ad38c7fa51f7dbd550496e15bc98b3cc61", "text": "\u6982\u8981\n\n\u901a\u5e38\u3001\u30c6\u30c8\u30ea\u30b9\u306f\u4e0a\u304b\u3089\u843d\u3061\u3066\u304f\u308b\u30d6\u30ed\u30c3\u30af\uff08\u30c6\u30c8\u30ea\u30df\u30ce\uff09\u3092\u64cd\u4f5c\u3057\u3066\u3001\u6a2a\u5217\u3092\u57cb\u3081\u3066\u6d88\u3057\u3066\u3044\u304f\u3088\u3046\u306b\u3057\u3066\u904a\u3076\u3002 \n\u4e00\u65b9\u3001\u30c6\u30c8\u30ea\u30b9\u30a2\u30fc\u30c8\u306f\u30d6\u30ed\u30c3\u30af\u3067\u30d5\u30a3\u30fc\u30eb\u30c9\u306b\u6a21\u69d8\u3092\u63cf\u304f\u3053\u3068\u3092\u76ee\u7684\u3068\u3059\u308b\u3002\uff08\u30d6\u30ed\u30c3\u30af\u3092\u6d88\u3059\u3053\u3068\u3092\u5fc5\u305a\u3057\u3082\u76ee\u7684\u3068\u3057\u306a\u3044\uff09 \n\u8a73\u3057\u304f\u306f\"\u30c6\u30c8\u30ea\u30b9\u30a2\u30fc\u30c8\"\u3067google\u691c\u7d22\u3092\u3057\u3066\u307f\u3066\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 151, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a50538d7-39ba-408d-b535-b62c3d5bb41b": {"__data__": {"id_": "a50538d7-39ba-408d-b535-b62c3d5bb41b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3b1cf835-65ab-47d4-9483-69be54ae419a", "node_type": null, "metadata": {}, "hash": "0b3701c1f0554ebe610074dd4a7fbc985132db34bc119a8288b65604490bd665"}}, "hash": "523daa000752621dd02c7bbe535117eb57fc497fd06633e184218b778093ba18", "text": "\u53d6\u308a\u7d44\u307f\u65b9\n\n1.\u307e\u305a\u3001\u30d6\u30ed\u30c3\u30af\u3067\u30d5\u30a3\u30fc\u30eb\u30c9\u306b\u63cf\u304d\u305f\u3044\u6a21\u69d8\u3092\u30a4\u30e1\u30fc\u30b8\u3059\u308b\u3002 \n2.\u6b21\u306b\u3001\u6a21\u69d8\u304c\u30c6\u30c8\u30ea\u30b9\u4e0a\u3067\u5b9f\u73fe\u53ef\u80fd\u304b\u3069\u3046\u304b\u3092\u691c\u8a0e\u3059\u308b\u3002\uff08\u4f5c\u56f3\u306a\u3069\u304c\u304a\u52e7\u3081\u3067\u3059\uff09 \n3.\u5b9f\u73fe\u53ef\u80fd\u3067\u3042\u308c\u3070\u30d6\u30ed\u30c3\u30af\u3092\u64cd\u4f5c\u3057\u3066\u6a21\u69d8\u3092\u4f5c\u6210\u3059\u308b\u3002\n \n3.\u306b\u3088\u308a\u30d6\u30ed\u30c3\u30af\u3092\u64cd\u4f5c\u3059\u308b\u5834\u5408\u3001\u5f8c\u8ff0\u3059\u308bconfig\u30d5\u30a1\u30a4\u30eb\u3092\u4f7f\u3044\u5404\u30d6\u30ed\u30c3\u30af\u306e\u8272\u3084\u51fa\u73fe\u9806\u5e8f(index)\u306a\u3069\u3092\u8abf\u6574\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u308b\u3002", "start_char_idx": 0, "end_char_idx": 183, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "18ca8e93-92d9-4eff-be6a-b6e49099476b": {"__data__": {"id_": "18ca8e93-92d9-4eff-be6a-b6e49099476b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b427ca59-425d-41f5-a205-da189a350d02", "node_type": null, "metadata": {}, "hash": "241f0ef77e9c284f21e5c7b7de30766c33bf86c3ca527cc93bd864b1e16de475"}}, "hash": "cd982df25458c8d1e172609c5fbb409ea7bf79d292ae2a8ce7c6f50bc44d2d67", "text": "\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\n\n`python start.py`\u5b9f\u884c\u6642\u306b\u4ee5\u4e0b\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u6307\u5b9a\u3059\u308b\u3068\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u304c\u5b9f\u884c\u3055\u308c\u308b \nyoutube-link: tetris art sample \n\n`1:onigiri`\n\n```\npython start.py -l1 -m art --art_config_filepath config/art/art_config_sample1.json\n```\n\n!Screenshot\n\n`2:manji`\n\n```\npython start.py -l1 -m art --art_config_filepath config/art/art_config_sample2.json\n```\n\n!Screenshot\n\n`3:cartoon charactor`\n\n```\npython start.py -l1 -m art --art_config_filepath config/art/art_config_sample3.json\n```\n\n!Screenshot\n\nother sample \n\n| name | --art_config_filepath | note |\n| ---- | ---- | ---- |\n| 4:heart | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample4.json | - |\n| 5:hamburger_shop | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample5.json | - |\n| 6:parking | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample6.json | - |\n| 7:team rocket | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample7.json | - |\n| 8:happy_new_year_2023 | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample8.json | - |\n| 9:taka | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample9.json | - |\n| ... | - | - |\n\ncontribution\n\n| name | --art_config_filepath | thanks |\n| ---- | ---- | ---- |\n| mario | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample_tanaka_2.json | @tanaken |\n| ... | - | - |", "start_char_idx": 0, "end_char_idx": 1478, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "aa3dd74c-bf10-4fec-98e2-94661eeb3270": {"__data__": {"id_": "aa3dd74c-bf10-4fec-98e2-94661eeb3270", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b8d70b19-f260-4c43-9334-2671cddcf503", "node_type": null, "metadata": {}, "hash": "98f7a72f9d9c47a6fdb3eeae2961938cb8341b04a4b9c552f42bb17a079ec6ac"}}, "hash": "fd88b396bb1bc574f20b43abd3f56a70dd6a53620563ef95cc8d2eaf3b13233b", "text": "config\u30d5\u30a1\u30a4\u30eb\u306e\u8aac\u660e\n\n\u30c6\u30c8\u30ea\u30b9\u30a2\u30fc\u30c8\u3092\u4f5c\u6210\u3057\u6613\u304f\u3059\u308b\u305f\u3081\u306b\u5404\u30d6\u30ed\u30c3\u30af\u306e\u8272\u3084\u51fa\u73fe\u9806\u5e8f(index)\u306a\u3069\u3092config\u30d5\u30a1\u30a4\u30eb\u3067\u8abf\u6574\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u308b\u3002 \n\u4ee5\u4e0b\u306fconfig\u30d5\u30a1\u30a4\u30eb\u306e\u8aac\u660e\u3067\u3042\u308b\u3002 \n\nart\u7528config\u30d5\u30a1\u30a4\u30eb\u30b5\u30f3\u30d7\u30eb \nconfig/art/art_config_sample_default.json \n\n```\n{\n // \u5404\u30d6\u30ed\u30c3\u30af\u306e\u8272\u3092\u8abf\u6574\u3059\u308b\u3002\u8272\u306b\u3064\u3044\u3066\u306f\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002\n \"color\": {\n \"shapeI\": \"0xCC6666\",\n \"shapeL\": \"0x66CC66\",\n \"shapeJ\": \"0x6666CC\",\n \"shapeT\": \"0xCCCC66\",\n \"shapeO\": \"0xCC66CC\",\n \"shapeS\": \"0x66CCCC\",\n \"shapeZ\": \"0xDAAA00\"\n },\n // \u5404\u30d6\u30ed\u30c3\u30af\u306e\u51fa\u73fe\u9806\u5e8f(index)/direction/x/y\u3092\u8abf\u6574\u3059\u308b\u3002\n // \u884c\u3092\u8ffd\u52a0\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u308a\u3001\u8ffd\u52a0\u3059\u308b\u3068\u8a18\u8f09\u306b\u5fdc\u3058\u305f\u30d6\u30ed\u30c3\u30af\u304c\u51fa\u73fe\u3059\u308b\u3002\n \"block_order\": [ \n [1,0,0,1],\n [2,0,0,1],\n [3,0,0,1],\n [4,0,0,1],\n [5,0,0,1],\n [6,0,0,1],\n [7,0,0,1]\n ]\n}\n```\n\n\u30d6\u30ed\u30c3\u30af\u306e\u8272\u306b\u3064\u3044\u3066\u306f\u4ee5\u4e0b\u306e\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9\u3092\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \n\n> \u8272\u306e\u540d\u524d\u3068\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9\u304c\u4e00\u76ee\u3067\u308f\u304b\u308bWEB\u8272\u898b\u672c \u539f\u8272\u5927\u8f9e\u5178 - HTML\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9 \n\n\u5404\u30d6\u30ed\u30c3\u30af\u306e\u51fa\u73fe\u9806\u5e8f(index)/direction/x/y\u306b\u3064\u3044\u3066\u306f\u4ee5\u4e0b\u3092\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \n\n> \u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066", "start_char_idx": 0, "end_char_idx": 804, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b5acf5c9-7372-4cad-936f-15baf28828d4": {"__data__": {"id_": "b5acf5c9-7372-4cad-936f-15baf28828d4", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b8162150-85b3-4fbb-9da9-a5b54c5e7f88", "node_type": null, "metadata": {}, "hash": "c4caf941936c3146d61ce1c92cb4a76d88371ee8d948cc4d306bd275699c13f2"}}, "hash": "2068860e6279cdc790a0d773ecc69f0bdc6a9ae2e3f31fae23ef38701d360966", "text": "\u5b9f\u884c\u65b9\u6cd5\n\n\u4ee5\u4e0b\u306e\u3088\u3046\u306b`-m art`,`--art_config_filepath`\u306b\u3088\u308a\u30e2\u30fc\u30c9\u53ca\u3073config\u30d5\u30a1\u30a4\u30eb\u3092\u6307\u5b9a\u3059\u308b\u3002\n\n```", "start_char_idx": 0, "end_char_idx": 73, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ed8e6383-00ce-4f6e-9f26-c13c25f41b52": {"__data__": {"id_": "ed8e6383-00ce-4f6e-9f26-c13c25f41b52", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "179127c9-d49d-4e15-a6d1-d977f107853f", "node_type": null, "metadata": {}, "hash": "b24d3531c7c9b954841c494370230b370299e8c978c81ece8f6cc7da3098e951"}}, "hash": "280517e795fd667f442c014cb0e26adeb0caf689a3c0aba8e8ff65afb15c116a", "text": "\"art_config_sample.json\"\u3092\u6307\u5b9a\u3057\u3066\u5b9f\u884c\npython start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json", "start_char_idx": 0, "end_char_idx": 114, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "be96e7c0-2f2d-44e9-bfc5-6deaf6a7f934": {"__data__": {"id_": "be96e7c0-2f2d-44e9-bfc5-6deaf6a7f934", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cd5e317d-895c-4369-acaa-22c1f375024d", "node_type": null, "metadata": {}, "hash": "c3d9eec47d8fd6ffd968f2d4c04dfcb0b0d21ddc2e867cbe3329156c6953e88a"}}, "hash": "44d61049a75d406d963e00956e8f7afca4ba464b979f25d509c55abd9eb22cd5", "text": "\u843d\u4e0b\u901f\u5ea6\u3092\u65e9\u304f\u3059\u308b\u5834\u5408\u306f\"-d500\"\u3092\u52a0\u3048\u3066\u5b9f\u884c\npython start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json -d500\n```\n\n\u81ea\u4f5c\u3057\u305fart\u7528config\u30d5\u30a1\u30a4\u30eb(`xxx.json`)\u3092\u4f5c\u6210\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30d5\u30a1\u30a4\u30eb\u30b3\u30d4\u30fc\u3057\u3066\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \n\n```", "start_char_idx": 0, "end_char_idx": 189, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a483c1ee-77b4-48a7-be28-b9a92ec5fb65": {"__data__": {"id_": "a483c1ee-77b4-48a7-be28-b9a92ec5fb65", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "47c50451-9153-40cf-8a3e-2df754742596", "node_type": null, "metadata": {}, "hash": "5bce6842462076a5125189a6d46001d63f966f3dfbbdb0b150b6b5ba7f0ccaac"}}, "hash": "b16c439685f32156613e8b97e925112c860d09dfa55c9c0db0bfba57dd44e67d", "text": "\u4e8b\u524d\u306b\u30b5\u30f3\u30d7\u30eb\u3092\u30b3\u30d4\u30fc\u3057\u3066\u304a\u304f(art_config_sample.json --> xxx.json)\ncp config/art/art_config_sample.json config/art/xxx.json", "start_char_idx": 0, "end_char_idx": 109, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "33e9e11c-0356-4c36-9d24-127d9ec3f40d": {"__data__": {"id_": "33e9e11c-0356-4c36-9d24-127d9ec3f40d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dd1508fc-36b2-4ffe-877f-2e078e0e6d93", "node_type": null, "metadata": {}, "hash": "18e014e44336e882707823eaa7ed6b57306749fa63c589a1058bac2985a6659f"}}, "hash": "070b95546592927794f493e39ff749774918dd0f16863e30772eeacf7486a420", "text": "\u5b9f\u884c\npython start.py -l1 -m art --art_config_filepath config/art/xxx.json\n```", "start_char_idx": 0, "end_char_idx": 75, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9355f9f7-4d0f-4eb7-8c26-b117bc816adc": {"__data__": {"id_": "9355f9f7-4d0f-4eb7-8c26-b117bc816adc", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e59201dd-50da-41fb-bcf7-828be248612b", "node_type": null, "metadata": {}, "hash": "c400ac41ff6b8669eefb505828d06a462b47a03d7711149ecab4cbe87782ef78"}, "3": {"node_id": "11bb893b-06dd-46fa-9c3b-05d0561e330b", "node_type": null, "metadata": {}, "hash": "dd6f903576cf2641f9dcd7b0fa519b6b22dc83511dbd9401d52ce7acbef22ab1"}}, "hash": "108159e9e03b044159141b8ce411db1ee52558784d6e5a4929e6de1507e186b9", "text": "\u4f5c\u6210\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4f8b\n\u6a2a10\u30de\u30b9\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\u306b\u30c6\u30c8\u30ea\u30b9\u30df\u30ce\u3092\u7f6e\u3044\u305f\u5f8c\u306b\u6b8b\u308b\u30d6\u30ed\u30c3\u30af\u306e\u500b\u6570\u306f 4n-10m \u500b\u3067\u3059\u3002 \n\u305d\u306e\u305f\u3081\u30c6\u30c8\u30ea\u30b9\u30a2\u30fc\u30c8\u3092 4n-10m \u500b\u306e\u30c9\u30c3\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u5206\u89e3\u3059\u308b\u3053\u3068\u3067\u697d\u306b\u4f5c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 \n\u6c4e\u7528\u6027\u304c\u9ad8\u3044\u306e\u306f2\u30c9\u30c3\u30c8\u6b8b\u3057\uff0832\u30d6\u30ed\u30c3\u30af\uff1an=8, m=3 \u3084 52\u30d6\u30ed\u30c3\u30af\uff1an=13, m=5 \u306a\u3069\uff09\u3002 \n\u30a2\u30fc\u30c8\u306b\u306f1\u884c\u3042\u305f\u308a\u6700\u59279\u500b\u306e\u30c9\u30c3\u30c8\u3092\u7f6e\u3051\u307e\u3059\u3002 \n\u305d\u306e\u305f\u3081\u57fa\u672c\u7684\u306b\u5404\u884c\u3092 2\\*4 + 1\u30c9\u30c3\u30c8\u306b\u5206\u89e3\u3057\u3001\u4f59\u3063\u305f1\u540c\u58eb\u30672\u30c9\u30c3\u30c8\u3092\u69cb\u6210\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u307e\u3059\u3002\n```\n2\u30c9\u30c3\u30c8\u3092\u540c\u3058\u884c\u306b\u6b8b\u3059\u5834\u5408\u3001\u305d\u306e\u7d44\u307f\u5408\u308f\u305b\u306f\u6050\u3089\u304f2205\u901a\u308a\uff08x\u5ea7\u6a1910*9\u00f72\u3001shape7*7\uff09\u3002 \n2\u30c9\u30c3\u30c8\u3092\u9055\u3046\u884c\u306b\u6b8b\u3059\u5834\u5408\u3001\u305d\u306e\u7d44\u307f\u5408\u308f\u305b\u306f\u6050\u3089\u304f4900\u901a\u308a\uff08x\u5ea7\u6a1910*10\u3001shape7*7\uff09\u3002 \n\u30a2\u30fc\u30c8\u3092\u69cb\u6210\u3059\u308b2\u30c9\u30c3\u30c8\u3092\u6b8b\u305b\u308b\u51fa\u73fe\u9806\u5e8f [index,direction,x,y] \u3092\u3042\u3089\u304b\u3058\u3081\u7528\u610f\u3059\u308c\u3070\u3001config\u4f5c\u6210\u306e\u81ea\u52d5\u5316\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002 \n```\n\n\n**Step1\uff1a\u4efb\u610f\u306e2\u30c9\u30c3\u30c8\u3092\u6b8b\u3059\u51fa\u73fe\u9806\u5e8f\u3092\u307e\u3068\u3081\u308b** \n`art_lib.py`\uff1a\n> https://github.com/mattshamrock/tetris/blob/art/art_lib.py \nxs_0000 \u2190 \u540c\u3058\u884c\u306b\u6b8b\u3059\u5834\u5408\u3002\u4f8b\u3048\u3070 'xs_5294'\u306a\u3089\u3001x=5\u306bshapeindex=2\uff08L\u30df\u30ce\uff09\u3001x=9\u306bshapeindex=4\uff08T\u30df\u30ce\uff09\u304c\u6b8b\u308b\u7f6e\u304d\u65b9\u3002\nxs_00_00 \u2190 \u9055\u3046\u884c\u306b\u6b8b\u3059\u5834\u5408\u3002\u524d\u534a\u304c1\u884c\u76ee\u306ex\u5ea7\u6a19\u3068shapeindex\u3001\u5f8c\u534a\u304c2\u884c\u76ee\u306ex\u5ea7\u6a19\u3068shapeindex\u3002 \n\u672a\u767a\u898b\u306e\u5834\u5408\u306f False \u3068\u8a18\u8f09 \n\n```\nart_lib.py\u306e\u73fe\u72b6\uff1a \n\u30102023/3/E\u6642\u70b9\u3011 \n \u30fb'xs_0000'\u306e\u3046\u306132\u30d6\u30ed\u30c3\u30af\u4f7f\u7528\u3067\u5b9f\u73fe\u53ef\u80fd\u306a\u3082\u306e\uff1a\u5168\u63a2\u7d22\u6e08\u307f; \u5f8c\u8ff0 \n \u30fb'xs_0000'\u306e\u3046\u306152\u30d6\u30ed\u30c3\u30af\u4ee5\u4e0a\u4f7f\u7528\u3067\u5b9f\u73fe\u53ef\u80fd\u306a\u3082\u306e\uff1a\u624b\u4f5c\u696d\u3002\u4e0d\u5b8c\u5168 \n \u30fb'xs_00_00'\u306e\u3046\u3061\u898b\u3064\u3051\u305f\u3082\u306e\uff1a\u624b\u4f5c\u696d\u3002\u3068\u3066\u3082\u4e0d\u5b8c\u5168 \n```\n\n`art_lib.py`\u7528\u306e\u63a2\u7d22\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff1a\n>", "start_char_idx": 0, "end_char_idx": 920, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "11bb893b-06dd-46fa-9c3b-05d0561e330b": {"__data__": {"id_": "11bb893b-06dd-46fa-9c3b-05d0561e330b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e59201dd-50da-41fb-bcf7-828be248612b", "node_type": null, "metadata": {}, "hash": "c400ac41ff6b8669eefb505828d06a462b47a03d7711149ecab4cbe87782ef78"}, "2": {"node_id": "9355f9f7-4d0f-4eb7-8c26-b117bc816adc", "node_type": null, "metadata": {}, "hash": "108159e9e03b044159141b8ce411db1ee52558784d6e5a4929e6de1507e186b9"}, "3": {"node_id": "f53eefe8-eb19-4cd3-94db-225026bd31af", "node_type": null, "metadata": {}, "hash": "455d244f15adf7663d8db3c336ec36bd76de7d408b498b5062a77c925467c4c1"}}, "hash": "dd6f903576cf2641f9dcd7b0fa519b6b22dc83511dbd9401d52ce7acbef22ab1", "text": "https://github.com/mattshamrock/tetris/blob/art/artlib_search_32.py \n\u5404xs_0000\u306b\u3064\u3044\u3066\u300132\u30c9\u30c3\u30c8\uff088\u30df\u30ce\uff09\u306e\u51fa\u73fe\u9806\u5e8f\u3092\u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3057\u3001\u6307\u5b9a\u306e2\u30c9\u30c3\u30c8\u306e\u307f\u304c\u6b8b\u308b\u304b\u3092\u5224\u5b9a\u3057\u307e\u3059\u3002 \n\u679d\u5207\u308a\u3092\u3059\u308b\u3053\u3068\u3067\u63a2\u7d22\u6642\u9593\u3092\u6e1b\u3089\u305d\u3046\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u624b\u5143PC\u3067\u306f36\u6642\u9593\u3067\u534a\u5206\u306e\u7d041200\u901a\u308a\u304c\u51fa\u529b\u3067\u304d\u307e\u3059\u3002 \n\u5f8c\u8ff0\u306e\u300c\u53cd\u8ee2\u300d\u3068\u7d44\u307f\u5408\u308f\u305b\u308b\u3068\u7d0436\u6642\u9593\u3067\u5b8c\u4e86\u3057\u307e\u3059\u3002\n\n`art_lib.py`\u7528\u306e\u53cd\u8ee2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff1a\n> https://github.com/mattshamrock/tetris/blob/art/artlib_reverse.py \n\u6a2a10\u30de\u30b9\u306e\u30c6\u30c8\u30ea\u30b9\u306f\u3059\u3079\u3066\u306e\u914d\u7f6e\u3092\u5de6\u53f3\u53cd\u8ee2\u3055\u305b\u3066\u3082\u6210\u308a\u7acb\u3061\u307e\u3059\u3002 \n\u63a2\u7d22\u3067art_lib.py\u3092\u57cb\u3081\u3066\u3044\u304f\u969b\u3001\u7d50\u679c\u304c\u51fa\u305f\u51fa\u73fe\u9806\u5e8f\u3092\u53cd\u8ee2\u3055\u305b\u308c\u3070\u63a2\u7d22\u306e\u307f\u3067\u57cb\u3081\u308b\u7d04\u534a\u5206\u306e\u6642\u9593\u3067\u5b8c\u4e86\u3067\u304d\u308b\u306f\u305a\u3067\u3059\u3002 \n\u6ce8\u610f\u2026\u57fa\u6e96\u70b9\u30ba\u30ec\u306e\u8abf\u6574\u304c\u5fc5\u8981\u306a\u7d44\u307f\u5408\u308f\u305b\u304c\u5b58\u5728\u3057\u307e\u3059\u3002\uff08\u4f8b\uff1ashape:S\u306e\u5f62\u72b6:1 \u306e\u53cd\u8ee2\u3068shape:Z\u306e\u5f62\u72b6:1 \u306f\u57fa\u6e96\u70b9\u304c1\u30de\u30b9\u9055\u3046\uff09 \n\n\n \n \n**Step2\uff1a\u30c9\u30c3\u30c8\u30a2\u30fc\u30c8\u3092\u63cf\u304f** \n\u73fe\u72b6\u306f\u624b\u4f5c\u696d\u3067\u3059 \n\u3000\u3000\n\n\n**Step3\uff1a\u30c9\u30c3\u30c8\u30a2\u30fc\u30c8\u3092\u6570\u5024\u306b\u5909\u63db\u3059\u308b** \n\u30d6\u30ed\u30c3\u30af\u306eindex\u5024\u306b\u5909\u63db\u3057\u307e\u3059\u3002 \nStep4\u306e\u5165\u529b\u306b\u5408\u308f\u305b\u3066\u6587\u5b57\u6570=10\u306estr\u306b\u5909\u63db\u3057\u3066\u3044\u307e\u3059\u3002 \n\u7a7a\u767d=0\u3067\u3059\u3002 \n\u73fe\u72b6\u306fexcel\u3067\u4f5c\u696d\u3057\u3066\u3044\u307e\u3059 \n\u3000\u3000\n\n**Step4\uff1aart_lib.py\u306b\u3042\u308b\u5b9f\u73fe\u53ef\u80fd\u306a2\u30c9\u30c3\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u63a2\u7d22\u3059\u308b** \n`art_lib.py`\u306e\u7d44\u307f\u5408\u308f\u305b\u63a2\u7d22\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff1a\n> https://github.com/mattshamrock/tetris/blob/art/create_art.py \n10\u500b\u306eindex\u5024\u30922\u30c9\u30c3\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u5206\u89e3\u3057\u307e\u3059\u3002 \nart_lib.py\u3067\u306f'xs_0000'\u306e\u51fa\u73fe\u9806\u5e8f\u304c\u672a\u767a\u898b\u306e\u5834\u5408\u306bFalse\u3092\u8fd4\u3059\u3053\u3068\u3092\u5229\u7528\u3057\u3066\u3001\u5b9f\u73fe\u53ef\u80fd\u306a2\u30c9\u30c3\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u63a2\u7d22\u3067\u304d\u307e\u3059\u3002 ", "start_char_idx": 921, "end_char_idx": 1819, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f53eefe8-eb19-4cd3-94db-225026bd31af": {"__data__": {"id_": "f53eefe8-eb19-4cd3-94db-225026bd31af", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e59201dd-50da-41fb-bcf7-828be248612b", "node_type": null, "metadata": {}, "hash": "c400ac41ff6b8669eefb505828d06a462b47a03d7711149ecab4cbe87782ef78"}, "2": {"node_id": "11bb893b-06dd-46fa-9c3b-05d0561e330b", "node_type": null, "metadata": {}, "hash": "dd6f903576cf2641f9dcd7b0fa519b6b22dc83511dbd9401d52ce7acbef22ab1"}}, "hash": "455d244f15adf7663d8db3c336ec36bd76de7d408b498b5062a77c925467c4c1", "text": " \n1\u884c\u306b\u5947\u6570\u500b\u306e\u30c9\u30c3\u30c8\uff08\u7a7a\u767d\u3092\u9664\u304f\uff09\u304c\u3042\u308b\u5834\u5408\u3001\u4f59\u3063\u305f\u30c9\u30c3\u30c8\u306ex\u5ea7\u6a19\u3068index\u5024\u3082\u8fd4\u3057\u307e\u3059\u3002\u3053\u308c\u304c'xs_00_00'\u306e\u524d\u534a\u304b\u5f8c\u534a\u306e\u3044\u305a\u308c\u304b\u306b\u306a\u308a\u307e\u3059\u3002\n \n\u3000 \n \n**Step5\uff1aStep4\u306e\u7d50\u679c\u3092\u8abf\u6574\u3059\u308b** \n\u30fb'xs_00_00' \u306f\u4f59\u308a\u3068\u3057\u3066\u8fd4\u3055\u308c\u305f\u60c5\u5831\u3092\u57fa\u306b\u81ea\u529b\u3067\u767a\u898b\u3057\u3001`art_lib.py`\u306b\u8ffd\u52a0\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 \n\u30fb\u4f59\u308a\u3068\u3057\u3066\u8fd4\u308b\u30c9\u30c3\u30c8\u306e\u307f\u3067'xs_00_00'\u304c\u69cb\u6210\u3055\u308c\u308b\u3053\u3068\u3092\u524d\u63d0\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u5404\u884c\u306e\u30c9\u30c3\u30c8\u6570\uff08\u7a7a\u767d\u3092\u9664\u304f\uff09\u306e\u5076\u5947\u306b\u3088\u3063\u3066\u306f\u6210\u7acb\u3057\u307e\u305b\u3093\u3002 \n\u30fb'xs_0000' \u306b\u304a\u3044\u3066\u3001\u3059\u3050\u4e0b\u306e\u30de\u30b9\u304c\u7a7a\u767d\u306e\u5834\u5408\u306b\u3001\u305d\u3053\u306b\u30cf\u30de\u308b\u5f62\u3067\u610f\u56f3\u3057\u306a\u3044\u7d50\u679c\u306b\u306a\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u5de6\u53f3\u306e\u30de\u30b9\u306b\u30d6\u30ed\u30c3\u30af\u304c\u3042\u308c\u3070\u5f15\u3063\u639b\u304b\u308b\u305f\u3081\u56de\u907f\u53ef\u80fd\u306a\u53ef\u80fd\u6027\u304c\u9ad8\u304f\u3001\u305d\u306e\u5834\u5408\u306f\u9806\u756a\u3092\u5909\u66f4\u3059\u308b\u3053\u3068\u3067\u89e3\u6c7a\u3067\u304d\u307e\u3059\u3002 \n\u3000\u3000\n\n**Step6\uff1a2\u30c9\u30c3\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u3092config\u30d5\u30a1\u30a4\u30eb\u306e\u51fa\u73fe\u9806\u5e8f\u306b\u5909\u63db\u3059\u308b** \nStep4-5\u3067\u5f97\u3089\u308c\u305f 'xs_0000' \u3068 'xs_00_00' \u306e\u7f85\u5217\u3092`art_lib.py`\u306e`artdict`\u306e\u30ad\u30fc\u3068\u3057\u3066\u5229\u7528\u3057\u3001\u51fa\u529b\u3057\u307e\u3059\u3002 \n\u73fe\u72b6\u306f\u4f59\u8a08\u306a\u4e8c\u91cd\u62ec\u5f27\u3000[[]] \u304c\u5165\u3063\u3066\u304d\u3066\u3057\u307e\u3044\u307e\u3059\u3002\u7f6e\u63db\u7b49\u3067\u53d6\u308a\u9664\u304d\u307e\u3059\u3002 \n\u5404\u51fa\u529b\u306e\u6700\u5f8c\u306e\u30ab\u30f3\u30de , \u304c\u4e0d\u8db3\u3057\u3066\u3044\u307e\u3059\u3002\u4e8c\u91cd\u62ec\u5f27\u3068\u5408\u308f\u305b\u3066\u7f6e\u63db\u3067\u5bfe\u5fdc\u3057\u307e\u3059\u3002 \n\u3000\u3000\n\n**Step7\uff1aconfig\u30d5\u30a1\u30a4\u30eb\u306b\u8ee2\u8a18\u3059\u308b** \n\u8ee2\u8a18\u3059\u308c\u3070OK\u3002 \n\u8272\u306e\u8abf\u6574\u306f\u624b\u4f5c\u696d\u3067\u3059\u3002 \n\u3000\u3000\n\n**\u4eca\u5f8c\u306b\u5411\u3051\u3066\uff1a** \n\u30fb`art_lib.py`\u306e\u629c\u3051\u3066\u3044\u308b\u90e8\u5206\u3092\u57cb\u3081\u308b\u300252\u30d6\u30ed\u30c3\u30af\uff0813\u624b\uff09\u4ee5\u4e0a\u3092\u52b9\u7387\u7684\u306b\u63a2\u7d22\u3059\u308b\u3002 \n\u30fb`art_lib.py`\u306e\u629c\u3051\u3066\u3044\u308b\u90e8\u5206\u3092\u57cb\u3081\u308b\u30022\u884c\u306b\u5206\u304b\u308c\u308b\u30d6\u30ed\u30c3\u30af\u3092\u52b9\u7387\u7684\u306b\u63a2\u7d22\u3059\u308b\u300232\u30de\u30b9\u30928\u3064\u306b\u5206\u5272\u3057\u305f\u7d50\u679c\u304c\u30c6\u30c8\u30ea\u30b9\u30df\u30ce\u306e\u5f62\u306b\u306a\u308b\u304b\u5224\u5b9a\u3059\u308b\u3001\u3068\u3044\u3046\u63a2\u3057\u65b9\u304c\u826f\u3044\u6c17\u304c\u3059\u308b\u3002 \n\u30fbStep5\u306e\u8abf\u6574\u3092\u81ea\u52d5\u5316\u3059\u308b\u3002 \n\u30fbStep2\u3092\u81ea\u52d5\u5316\u3059\u308b\uff08\u753b\u50cf\u751f\u6210AI\u7684\u306a\uff09", "start_char_idx": 1819, "end_char_idx": 2666, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b9fdbcb9-5edc-43ae-8050-dd72079dac26": {"__data__": {"id_": "b9fdbcb9-5edc-43ae-8050-dd72079dac26", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cebfd4cd-b7a7-4e2c-ada9-78695d54f04a", "node_type": null, "metadata": {}, "hash": "4f191e64dbf81c12453e4cb81ff389def46c83813b1f2cb5a51551559b6956ef"}}, "hash": "87d4604660f5cac48c8cf36e1939b9184780d0bdbe41c94369a4570fc8c562bc", "text": "\u53c2\u8003\n\u300c\u30c6\u30c8\u30ea\u30b9\u300d\u306e\u65ac\u65b0\u3059\u304e\u308b\u904a\u3073\u65b9\u304c\u8a71\u984c\u306b\u3002\u7a4d\u307f\u4e0a\u3052\u305f\u30d6\u30ed\u30c3\u30af\u3067\u30de\u30ea\u30aa\u3084\u30eb\u30a4\u30fc\u30b8\u3092\u518d\u73fe!? \n\u8272\u306e\u540d\u524d\u3068\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9\u304c\u4e00\u76ee\u3067\u308f\u304b\u308bWEB\u8272\u898b\u672c \u539f\u8272\u5927\u8f9e\u5178 - HTML\u30ab\u30e9\u30fc\u30b3\u30fc\u30c9 \nyoutube-link: tetris art sample", "start_char_idx": 0, "end_char_idx": 126, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8bb5fe02-f66d-4302-85cd-4c0c956bf6c0": {"__data__": {"id_": "8bb5fe02-f66d-4302-85cd-4c0c956bf6c0", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ca65780f-bff0-4525-98af-59ce39554b0f", "node_type": null, "metadata": {}, "hash": "508db7480e2f3f91a5d3ea8b5be3c889160e8be6cd0bf959753158c6f604e05b"}}, "hash": "41f02c59f86f79b7c2116cab90d5ae43cced566f8310b9204818c38d561566ef", "text": "\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066\n\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u306f\u3001\u30dc\u30fc\u30c9\u60c5\u5831\u304b\u3089\u30d6\u30ed\u30c3\u30af\u306e\u6b21\u306e\u52d5\u4f5c\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002\n\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u306e`def GetNextMove`\u95a2\u6570\u306b\u3066\u3001\u30dc\u30fc\u30c9\u60c5\u5831`GameStatus`\u3092\u5165\u529b\u3068\u3057\u3066\u53d7\u3051\u53d6\u308a\u3001\u6b21\u306e\u52d5\u4f5c\u6c7a\u5b9a\u7528\u306e\u30c7\u30fc\u30bf`nextMove`\u3092\u8fd4\u3059\u4e8b\u3067\u30d6\u30ed\u30c3\u30af\u304c\u52d5\u304f\u4ed5\u69d8\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\n\n\u4ee5\u4e0b\u306f\u3001\u30dc\u30fc\u30c9\u60c5\u5831`GameStatus`\u3001\u6b21\u306e\u52d5\u4f5c\u6c7a\u5b9a\u7528\u306e\u30c7\u30fc\u30bf`nextMove`\u306b\u3064\u3044\u3066\u8a18\u8f09\u3057\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 212, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9b546d02-7c84-40d5-aaae-19fad4f818b9": {"__data__": {"id_": "9b546d02-7c84-40d5-aaae-19fad4f818b9", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c3f31aa3-3a65-4566-810f-304800b43281", "node_type": null, "metadata": {}, "hash": "6b3dd27be4c210aa3f1f143be6734678413ad9bf45fe8fdf4272bac657ef948d"}}, "hash": "bd90ff6b8d83339ced524d0ceb997bb9ca67cddd59238d8bb5ecc833cfca807e", "text": "\u30dc\u30fc\u30c9\u60c5\u5831\n\u30dc\u30fc\u30c9\u60c5\u5831\u306b\u306f\u4ee5\u4e0b\u304c\u542b\u307e\u308c\u307e\u3059\u3002\n\u8a73\u7d30\u306f\u3001`GameStatus`\u30c7\u30fc\u30bf\u69cb\u9020\u3084\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u306e\u3088\u3046\u306bGameStatus\u3092\u30ed\u30b0\u51fa\u529b\u3059\u308b\u3053\u3068\u3067\u78ba\u8a8d\u53ef\u80fd\u3067\u3059\u3002\n\n```\n* field_info : \u30d5\u30a3\u30fc\u30eb\u30c9\u60c5\u5831\n* block_info : \u30d6\u30ed\u30c3\u30af\u60c5\u5831\n* judge_info : \u5be9\u5224\u60c5\u5831\n* debug_info : \u30c7\u30d0\u30c3\u30b0\u60c5\u5831\n```", "start_char_idx": 0, "end_char_idx": 180, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b689de3d-49d0-4f72-b07a-a561248ccc58": {"__data__": {"id_": "b689de3d-49d0-4f72-b07a-a561248ccc58", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "500bd4a9-9d6c-46d1-a7ba-68c367c65b19", "node_type": null, "metadata": {}, "hash": "006357ba9d44c8bc896c2fbea77170ae63079a63524bc04573e66cc08366ea06"}}, "hash": "a111a124710372db7c10aa64d860d44e4eca5892c35802311a3a566f46308a1c", "text": "\u30d5\u30a3\u30fc\u30eb\u30c9\u60c5\u5831", "start_char_idx": 0, "end_char_idx": 7, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "427e9668-cbaf-457e-ab76-98b61061e9b9": {"__data__": {"id_": "427e9668-cbaf-457e-ab76-98b61061e9b9", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4699fa1d-4331-4faa-9df4-dd354731616d", "node_type": null, "metadata": {}, "hash": "db34f03b3b395a8f50c539e77ad5b4cc6317f350066bd8c6691ab29b2ccb0ef8"}}, "hash": "8e842aa62012f9c9b229ce84f2338d4466b8e1bfbf9458d5d6282d385b633acc", "text": "\u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u5b9a\u7fa9\n\u30d5\u30a3\u30fc\u30eb\u30c9\u306f\u6a2a\u5e4510\u00d7\u9ad8\u305522\u30d6\u30ed\u30c3\u30af\u5206\u306e\u5927\u304d\u3055\u3067\u3059\u3002\n\u5ea7\u6a19\u7cfb\u306f\u3001\u5de6\u4e0a\u3092\u539f\u70b9(0,0)\u3068\u3057\u3066\u3001x\u8ef8\u306f\u53f3\u5074\u3001y\u8ef8\u306f\u4e0b\u5074\u3092\u6b63\u306e\u65b9\u5411\u3068\u3057\u3066\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059\u3002\n\n!Screenshot\n\n\u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u30c7\u30fc\u30bf\u306f\u3001\u4e00\u6b21\u5143\u914d\u5217\u3068\u3057\u3066\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059\u3002\n\u5ea7\u6a19(x, y)\u306e\u30c7\u30fc\u30bf\u306f\u3001\u4e00\u6b21\u5143\u914d\u5217\u306e[x + y * \u6a2a\u5e45]\u756a\u76ee\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3053\u3068\u3067\u53d6\u5f97\u53ef\u80fd\u3067\u3059\u3002\n\n| | \u30d5\u30a3\u30fc\u30eb\u30c9 | \u4e00\u6b21\u5143\u914d\u5217 |\n| --- | --- | --- |\n| \u30c7\u30fc\u30bf | !Screenshot | !Screenshot |", "start_char_idx": 0, "end_char_idx": 262, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "308f55fd-5f56-42b0-b473-9a57716dced2": {"__data__": {"id_": "308f55fd-5f56-42b0-b473-9a57716dced2", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d17cb98f-78b0-4313-add5-4f6024cc023a", "node_type": null, "metadata": {}, "hash": "c2d78e5ea966f8b5f0d8774fd677d1fe2c1fbc9711f83ec2ca56e8343c061252"}}, "hash": "ebea327d2061c05b5ad67c188cad198864c1c8638dda971adc5eb09a79cda953", "text": "\u30d5\u30a3\u30fc\u30eb\u30c9\u60c5\u5831\u306b\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\n\n\u4e0a\u8a18\u306e\u4e00\u6b21\u5143\u914d\u5217\u306e\u5404\u5ea7\u6a19\u306b\u30d6\u30ed\u30c3\u30af\u304c\u6709\u308b\u304b\u7121\u3044\u304b\u3092\u7ba1\u7406\u3057\u3066\u3044\u307e\u3059\u3002\n\n```\n* `0\u4ee5\u5916` : \u30d6\u30ed\u30c3\u30af\u6709\u308a\n* `0` : \u30d6\u30ed\u30c3\u30af\u7121\u3057\n```\n\n\u30d6\u30ed\u30c3\u30af\u304c\u6709\u308b\u5834\u5408\u306f\u3001\u5404\u30d6\u30ed\u30c3\u30af\u306eIndex\u5024\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\nIndex\u5024\u306f\u30d6\u30ed\u30c3\u30af\u60c5\u5831\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 149, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f50169a5-63d6-41e0-8f71-5b49f72769c9": {"__data__": {"id_": "f50169a5-63d6-41e0-8f71-5b49f72769c9", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "05a2ef2d-d2ac-4130-bdf8-e36c5df8abec", "node_type": null, "metadata": {}, "hash": "683f3e800ede901bd0e8d346c931b9136685a97be06e15241e3fd4202ae2de5b"}}, "hash": "10232c09d1dbd5d36aef8fb02aae700ca00a7468f2ed2ce8718756ece2ef1ed8", "text": "\u30d6\u30ed\u30c3\u30af\u60c5\u5831", "start_char_idx": 0, "end_char_idx": 6, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f83a5752-4020-41c1-bc2c-9d74e407fdfc": {"__data__": {"id_": "f83a5752-4020-41c1-bc2c-9d74e407fdfc", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dfe8ba87-12c6-4352-881e-007366b9329a", "node_type": null, "metadata": {}, "hash": "2fd77e813340dfeb6868f6785e49fb498a163aa5110044dd40ab5fa02df56e8d"}}, "hash": "bb96f55bb8c14ff856e3d0c7b2c0853a1f5a03a9a21d33914f1e11efc061f0be", "text": "\u30d6\u30ed\u30c3\u30af\u306e\u5b9a\u7fa9\n\u30d6\u30ed\u30c3\u30af\u306f\uff17\u7a2e\u985e\u3042\u308a\u3001Index\u5024\u3068\u521d\u671f\u5f62\u72b6\u3092\u4ee5\u4e0b\u306e\u901a\u308a\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059\u3002\n\u6a2a\u79fb\u52d5\u3068\u56de\u8ee2\u304c\u53ef\u80fd\u3067\u3059\u3002\n\u56de\u8ee2\u306f\u6642\u8a08\u56de\u308a\u306b\u56de\u8ee2\u3057\u307e\u3059\u3002\u30d6\u30ed\u30c3\u30af\u306b\u3088\u3063\u3066\uff11\u5468\u306e\u56de\u8ee2\u306b\u5fc5\u8981\u306a\u56de\u6570\u304c\u7570\u306a\u308a\u307e\u3059\u3002\n\n| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ |\n| --- | --- | --- | --- | --- | --- | --- | --- | \n| index\u5024 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | \n| \u521d\u671f\u5f62\u72b6 | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | \n| \uff11\u5468\u306e\u56de\u8ee2\u306b\u5fc5\u8981\u306a\u56de\u6570 | 2 | 4 | 4 | 4 | 1 | 2 | 2 |", "start_char_idx": 0, "end_char_idx": 427, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e70c7fff-b77c-46dd-9009-dc930ae0692d": {"__data__": {"id_": "e70c7fff-b77c-46dd-9009-dc930ae0692d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "22419697-3d51-4f4b-b3c5-517fc93b85eb", "node_type": null, "metadata": {}, "hash": "8cd23fc8edaa42527444fb070d7a7c2a46024eaa14a03b11805de4d12cc38a04"}, "3": {"node_id": "f1b73661-889e-4a8e-b150-42821788ceb9", "node_type": null, "metadata": {}, "hash": "b29939d2661b1dea098ce776b9291e31e0b326b40dc398b7d575f30d97c54fb5"}}, "hash": "ad4ffaad2d9da9c6e5d60bd5a33de5f66c8e8b6f0dd7737613dffd829f8b6ec2", "text": "\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9\n\u5404\u30d6\u30ed\u30c3\u30af\u306b\u306f\u64cd\u4f5c\u306e\u57fa\u6e96\u3068\u306a\u308b\u70b9\uff08\u4ee5\u4e0b\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9\uff09\u304c\u3042\u308a\u307e\u3059\u3002\n\u307e\u305f\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9\u4ee5\u5916\u306e\u5ea7\u6a19\u3082\u53d6\u5f97\u53ef\u80fd\u3067\u3059\u3002\u57fa\u6e96\u70b9(x,y)\u3068\u305d\u306e\u5468\u56f2\u306e\u5ea7\u6a19\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u3057\u307e\u3059\u3002\n\n!Screenshot\n\n\u30d6\u30ed\u30c3\u30af\u6bce\u306b\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y)\u306f\u7570\u306a\u308a\u307e\u3059\u3002\u5404\u30d6\u30ed\u30c3\u30af\u306e\u5f62\u72b6\u6bce\u306e\u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002\n\n| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ |\n| --- | --- | --- | --- | --- | --- | --- | --- | \n| \u5f62\u72b60\u306b\u304a\u3051\u308b\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y) | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | \n| \u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19 | (x,y-1),(x,y),(x,y+1),(x,y+2) | (x,y-1),(x,y),(x,y+1),(x+1,y+1) | (x,y-1),(x,y),(x,y+1),(x-1,y+1) | (x,y-1),(x,y),(x+1,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x+1,y) | (x,y-1),(x+1,y-1),(x-1,y),(x,y) | (x-1,y-1),(x,y-1),(x,y),(x+1,y) | \n| \u5f62\u72b61\u306b\u304a\u3051\u308b\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y) | !Screenshot | !Screenshot | !Screenshot | !Screenshot | --- | !Screenshot | !Screenshot | \n| \u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19 | (x-2,y),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x-1,y+1) | (x-1,y-1),(x-1,y),(x,y),(x+1,y) | (x,y+1),(x-1,y),(x,y),(x+1,y) | --- | (x,y-1),(x,y),(x+1,y),(x+1,y+1) | (x+1,y-1),(x,y),(x+1,y),(x,y+1) | \n| \u5f62\u72b62\u306b\u304a\u3051\u308b\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y) | --- | !Screenshot | !Screenshot | !Screenshot | --- | --- | --- | \n| \u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19 | --- | (x-1,y-1),(x,y-1),(x,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x,y+1) | (x,y-1),(x-1,y),(x,y),(x,y+1) | --- | --- | --- | \n|", "start_char_idx": 0, "end_char_idx": 1250, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f1b73661-889e-4a8e-b150-42821788ceb9": {"__data__": {"id_": "f1b73661-889e-4a8e-b150-42821788ceb9", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "22419697-3d51-4f4b-b3c5-517fc93b85eb", "node_type": null, "metadata": {}, "hash": "8cd23fc8edaa42527444fb070d7a7c2a46024eaa14a03b11805de4d12cc38a04"}, "2": {"node_id": "e70c7fff-b77c-46dd-9009-dc930ae0692d", "node_type": null, "metadata": {}, "hash": "ad4ffaad2d9da9c6e5d60bd5a33de5f66c8e8b6f0dd7737613dffd829f8b6ec2"}}, "hash": "b29939d2661b1dea098ce776b9291e31e0b326b40dc398b7d575f30d97c54fb5", "text": "| --- | --- | --- | \n| \u5f62\u72b63\u306b\u304a\u3051\u308b\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y) | --- | !Screenshot | !Screenshot | !Screenshot | --- | --- | --- |\n| \u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19 | --- | (x+1,y-1),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x+1,y+1) | (x,y-1),(x-1,y),(x,y),(x+1,y) | --- | --- | --- | \n\n\u5c1a\u3001\u57fa\u6e96\u70b9\u3068\u305d\u308c\u4ee5\u5916\u306e\u5ea7\u6a19\u306b\u3064\u3044\u3066\u306f\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u60c5\u5831\uff08\u30e1\u30bd\u30c3\u30c9`Shape_class.getCoords()`\uff09\u304b\u3089\u53d6\u5f97\u53ef\u80fd\u3067\u3059\u3002\n\u307e\u305f\u3001\u5404\u5f62\u72b6\u306b\u304a\u3051\u308bx,y\u65b9\u5411\u306e\u6700\u5927\u9577\u306f\u3001\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u60c5\u5831\uff08\u30e1\u30bd\u30c3\u30c9`Shape_class.getBoundingOffsets()`)\u304b\u3089\u53d6\u5f97\u53ef\u80fd\u3067\u3059\u3002\n\u53d6\u5f97\u306e\u5177\u4f53\u7684\u306a\u5b9f\u88c5\u65b9\u6cd5\u306f\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u53c2\u7167\u304f\u3060\u3055\u3044\u3002", "start_char_idx": 1228, "end_char_idx": 1665, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d930e5fa-ab0d-4a54-a85b-c8683ca6ce79": {"__data__": {"id_": "d930e5fa-ab0d-4a54-a85b-c8683ca6ce79", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1b12cd2c-6cbc-4b8b-bc11-b0f07bc21165", "node_type": null, "metadata": {}, "hash": "fad388913de2828ed166ba8244cc1a3ae2d66832b15f9118392e02f8d90e76cf"}}, "hash": "00028214bb75139ca54dcda14a6b1d8112fca009a1015f13625fea92a0ccbbe1", "text": "\u30d6\u30ed\u30c3\u30af\u51fa\u73fe\u6642\u306e\u521d\u671f\u4f4d\u7f6e\n\u30d6\u30ed\u30c3\u30af\u306f\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9\u304c\u5ea7\u6a19(5,1)\u306b\u306a\u308b\u3088\u3046\u306b\u51fa\u73fe\u3057\u307e\u3059\u3002\n\n!Screenshot\n\n\u5c1a\u3001\u30d6\u30ed\u30c3\u30af\u306e\u51fa\u73fe\u6642\u3001\u65e2\u306b\u57fa\u6e96\u70b9(x, y)\u306b\u30d6\u30ed\u30c3\u30af\u304c\u5b58\u5728\u3057\u3066\u3044\u308b\u3068`game over`\u3068\u306a\u308a\u30d5\u30a3\u30fc\u30eb\u30c9\u304c\u30ea\u30bb\u30c3\u30c8\u3055\u308c\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 128, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "00eddcdb-b8ed-41b9-838a-2d79d8fb57ab": {"__data__": {"id_": "00eddcdb-b8ed-41b9-838a-2d79d8fb57ab", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f42aab04-8e78-4f6a-854d-96e38f4a5861", "node_type": null, "metadata": {}, "hash": "062fedfa1f46f31e5211d637733934cded8adbbf2a305f9abf596bf4c3e29be7"}}, "hash": "42336a5edd25dd0dff1b4abe26c4e3b399aec4644745e32ed6876fe5dcd62455", "text": "\u30d6\u30ed\u30c3\u30af\u60c5\u5831\u306b\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\n\n\u4e3b\u306b\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n* `\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9(x,y)`\n* `\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u306eIndex\u5024`\n* `\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u306eIndex\u5024`", "start_char_idx": 0, "end_char_idx": 93, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7ad21d09-559c-4e47-92c4-2a2e60192654": {"__data__": {"id_": "7ad21d09-559c-4e47-92c4-2a2e60192654", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f95c4018-54ee-4594-ba99-26613ce4b0b9", "node_type": null, "metadata": {}, "hash": "47cdc546c353e366576258cb5c918d99f3cbde463c6df993156842a0a227cb0a"}}, "hash": "7791fbe63449f6b6af11b8625153143f5920270cc73ecf202e07b646a9a504f2", "text": "\u5be9\u5224\u60c5\u5831\n\n\u4ee5\u4e0b\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n* `\u7d4c\u904e\u6642\u9593[s]` : \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u306e\u7d4c\u904e\u6642\u9593\n* `\u30b9\u30b3\u30a2[\u70b9]` : \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u7372\u5f97\u3057\u305f\u5408\u8a08\u30b9\u30b3\u30a2\n* `\u30e9\u30a4\u30f3\u6570` : \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u6d88\u3057\u305f\u30e9\u30a4\u30f3\u306e\u6570\n* `\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u56de\u6570` : \u30b2\u30fc\u30e0\u958b\u59cb\u6642\u304b\u3089\u73fe\u5728\u307e\u3067\u306b\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u306b\u306a\u3063\u305f\u56de\u6570", "start_char_idx": 0, "end_char_idx": 158, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8fe56bf8-c709-451f-9cb5-3d45acff76a6": {"__data__": {"id_": "8fe56bf8-c709-451f-9cb5-3d45acff76a6", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dcfb8e33-0dd5-4a5a-b143-767385e89c45", "node_type": null, "metadata": {}, "hash": "0b082d82d58c8ce8cd5e4e188940f2327a5ca773d0ef08a3a4addb33a4375941"}}, "hash": "91eca5398c501bd894b9de55566fe48cc9f36b0d9cc33e9399d563d32cbee8eb", "text": "\u30c7\u30d0\u30c3\u30b0\u60c5\u5831\n\n\u4ee5\u4e0b\u7b49\u3092\u683c\u7d0d\u3057\u3066\u3044\u307e\u3059\u3002\n\n* `\u30b9\u30b3\u30a2\u5185\u8a33` : \u5408\u8a08\u30b9\u30b3\u30a2\uff08\u6d88\u3057\u305f\u30e9\u30a4\u30f3\u3001\u843d\u4e0b\u30dc\u30fc\u30ca\u30b9\uff09\u306e\u5185\u8a33\n\n\u305d\u306e\u4ed6\u3001\u8a73\u7d30\u306f\u30b3\u30fc\u30c9\u4e0a\u306eGameStatus\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 88, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "283ffde8-e3e6-462e-ac07-8d9c0aceda45": {"__data__": {"id_": "283ffde8-e3e6-462e-ac07-8d9c0aceda45", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "851eae1a-b9e2-4830-8dac-bb1210d3f0cf", "node_type": null, "metadata": {}, "hash": "19865fe7e59a03ed954ecaa14210a281ce5ab496a8fbe614faccf0441c3663d9"}}, "hash": "38ca9236e70af3d74a7ca94a3e6eabd9198992ef26a8af7afae9d9169c2a0824", "text": "\u6b21\u306e\u52d5\u4f5c\u6c7a\u5b9a\u7528\u306e\u30c7\u30fc\u30bf\n\n\u4ee5\u4e0b\u306e\u30c7\u30fc\u30bf\u3092\u3001\u6b21\u306e\u52d5\u4f5c\u6c7a\u5b9a\u7528\u306e\u30c7\u30fc\u30bf`nextMove`\u306b\u4e0e\u3048\u308b\u3053\u3068\u3067\u6b21\u306e\u52d5\u4f5c\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002\n`nextMove`\u306f\u8f9e\u66f8\u578b\u306e\u30c7\u30fc\u30bf\u3067\u3042\u308a\u3001`nextMove[\"strategy\"]`\u4ee5\u4e0b\u306e\u5404\u5024\u306b\u5fdc\u3058\u3066\u30d6\u30ed\u30c3\u30af\u304c\u52d5\u304d\u307e\u3059\u3002\n\n* `direction` : \u73fe\u5728\u306e\u5f62\u72b6\u304b\u3089\u6642\u8a08\u56de\u308a\u306b\u4f55\u56de\u56de\u8ee2\u3059\u308b\u304b\uff08\u7bc4\u56f2\uff1a0\u301c(1\u5468\u306e\u56de\u8ee2\u306b\u5fc5\u8981\u306a\u56de\u6570-1) \uff09\n* `x` : x\u5ea7\u6a19\u306e\u3069\u306e\u4f4d\u7f6e\u306b\u79fb\u52d5\u3059\u308b\u304b\uff08\u7bc4\u56f2\uff1a0-9\u3001x\u5ea7\u6a19\u306e\u7d76\u5bfe\u5024\u3092\u6307\u5b9a\u3059\u308b\uff09\n* `y_operation` : y\u5ea7\u6a19\u65b9\u5411\u306e\u64cd\u4f5c\uff081:\u843d\u4e0b\u30010:\u79fb\u52d5\uff09\n* `y_moveblocknum` : y\u5ea7\u6a19\u65b9\u5411\u306b\u79fb\u52d5\u3059\u308b\u5834\u5408\u3001y\u5ea7\u6a19\u306b\u4f55\u30d6\u30ed\u30c3\u30af\u5206\u79fb\u52d5\u3059\u308b\u304b\uff08\u7bc4\u56f2\uff1a1-21\uff09\n\n!Screenshot\n\n\uff08\u4eca\u5f8c\u30eb\u30fc\u30eb\u5909\u66f4\u304c\u3042\u308b\u5834\u5408\u306f\u3001\u30eb\u30fc\u30eb\u306b\u5fdc\u3058\u3066`nextMove`\u3082\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u3057\u3066\u3044\u304f\u4e88\u5b9a\u3067\u3059\u3002\uff09", "start_char_idx": 0, "end_char_idx": 389, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "17dc908d-97bd-4f70-a113-da91d42944fc": {"__data__": {"id_": "17dc908d-97bd-4f70-a113-da91d42944fc", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5abc6f30-b9c4-440e-bfc0-e6ace08009d1", "node_type": null, "metadata": {}, "hash": "976f58f209b7983e08bc43a91854a80d0e6c21518d0e2c92cf4319509fddec92"}}, "hash": "352900f6c92eb598e66479c7ddc2df373a225b4d4cd954fb9b8fd173a7972561", "text": "HOLD\u6a5f\u80fd\n\nHOLD\u6a5f\u80fd\u3068\u306f\u3001\u30d6\u30ed\u30c3\u30af\u3092\u4fdd\u6301\u3059\u308b\u6a5f\u80fd\u3067\u3059\u3002\u3053\u306e\u6a5f\u80fd\u3092\u4f7f\u3046\u3068\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304cHOLD\u6271\u3044\u306b\u306a\u308a\u307e\u3059\u3002\n\u305d\u306e\u5f8c\u3001\u3082\u3057\u4fdd\u6301\u3057\u3066\u3044\u308b\u30d6\u30ed\u30c3\u30af\u304c\u306a\u3044\u5834\u5408\u306f\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u304c\u843d\u3061\u3066\u304d\u307e\u3059\u3002\n\u3082\u3057\u4fdd\u6301\u3057\u3066\u3044\u308b\u30d6\u30ed\u30c3\u30af\u304c\u65e2\u306b\u3042\u308b\u5834\u5408\u306f\u305d\u306e\u30d6\u30ed\u30c3\u30af\u304c\u518d\u3073\u843d\u3061\u3066\u304d\u307e\u3059\u3002\n\n\u30d7\u30ed\u30b0\u30e9\u30e0\u304b\u3089\u4f7f\u7528\u3059\u308b\u5834\u5408\u306f`nextMove[\"strategy\"]`\u306e\u4ee5\u4e0b\u306e\u5024\u3092\u8abf\u6574\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n* `use_hold_function` : HOLD\u6a5f\u80fd\u3092\u4f7f\u3046\u304b\u3069\u3046\u304b\uff08\"y\":\u4f7f\u3046\u3001\"n\":\u4f7f\u308f\u306a\u3044\uff09\n\n!Screenshot", "start_char_idx": 0, "end_char_idx": 254, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b47de4d3-efca-437f-b86f-a511a8a488c2": {"__data__": {"id_": "b47de4d3-efca-437f-b86f-a511a8a488c2", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c6e71118-ef41-407c-ae38-6c9531a21885", "node_type": null, "metadata": {}, "hash": "4502424dac88298114584fb0eefe7dc0e4cda3ffb0367c63f7e12f8ab51ce620"}}, "hash": "8e8a3d38431e6288a4f8464a901b05ce880c241ca0a0714d2c5eec0d4c229ce3", "text": "\u4f7f\u7528\u4f8b\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u53c2\u7167\u304f\u3060\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 28, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "470d7749-2dac-40b7-809a-2f8e5dda995f": {"__data__": {"id_": "470d7749-2dac-40b7-809a-2f8e5dda995f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e06a2dad-4079-46f4-86ee-e7fefdd32765", "node_type": null, "metadata": {}, "hash": "4cb778c53a93ef636b0d8aa949e4f0d6e06347e11d6af9ea900464c6edcbe2df"}}, "hash": "5133cd431a25c7a45300c247aedc783a0c3744c7d34ef0451646f4429da06e8a", "text": "\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\n\n\u5b9f\u884c\u65b9\u6cd5\n\n```\npython start.py -m sample\n```\n\n\u4e00\u822c\u7684\u306b\u3001\u30c6\u30c8\u30ea\u30b9\u306f\u4ed6\u30b2\u30fc\u30e0\u540c\u69d8\u306b\u7d44\u5408\u305b\u6700\u9069\u5316\u554f\u984c\u3068\u6349\u3048\u308b\u4e8b\u304c\u53ef\u80fd\u3067\u3042\u308a\u3001\u904e\u53bb\u306b\u69d8\u3005\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u63d0\u6848\u3055\u308c\u3066\u3044\u308b\u3002\uff08\u4f8b\u3048\u3070\u4e0b\u8a18\uff09\n- \u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u305f \u30c6\u30c8\u30ea\u30b9\u30b3\u30f3\u30c8\u30ed\u30fc\u30e9\u306e\u958b\u767a\n- \u30c6\u30c8\u30ea\u30b9\u306b\u304a\u3051\u308bAI\u306e\u958b\u767a\n- Tetris is Hard, Even to Approximate\n- ...\n\n\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306f\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u7df4\u7fd2\u3068\u5171\u306b\u3001\u63a2\u7d22\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4eee\u8aac\u691c\u8a3c\u306b\u3082\u5229\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u308b\u3002\n\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u306f\u3001\u4e00\u822c\u7684\u306a\u63a2\u7d22\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u306e\u57fa\u672c\u90e8\u5206\u3068\u306a\u308b\u3001\u73fe\u5728\u30d6\u30ed\u30c3\u30af\u306e\u63a2\u7d22\u65b9\u6cd5\u3001\u76e4\u9762\u8a55\u4fa1\u3092\u7c21\u6f54\u306b\u5b9f\u88c5\u3057\u3066\u3044\u308b\u3002", "start_char_idx": 0, "end_char_idx": 338, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c1bdce91-2f0b-48af-b4be-c8498c881ace": {"__data__": {"id_": "c1bdce91-2f0b-48af-b4be-c8498c881ace", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b98f2a03-09c3-4729-8aa2-edc392482b5d", "node_type": null, "metadata": {}, "hash": "4dc41dbb6bcdc35bc3e429f1e2c563a32ffd28c0458e39bd828613ead2d3aa69"}}, "hash": "8192788ef3686b1a5a26b73553987905f321e211f87c2b52a474d2a10785c638", "text": "step1.\u63a2\u7d22\u65b9\u6cd5\n\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306e`def GetNextMove(self, nextMove, GameStatus):`\u95a2\u6570\u306e\u3001\n`for direction0 in CurrentShapeDirectionRange:`\n`for x0 in range(x0Min, x0Max):`\u90e8\u5206\u304c\u8a72\u5f53\u3059\u308b\u3002\n\u3053\u3053\u3067\u306f\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u306e\u60c5\u5831\uff08\u4ee5\u4e0b\uff09\u3092\u4f7f\u3063\u3066\u5168\u63a2\u7d22\u3092\u884c\u3063\u3066\u3044\u308b\u3002\n- \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304c\u56de\u8ee2\u3067\u304d\u308b\u56de\u6570\n- \u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u304c\u79fb\u52d5\u3067\u304d\u308bx\u8ef8\u65b9\u5411\u306e\u79fb\u52d5\u91cf\n\n!Screenshot\n\n\u4e00\u822c\u7684\u306b\u3001\u8003\u616e\u3059\u3079\u304d\u7d44\u307f\u5408\u308f\u305b\u304c\u591a\u304f\u306a\u308b\u3068\u30b2\u30fc\u30e0\u6728\u3092\u5229\u7528\u3057\u305f\u63a2\u7d22\u7bc4\u56f2\u306e\u52b9\u7387\u5316\u304c\u884c\u308f\u308c\u308b\u3002\n\u4eca\u56de\u3082\uff11\u79d2\uff08\uff11\u30d5\u30ec\u30fc\u30e0\u66f4\u65b0\u983b\u5ea6\uff09\u4ee5\u5185\u306b\u6b21\u306e\u624b\u3092\u6c7a\u3081\u308b\u5fc5\u8981\u304c\u3042\u308a\u3001\u72b6\u6cc1\u306b\u3088\u3063\u3066\u306f\u30b2\u30fc\u30e0\u6728\u306e\u63a1\u7528\u304c\u5fc5\u8981\u3068\u601d\u308f\u308c\u308b\u3002\n\u305f\u3060\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u306f\u7c21\u6f54\u306b\u7406\u89e3\u3067\u304d\u308b\u3053\u3068\u3092\u512a\u5148\u3057\u3001\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u3092\u7528\u3044\u305f\u5168\u63a2\u7d22\u3092\u63a1\u7528\u3057\u3066\u3044\u308b\u3002", "start_char_idx": 0, "end_char_idx": 408, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9df26552-c77b-48f9-8468-7ee0482ee31c": {"__data__": {"id_": "9df26552-c77b-48f9-8468-7ee0482ee31c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c75b624b-0673-4028-a63b-5dca4caebad0", "node_type": null, "metadata": {}, "hash": "9062bee3f1c067761f285658f76782dd82db8cdad82f96deab4fb901471082ee"}}, "hash": "3a64095e455761305673d6ed9e596446b20865575fbcc418e78fde87f22e579b", "text": "step2.\u76e4\u9762\u8a55\u4fa1\n\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306e`def GetNextMove(self, nextMove, GameStatus):`\u95a2\u6570\u306e\u3001\n` EvalValue = self.calcEvaluationValueSample(board)`\u90e8\u5206\u304c\u8a72\u5f53\u3059\u308b\u3002\n\n\u3053\u3053\u3067\u306f\u4ee5\u4e0b\u3092\u8003\u3048\u305f\u76e4\u9762\u8a55\u4fa1\u3092\u884c\u3063\u3066\u3044\u308b\u3002\n- \u4eee\u306b\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u3092\u63a2\u7d22\u5019\u88dc\u7b87\u6240\u306b\u7f6e\u3044\u305f\u6642\u3001\u6d88\u305b\u308b\u30d6\u30ed\u30c3\u30af\u304c\u591a\u3044\u307b\u3069\u826f\u3044\u3002\n- \u540c\u69d8\u306b\u3001\u7a74\u307c\u3053\u3084\u5b64\u7acb\u3057\u305f\u30d6\u30ed\u30c3\u30af\u304c\u5c11\u306a\u3044\u307b\u3069\u826f\u3044\u3002\n- \u540c\u69d8\u306b\u3001\u9ad8\u3055\u306b\u3070\u3089\u3064\u304d\u304c\u5c11\u306a\u3044\u307b\u3069\u826f\u3044\u3002\n\n!Screenshot\n\n\n`EvalValue = self.calcEvaluationValueSample(board)`\u5185\u3067\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u5909\u6570\u3092\u6271\u3063\u3066\u3044\u308b\u3002\nblock_controller_sample.py#L140\n\n```\nBlockMaxY\uff1a\u3042\u308bX\u5ea7\u6a19\u306e\u30d6\u30ed\u30c3\u30af\u306e\u9ad8\u3055\u304c\u5165\u308b\nholeCandidates\uff1a\u3042\u308bX\u5ea7\u6a19\u306e\u7a74\u5019\u88dc\u306e\u6570\nholeConfirm\uff1a\u3042\u308bX\u5ea7\u6a19\u306e\u78ba\u5b9a\u3057\u305f\u7a74\u306e\u6570\uff08\u4e0b\u304b\u3089\"\u7a74\",\"\u7a74\",\u201d\u30d6\u30ed\u30c3\u30af\u201d,\"\u7a74\",\"\u7a74\",\u201d\u30d6\u30ed\u30c3\u30af\u201d,\"\u306a\u3057\",\"\u306a\u3057\",\u201d\u306a\u3057\u201d\u30fb\u30fb\u30fb\u306e\u5834\u5408\u306f\uff14\uff09\nnIsolatedBlocks\uff1a\u4e0b\u304c\u30d6\u30ed\u30c3\u30af\u306a\u3057\u306e\u30d6\u30ed\u30c3\u30af\u306e\u7dcf\u6570\nfullLines\uff1a\u3042\u308bY\u5ea7\u6a19\u306e\u6a2a\u4e00\u5217\u5168\u90e8\u306b\u30d6\u30ed\u30c3\u30af\u304c\u3042\u3063\u305f\u3089\uff11\u304c\u52a0\u7b97\u3055\u308c\u308b\uff08\u6700\u5927\u5024\u306f\uff14\uff09\nnHoles += abs(x)\uff1a\u5404X\u5ea7\u6a19\u306e\u7a74\u306e\u6570\u306e\u5408\u8a08\u5024\nabsDy += abd(x)\uff1a\u3067\u3053\u307c\u3053\u5177\u5408\u3092\u8868\u73fe\u3057\u3066\u3044\u308b\u3002\u3068\u306a\u308a\u306e\u5217\u3068\u306e\u9ad8\u3055\u306e\u5dee\u5206\u3092\u5408\u8a08\u3057\u3066\u3001\u5024\u304c\u5927\u304d\u3044\uff1d\u30d6\u30ed\u30c3\u30af\u306e\u9ad8\u3055\u304c\u51f8\u51f9\u3057\u3066\u308b\n```\n\n\u3057\u304b\u3057\u3001\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u65b9\u6cd5\u3067\u306f\u76e4\u9762\u306b\u7a74\u304c\u6b8b\u308b\u4e8b\u304c\u307e\u3060\u307e\u3060\u591a\u3044\u3002", "start_char_idx": 0, "end_char_idx": 730, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9e8f0265-a120-4449-ab49-84c01e1501fb": {"__data__": {"id_": "9e8f0265-a120-4449-ab49-84c01e1501fb", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "fb72cf94-871a-46a6-b2c2-e77ebfb0a841", "node_type": null, "metadata": {}, "hash": "aee4d7fba88881c3496a813ad2bf1960fa1ded4379575e65eafc66ec41dfe9bb"}}, "hash": "938b4e2ff091c49e6046c56d0c48e071a669831b2e0be0a9d25db6c08769d7e4", "text": "\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u8ab2\u984c\n\n\u4e0a\u8a18\u306e\u901a\u308a\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u6700\u5c0f\u9650\u306e\u6a5f\u80fd\u3057\u304b\u306a\u304f\u3001\u3044\u304f\u3064\u304b\u8ab2\u984c\u304c\u5b58\u5728\u3059\u308b\u3002\n\n- \u6570\u624b\u5148\u8aad\u307f\u63a2\u7d22\n - \u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u306f\u63a2\u7d22\u6642\u306b\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u3057\u304b\u8003\u616e\u3067\u304d\u3066\u3044\u306a\u3044\u3002\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u3084\u4ee5\u964d\u3092\u8003\u616e\u3059\u308b\u65b9\u304c\u826f\u3044\u306f\u305a\u3002\n- \u76e4\u9762\u8a55\u4fa1\u306e\u6539\u826f\n - \u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u306f\u76ee\u5148\u306e\uff11\u30d6\u30ed\u30c3\u30af\u6d88\u3059\u4e8b\u3092\u512a\u5148\u3057\u3066\u3044\u308b\u3002\uff14\u30d6\u30ed\u30c3\u30af\u6d88\u3059\u305f\u3081\u306e\u624b\u3092\u9078\u629e\u3067\u304d\u3066\u3044\u306a\u3044\u3002\n - \u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u624b\u6cd5\u3067\u306f\u76e4\u9762\u306b\u7a74\u304c\u6b8b\u308b\u4e8b\u304c\u307e\u3060\u307e\u3060\u591a\u3044\u3002\n- \u6700\u9069\u89e3\u3092\u4eba\u304c\u8003\u3048\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002\n - AI\u306e\u3088\u3046\u306b\u81ea\u3089\u5b66\u7fd2\u3059\u308b\u65b9\u304c\u697d\u306b\u6700\u9069\u89e3\u3092\u5f97\u3089\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002\n\n\u4e0a\u8a18\u3092\u89e3\u6c7a\u3059\u308b\u3068\u3088\u308a\u9ad8\u3044\u30b9\u30b3\u30a2\u304c\u7372\u5f97\u3067\u304d\u308b\u3068\u601d\u308f\u308c\u308b\u3002\n\u4ee5\u4e0b\u306e\u5148\u884c\u6587\u732e\u306b\u3088\u308b\u3068\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u63a1\u7528\u3057\u3066\u3044\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u4ee5\u5916\u3082\u5229\u7528\u3057\u3066\u304a\u308a\u3001\u6539\u826f\u306e\u4f59\u5730\u304c\u3042\u308b\u4e8b\u306f\u9593\u9055\u3044\u306a\u3044\u3002\n\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u305f \u30c6\u30c8\u30ea\u30b9\u30b3\u30f3\u30c8\u30ed\u30fc\u30e9\u306e\u958b\u767a", "start_char_idx": 0, "end_char_idx": 405, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "508c0633-27f9-41ce-8943-38a30ca38670": {"__data__": {"id_": "508c0633-27f9-41ce-8943-38a30ca38670", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d8b49b77-5ea7-4bb1-bd66-ce20ce838553", "node_type": null, "metadata": {}, "hash": "d659b6b9d54126e0a4d9460f4f279c88d2c9271273616e8c124529c3e4b81c4e"}}, "hash": "4d1f7bdabe25fa5cc4bb69560637fb84477946075e6eb5e6f18cf3d25bffa457", "text": "\u30dc\u30fc\u30c9\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066\n\n\u3053\u3053\u3067\u306f board_manager.py \u306e\u89e3\u8aac\u3092\u884c\u3046\u3002\n\n\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u306b\u306f\u30c6\u30c8\u30ea\u30df\u30ce\u306e \u5f62\u72b6\u3001\u56de\u8ee2\u65b9\u5411\u3092\u62c5\u3046 Shape Class \u3068\u3001\u753b\u9762\u30dc\u30fc\u30c9\u306e\u5404\u7a2e\u51e6\u7406\u3092\u884c\u3046 BoardData Class \u304c\u3042\u308b\u3002\nblock_controller.py \u5074\u304b\u3089\u3082\u3088\u304f\u4f7f\u3046\u30e1\u30bd\u30c3\u30c9\u304c\u591a\u3044\u306e\u3067\u89e3\u8aac\u3092\u66f8\u3044\u3066\u304a\u304f\u3002", "start_char_idx": 0, "end_char_idx": 169, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "de287235-bc55-42f3-98c4-6a2d542b2740": {"__data__": {"id_": "de287235-bc55-42f3-98c4-6a2d542b2740", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7267230c-b52c-407c-8454-190574d5418c", "node_type": null, "metadata": {}, "hash": "210f3bb2853ff91bd64866d37ce3a37b5ba5d45173a619e504e37e507affdb07"}}, "hash": "22985006f7ad800c37b09b3ca7c657b499bde9c4fa1965d62c53e0dd8a49dba7", "text": "Shape Class\n\n\u3053\u3053\u3067\u306f\u3001board_manager.py \u306e Shape Class \u306b\u3064\u3044\u3066\u89e3\u8aac\u3059\u308b\u3002\nShape Class \u306f\u4e0b\u8a18\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u5f62\u72b6\u306b\u3064\u3044\u3066\u4fdd\u6301\u3057\u3066\u3044\u308b\u30af\u30e9\u30b9\u3067\u3042\u308b\u3002\n\n| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ |\n| --- | --- | --- | --- | --- | --- | --- | --- | \n| index\u5024 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | \n| \u521d\u671f\u5f62\u72b6 | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | !Screenshot | \n| \uff11\u5468\u306e\u56de\u8ee2\u306b\u5fc5\u8981\u306a\u56de\u6570 | 2 | 4 | 4 | 4 | 1 | 2 | 2 | \n\n\u8a73\u7d30\u306f\u3001\u30d6\u30ed\u30c3\u30af\u60c5\u5831 \u306b\u8a73\u3057\u304f\u8a18\u8f09\u304c\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 452, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "23f1ecfa-8922-44a1-ac79-56b894736a62": {"__data__": {"id_": "23f1ecfa-8922-44a1-ac79-56b894736a62", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3ec15043-1a7f-464e-9c5c-d535579a0387", "node_type": null, "metadata": {}, "hash": "4e0e8787b446de498de486b9a600183a7b78eca94a8d0b7b1b204c0558dca06c"}}, "hash": "7cf2353c64656cb9f885578465342992b2458a0e2d6bde59e22deefbcf2690ef", "text": "getRotatedOffsets method\n\ngetRotatedOffsets \u30e1\u30bd\u30c3\u30c9\u306f \u30c6\u30c8\u30ea\u30df\u30ce\u5f62\u72b6\u3092\u56de\u8ee2\u3057\u305f\u57fa\u6e96\u5ea7\u6a19\u304b\u3089\u306e\u76f8\u5bfe\u5ea7\u6a19\u3092\u8fd4\u3057\u307e\u3059\u3002\n\u5f15\u6570\u3068\u3057\u3066 direction \u30c6\u30c8\u30ea\u30df\u30ce\u56de\u8ee2\u65b9\u5411\u3092\u6307\u5b9a\u3059\u308b\u3002\n\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u306e\u57fa\u6e96\u70b9\u306e\u5f62\u72b6\u756a\u53f7\u3092 direction \u3067\u6307\u5b9a\u3059\u308b\u3068\u3001\u3053\u3053\u3067\u66f8\u304b\u308c\u3066\u3044\u308b\u76f8\u5bfe\u5ea7\u6a19\u304c\u8fd4\u3055\u308c\u308b\u3002", "start_char_idx": 0, "end_char_idx": 166, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "15225548-ae59-44c6-b10d-3788fc066586": {"__data__": {"id_": "15225548-ae59-44c6-b10d-3788fc066586", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "69f6c39d-83e3-4f08-96b1-493af5b225e5", "node_type": null, "metadata": {}, "hash": "2d29f178f4568c4820f3e65032f1806bde1dd07e190238ac1d23439f92008fc4"}}, "hash": "afcf01bd711907b6bd87c9b25b020e16451bb35679e4da99702bfe710f2afe8c", "text": "getCoords method\n\ngetCoords \u30e1\u30bd\u30c3\u30c9\u306f \u3055\u3089\u306b x,y \u3092\u5f15\u6570\u306b\u6301\u3061\u3001x,y \u3092\u57fa\u6e96\u70b9\u306b\u7f6e\u3044\u305f\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u7d76\u5bfe\u5ea7\u6a19\u3092\u8fd4\u3059\u3002", "start_char_idx": 0, "end_char_idx": 75, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "5b34d215-2611-416b-b34a-7470223a687e": {"__data__": {"id_": "5b34d215-2611-416b-b34a-7470223a687e", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "12f3e9df-748d-4383-b5eb-8e0db339f84f", "node_type": null, "metadata": {}, "hash": "a53eedbc51275c54a45bee2be299e4cb29dbf1cc0ab69b421a90a5ca092fd1ba"}}, "hash": "a92e368b24aa95b103367b7c4552e937d82d2e25bf232d6fa79d4cea952ed2f8", "text": "getBoundingOffsets method\n\ngetBoundingOffsets \u30e1\u30bd\u30c3\u30c9\u306f\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u57fa\u6e96\u70b9\u304b\u3089\u306e\u6700\u5927 x, y \u6700\u5c0fx, y \u3092\u53d6\u5f97\u3059\u308b\u3002\ndirection \u3067\u56de\u8ee2\u65b9\u5411\u3092\u6307\u5b9a\u3057\u3001minX, maxX, minY, maxY \u304c\u8fd4\u3063\u3066\u304f\u308b\u3002", "start_char_idx": 0, "end_char_idx": 135, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "bcc8ce86-e983-490e-a23f-d7af513392d3": {"__data__": {"id_": "bcc8ce86-e983-490e-a23f-d7af513392d3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ca57654f-7e33-4731-932e-fa737b633546", "node_type": null, "metadata": {}, "hash": "ad390c0a18d5ad338bcf85457b9f4f1982973ca4d9589fb152cb6736ff944cf5"}}, "hash": "de2907ec868500107de5e34f817f203498b37138a01eb689425306f905117478", "text": "BoardData Class\n\n\u3053\u3053\u3067\u306f\u3001board_manager.py \u306e BoardData Class \u306b\u3064\u3044\u3066\u89e3\u8aac\u3059\u308b\u3002\nBoardData Class \u306f\u753b\u9762\u30dc\u30fc\u30c9\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u30c6\u30c8\u30ea\u30df\u30ce\u60c5\u5831\u304c\u683c\u7d0d\u3055\u308c\u3066\u304a\u308a\u3001\u753b\u9762\u30dc\u30fc\u30c9\u306e\u51e6\u7406\u306f\u3053\u306e Class \u306b\u304a\u3044\u3066\u884c\u3046\u3002", "start_char_idx": 0, "end_char_idx": 138, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "81393b81-5c50-483e-ac72-3ddb2466f498": {"__data__": {"id_": "81393b81-5c50-483e-ac72-3ddb2466f498", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c7cfdf36-eef9-4eba-9579-4bc1b556f2c9", "node_type": null, "metadata": {}, "hash": "15550c8af0ea33319f70da48f2bf9ca0650c23d88a40378233cbc8b5f31f8841"}}, "hash": "9db94f8ddf769fc925f014247df473c93638f4adce4b580c960648d198332e1d", "text": "\u753b\u9762\u30dc\u30fc\u30c9\u60c5\u5831\u53d6\u5f97\u7cfb\u30e1\u30bd\u30c3\u30c9", "start_char_idx": 0, "end_char_idx": 14, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a8490578-d792-4b2d-a91a-e3755211dad5": {"__data__": {"id_": "a8490578-d792-4b2d-a91a-e3755211dad5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "fb4931e0-f4e9-4d86-9e7c-0c10aa5d07f4", "node_type": null, "metadata": {}, "hash": "235b89a0327d28b3e2abc2f434621d2ef1c1a7703b08ab9766447cc189f8274e"}}, "hash": "3cdb6c80b98f7992b367f2d1e168dec89961dc6cda9ba16c16fa362b118612db", "text": "getData method\n\n\u73fe\u72b6\u306e\u753b\u9762\u30dc\u30fc\u30c9\u30c7\u30fc\u30bf\u3092\u8fd4\u3059\u3002\u4e00\u6b21\u5143\u914d\u5217\u3067\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 40, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "bc35b2fc-146f-4cc2-9a2f-0d1acc85fa16": {"__data__": {"id_": "bc35b2fc-146f-4cc2-9a2f-0d1acc85fa16", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "49976c04-4ec1-4ef2-8fe3-d92e41b0da88", "node_type": null, "metadata": {}, "hash": "b009e7d49deb66f46bb8f1e81970beec59288be412569e7c7a9a020df58847a6"}}, "hash": "9e3353f942bbfa832014f30f0e1ac6ecbc741dd3886f2c76ba6e1f89479a0df6", "text": "\u30c6\u30c8\u30ea\u30df\u30ce\u60c5\u5831\u53d6\u5f97\u95a2\u4fc2\u30e1\u30bd\u30c3\u30c9", "start_char_idx": 0, "end_char_idx": 15, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1846fdb4-bbab-4f8c-a1ce-295a684ef720": {"__data__": {"id_": "1846fdb4-bbab-4f8c-a1ce-295a684ef720", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "19ea8123-db68-412d-9d74-a03aabed8f14", "node_type": null, "metadata": {}, "hash": "45744ba5d52cf061d3d391a2c3bb7fd136d152ff76ce02947252ca203c0c00aa"}}, "hash": "5a11592030637a5a43bf50279471b3f4082a763a8f6315212de85f4fbb223858", "text": "getDataWithCurrentBlock method\n\n\u753b\u9762\u30dc\u30fc\u30c9\u30c7\u30fc\u30bf\u30b3\u30d4\u30fc\u3057\u52d5\u3044\u3066\u3044\u308b\u30c6\u30c8\u30ea\u30df\u30ce\u30c7\u30fc\u30bf\u3092\u4ed8\u52a0\u3057\u3001\u305d\u306e\u753b\u9762\u30dc\u30fc\u30c9\u30c7\u30fc\u30bf\u3092\u8fd4\u3059\u3002", "start_char_idx": 0, "end_char_idx": 76, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0cba1902-9045-405e-9a1d-64b1526ebb87": {"__data__": {"id_": "0cba1902-9045-405e-9a1d-64b1526ebb87", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5be45fc8-d6f1-43f4-878e-6b06ddbab2fe", "node_type": null, "metadata": {}, "hash": "68b308f20d02c01e503f8839d769ce55fbf46693af7946fea1b7292db296f953"}}, "hash": "d86ca2c441103084242728b24514cdeced3b75ecae1bbb2ba0d50bfee276f762", "text": "getValue method\n\n\u5f15\u6570\u3068\u3057\u3066 x,y \u3092\u6307\u5b9a\u3057\u753b\u9762\u30dc\u30fc\u30c9\u4e0a\u306e\u305d\u306e\u5ea7\u6a19\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u6709\u7121(\u304a\u3088\u3073\u305d\u306e\u7a2e\u985e)\u3092\u8fd4\u3059\u3002\n\u8fd4\u308a\u5024\u306f Shape Class \u306e index\u5024\u3002\u4f55\u3082\u306a\u3044\u306a\u3089 0\u3002", "start_char_idx": 0, "end_char_idx": 100, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ed1fe994-028b-48dc-bc10-80d78f76d4af": {"__data__": {"id_": "ed1fe994-028b-48dc-bc10-80d78f76d4af", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9f9b9538-89aa-431f-8cb3-a8cc338ee9d9", "node_type": null, "metadata": {}, "hash": "d87da45194870913061515eebae05ef7a0e1e825a4a393ad3b22d20ba74d690a"}}, "hash": "e7039fb78e964d75fe9a0210b025feaa55eeff077d48c19ea1878302fe7964b5", "text": "getCurrentShapeCoord method\n\ndirection (\u56de\u8ee2\u72b6\u614b)\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u5ea7\u6a19\u914d\u5217\u3092\u53d6\u5f97\u3057\u3001\u305d\u308c\u3092x,y\u306b\u914d\u7f6e\u3057\u305f\u5834\u5408\u306e\u5ea7\u6a19\u914d\u5217\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 81, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "4896d728-623a-4b34-ae7d-26404e9a13d6": {"__data__": {"id_": "4896d728-623a-4b34-ae7d-26404e9a13d6", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "24c4967d-f328-40cc-9eac-40174a638d4a", "node_type": null, "metadata": {}, "hash": "4ceb84c87e37670f78f6d4ebfea843970a93ac51f0e139f0e9e73f283202e7da"}}, "hash": "b72b4e0f830091ae46d3a1a949d42015c82e5d651a31ebfea8a5fad720039221", "text": "getShapeListLength method\n\n\u4e88\u544a\u30c6\u30c8\u30ea\u30df\u30ce\u914d\u5217\u306e\u9577\u3055\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 42, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0dba0ca0-6cb8-4433-8b21-00c2cecb4ad1": {"__data__": {"id_": "0dba0ca0-6cb8-4433-8b21-00c2cecb4ad1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dd7a0dd1-80ac-4acb-aae6-04abdd67eab7", "node_type": null, "metadata": {}, "hash": "b2320277a072a9818a5104eadfc514569f438da43b85f832ffa2913ddb85ab94"}}, "hash": "efa62ba335d424dc7fac56f48be2ca30eba717cac7dbee703336b880a9bee770", "text": "getShapeDataFromShapeClass method\n\n\u30c6\u30c8\u30ea\u30df\u30ce\u30af\u30e9\u30b9\u30c7\u30fc\u30bf, \u30c6\u30c8\u30ea\u30df\u30ce\u5ea7\u6a19\u914d\u5217\u3001\u30c6\u30c8\u30ea\u30df\u30ce\u56de\u8ee2\u7a2e\u985e\u3092\u8fd4\u3059\n\n\u5f15\u6570\n\n ShapeClass ... Shape \u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\n\n\u8fd4\u5024\n\n ShapeClass ... Shape \u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\n ShapeIdx ... Shape Class \u306e index\u5024\n ShapeRange ... Shape Class \u306e\u56de\u8ee2\u65b9\u5411\u914d\u5217\u3002O\u30df\u30ce\u306a\u3089 (0,) L\u30df\u30ce\u306a\u3089 (0,1,2,3)\u3002", "start_char_idx": 0, "end_char_idx": 251, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "6027cd04-5df8-4ec9-9a74-85729013e53f": {"__data__": {"id_": "6027cd04-5df8-4ec9-9a74-85729013e53f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8dd7b6e7-4a87-423e-991e-911c36ec1ff0", "node_type": null, "metadata": {}, "hash": "7e07da31b16ec9e7497ffd6d52d43d29b2dc7e3f31a346df267feff3c48e1da1"}}, "hash": "c292620cdd2daf6ab2e7c116c749a865acffbb91108408a7f641ea75cd48fbb1", "text": "getShapeData method\n\n\u30c6\u30c8\u30ea\u30df\u30ce\u306e index \u756a\u53f7\u304b\u3089 shape \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u8fd4\u3059\n\n\u5f15\u6570\n\n ShapeNumber ... \u30c6\u30c8\u30ea\u30df\u30ce\u306e index\n\n\u8fd4\u5024\n\n ShapeClass ... Shape \u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8", "start_char_idx": 0, "end_char_idx": 130, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f45620fd-aa92-4c7d-b48b-1b70fc3622ea": {"__data__": {"id_": "f45620fd-aa92-4c7d-b48b-1b70fc3622ea", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "210fa9a3-c7d1-41d7-b065-251a4c37174c", "node_type": null, "metadata": {}, "hash": "ab46ad700d58c9bd8694ec90d9bb372bc4dd2101e840fb8cc78aeff65407a835"}}, "hash": "36631506375b63482cbb25d97f815cb5716a42bea463a3eef5ba297306d631b1", "text": "HOLD \u95a2\u4fc2", "start_char_idx": 0, "end_char_idx": 7, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "085fcc40-c5a0-4dea-8039-113b0bd8d4e3": {"__data__": {"id_": "085fcc40-c5a0-4dea-8039-113b0bd8d4e3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "951d4750-1ce7-4a82-b1d1-3a7144b67547", "node_type": null, "metadata": {}, "hash": "ff096aa7cc4e3945e0a56c7184d66f401daa662b8c6bef256c9b00c157de7420"}}, "hash": "a584572129d6a1c7db781bbd550626a9d840d64b2e0ccf199f43ec28989be553", "text": "getholdShapeData method\n\n\u30db\u30fc\u30eb\u30c9\u3057\u3066\u3044\u308b Shape \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 49, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ed78f2a3-18af-4f13-9705-e9b9eca959ae": {"__data__": {"id_": "ed78f2a3-18af-4f13-9705-e9b9eca959ae", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "56ad45a3-f3cb-4aeb-b883-b9d54843d513", "node_type": null, "metadata": {}, "hash": "784419d8750230ce46da874caf2e9ab3666fdbf6c4881e8b074df279970f40cc"}}, "hash": "25921c4157fc9b827c1b59c8ef99e9f729f15d48f9277936a3dea6eb5312d961", "text": "ART \u95a2\u4fc2", "start_char_idx": 0, "end_char_idx": 6, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "45aadffd-70a5-4839-92f9-6a8f5f60e930": {"__data__": {"id_": "45aadffd-70a5-4839-92f9-6a8f5f60e930", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1149b717-c8ec-41ff-8bb2-a8c860e1b122", "node_type": null, "metadata": {}, "hash": "6d5b0b2c49192297bc6a62195c92c7bf3b421d5184499932ee7a81bb0dd96674"}}, "hash": "3c82a91e8dcd1c12f6d0765e77fb5c16885a585ff84995b790538f139b0a9d04", "text": "getcolorTable method\n\ncolorTable\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 35, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0ecd9cf8-ce03-4f27-8346-be248bb2b66a": {"__data__": {"id_": "0ecd9cf8-ce03-4f27-8346-be248bb2b66a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "aa0b9af2-ebf7-4edb-b567-c90e5d5932b5", "node_type": null, "metadata": {}, "hash": "b0d2ef577dc09063407228d9a60bb10a1651c1def31666ef28c65600069fd579"}}, "hash": "dfcbc084b2bd1f7b631e24fcc20d3c58d1c49b3d698583597e565b2b8b0bae47", "text": "getnextShapeIndexListDXY method\n\nindex \u3092\u6307\u5b9a\u3057\u3066\u3001nextShapeIndexList \u306e d, x, y \u3092\u8fd4\u3059\n\n\u5f15\u6570\n\n index\n\n\u8fd4\u5024\n\n d\n x\n y", "start_char_idx": 0, "end_char_idx": 115, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "76a79005-725c-4eeb-a2cb-d05bb8a0cb88": {"__data__": {"id_": "76a79005-725c-4eeb-a2cb-d05bb8a0cb88", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f12dbc0f-04f1-408f-b9c4-6b5eafb63498", "node_type": null, "metadata": {}, "hash": "5e85f58fc650effe7c4fe86eab16f31bfeba60f4279413cc48cb513bc367bbd3"}}, "hash": "230ec558e5c611e7036407fe5254afa9dc4277b04986c1e625c1acb303a69bc9", "text": "\u30b2\u30fc\u30e0\u30bf\u30fc\u30f3\u9032\u884c\u95a2\u4fc2\u30e1\u30bd\u30c3\u30c9", "start_char_idx": 0, "end_char_idx": 14, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d04b1468-78b1-4115-855e-de536d9f0c9f": {"__data__": {"id_": "d04b1468-78b1-4115-855e-de536d9f0c9f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "764e76f4-d0e9-4e94-9570-fbca5547244e", "node_type": null, "metadata": {}, "hash": "11b9f106ee17cb0c9143a08e3321ea6c125a14a83473beb9a810123ea6a09bcd"}}, "hash": "5cc2e105e13627627db1a8852f8b7bfed4468791758590e6ac7c0e62879d6271", "text": "mergePiece method\n\n\u753b\u9762\u30dc\u30fc\u30c9\u306b\u56fa\u7740\u3057\u305f\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u66f8\u304d\u8fbc\u3080", "start_char_idx": 0, "end_char_idx": 39, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c4e7b5cd-3352-42a8-983d-494bcf6582a1": {"__data__": {"id_": "c4e7b5cd-3352-42a8-983d-494bcf6582a1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dfa154f0-f996-4e89-bc94-d40c9bd718e1", "node_type": null, "metadata": {}, "hash": "cd2c8330e7aa0da05536db4bc139078f6a753fa761639cbbd85932877ab55c7b"}}, "hash": "aca0c167e81a1ae7a03279df6358a93ff332efef4676ffcc12ac97b199bb36dd", "text": "getNewShapeIndex method\n\n\u6b21\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u306e index \u53d6\u5f97", "start_char_idx": 0, "end_char_idx": 42, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a3a7149a-804f-4666-8986-1cd855092f7d": {"__data__": {"id_": "a3a7149a-804f-4666-8986-1cd855092f7d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5f47e28e-8f87-443e-b11d-00173bc03553", "node_type": null, "metadata": {}, "hash": "a4465c9daac4376a7306ddc2e0129816316e4f3b1b192aaebd9fa9624dd61966"}}, "hash": "f1c2670233ac328f55bd331eca3de450b80c6ac1a2405553d71cbc2d2515a24c", "text": "createNewPiece method\n\n\u753b\u9762\u30dc\u30fc\u30c9\u4e0a\u306b\u65b0\u3057\u3044\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u914d\u7f6e\u3002\u914d\u7f6e\u3067\u304d\u306a\u304b\u3063\u305f\u3089 False \u3092\u8fd4\u3059\u3002", "start_char_idx": 0, "end_char_idx": 62, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "78af704b-28bf-400e-ab15-0860f9ff21f0": {"__data__": {"id_": "78af704b-28bf-400e-ab15-0860f9ff21f0", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a4663894-f9b2-4950-bffc-f07ffb9a0093", "node_type": null, "metadata": {}, "hash": "434b8b950ffa15fefa02e9a47f4b174ab46c0564fa50921622d716b554710fee"}}, "hash": "3f5425626ddf5735f3acc2a112c9118bd136b8eee3d0bc762c80306c8ed741d4", "text": "tryMoveNext method\n\ndirection \u65b9\u5411\u3067 x,y \u30782\u56de\u914d\u7f6e\u3057\u3066\u52d5\u304b\u305b\u306a\u304b\u3063\u305f\u3089 Reset", "start_char_idx": 0, "end_char_idx": 59, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c6701200-393e-4d5f-9fc1-4ecb8bef309f": {"__data__": {"id_": "c6701200-393e-4d5f-9fc1-4ecb8bef309f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cfaf86ce-55f8-4538-96ed-3fe4df0deb58", "node_type": null, "metadata": {}, "hash": "2c31eeded8b9936fa0892563efe1768402fdf6aa9a881863dda7335ecc6a0137"}}, "hash": "ab5d3b6c58a45d3ecc90fd7dfa1c7182705f2df86b61e90eb711c0e3ab185974", "text": "exchangeholdShape method\n\nHOLD \u5165\u308c\u66ff\u3048", "start_char_idx": 0, "end_char_idx": 35, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a9c9ac03-52ba-4828-a891-4f63dcf7e4ea": {"__data__": {"id_": "a9c9ac03-52ba-4828-a891-4f63dcf7e4ea", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8fca467c-aba7-4536-8adc-80e67a334b58", "node_type": null, "metadata": {}, "hash": "1a29f18f1f1c2d76eab9da4b167f037726f0e194bc552e63aa14686bb50927ff"}}, "hash": "817e1024b87ee491a3503da3851b549cc5cfb4e15bc52b5a05f05768cdca07f4", "text": "removeFullLines method\n\n\u753b\u9762\u30dc\u30fc\u30c9\u306e\u6d88\u53bb\u3067\u304d\u308b\u30e9\u30a4\u30f3\u3092\u63a2\u3057\u3066\u6d88\u53bb\u3057\u3001\u753b\u9762\u30dc\u30fc\u30c9\u3092\u66f4\u65b0\u3001\u305d\u3057\u3066\u6d88\u3057\u305f Line \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 70, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c9b38449-e0cc-4a69-a302-65784caaca9c": {"__data__": {"id_": "c9b38449-e0cc-4a69-a302-65784caaca9c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f52f19e2-8471-4e77-bc32-a7f01b22e2a3", "node_type": null, "metadata": {}, "hash": "29c83fac7a58d5f5d12b09799c73d7479e946d920cc0a44e48c6da365debf6f9"}}, "hash": "dd94461e2dde6bff079b09d6fb7e947260af02fa26e08846374a09b159dff646", "text": "\u30c6\u30c8\u30ea\u30df\u30ce\u914d\u7f6e\u78ba\u8a8d\u95a2\u4fc2\u30e1\u30bd\u30c3\u30c9", "start_char_idx": 0, "end_char_idx": 15, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "236fce0f-14ec-45a2-98c7-63852575335f": {"__data__": {"id_": "236fce0f-14ec-45a2-98c7-63852575335f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9556f22a-8c45-4dcc-89a7-fc8d772fa2c2", "node_type": null, "metadata": {}, "hash": "e3ff832b1aef6eab2341c0ed6f6beef8d5c939cbf79ae7862633700a90693004"}}, "hash": "1cea94d706310aa3316075fd814878b4fe34136bd4fc117d15e1eba631356da9", "text": "tryMoveCurrent method\n\n\u30c6\u30c8\u30ea\u30df\u30ce\u3092 direction \u65b9\u5411\u3067 x,y \u306b\u52d5\u304b\u305b\u308b\u304b\u3069\u3046\u304b\u78ba\u8a8d\u3059\u308b\n\u52d5\u304b\u305b\u306a\u3044\u5834\u5408 tryMove \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u3044 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 95, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b6b19753-0021-4092-92a5-82c6385d1ea7": {"__data__": {"id_": "b6b19753-0021-4092-92a5-82c6385d1ea7", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "667fe628-926a-4bbd-9bae-3031da839aa3", "node_type": null, "metadata": {}, "hash": "5ca4da78eae6572b20c5694b41d7459c5a736003169b4ed69bcba288634bf9ab"}}, "hash": "b9606100fc6c56e7a0c9eed521c92b05af8a7eb7ea58f126f424cc0a518d3bd5", "text": "tryMove method\n\ndirection (\u56de\u8ee2\u72b6\u614b)\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u5ea7\u6a19\u914d\u5217\u3092\u53d6\u5f97\u3057\u3001\u305d\u308c\u3092x,y\u306b\u914d\u7f6e\u53ef\u80fd\u304b\u5224\u5b9a\u3059\u308b\n\u914d\u7f6e\u3067\u304d\u306a\u3044\u5834\u5408 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 82, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3677f763-add2-4a4e-a9bc-3285c05feea5": {"__data__": {"id_": "3677f763-add2-4a4e-a9bc-3285c05feea5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "343bd849-6f9e-41fc-a6b5-fa20ae31b338", "node_type": null, "metadata": {}, "hash": "a58fa80183ff8819b7f618ebf115a9d224a8ef6e2ee381d532d205f9660554ec"}}, "hash": "fba7c94afc1e5304482dcdee96196e133be2f18c3175dd24df5a6d5349afa619", "text": "moveDown method\n\n\u30c6\u30ce\u30ea\u30df\u30ce\u30921\u3064\u843d\u3068\u3057\u6d88\u53bb\u30e9\u30a4\u30f3\u3068\u30c6\u30c8\u30ea\u30df\u30ce\u843d\u4e0b\u6570\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 45, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e906b892-59c3-47ea-8db5-9b62513213dd": {"__data__": {"id_": "e906b892-59c3-47ea-8db5-9b62513213dd", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5d2e75cd-1c01-4bf5-a23c-32904cb3f9d4", "node_type": null, "metadata": {}, "hash": "7ebed55e287d9155302ba42d883fd9924f3944cc5af2ec380e9b552253666f80"}}, "hash": "ed3a7b62b389066003a43683600c3ddc9b4716e08ad1846571d70938c3eb69c1", "text": "dropDown method\n\n\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u4e00\u756a\u4e0b\u307e\u3067\u843d\u3068\u3057\u6d88\u53bb\u30e9\u30a4\u30f3\u3068\u30c6\u30c8\u30ea\u30df\u30ce\u843d\u4e0b\u6570\u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 48, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "6c060f29-393f-4866-9884-d6732a831213": {"__data__": {"id_": "6c060f29-393f-4866-9884-d6732a831213", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1cb13e86-3aec-4ca4-8226-5155bf18f1d8", "node_type": null, "metadata": {}, "hash": "47fa02b6df2657ed01ea516efd5299b6db2dc340b2a2d2e67eaf0261cc732679"}}, "hash": "990e0f8c44856872c5d86bb61e2a85a8d6d5dd06ae75bfcc1879037a6d03c7a5", "text": "moveLeft method\n\n\u5de6\u3078\u30c6\u30c8\u30ea\u30df\u30ce\u30921\u3064\u52d5\u304b\u3059\n\u5931\u6557\u3057\u305f\u3089 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 46, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "be92606c-f3e3-4dc0-8a4f-c9095ab46d10": {"__data__": {"id_": "be92606c-f3e3-4dc0-8a4f-c9095ab46d10", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "12bbab1f-2548-4095-8f24-24acf4f783cb", "node_type": null, "metadata": {}, "hash": "5b40d125df82c5a22180caf36236c844b7498390f9f18e90d6fa8dbc9077f2fe"}}, "hash": "be61fb49971cf3b76113be7ce1974e4583506601e421fa1e80b8b6fecd6891e3", "text": "moveRight method\n\n\u53f3\u3078\u30c6\u30c8\u30ea\u30df\u30ce\u30921\u3064\u52d5\u304b\u3059\n\u5931\u6557\u3057\u305f\u3089 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 47, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a80a896e-df16-444e-ba40-7e566075f3f7": {"__data__": {"id_": "a80a896e-df16-444e-ba40-7e566075f3f7", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "91c987ce-90bd-4d65-a079-868c90a62b4e", "node_type": null, "metadata": {}, "hash": "fb891ffaca411cd8e963f94b40fe583538edbc61d866121d28d724485a019882"}}, "hash": "ca8ddf63fbd6e64609eeb6431328c1bf85b4c2925281264d44b7a3100fbfb3e2", "text": "rotateRight method\n\n\u53f3\u56de\u8ee2\u3055\u305b\u308b\n\u5931\u6557\u3057\u305f\u3089 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 42, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "bb09d21a-e0ce-4bd4-b30e-71b1502184db": {"__data__": {"id_": "bb09d21a-e0ce-4bd4-b30e-71b1502184db", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ca6aa865-a510-45fb-a604-16484692ca9c", "node_type": null, "metadata": {}, "hash": "587a2b3af8d6225b4e15a184efcd8e5c119179eeca69fb0cb581290e156c1010"}}, "hash": "143c46ea9efd95593692ed40b15caa10fdba16b8d7580d0a4afc09f57bb87ab6", "text": "rotateLeft method\n\n\u5de6\u56de\u8ee2\u3055\u305b\u308b\n\u5931\u6557\u3057\u305f\u3089 False \u3092\u8fd4\u3059", "start_char_idx": 0, "end_char_idx": 41, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "007f28db-1a5c-40d3-919b-4d993ff0d192": {"__data__": {"id_": "007f28db-1a5c-40d3-919b-4d993ff0d192", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "0b151f23-6052-4c2b-9730-3c0797a30e9a", "node_type": null, "metadata": {}, "hash": "f0b58ad6f1427a4ec4ea864f3b398f5297688e22ec4a243f7a5055031b91ee15"}}, "hash": "65933d35747751541211c2c417d3a0eba4e9de50f229db76263f76918c3131ae", "text": "\u521d\u671f\u914d\u7f6e\u95a2\u4fc2\u30e1\u30bd\u30c3\u30c9", "start_char_idx": 0, "end_char_idx": 10, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d44e0165-c1b2-4295-9dcc-bf9de3a9fd06": {"__data__": {"id_": "d44e0165-c1b2-4295-9dcc-bf9de3a9fd06", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "86f9492c-a828-4b56-928e-2d9092ec3ca8", "node_type": null, "metadata": {}, "hash": "071fa4ac28874ce4176f3fabae0c943514121b8dadd7c277d3cc43f3409457fa"}}, "hash": "545c9414376f0544bf7a8a348a4fa6d06eff58e64f66c24423c66ec244aa5f0f", "text": "clear method\n\n\u753b\u9762\u30dc\u30fc\u30c9\u3068\u73fe\u30c6\u30c8\u30ea\u30df\u30ce\u60c5\u5831\u3092\u30af\u30ea\u30a2", "start_char_idx": 0, "end_char_idx": 32, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f8833e02-c260-4885-8532-49ed310cac67": {"__data__": {"id_": "f8833e02-c260-4885-8532-49ed310cac67", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f7d6244b-f7d4-456f-923f-621bc30a84ae", "node_type": null, "metadata": {}, "hash": "23904481f0c8040736535212bb54472f586452051bb2d351f5dad4843bcf6896"}}, "hash": "e0f732c56e8f163a40b3575308c6deafa2b1506e20663bcb7574abc59fb63c17", "text": "addobstacle method\n\n\u521d\u671f\u969c\u5bb3\u7269\u914d\u7f6e\u3002Level3 Level4 \u7528\u3002", "start_char_idx": 0, "end_char_idx": 44, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "963f875d-783b-4902-9e2b-433010ef739a": {"__data__": {"id_": "963f875d-783b-4902-9e2b-433010ef739a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e3d1d171-77c2-4675-9891-7ec48fbabc91", "node_type": null, "metadata": {}, "hash": "66b8d0b19164c8c43e57c6d258d3723e82ae63b3845f72ee84964852bf1280c6"}}, "hash": "4814ba22a30e0b72cb228af87877808100083b15f645bbc29feb75f490398f19", "text": "Tetris Game docker", "start_char_idx": 0, "end_char_idx": 18, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8719fd0e-5005-4414-bf3c-41d6a3057de9": {"__data__": {"id_": "8719fd0e-5005-4414-bf3c-41d6a3057de9", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "aa6d1cbc-a686-4d84-93ba-d2a2cb880313", "node_type": null, "metadata": {}, "hash": "15d836ad4f6cb4a5fd1a28a8ad4cd660d72f7ac94821c12beb6e5df3c9674d81"}}, "hash": "0d4bc5ff76aeb03741ba026d05948711db115e1f40d48b83fa28cea50d96ae87", "text": "step0. docker \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\n\nInstall Docker Engine on Ubuntu \nInstall Docker Desktop on Mac \nInstall Docker Desktop on Windows", "start_char_idx": 0, "end_char_idx": 122, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "00c06687-6593-44db-9462-6ec51c254b20": {"__data__": {"id_": "00c06687-6593-44db-9462-6ec51c254b20", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "972df85b-4215-4c8a-b4c9-4b3545e8d879", "node_type": null, "metadata": {}, "hash": "c278188233ffe3edc432c541c1e62be795209a98bc92a4f9475563b47681d033"}}, "hash": "3c1c813ce4f0e160086922b89dae92ccbf2d37dcf06126158653f016047d736d", "text": "step1. docker \u30b3\u30f3\u30c6\u30ca\u3092\u8d77\u52d5\u3059\u308b\n\ntetris \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```\ndocker-compose up\n```\n\n`pytorch`\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6e08\u30b3\u30f3\u30c6\u30ca\u3092\u4f7f\u3044\u305f\u3044\u5834\u5408\u3001\u4e0a\u8a18\u306e\u4ee3\u308f\u308a\u306b\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```\ndocker-compose -f docker-compose.pytorch.yaml up\n```", "start_char_idx": 0, "end_char_idx": 177, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0c575bac-b8db-4ea8-abc2-0b512363d662": {"__data__": {"id_": "0c575bac-b8db-4ea8-abc2-0b512363d662", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3d5eafa6-af75-41f1-bf38-4d95162c8ebc", "node_type": null, "metadata": {}, "hash": "387f370518f9578b5577fd5c789ec83a33fb54df0ed709129ad66ed8961d9b3f"}}, "hash": "4b4a64dc8a85975c4108f20a7dda8d58781c0b9f4aa45baa1ba6613719b90e37", "text": "step2. docker \u30b3\u30f3\u30c6\u30ca\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\n\n\u30b3\u30f3\u30c6\u30ca\u5185\u306b\u5165\u3063\u3066\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u8d77\u52d5\n\n```\ndocker exec -it tetris-container bash\n```", "start_char_idx": 0, "end_char_idx": 91, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "47443c42-8394-4e1a-a1ae-748f0d235d1e": {"__data__": {"id_": "47443c42-8394-4e1a-a1ae-748f0d235d1e", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1ea78f64-0de3-408c-baed-69f05b82be79", "node_type": null, "metadata": {}, "hash": "ba686df37fe49864ae31026384c4d0965445c069ba021a79b095450318087696"}}, "hash": "d96501dbd486be8ad3cad4af032c12740ef5a49053a10a674468e4d2f9e8243a", "text": "step3. \u8d77\u52d5\u30db\u30b9\u30c8 OS \u5074\u3067 GUI \u8d77\u52d5\u306e\u305f\u3081\u306e\u6e96\u5099\n\ndocker \u30b3\u30f3\u30c6\u30ca\u5185\u3067\u30c6\u30c8\u30ea\u30b9\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c\u3059\u308b\u5834\u5408\u3001GUI \u3092\u8868\u793a\u3055\u305b\u308b\u305f\u3081\u306b\u306f\u30db\u30b9\u30c8 OS \u5074\u3067x \u30b5\u30fc\u30d0\u3092\u8d77\u52d5\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 \nx \u30b5\u30fc\u30d0\u306e\u8d77\u52d5\u65b9\u6cd5\u306f\u4ee5\u4e0b\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\n- Windows\u2192\u3053\u3061\u3089\n- Linux\u2192", "start_char_idx": 0, "end_char_idx": 154, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7e115d34-80b1-444b-9a9b-cc54eca947cc": {"__data__": {"id_": "7e115d34-80b1-444b-9a9b-cc54eca947cc", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ff8a7032-6fbd-4cd7-89d1-e7bdd1038adb", "node_type": null, "metadata": {}, "hash": "4cd77461e6d6a9838b892305385615c9fcee3fc56c1b6016b0b67adca2f573a9"}}, "hash": "b738435d6fc9e31364d471e8ff2a8bafd15a9b69ea200d81bcf4cc8eaab97bc1", "text": "step3'. GUI \u306a\u3057\u3067\u30c6\u30c8\u30ea\u30b9\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c\n\nGUI \u306e\u8d77\u52d5\u304c\u5fc5\u8981\u306a\u3044\u5834\u5408\u306b\u306f\u5148\u7a0b\u7acb\u3061\u4e0a\u3052\u305f\u30b3\u30f3\u30bd\u30fc\u30eb\u304b\u305f\u30b3\u30f3\u30c6\u30ca\u5185\u306e\u74b0\u5883\u5909\u6570`QT_QPA_PLATFORM`\u3092`offscreen`\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002\n\n```\nexport QT_QPA_PLATFORM=offscreen\n```", "start_char_idx": 0, "end_char_idx": 147, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "6055200f-29d8-477f-ac6b-e4bede10878a": {"__data__": {"id_": "6055200f-29d8-477f-ac6b-e4bede10878a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b643bfd3-6ec9-411d-a563-00ddadf3f830", "node_type": null, "metadata": {}, "hash": "b5a3c3f89c5ef669972135bf15b1729f1d0c4bcdfc3537916ba2b98f73c05cc1"}}, "hash": "0ef201675882819641d70ead25c0e2c5819c704f20f671ef2fd8811fba524df0", "text": "step4. \u30c6\u30c8\u30ea\u30b9\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u5b9f\u884c\n\n`/tetris`\u306b\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3001\u4ee5\u4e0b\u3092\u5b9f\u884c\n\n```\npython start.py\n```\n\n\u30c6\u30c8\u30ea\u30b9\u306e\u753b\u9762\u304c\u8868\u793a\u3055\u308c\u308c\u3070\u7121\u4e8b\u5b8c\u4e86\u3067\u3059\u3002", "start_char_idx": 0, "end_char_idx": 93, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "4cc49e02-40a2-48f8-88b6-76b37c9bdd81": {"__data__": {"id_": "4cc49e02-40a2-48f8-88b6-76b37c9bdd81", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b0f9fcc2-bf2b-4a6c-821c-83f09d8d90ed", "node_type": null, "metadata": {}, "hash": "394963175d4a7b49acc42265681581ef635ef0ab34624bc0f88e72d1b87b3c7f"}}, "hash": "fecbeea9ebef5034b05cc0dcf4f6c9683166d336dae96eaf23ed7ee2a3ddf85b", "text": "\u30b3\u30f3\u30c6\u30ca\u306e\u505c\u6b62\n\n\u30db\u30b9\u30c8 OS \u5074\u306e tetris \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304b\u3089\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```\ndocker-compose stop\n```\n\n\u3053\u306e\u5834\u5408\u306b\u306f\u3001\u30b3\u30f3\u30c6\u30ca\u306f\u524a\u9664\u3055\u308c\u305a\u306b\u6b8b\u308a\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 96, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f6db1301-4642-4407-b005-e75982801961": {"__data__": {"id_": "f6db1301-4642-4407-b005-e75982801961", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5130e796-5a64-40a5-a8c0-2a4354e451e6", "node_type": null, "metadata": {}, "hash": "9de430106dfa3a13e6f35b87a9675c9aa32b92859e7562acecd24bcab1f2e3e5"}}, "hash": "790ef6c1b1174ce6151e7ffbeddf87ca21d2ebc07ecb08b8bf13bc508004a687", "text": "\u30b3\u30f3\u30c6\u30ca\u306e\u505c\u6b62\u3001\u524a\u9664\n\n\u30db\u30b9\u30c8 OS \u5074\u306e tetris \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304b\u3089\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```\ndocker-compose down\n```\n\n\u3053\u306e\u5834\u5408\u306b\u306f\u3001\u30b3\u30f3\u30c6\u30ca\u306f\u524a\u9664\u3055\u308c\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 95, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3fc35171-bc33-4726-af97-d66dd2070531": {"__data__": {"id_": "3fc35171-bc33-4726-af97-d66dd2070531", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f4a606d9-2489-4aa9-b68e-f476ddf8d5d3", "node_type": null, "metadata": {}, "hash": "692564c7fd013e6b89db41ebd2b9167a1482ef8acb38c5690d85239771c770f7"}}, "hash": "2046c26e6b11bd474b56c0e8521c6c1de15b8614504cd1f7348df6e9a01ac006", "text": "\u30b2\u30fc\u30e0\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066", "start_char_idx": 0, "end_char_idx": 15, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3b9b8a00-cdac-42d3-b5d0-c5e7f87345a6": {"__data__": {"id_": "3b9b8a00-cdac-42d3-b5d0-c5e7f87345a6", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "28d7174d-852f-4886-8a8b-e63a916a144e", "node_type": null, "metadata": {}, "hash": "436781b55d4ca24a19cd6b402189dcad2bc5086192979209da398f69b9e3afd0"}}, "hash": "3a54543a7223236143c2d683c7944a75db0e01230c41f74d9721447511526cc0", "text": "start.py \u306e Option \u8a2d\u5b9a\n\n\u3053\u3053\u3067\u306f start.py \u3067\u6307\u5b9a\u3059\u308b option \u306b\u3064\u3044\u3066\u89e3\u8aac\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 58, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "5fbdf692-ad99-4031-a69d-798144197097": {"__data__": {"id_": "5fbdf692-ad99-4031-a69d-798144197097", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "35dc437a-7626-40c7-8567-2a5856790c16", "node_type": null, "metadata": {}, "hash": "e974fd9aeb55a5ca57e83eab85c6e05aa618f8d99f6e3340c3ed5d908442407a"}}, "hash": "e69258ecfd09dac9c43cd8d9feb273e76cecc7c2f5199a322de703c1a5574f99", "text": "-m --mode \u30e2\u30fc\u30c9\n default : \u30eb\u30fc\u30eb\u30d9\u30fc\u30b9\n train : AI\u5b66\u7fd2\n predict : AI \u63a8\u8ad6\uff08\u4e88\u6e2c\u5b9f\u884c)\n predict_sample : AI MLP \u63a8\u8ad6\uff08\u4e88\u6e2c\u5b9f\u884c)\n predict_sample2 : AI DQN \u63a8\u8ad6\uff08\u4e88\u6e2c\u5b9f\u884c)\n keyboard : \u30ad\u30fc\u30dc\u30fc\u30c9\u64cd\u4f5c\n gamepad : \u30b2\u30fc\u30e0\u30d1\u30c3\u30c9\u64cd\u4f5c", "start_char_idx": 0, "end_char_idx": 198, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9c38a9cf-92ca-492e-bf14-e332287c02ad": {"__data__": {"id_": "9c38a9cf-92ca-492e-bf14-e332287c02ad", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "48c58cf9-dd43-4193-aa02-f86073982a26", "node_type": null, "metadata": {}, "hash": "8576def2aef913753e3715ce87e695f6bc3ec6a6c5a2eba6c4a10cbb3202df5d"}}, "hash": "9ffe6f35a1b9b2e703ca1e1d77d7c8a0d56613dca63a169f4162a13f7cd6dc31", "text": "-l --game_level\u30ec\u30d9\u30eb\n 1 : Level 1 \u56fa\u5b9a\u30c6\u30c8\u30ea\u30df\u30ce\n 2 : Level 2 \u30e9\u30f3\u30c0\u30e0\u30c6\u30c8\u30ea\u30df\u30ce\n 3 : Level 3 \u521d\u671f\u30d6\u30ed\u30c3\u30af\u3042\u308a\u30c6\u30c8\u30ea\u30df\u30ce\n 4 : Level 4 \u521d\u671f\u30d6\u30ed\u30c3\u30af\u3042\u308a\u30c6\u30c8\u30ea\u30df\u30ce 0.001\u79d2\u66f4\u65b0", "start_char_idx": 0, "end_char_idx": 137, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2ef6c2b4-8a75-4214-97a7-ba78b4f7fcc8": {"__data__": {"id_": "2ef6c2b4-8a75-4214-97a7-ba78b4f7fcc8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "99a013c1-3b3a-4f1b-a656-9e249c618656", "node_type": null, "metadata": {}, "hash": "52075d3d991c611feb7cb648ef17f29f376b15979559fd031c74083a6817f44f"}}, "hash": "59774ca0333a29815c0500ccb4a66570c2e129e839d2306887245432cff15ed7", "text": "-d --drop_interval \u66f4\u65b0\u9593\u9694\n default => 1000 (=1\u79d2)\n ms \u6307\u5b9a\n \u203b \u30c6\u30c8\u30ea\u30df\u30ce\u3092 DROP \u3059\u308c\u3070 \u3053\u306e\u6642\u9593\u5f85\u3066\u3070\u6b21\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u304c\u51fa\u308b\u304c\u3001\u964d\u4e0b\u3060\u3051\u3055\u305b\u305f\u5834\u5408\u306f\u6b21\u306e\u64cd\u4f5c\u304c\u3067\u304d\u308b\u307e\u3067\u3053\u306e\u6642\u9593\u5206\u505c\u6b62\u3059\u308b\u3002\n \u203b Level 4\u6307\u5b9a\u3057\u305f\u5834\u5408\u306f\u7121\u52b9", "start_char_idx": 0, "end_char_idx": 162, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "5b87a716-610c-4a52-9706-58146e9b5f5b": {"__data__": {"id_": "5b87a716-610c-4a52-9706-58146e9b5f5b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "73b731ff-ee83-48d5-82c7-3ac9cc4dd38c", "node_type": null, "metadata": {}, "hash": "ea0391e57ceed0e109ab8437e3ca139d55023d1963d31fb1fc56cc9393377da6"}}, "hash": "72cea9ef365cce8844f23925e9263aba691ee1592b2a3092ef65194129ff3286", "text": "-t --game_time \u30b2\u30fc\u30e0\u6642\u9593\n default => 180\n \u79d2\u6307\u5b9a\n -1 \u3067\u5236\u9650\u306a\u3057 \u5b66\u7fd2\u6642\u306f -1 \u6307\u5b9a\u63a8\u5968\u3002", "start_char_idx": 0, "end_char_idx": 74, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1349852d-6cd2-4a66-b4a1-b258a4147807": {"__data__": {"id_": "1349852d-6cd2-4a66-b4a1-b258a4147807", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "584d5baf-1356-46c0-a46e-860aec0e8007", "node_type": null, "metadata": {}, "hash": "151b08f3d0949616a4f13c0aab8044612f90900d6677f570ca464c48a5aaed26"}}, "hash": "79253432ef348d97fb1b59affd83325efd91f9f64f72a0120b45a0747683a3c7", "text": "-r --random_seed \u4e71\u6570\u306e\u30bf\u30cd\n \u6574\u6570\u6307\u5b9a\u3002\u7279\u5225\u306b\u5fc5\u8981\u306a\u3051\u308c\u3070\u6307\u5b9a\u3057\u306a\u3044\u3002", "start_char_idx": 0, "end_char_idx": 47, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "019057ca-a153-4f2f-bd21-65fbb4dfcce6": {"__data__": {"id_": "019057ca-a153-4f2f-bd21-65fbb4dfcce6", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "571308b4-3cd1-47f6-85b4-faac592e0a46", "node_type": null, "metadata": {}, "hash": "cb0c8fb554b1f59b3239c019bd39c5fe8e93bc5ac8ada325506a1eb7245b8f74"}}, "hash": "4caa103d9b1b3626e3a831a4c023d45edd663a5f47d371fcb82ef1923cded9f9", "text": "-f --resultlogjson\n default => result.json\n \u7d50\u679c\u306e json \u30d5\u30a1\u30a4\u30eb\u6307\u5b9a", "start_char_idx": 0, "end_char_idx": 65, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "40d8ea74-d247-413f-9230-20c95127dcdb": {"__data__": {"id_": "40d8ea74-d247-413f-9230-20c95127dcdb", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "bdacaa2a-bd38-4606-bbef-cd07c48c7869", "node_type": null, "metadata": {}, "hash": "c87d98e80b3bd36c01ce9e9d3a42264becf8effe8908a9cfe6cb22aa92903f89"}}, "hash": "e8e10a221b20ed700cd1a9cdbcf531d67c95b6a798b7d21f385a2619016a24d3", "text": "--train_yaml\n default => config/default.yaml\n AI \u5b66\u7fd2\u63a8\u8ad6\u6642 (train, predict)\u306e\u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\u6307\u5b9a", "start_char_idx": 0, "end_char_idx": 86, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "71f70f07-2860-4a61-b3a1-372762fedfb8": {"__data__": {"id_": "71f70f07-2860-4a61-b3a1-372762fedfb8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "0d846675-3005-40e5-bc51-bba6923267dd", "node_type": null, "metadata": {}, "hash": "1c280be2e432823afb829681364800e722da61277af2f606bca09adb793ef042"}}, "hash": "82cac1b23006cb6cd3dac4e295cf5c5bcc74c79befc75c459bcf76f777729438", "text": "--predict_weight\n default => outputs/latest/best_weight.pt\n AI \u63a8\u8ad6\u6642 (predict) \u306e\u5b66\u7fd2\u7d50\u679c weight \u30d5\u30a1\u30a4\u30eb\u6307\u5b9a", "start_char_idx": 0, "end_char_idx": 102, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8285f94c-d182-4f5e-b8ea-8916ebc54544": {"__data__": {"id_": "8285f94c-d182-4f5e-b8ea-8916ebc54544", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e91a5a0e-ac47-48f6-bae9-5b7b2c6c39c6", "node_type": null, "metadata": {}, "hash": "73c9748471fb0f5e7760990af13556fc0f887c35f4e05dc9648db7707d468ade"}}, "hash": "90a35e151589b4b619ae11bda997e374c344c81df8d80aa9ed074a8a1c9674e1", "text": "Game_Manager Class\n\n\u3053\u3053\u3067\u306f game_manager.py \u306e Game_Manager Class \u3092\u89e3\u8aac\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 68, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0de6c6a7-1c6a-4840-8e96-e09d0d995e52": {"__data__": {"id_": "0de6c6a7-1c6a-4840-8e96-e09d0d995e52", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "555311ab-c836-4c20-a3dc-24efaf7ccace", "node_type": null, "metadata": {}, "hash": "8a859f5412a5231a1e885cad3c5db653d382ef0c3c5948f6833765b338a06067"}}, "hash": "912ffed6ebe37ad194228165c21edafffbe97bce9d78e6e600608f9185cf0d33", "text": "\u6982\u8981\n\n\u6700\u521d\u306b start.py \u304b\u3089\u547c\u3073\u51fa\u3055\u308c\u308b\u3002\u3053\u306e\u30b2\u30fc\u30e0\u306e\u57fa\u672c\u7684\u306a\u52d5\u4f5c\u306f\u3053\u306e\u30af\u30e9\u30b9\u304b\u3089\u5404\u30af\u30e9\u30b9\u3001\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u308b\u3002", "start_char_idx": 0, "end_char_idx": 71, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c180cbf8-8012-4f58-a408-4b6a6072477a": {"__data__": {"id_": "c180cbf8-8012-4f58-a408-4b6a6072477a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d3396fc0-63bc-4aa5-95f9-6cd3ea4d174d", "node_type": null, "metadata": {}, "hash": "d7f1b32fda2f36456e99ab1cb83800698f862b727afbe378638703f9601646c7"}}, "hash": "a5c0a53a1a6ba27033cf61a3663b7e0810cfe4496ceec05a39a7598a6db0ff2c", "text": "\u521d\u671f\u5316\n\n\\_\\_init\\_\\_ \u306b\u3066 Option \u3084\u521d\u671f\u8a2d\u5b9a\u5024\u3092\u5404\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5909\u6570\u306b\u683c\u7d0d\u3059\u308b\u3002\n\\_\\_init\\_\\_ \u306b\u3042\u308b initUI \u30e1\u30bd\u30c3\u30c9\u306b\u3066\u3001Window \u751f\u6210\u3001\u4f4d\u7f6e\u8a2d\u5b9a\u8868\u793a\u304c\u884c\u308f\u308c\u308b\u3002\u3053\u3053\u3067 SidePanel Class, Board Class \u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3082\u751f\u6210\u3055\u308c\u308b\u3002\ninitUI \u306e\u4e2d\u306e start \u30e1\u30bd\u30c3\u30c9\u3067\u3001\u30b9\u30b3\u30a2\u3001\u753b\u9762\u30dc\u30fc\u30c9\u3001\u4e88\u544a\u30c6\u30c8\u30ea\u30df\u30ce\u3001\u30b9\u30c6\u30fc\u30bf\u30b9\u30d0\u30fc\u304c\u30af\u30ea\u30a2\u3055\u308c\u308b\u3002\u307e\u305f\u3001\u3053\u306e\u4e2d\u3067 timerEvent \u3082\u751f\u6210\u3055\u308c\u308b\u3002timerEvent \u306e\u9593\u9694\u306f\u4e0a\u8a18\u306e drop_interval \u3068\u306a\u308b\u3002\u3053\u308c\u306b\u3088\u308a\u6b21\u306e\u64cd\u4f5c\u304c\u53ef\u80fd\u3068\u306a\u308b\u3002", "start_char_idx": 0, "end_char_idx": 287, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e3ba026b-5908-4c73-9833-67cb82a75746": {"__data__": {"id_": "e3ba026b-5908-4c73-9833-67cb82a75746", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "91fa9261-3de4-42a5-b5b6-d2559d0006c2", "node_type": null, "metadata": {}, "hash": "386477466fb7f3e20a3d5559e4439afecd6eee65919c43b1750f1c02cff41097"}}, "hash": "984c1d4840d965ae471bfd5828b4464c370a94599e9e7e45bf6a37c9d770508c", "text": "timerEvent method\n\n\u4e0a\u8a18\u306e\u521d\u671f\u5316\u306b\u3088\u308a timerEvent \u304c drop_interval \u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u3066\u6307\u5b9a\u3055\u308c\u305f\u9593\u9694\u3067\u5b9f\u884c\u3055\u308c\u308b\u3002\n\u3053\u306e Event \u306b\u3088\u308a\u30b2\u30fc\u30e0\u304c\u9032\u884c\u3057\u3066\u3044\u304f\u3002\n\u306a\u304a\u3001timerEvent \u30e1\u30bd\u30c3\u30c9\u5b9f\u884c\u4e2d\u306b drop_interval \u6307\u5b9a\u306e\u6642\u9593\u3092\u8d85\u3048\u3066\u3082\u3001\u6b21\u306e timerEvent \u306f\u767a\u751f\u3057\u306a\u3044\u3002\n\u3088\u3063\u3066\u3001drop_interval \u304c\u975e\u5e38\u306b\u77ed\u3044 (1ms \u306a\u3069 = Level 4) \u306e\u5834\u5408\u306f\u3001timerEvent \u306e\u30ed\u30b9\u304c\u591a\u767a\u3059\u308b\u3002\n\u305d\u3046\u3044\u3063\u305f\u30eb\u30fc\u30eb\u306e\u5834\u5408\u306f\u3044\u304b\u306b\u65e9\u304f timerEvent \u3092\u7d42\u4e86\u3055\u305b\u308b\u304b\u304c\u809d\u3068\u306a\u308b\u3002\n\ntimerEvent \u306b\u304a\u3044\u3066\u5404 mode \u306b\u5bfe\u5fdc\u3059\u308b block_controller.py (or block_controller_train*.py) \u306e GetNextMove \u304c\u547c\u3073\u51fa\u3055\u308c\u308b\u3002\n\u3053\u3053\u3067\u3001GameStatus \u304c GetNextMove \u306b\u5f15\u304d\u6e21\u3055\u308c\u3001\u5404\u81ea\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u6307\u793a\u3057\u305f\u64cd\u4f5c\u7d50\u679c\u304c nextMove \u5909\u6570\u306b\u683c\u7d0d\u3055\u308c\u308b\u3002\n\n\u3053\u306e\u7d50\u679c\u3092\u3082\u3063\u3066\u3001\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u30db\u30fc\u30eb\u30c9\u3001\u6a2a\u79fb\u52d5\u3001\u56de\u8ee2\u3001\u843d\u4e0b\u3001\u964d\u4e0b\u51e6\u7406\u3092\u884c\u3046\u3002\n\n\u30db\u30fc\u30eb\u30c9\u306f\u30db\u30fc\u30eb\u30c9\u753b\u9762\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u3068\u306e\u5165\u308c\u66ff\u3048\u3092\u884c\u3046\u3002\u305f\u3060\u3057\u3001\u30db\u30fc\u30eb\u30c9\u753b\u9762\u306b\u4f55\u3082\u306a\u3044\u5834\u5408\u306f\u30db\u30fc\u30eb\u30c9\u753b\u9762\u306b\u73fe\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u5165\u308c\u3066 timerEvent \u3092\u7d42\u4e86\u3059\u308b\u3002\n\n\u6a2a\u79fb\u52d5\u306f\u753b\u9762\u30dc\u30fc\u30c9\u306e\u7aef\u3084\u30c6\u30c8\u30ea\u30df\u30ce\u306b\u3076\u3064\u304b\u308b\u3068\u505c\u6b62\u3059\u308b\u3002\n\n\u56de\u8ee2\u306f**\u53f3\u56de\u8ee2** (rotateRight)\u3067\u884c\u3044\u3001\u753b\u9762\u30dc\u30fc\u30c9\u306e\u7aef\u3084\u30c6\u30c8\u30ea\u30df\u30ce\u306b\u3076\u3064\u304b\u308b\u3068\u30a8\u30e9\u30fc\u3068\u306a\u308atimerEvent\u7121\u52b9\u3068\u306a\u308b\u306e\u3067\u8981\u6ce8\u610f\u3067\u3042\u308b\u3002\n\n\u964d\u4e0b (move down) \u51e6\u7406\u306e\u5834\u5408\u306f\u6307\u5b9a\u6570\u5206\u964d\u4e0b\u3055\u305b\u308b\u3002\n\n\u843d\u4e0b (drop down) \u51e6\u7406\u304c\u884c\u308f\u308c\u308b\u3068\u3001\u305f\u3060\u3061\u306b\u30c6\u30c8\u30ea\u30df\u30ce\u306f\u4e00\u756a\u4f4e\u3044\u3068\u3053\u308d\u307e\u3067\u843d\u3068\u3055\u308c\u3001\u30c6\u30c8\u30ea\u30df\u30ce\u306f\u56fa\u5b9a\u3055\u308c\u308b\u3002\n\n\u3044\u305a\u308c\u306b\u3057\u3066\u3082\u30c6\u30c8\u30ea\u30df\u30ce\u306e\u4e0b\u5074\u304c\u753b\u9762\u30dc\u30fc\u30c9\u4e0b\u9650\u304b\u30c6\u30c8\u30ea\u30df\u30ce\u306b\u5f53\u305f\u3063\u3066\u3044\u308b\u5834\u5408\u306f\u3001\u6d88\u53bb\u30e9\u30a4\u30f3\u6570\u8a08\u7b97(moveDown)\u3068\u30b9\u30b3\u30a2\u8a08\u7b97(UpdateScore)\u304c\u5b9f\u65bd\u3055\u308c\u3001\u6b21\u306e timerEvent \u3067\u306f\u6b21\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u304c\u51fa\u308b\u3002\n\u306a\u304a\u3001\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u306b\u306a\u308c\u3070\u30b2\u30fc\u30e0\u30aa\u30fc\u30d0\u30fc\u5206\u306e\u30b9\u30b3\u30a2\u3092\u6e1b\u3089\u3055\u308c\u308c\u3001\u753b\u9762\u30dc\u30fc\u30c9\u3092\u30af\u30ea\u30a2\u3057\u3066\u6b21\u306e timerEvent \u3068\u306a\u308b\u3002", "start_char_idx": 0, "end_char_idx": 963, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "885c5a0b-2e15-4c7d-9257-d9a01c458eba": {"__data__": {"id_": "885c5a0b-2e15-4c7d-9257-d9a01c458eba", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c2c25875-bcce-49a6-accd-80125c88ea8e", "node_type": null, "metadata": {}, "hash": "50d866b2f168b7330dd786a7a79263de8163254ff8bc6a307922d1aab06d3400"}}, "hash": "97a737ba3656432616e13de13a03bbb7a690c09d918bffc2b96839a68b063ec1", "text": "keyPressEvent method\n\n\u4e0b\u8a18\u30ad\u30fc\u64cd\u4f5c\u306b\u3088\u308a Event \u304c\u767a\u751f\u3057\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u64cd\u4f5c\u3059\u308b\u3002\n\u306a\u304a\u3001GetNextMove \u3067\u6307\u5b9a\u3059\u308b\u5834\u5408\u3068\u3053\u3068\u306a\u308a**\u5de6\u56de\u8ee2** (rotateLeft) \u3067\u3042\u308b\u70b9\u306b\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3042\u308b\u3002\n\n| \u624b\u52d5\u64cd\u4f5c | PC\u64cd\u4f5c\u6e96\u62e0 | \u30b2\u30fc\u30e0\u6a5f\u30b3\u30f3\u30c8\u30ed\u30fc\u30e9\u6e96\u62e0 |\n| ---- | ---- | ---- |\n| \u5b9f\u884c\u30b3\u30de\u30f3\u30c9 | python start.py -m keyboard | python start.py -m gamepad |\n| *up* key | **\u5de6\u56de\u8ee2** | \u843d\u4e0b |\n| *left* key | \u5de6\u306b\u79fb\u52d5 | \u5de6\u306b\u79fb\u52d5 |\n| *right* key | \u53f3\u306b\u79fb\u52d5 | \u53f3\u306b\u79fb\u52d5 |\n| *m* key | \u964d\u4e0b\uff08\u4e0b\u306b\u79fb\u52d5\uff09 | \u964d\u4e0b\uff08\u4e0b\u306b\u79fb\u52d5\uff09 |\n| *space* key | \u843d\u4e0b | **\u5de6\u56de\u8ee2** |\n| *P* key | Pause | Pause |\n| *c* key | hold | hold |", "start_char_idx": 0, "end_char_idx": 504, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "961b4e50-41d2-4353-9e78-885db2028a62": {"__data__": {"id_": "961b4e50-41d2-4353-9e78-885db2028a62", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "76a37b38-3207-45cd-bbda-1c8f78765275", "node_type": null, "metadata": {}, "hash": "b8c0ec821bdfdd47bb2200959ad5544de2db65560f7c72f1e99962416ae4f8b8"}}, "hash": "388638d7b5e393c5e4c51247dd0c8d4ae6dab36c4d29b93ef874acb4db0321fa", "text": "SidePanel Class\n\n\u3053\u3053\u3067\u306f game_manager.py \u306e SidePanel Class \u3092\u89e3\u8aac\u3059\uff52\u3002\n\u6a2a\u306e\u4e88\u544a\u30c6\u30c8\u30ea\u30df\u30ce\u3001\u304a\u3088\u3073\u30db\u30fc\u30eb\u30c9\u30c6\u30c8\u30ea\u30df\u30ce\u63cf\u753b\u753b\u9762 Class \u3067\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 100, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a7d5cd60-d35b-43c4-ad2d-469147d85e1d": {"__data__": {"id_": "a7d5cd60-d35b-43c4-ad2d-469147d85e1d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "50e94990-40c3-4321-9cfc-6aacfb48bc5d", "node_type": null, "metadata": {}, "hash": "63c74f5ccf5a989c745c97dfef5753563e6135ee36026ab512bf0c83a7fe62a1"}}, "hash": "356827cdad01a03c5f6b899e48c1bee7f904d834945125a68e86e8590ce11ea1", "text": "paintEvent method\n\n\u3053\u3053\u3067\u306f\u4e0a\u8a18\u306e\u3088\u3046\u306b 4\u3064\u306e\u4e88\u544a\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u63cf\u753b\u3059\u308b\u3002\n\u307e\u305f\u3001\u4e0b\u306b\u30db\u30fc\u30eb\u30c9\u753b\u9762\u304c\u3042\u308a\u305d\u3053\u3067\u30db\u30fc\u30eb\u30c9\u3057\u305f\u30c6\u30c8\u30ea\u30df\u30ce\u3082\u8868\u793a\u3055\u308c\u308b\u3002\ntimerEvent \u3084 keyEvent \u306a\u3069\u306b\u3088\u308a\u547c\u3073\u51fa\u3055\u308c\u63cf\u753b\u3055\u308c\u308b\u3002", "start_char_idx": 0, "end_char_idx": 121, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7b7444e8-5e2c-4d2b-8d41-a5c7180e5747": {"__data__": {"id_": "7b7444e8-5e2c-4d2b-8d41-a5c7180e5747", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3b40b0c5-d27e-4717-9765-79254f86ca99", "node_type": null, "metadata": {}, "hash": "65254f68fafb7b3b397001f40c7b86718cb587bdda79595bea6153a304be1211"}}, "hash": "f8da3f3ba7f5d2b5da8518df0ed65182d70ef12c951850af040822babe179a16", "text": "Board Class\n\n\u3053\u3053\u3067\u306f game_manager.py \u306e Board Class \u3092\u89e3\u8aac\u3059\u308b\u3002\n\u30b2\u30fc\u30e0\u306e\u753b\u9762\u30dc\u30fc\u30c9\u304a\u3088\u3073\u30b9\u30c6\u30fc\u30bf\u30b9\u30d0\u30fc\u306e Class \u3067\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 86, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3454cfe3-51a9-40f2-b7d4-28efeb36b30b": {"__data__": {"id_": "3454cfe3-51a9-40f2-b7d4-28efeb36b30b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5a6656fd-78b4-4668-8c2f-a0181814e07d", "node_type": null, "metadata": {}, "hash": "0cd97796966adaea953b44d15be79cb5a12e08303a9f3825dc759e94403d0d8d"}}, "hash": "93167526363687976cde22385a8dc41913cd2c2c88d87f209c37ef7b961df762", "text": "paintEvent method\n\n\u3053\u3053\u3067\u306f\u7e2622,\u6a2a10\u306e\u753b\u9762\u30dc\u30fc\u30c9\u3092\u63cf\u753b\u3059\u308b\u3002\n\u56fa\u5b9a\u3055\u308c\u305f\u30c6\u30c8\u30ea\u30df\u30ce\u304a\u3088\u3073\u52d5\u4f5c\u4e2d\u306e\u30c6\u30c8\u30ea\u30df\u30ce\u3092\u63cf\u753b\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 71, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "34002b91-014c-4697-8737-4a15527847f5": {"__data__": {"id_": "34002b91-014c-4697-8737-4a15527847f5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8a0351b2-2b88-45ec-a4ef-77fa0ca33582", "node_type": null, "metadata": {}, "hash": "c47f4e9765b4c9fad64533acb94ae51e3f8bf2d141d1c826c3685847bf4cad72"}}, "hash": "76965c537be7710c56db298719a65c71d9bf7b9fe88c46a1ae0c6335b315c888", "text": "updateData method\n\n\u30b9\u30c6\u30fc\u30bf\u30b9\u30d0\u30fc\u306e\u6587\u5b57\u306e\u66f4\u65b0\u3092\u884c\u3046\u3002\n\u307e\u305f\u3001 game_time \u7d4c\u904e\u3001\u3082\u3057\u304f\u306f\u30d6\u30ed\u30c3\u30af\u6570\u4e0a\u9650\u306b\u9054\u3057\u305f\u3089\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u7d42\u4e86\u3057\u3001Result \u3092\u8868\u793a\u3059\u308b\u3002\n\u5b66\u7fd2\u30e2\u30fc\u30c9\u3067 game_time \u3092 -1 \u306b\u3057\u3066\u3044\u306a\u3044\u3068\u3001\u3053\u3053\u3067\u7d42\u4e86\u3057\u3066\u3057\u307e\u3046\u3002", "start_char_idx": 0, "end_char_idx": 135, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3b6bef66-ce49-4c09-8fc7-14270f1a72db": {"__data__": {"id_": "3b6bef66-ce49-4c09-8fc7-14270f1a72db", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "2f1409cd-fc75-4462-be08-151a3d7f807b", "node_type": null, "metadata": {}, "hash": "39167b4a75b43775f38ddf33831aade18a1358c4c031ca090ec82a8dceaf3861"}}, "hash": "5a0648e28d9c725f9ca795f1b10536262bbe3dd73c2d4d727011eecd32f0c9dc", "text": "\u5b9f\u884c\u74b0\u5883(Mac\u306e\u5834\u5408)\n\nFinder\u2192Application\u2192Utility\u2192Terminal\u304b\u3089\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u753b\u9762\u3092\u8d77\u52d5\u3057\u3066\u4ee5\u4e0b\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```", "start_char_idx": 0, "end_char_idx": 81, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2c2f9276-e991-4f8c-8ce0-bd87ea6d42bf": {"__data__": {"id_": "2c2f9276-e991-4f8c-8ce0-bd87ea6d42bf", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "382c1175-574c-4da1-b8ba-e48946e55529", "node_type": null, "metadata": {}, "hash": "190877e6fa5e2d4a8c66cc81bf257cbd5df843bb71a7bd29c615c68d97297da8"}}, "hash": "dc20036dbae8d15e80e0c18c834bf8e150741e5353264f73b394d9fedc5ef1a5", "text": "install pyqt5 and NumPy\nbrew install python3\nbrew install pyqt5\nbrew install numpy\n#pip3 install pyqt5\n#pip3 install numpy", "start_char_idx": 0, "end_char_idx": 122, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c58d2cf1-6713-4d9a-9d37-6afa53baa3fd": {"__data__": {"id_": "c58d2cf1-6713-4d9a-9d37-6afa53baa3fd", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "94c1534c-c2b4-48d4-9678-9c717953ff17", "node_type": null, "metadata": {}, "hash": "1e03a03190de3114e69abf6e9d02f97ba00d5bf743cd2f73d37a9d45c086c5a4"}}, "hash": "4d8c0a7f2c875242c3a5ecfac4c2d769ca0061d108afdecb533046959b95c7d6", "text": "install other packages\nbrew install git\n```\n\nMac(M1)\u74b0\u5883\u7b49\u3067\u306f`pip3 install pyqt5`\u3067\u30a8\u30e9\u30fc\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308b\u3002`brew`\u3092\u4f7f\u3046\u3053\u3068\u3067\u89e3\u6c7a\u3059\u308b\u3053\u3068\u304c\u3042\u308b\u3002 \nMac(M1)\u306bpip3 install pyqt5\u3067pyqt5\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3088\u3046\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u30a8\u30e9\u30fc\u306b\u306a\u308b\uff08Preparing metadata (pyproject.toml) ... error\uff09 \n\n```\nbrew install pyqt5\n```\n\n`pytorch`\u3092\u4f7f\u3046\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308c\u3070OK \nMacBook Pro (Apple Silicon, M1 PRO, 2021) \u3067Python\u958b\u767a\u74b0\u5883\u3092\u6574\u3048\u308b #49 \nAI\u306b\u3064\u3044\u3066 \n\n```", "start_char_idx": 0, "end_char_idx": 355, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "48bf9617-e70d-4329-bdd5-b63b261e0103": {"__data__": {"id_": "48bf9617-e70d-4329-bdd5-b63b261e0103", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1095fffc-e023-454c-88da-e8c24bc28e82", "node_type": null, "metadata": {}, "hash": "3d22a333d758164bf30058bd301d9dfbfd0487d43064b2ebabc63f88e5837ee8"}}, "hash": "328db0d9d404bde5ad0368e647dcd812c57259316ba7d8b2e467c009aca263dc", "text": "install\npip3 install -U pip\npip3 install torch\npip3 install tensorboardX\npython3 -c \"import torch\"", "start_char_idx": 0, "end_char_idx": 98, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cd8cbd8b-e585-4ef7-b12b-413e6b598c04": {"__data__": {"id_": "cd8cbd8b-e585-4ef7-b12b-413e6b598c04", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3bcfa11a-3d3d-4fff-bd31-a9cec14dbe9c", "node_type": null, "metadata": {}, "hash": "3c1a1dc3f0a8082e0d0dc9cb7a09c06d31b9ea6cfe106dda7df5a34a5c962e5d"}}, "hash": "1bf9298b663b38208b3e05df4a829b8affda9e1e5390d8754cc7c1b944cd297c", "text": "\u5b9f\u884c\u4f8b (\u8a73\u7d30\u306f\"AI\u306b\u3064\u3044\u3066\"\u30ea\u30f3\u30af\u53c2\u7167)\npython3 start.py -m train_sample -d 1 -l 2 -t -1\npython3 start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt\n```\n\n\u3082\u3057\u4ed6\u306b\u3082\u8db3\u308a\u306a\u3044\u3053\u3068\u304c\u3042\u308c\u3070`pull request`\u9802\u3051\u308b\u3068\u52a9\u304b\u308a\u307e\u3059", "start_char_idx": 0, "end_char_idx": 200, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2842b791-dc81-4391-ae53-183965f45efb": {"__data__": {"id_": "2842b791-dc81-4391-ae53-183965f45efb", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "0bc53677-2697-47b6-800f-89ff1da2f2f7", "node_type": null, "metadata": {}, "hash": "40565796c4ba0333fc06f00cc4e2755a9feea3980dadc5a93a3fdd4fa0f3cbde"}}, "hash": "e680d408d057b2a19030bf0e39b7136e5bc6f031659d7cfd4b2c8b24c1ad5aa0", "text": "\u5b9f\u884c\u74b0\u5883(Ubuntu/JetsonNano\u306e\u5834\u5408)\n\nNeed python3, PyQt5 and NumPy to be installed.\n\n```", "start_char_idx": 0, "end_char_idx": 79, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "73ce240b-3c09-48de-99f4-0194dc1faaf2": {"__data__": {"id_": "73ce240b-3c09-48de-99f4-0194dc1faaf2", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f4f00ff3-56fa-442d-8ef2-9c4ca4736376", "node_type": null, "metadata": {}, "hash": "a7c589c43a0bdc6c4fc8b343bfa65bf936099277a9889a18e2231ce8dd56a244"}}, "hash": "e09733aaa7039860844afdc7410f0756be2f827a60cf2c91227c7621fd433e49", "text": "install pyqt5 and NumPy\nsudo apt-get install -y python3-pip\npip3 install --upgrade pip\npip3 install numpy\npip3 install pyqt5\n```\n\n\u305d\u306e\u4ed6\u3001\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\n```\nsudo apt-get install -y git\n```\n\nubuntu20.04LTS\u4ee5\u964d\u306e\u5834\u5408\u306f`python`\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u3046\u305f\u3081\u306b\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\n\n```\nsudo apt install python-is-python3\n```\n\n\u305d\u306e\u5f8c\u306f\u3001\u5b9f\u884c\u65b9\u6cd5\u3092\u53c2\u7167", "start_char_idx": 0, "end_char_idx": 288, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "6d444950-9e43-4284-9d89-6948cad48e74": {"__data__": {"id_": "6d444950-9e43-4284-9d89-6948cad48e74", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ab94c84e-faa3-46ad-b0cd-8954bc2ef260", "node_type": null, "metadata": {}, "hash": "6c836a899825d710e64c161858bbeed147fcc10edd93df6f301e87a7386e89ef"}}, "hash": "140784974be86dcafe455bccd67282877b8a9e45ef62f4a1925d87acf9e6603c", "text": "\u5b9f\u884c\u74b0\u5883(windows\u306e\u5834\u5408)\ndocker for windows\u3092\u4f7f\u3044\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 41, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c672c616-984e-4cb1-8d30-e8a926d7fe1c": {"__data__": {"id_": "c672c616-984e-4cb1-8d30-e8a926d7fe1c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "50f7803c-7efb-45d5-ba28-635155c5fc4f", "node_type": null, "metadata": {}, "hash": "c32124e42306b479c58f93cd249571547c7f57f6aa3461f59770d2148741ccee"}}, "hash": "ebe9abe690e7835f4c81ef9305242b8be7e44ce31b0dd809a1b6eba07684bbfe", "text": "step0. DockerDesktop\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\n\u4ee5\u4e0b\u3092\u53c2\u7167\u3059\u308b\u3002\nDockerDeskop \nWindows10\u7528Windows Subsystem for Linux\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb \u30ac\u30a4\u30c9\n\n\u4ee5\u4e0b\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6642\u306e\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u3092\u307e\u3068\u3081\u307e\u3057\u305f\u3002\nFAQ/Windows Docker install\u6642\u306e\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0", "start_char_idx": 0, "end_char_idx": 172, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "45b837a5-3b51-4f6f-9806-ce2f512426fa": {"__data__": {"id_": "45b837a5-3b51-4f6f-9806-ce2f512426fa", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6ae5d33d-30b7-47d2-b945-6f71791af405", "node_type": null, "metadata": {}, "hash": "5fb4b05fb5c177c4266ce99da61b24787713d13344421a910b913994c4920cdf"}}, "hash": "6a2c6277756d6ad487a94c7b07d8b4dc41b245236df6727bcf6573af9cc26abc", "text": "step1. \u30d1\u30ef\u30fc\u30b7\u30a7\u30eb\u4e0a\u3067\u30b3\u30f3\u30c6\u30ca\u3092\u8d77\u52d5\u3059\u308b
\n\n`Windows\u30dc\u30bf\u30f3`\u304b\u3089`Windows PowerShell`\u3092\u8d77\u52d5\u3057\u3066terminal\u304b\u3089\u4ee5\u4e0b\u5b9f\u884c\u3059\u308b\u3002\n\u30b3\u30f3\u30c6\u30ca\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u301c\u8d77\u52d5\u304c\u5b8c\u4e86\u3059\u308b\u307e\u3067\u5c11\u3057\u5f85\u3064\u3002\n\n```\ndocker run -p 6080:80 --shm-size=512m seigott/tetris_docker\n```", "start_char_idx": 0, "end_char_idx": 182, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ab3b4ded-a0e5-4d00-94d1-2fcee1efeba3": {"__data__": {"id_": "ab3b4ded-a0e5-4d00-94d1-2fcee1efeba3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6daf950b-9c88-438f-9757-2b6c544d0b04", "node_type": null, "metadata": {}, "hash": "b88df71729a9135a15a20309a4f2e41acc9ef6df6143964460c284a4df46564f"}}, "hash": "25f0bb6a25512fe8916b3f508895152c9bea3d55f5c06520ee07eafb23b50e3e", "text": "step2. \u30d6\u30e9\u30a6\u30b6\u304b\u3089docker\u30b3\u30f3\u30c6\u30ca\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\n\n```\nlocalhost:6080\n```\n\n\u30a2\u30af\u30bb\u30b9\u3067\u304d\u305f\u3089\u4ee5\u4e0b\u306b\u3088\u308a\u52d5\u4f5c\u691c\u8a3c\u3059\u308b\n\n\u5de6\u4e0b\u30a2\u30a4\u30b3\u30f3 --> system tools --> terminator\n\nTerminal\u3092\u7acb\u3061\u4e0a\u3052\u3066\u4ee5\u4e0b\u3092\u5b9f\u884c\n\n```\ncd ~/tetris\npython start.py\n```\n\n\u6ce8\u610f\uff09\u30d1\u30ef\u30fc\u30b7\u30a7\u30eb\u3092\u9589\u3058\u305f\u308a\u96fb\u6e90OFF\u3059\u308b\u3068\u30b3\u30f3\u30c6\u30ca\u3082\u7d42\u4e86\u3057\u307e\u3059\u3002\u4f5c\u6210\u4e2d\u30c7\u30fc\u30bf\u304c\u5931\u308f\u308c\u306a\u3044\u3088\u3046\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 231, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "62b01dae-a988-438c-8405-cc7b9e34f01c": {"__data__": {"id_": "62b01dae-a988-438c-8405-cc7b9e34f01c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3c6dcf35-7412-4f68-ac74-adde2f7b5134", "node_type": null, "metadata": {}, "hash": "643df0902ea9fb53fb774bfdf0834a49d0f53805255f3a70ca6c1d7146825f8c"}}, "hash": "37344f24a53a946a08064ded28187875a7e98ad7a558612dc1807c3bce492535", "text": "windows\u306epowershell\u3092\u4f7f\u3063\u3066\u30c6\u30c8\u30ea\u30b9\u74b0\u5883\u3092\u69cb\u7bc9\u3059\u308b", "start_char_idx": 0, "end_char_idx": 33, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "63c48eac-6dd9-46ff-a6f2-e00f5dcb0e7f": {"__data__": {"id_": "63c48eac-6dd9-46ff-a6f2-e00f5dcb0e7f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dd994aa9-ffd5-4886-a1df-5f8a21286da6", "node_type": null, "metadata": {}, "hash": "c56e32afdbce0051efe0c49418b208c7d80f4f169f72661d7c798914b1cdec32"}}, "hash": "8441a8d5e8398af5bd25d1cf1a9843e0fd1d7e308d257707212bd3649e69d9ce", "text": "powershell\u3068\u306f\n\nwindows\u3067\u5229\u7528\u3067\u304d\u308b\u3001\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3002 \n\u300cWindows\u300d\uff0b\u300cS\u300d\u3067\u691c\u7d22\u30dc\u30c3\u30af\u30b9\"powershell\"\u3068\u5165\u529b\u3059\u308b\u3068\u898b\u3064\u304b\u308b\u306f\u305a\u3002 \n> Windows10 - PowerShell(\u30d1\u30ef\u30fc\u30b7\u30a7\u30eb)\u306e\u958b\u304d\u65b9", "start_char_idx": 0, "end_char_idx": 131, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "3f83359c-54f8-428b-b1e4-a1fb21c29b8c": {"__data__": {"id_": "3f83359c-54f8-428b-b1e4-a1fb21c29b8c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "296249e0-01bf-4dec-9622-c0850f83ecc5", "node_type": null, "metadata": {}, "hash": "dc451067fc3b63bb5c4429a5f0ba7585ba902e6226336813d8a9406718b5eaa8"}}, "hash": "4cfda5373bbe89057acabcd4da7b57fe4d720b4451a84e5d25d24c01d8d02925", "text": "1. git \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb", "start_char_idx": 0, "end_char_idx": 14, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e7b19daa-0975-4df6-a2d9-19f1be4fed5c": {"__data__": {"id_": "e7b19daa-0975-4df6-a2d9-19f1be4fed5c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a262d057-8d77-42c5-9a89-3188d79b9893", "node_type": null, "metadata": {}, "hash": "1fb07f9757df1b1257ea036faff1c428968608880ce7f2a4dfe7fb71f58429d5"}}, "hash": "00b5dd0d972cb82621cf386820eeceed93416307b7b97ba64854531138d882f7", "text": "Windows Package Manager(winget)\u304c\u3042\u308b\u5834\u5408\n\nWindows Package Manager(winget)\u304c\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u3002 \npowershell\u3092\u8d77\u52d5\u3057\u3066\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308c\u3070\u78ba\u8a8d\u3067\u304d\u308b\u3002\n\n```\nwinget --version", "start_char_idx": 0, "end_char_idx": 134, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "027179e2-6c33-43b0-b609-f76b6c776cd2": {"__data__": {"id_": "027179e2-6c33-43b0-b609-f76b6c776cd2", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1ebb5f71-2f5d-4611-a975-b1d8c32fe040", "node_type": null, "metadata": {}, "hash": "43db77903747859536097668d1c1995d0ec1409c87fb3c422bb403898d39cfc1"}}, "hash": "e99e3905966b0a38f226e12f555d83356f6e7a24fa9fdc5e36c529914d36c461", "text": "v1.1.13405 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070\u3001Windows Package Manager(winget)\u304c\u3042\u308b\n```\n\ngit\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\n\n```\nwinget install Git.Git", "start_char_idx": 0, "end_char_idx": 101, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "227654cd-983d-4662-8ffd-2b2c2f3929a3": {"__data__": {"id_": "227654cd-983d-4662-8ffd-2b2c2f3929a3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "94b20e90-7916-40ef-8e41-9632f6844787", "node_type": null, "metadata": {}, "hash": "9edacd202d223b477110b8086a527c52cc9cb32ec1789a43a03bf9be80c442d9"}}, "hash": "2bc06ac3794b63f8e4e21341f2225b076a2bc0e024a9cb017ac86223f22bb0ca", "text": "\u3082\u3057 y/n \u3068\u51fa\u308b\u5834\u5408\u306f\u3001y \u3068\u5165\u529b\u3059\u308b\u3002y=yes \u3068\u3044\u3046\u610f\u5473\u3067\u3042\u308b\u3002\n```\n\n\u65b0\u3057\u304fpowershell\u3092\u8d77\u52d5\u3057\u3001\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066\u7d50\u679c\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```\ngit --version", "start_char_idx": 0, "end_char_idx": 98, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ca018018-d46c-4cc7-815c-7ed899383cc8": {"__data__": {"id_": "ca018018-d46c-4cc7-815c-7ed899383cc8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "821040d3-9adc-47af-9e8f-95caa036491d", "node_type": null, "metadata": {}, "hash": "d201c4cf4e56ea80fa69e2c228d3e2f3c6ae09ff0abe89aff3396d57769e7d38"}}, "hash": "1313c2fe247f0c5c70d8084edd6ae889895438bdfd0c73a958719929fe4835cf", "text": "ex.) git version 2.34.1.windows.1 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\n```", "start_char_idx": 0, "end_char_idx": 63, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7d9ff17a-3463-4125-a2f7-06aadcd0e118": {"__data__": {"id_": "7d9ff17a-3463-4125-a2f7-06aadcd0e118", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "303c678b-4232-47a9-9455-d9391baaaa49", "node_type": null, "metadata": {}, "hash": "2b0d2ec8df52c26fba3a84de55b8eb1e4fdcffa76c2a82f451a38124ece6fa63"}}, "hash": "7bc11db3254a4efa6e3e5a7d478c49886330e0c384b4de97cc6b8b2020a6fa61", "text": "\u4e0a\u8a18\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u3067\u304d\u306a\u3044\u5834\u5408\n\n\u4e0a\u8a18\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001\u516c\u5f0f\u30b5\u30a4\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30e9\u30fc\u3092\u4f7f\u3046\u3002\u4ee5\u4e0b\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u3002\n\n> git Downloading Git \n> 64-bit Git for Windows Setup \n\n\u4ee5\u4e0b\u306e\u300cGit\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u300d\u90e8\u5206\u8a18\u8f09\u306e\u901a\u308a\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u9032\u3081\u308b\u3002\u4ee5\u4e0b\u306e\u8a18\u8f09\u90e8\u5206\u306f\u9078\u629e\u304c\u5fc5\u8981\u3002\n\n> Windows 10 \u3068 Powershell\uff08WSL\u542b\u3080\uff09 \u3067 git \u3092\u5229\u7528\u3059\u308b \n> https://qiita.com/kerobot/items/78372640127771f92ee0#git-%E3%81%AE%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB \n> > \u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3084PowerShell\u306a\u3069\u304b\u3089Git\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5229\u7528\u3059\u308b\u305f\u3081\u300cGit from the command line and also from 3rd-party software\u300d\u3092\u9078\u629e\u3057\u307e\u3059\u3002\n\npowershell\u4e0a\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066\u7d50\u679c\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```\ngit --version", "start_char_idx": 0, "end_char_idx": 506, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "a9cdbcc2-2f08-45ad-9dbe-141195027deb": {"__data__": {"id_": "a9cdbcc2-2f08-45ad-9dbe-141195027deb", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f4a19dd-5982-4ac6-be64-54dff7cbb176", "node_type": null, "metadata": {}, "hash": "d201c4cf4e56ea80fa69e2c228d3e2f3c6ae09ff0abe89aff3396d57769e7d38"}}, "hash": "1313c2fe247f0c5c70d8084edd6ae889895438bdfd0c73a958719929fe4835cf", "text": "ex.) git version 2.34.1.windows.1 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\n```", "start_char_idx": 0, "end_char_idx": 63, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f33c9b34-17e2-471c-8474-f72b8cb2cae8": {"__data__": {"id_": "f33c9b34-17e2-471c-8474-f72b8cb2cae8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8799e86a-5519-4697-b866-797972f40d5c", "node_type": null, "metadata": {}, "hash": "e3691977971ec47d1372acabe081b5f23931600f4a681cf7883da8fdc4256218"}}, "hash": "956320325aeb4663f661beff8755bc087288dcb84f4f5ede8fad78ccb6a43e39", "text": "2. python \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb", "start_char_idx": 0, "end_char_idx": 17, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2285791b-bcd9-4594-8a59-e3111636859e": {"__data__": {"id_": "2285791b-bcd9-4594-8a59-e3111636859e", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c465083c-dfcb-443d-a011-e05a7d8b6572", "node_type": null, "metadata": {}, "hash": "438301f0a5de1c82e38f1faefcf57880c223c01b9c77c48220438a35fd74807d"}}, "hash": "570e0f507de8db1971df21a9079398e8280a7f3668c497e19bba9491e1338313", "text": "microsoft store\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\n\npowershell\u4e0a\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c\u3001\u3082\u3057\u304f\u306f`microsoft store`\u4e0a\u3067`python`\u3068\u691c\u7d22\u3057\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30da\u30fc\u30b8\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\n\n```\npython\n```\n\n!Screenshot\n\n\u65b0\u3057\u304fpowershell\u3092\u8d77\u52d5\u3057\u3001\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066\u7d50\u679c\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```\npython --version", "start_char_idx": 0, "end_char_idx": 190, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "228c06bb-f645-4014-b659-023b13b7411f": {"__data__": {"id_": "228c06bb-f645-4014-b659-023b13b7411f", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eaf7d3e7-bad4-4848-af23-c50cafbb009f", "node_type": null, "metadata": {}, "hash": "09be80cb540eef7ca96b755561871e9a7a8b81253392b64367c79f48b96ce6d2"}}, "hash": "aebc1515f35d4bcae6ed6d2e9dfb435f300912336a204c9ee3e27cec33b1822c", "text": "ex.) Python 3.10.9 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\n```", "start_char_idx": 0, "end_char_idx": 48, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "51d4d8a9-2dc7-41a8-b8e1-69681f8e3f98": {"__data__": {"id_": "51d4d8a9-2dc7-41a8-b8e1-69681f8e3f98", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cf26ff9b-e2cd-47b3-9f91-ba621cc1b131", "node_type": null, "metadata": {}, "hash": "2ef79b0640443d4aff8fe72d4846d00b6788a39314839c1ecc7030e5f5ccdb6f"}}, "hash": "345c88546d27466248ff5d4cecbfaa45efbf7d0d9cc1287757f2da0b29dcbdd8", "text": "\u53e4\u3044\u30d0\u30fc\u30b8\u30e7\u30f3\u306epython\u304c\u8868\u793a\u3055\u308c\u308b\u5834\u5408\n\n\u904e\u53bb\u306b\u53e4\u3044\u30d0\u30fc\u30b8\u30e7\u30f3\u306epython\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3044\u308b\u5834\u5408\u306b\u305d\u3061\u3089\u304c\u512a\u5148\u3055\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002 \n\u300cWindows\u300d\uff0b\u300cpython\u300d\u3067\u691c\u7d22\u30dc\u30c3\u30af\u30b9\u304b\u3089\u53e4\u3044python\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u30a2\u30f3\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u518d\u5ea6\u65b0\u3057\u3044python\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3068\u89e3\u6c7a\u3059\u308b\u3053\u3068\u304c\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 156, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "660c68c6-21fc-4598-a6ea-c3f54b19cabf": {"__data__": {"id_": "660c68c6-21fc-4598-a6ea-c3f54b19cabf", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7797fb4b-430f-4807-8b24-883e270a3a3d", "node_type": null, "metadata": {}, "hash": "45b68b8d29a16e508cf2a74b52f555c73599b751cfb78f92ef8acfae8665137b"}}, "hash": "136bcf5341cd5c7b81262521c42d85b5dbb9fe0f1e31b5d645c02d6704fab62e", "text": "\u4e0a\u8a18\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u3067\u304d\u306a\u3044\u5834\u5408\n\n\u4e0a\u8a18\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001\u516c\u5f0f\u30b5\u30a4\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30e9\u30fc\u3092\u4f7f\u3046\u3002 \n\u4ee5\u4e0b\u8a18\u4e8b\u3092\u53c2\u7167\u3057\u3001\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\uff5ePowerShell\u306e\u74b0\u5883\u8a2d\u5b9a\u307e\u3067\u3092\u5b9f\u65bd\u3059\u308b\n\n> Windows\u7248Python\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb \n\n2023.1\u6642\u70b9\u3067\u306fpython\u306e\u30d0\u30fc\u30b8\u30e7\u30f33.10.9\u304c\u5229\u7528\u3057\u6613\u305d\u3046\u3067\u3042\u308b\u3002 \n\u3053\u306e\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u30ea\u30f3\u30af\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30e9(`.exe`)\u3092\u53d6\u5f97\u3059\u308b\u3068\u3088\u3044\u3002 \n\n> \u975e\u516c\u5f0fPython\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u30ea\u30f3\u30af \n\n\u7279\u306b\u4ee5\u4e0b\u306e\u70b9\u3092\u5fd8\u308c\u305a\u306b\u5b9f\u65bd\u3059\u308b\u3002\n\n> > \"Add Python 3.x to PATH\" \u3092\u30c1\u30a7\u30c3\u30af\u3059\u308b\u306e\u3092\u5fd8\u308c\u306a\u3044\u3088\u3046\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \n> > \u3082\u3057\u5fd8\u308c\u305f\u3089\u3001\u3042\u308f\u3066\u305a\u843d\u3061\u7740\u3044\u3066\u3082\u3046\u4e00\u5ea6\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u3087\u3046\u3002\u4f55\u5ea6\u3067\u3082\u7e70\u308a\u8fd4\u3057\u3066\u5927\u4e08\u592b\u3067\u3059\u3002 \n>\n> > PowerShell\u306e\u74b0\u5883\u8a2d\u5b9a\u00b6 \n> > PowerShell\u3067\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u3092\u8a31\u53ef\u3057\u3066\u304a\u304d\u307e\u3059\u3002 \n> > \u30b9\u30bf\u30fc\u30c8\u30e1\u30cb\u30e5\u30fc\u3067 Windows PowerShell | Windows PowerShell \u3092\u8d77\u52d5\u3057\u3001\u6b21\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002 \n> Set-ExecutionPolicy RemoteSigned -Scope CurrentUser -Force \n\npowershell\u4e0a\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066\u7d50\u679c\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```\npython --version", "start_char_idx": 0, "end_char_idx": 621, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1036624e-f299-4634-8358-ea02d80ebfdb": {"__data__": {"id_": "1036624e-f299-4634-8358-ea02d80ebfdb", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "904a3afc-d116-4c75-b30c-bb2c37e0725b", "node_type": null, "metadata": {}, "hash": "09be80cb540eef7ca96b755561871e9a7a8b81253392b64367c79f48b96ce6d2"}}, "hash": "aebc1515f35d4bcae6ed6d2e9dfb435f300912336a204c9ee3e27cec33b1822c", "text": "ex.) Python 3.10.9 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\n```", "start_char_idx": 0, "end_char_idx": 48, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "efbff208-3cc2-4169-adc8-1aadfd164458": {"__data__": {"id_": "efbff208-3cc2-4169-adc8-1aadfd164458", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "417aec6d-529e-492c-b069-156861142815", "node_type": null, "metadata": {}, "hash": "94e7bc6a56a4fae3e93bf41b287bc12e55c40c0040fa87c8ec585616b48c92cf"}}, "hash": "88e3fd6b0693a0c1d0960ea6b3b539d83d607dede9baf249cfa878c12e74366d", "text": "3. powershell\u304b\u3089tetris\u3092\u5b9f\u884c\u3059\u308b\n\npowershell\u3092\u65b0\u305f\u306b\u8d77\u52d5\u3059\u308b \n(\u300cWindows\u300d\uff0b\u300cS\u300d\u3067\u691c\u7d22\u30dc\u30c3\u30af\u30b9\"powershell\"\u3068\u5165\u529b\u3059\u308b\u3068\u898b\u3064\u304b\u308b\u306f\u305a\u3002\u898b\u3064\u304b\u3063\u305f\u3089\u30af\u30ea\u30c3\u30af\u3057\u3066\u8d77\u52d5\u3059\u308b\u3002) \n \n \n\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u78ba\u8a8d\u3059\u308b \n\u3053\u3053\u3067\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u4f55\u3082\u8868\u793a\u3055\u308c\u306a\u3051\u308c\u3070\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u6210\u529f\u3057\u3066\u3044\u306a\u3044\u305f\u3081\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304b\u3089\u3084\u308a\u76f4\u3059\n \n```\ngit --version", "start_char_idx": 0, "end_char_idx": 204, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "bcd5627d-3241-4022-9659-0a39c43d1017": {"__data__": {"id_": "bcd5627d-3241-4022-9659-0a39c43d1017", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1a731606-d439-4f76-9e83-c40360894e19", "node_type": null, "metadata": {}, "hash": "c1f9d57760632d0cc0459dd38b2ef1edcd511c377f723b6c6727c90b6fbb3b15"}}, "hash": "4be28e68dc2760ca5d43a658b65bed02930220ae9fc40a32efa33ce16de3ca8f", "text": "ex.) git version 2.34.1.windows.1 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\npython --version", "start_char_idx": 0, "end_char_idx": 76, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2d5d975a-c3d1-4c28-ba1f-ff97bb1fc7c2": {"__data__": {"id_": "2d5d975a-c3d1-4c28-ba1f-ff97bb1fc7c2", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5ad3f174-df8b-4315-941d-b50415aaeb77", "node_type": null, "metadata": {}, "hash": "9243df71043f44d89c45b5e1add7a4a7cdc0be88f2c911e78f813237eda9fb6d"}}, "hash": "8416d8fc9f7d92e6e72092668ef800504400b1a53d2af0ac04081bd6f0b9fa1f", "text": "ex.) Python 3.10.9 \u7b49\u3068\u51fa\u529b\u3055\u308c\u308c\u3070OK\uff08\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308c\u3070OK\uff09\n```\n\n\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\n\n```\npip install pyqt5\npip install numpy\n```\n\n\u4ee5\u4e0b\u306e\u901a\u308a\u5b9f\u884c\u3057\u3001tetris\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```\ncd ~\nmkdir work\ncd work\ngit clone https://github.com/seigot/tetris\ncd tetris\npython start.py # \u3053\u3053\u3067tetris\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n```\n\n\u30d7\u30ed\u30b0\u30e9\u30e0\u5185\u90e8\u306e\u8aac\u660e\u306b\u3064\u3044\u3066\u306f\u4ee5\u4e0b\u306a\u3069\u53c2\u7167\u304f\u3060\u3055\u3044\n\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066 \nAI\u306b\u3064\u3044\u3066 \ntetris art\u306b\u3064\u3044\u3066 \nREADME.md", "start_char_idx": 0, "end_char_idx": 349, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9dea7fe1-20f0-4f2b-9256-e5ccf033967c": {"__data__": {"id_": "9dea7fe1-20f0-4f2b-9256-e5ccf033967c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6373be1e-2941-4482-8d7e-feb5316a160c", "node_type": null, "metadata": {}, "hash": "a89425e1aa78f9cbbab47f9836bffb41635ac31ad51614c6ddc10687b2d3097f"}}, "hash": "f90c40e646fcd1f097be27b4b07a6646cd7620afbb9d3aba4e521adee16ca3e3", "text": "option. \u30a8\u30c7\u30a3\u30bf\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\n\u6700\u8fd1\u6d41\u884c\u308a\u306e\u30a8\u30c7\u30a3\u30bf\u3067\u3042\u308b`visual studio code`\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304a\u304f\u3068\u5f8c\u306e\u30b3\u30fc\u30c9\u7de8\u96c6\u306b\u4fbf\u5229\u3067\u3042\u308b \npowershell\u4e0a\u3067winget\u304c\u4f7f\u3048\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u3092\u5b9f\u884c\u3001 \n\n```", "start_char_idx": 0, "end_char_idx": 120, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "22b13e1d-dad8-43f3-ad56-a2ce9d179da3": {"__data__": {"id_": "22b13e1d-dad8-43f3-ad56-a2ce9d179da3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6541688c-1f56-4cde-a319-67a1c85e6e02", "node_type": null, "metadata": {}, "hash": "b6727b3753593e72745f06e4ec678c08316c66e008e270245c1bb9fa05a5498b"}}, "hash": "d6da92870fc8ff054401a976d5c6b5d8c0d009822a8fd05b1ceadb1b6e815695", "text": "powershell\u4e0a\u3067winget\u304c\u4f7f\u3048\u308b\u5834\u5408\nwinget install vscode\n```\n\n\u3082\u3057\u304f\u306f`microsoft store`\u4e0a\u3067`visual studio code`\u3068\u691c\u7d22\u3057\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30da\u30fc\u30b8\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\n\n!Screenshot\n\npowershell\u4e0a\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066`vscode`\u3068\u5bfe\u8c61\u306e\u30b3\u30fc\u30c9\u304c\u8868\u793a\u3055\u308c\u308c\u3070OK\n\n```", "start_char_idx": 0, "end_char_idx": 183, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9cab7fb6-4610-46eb-bd11-bb7ec3eede56": {"__data__": {"id_": "9cab7fb6-4610-46eb-bd11-bb7ec3eede56", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1bddf82c-eeeb-459c-a298-bff48a82a43e", "node_type": null, "metadata": {}, "hash": "cbb9e5b289b9dd32896328034c5d836c22e1253f562d5bc775cfea136d9c2663"}}, "hash": "bc03fb5dd36377d630137aacad76fc72051c95f78d29fabde620cfbd4dcea3c9", "text": "cd ~/work/tetris \u7b49\u3067\u4e8b\u524d\u306bclone\u3057\u305f\u5834\u6240\u306b\u79fb\u52d5\u3057\u3066\u304a\u304f\ncode game_manager/block_controller.py\n```", "start_char_idx": 0, "end_char_idx": 80, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "30108c3b-c684-4ff2-964f-5b5eae299db8": {"__data__": {"id_": "30108c3b-c684-4ff2-964f-5b5eae299db8", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e86b2357-ca10-46cf-89a0-65ec5b942f8d", "node_type": null, "metadata": {}, "hash": "755daf4c2eb87432e307a14b69bafac63c7ed5e59a5036701754f2dae7738024"}}, "hash": "4dcb357af645d66da606163f4f0a5908ccf648192e7aca85694b6cc41618b541", "text": "`python start.py`\u5b9f\u884c\u5f8c\u306btetris\u304c\u8868\u793a\u3055\u308c\u306a\u3044\u5834\u5408\n\n\u5b9f\u884c\u74b0\u5883\u306e\u9055\u3044\u306b\u3088\u308a`python start.py`\u5b9f\u884c\u5f8c\u306btetris\u304c\u8868\u793a\u3055\u308c\u306a\u3044\u3053\u3068\u304c\u305f\u307e\u306b\u3042\u308b\u3002 \n\u3053\u306e\u5834\u5408\u306fwarning\u3084\u30a8\u30e9\u30fc\u30ed\u30b0\u304c\u51fa\u529b\u3055\u308c\u308b\u306f\u305a\u306a\u306e\u3067\u3001\u4ee5\u4e0b\u306e\u5b9f\u884c\u6210\u529f\u6642\u306e\u30ed\u30b0\u3068\u6bd4\u8f03\u3059\u308b\u3068\u89e3\u6c7a\u306b\u8fd1\u3065\u304f\u3068\u601d\u308f\u308c\u308b\u3002\n\n```", "start_char_idx": 0, "end_char_idx": 158, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d4901a71-eb19-4e06-8bce-ca8451429be1": {"__data__": {"id_": "d4901a71-eb19-4e06-8bce-ca8451429be1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b30bdd19-acfe-4cb4-a6a5-20d7759e8ec9", "node_type": null, "metadata": {}, "hash": "499914babf22067be95b1eff5257762c0192fcd9b6ec1fc86c7904a86c7d42cf"}}, "hash": "caf8814f9c52a2b4963de330b49dad4a6404c6f52ebc7e4bce15ac06ce5effff", "text": "\uff08\u4f8b\uff09\u5b9f\u884c\u6210\u529f\u6642\u306e\u30ed\u30b0\n$ python start.py\ngame_level: 1\ngame_time: 180\nRANDOM_SEED: 0\nIS_MODE :default\nOBSTACLE_HEIGHT: 0\nOBSTACLE_PROBABILITY: 0\nUSER_NAME: window_sample\nSHAPE_LIST_MAX: 6\nBLOCK_NUM_MAX: -1\nRESULT_LOG_JSON: result.json\nTRAIN_YAML: config/default.yaml\nPREDICT_WEIGHT: outputs/latest/best_weight.pt\nPython 3.7.1\nCompletedProcess(args='python --version', returncode=0, stderr='')\n=================================================>\n...(\u4ee5\u964d\u3001tetris\u30c7\u30d0\u30c3\u30b0\u60c5\u5831\u304c\u8868\u793a\u3055\u308c\u308b)\n```", "start_char_idx": 0, "end_char_idx": 463, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "10bcde16-c549-4dc8-b38c-13526db9e2ec": {"__data__": {"id_": "10bcde16-c549-4dc8-b38c-13526db9e2ec", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "5fe4e490-a421-426d-9c76-f34a4859f9d1", "node_type": null, "metadata": {}, "hash": "daddee51f66d041a845aef95d718e1967c326490ee76d534258686754da04dad"}}, "hash": "f9df382ee871a5f60820f657d4535b0f0ec0139c40a7c88df5260b80d56ffb15", "text": "\u53c2\u8003\ngit Downloading Git \nWindows 10 \u3068 Powershell\uff08WSL\u542b\u3080\uff09 \u3067 git \u3092\u5229\u7528\u3059\u308b \nWindows\u7248Python\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb \n\u975e\u516c\u5f0fPython\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u30ea\u30f3\u30af \nWindowsPowerShell\u3067Python\u3092\u5229\u7528\u3059\u308b \nGNU Emacs for Windows\u518d\u5165\u9580 \nvisual studio code\u3092install \nWindows\u306bBash\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u65b9\u6cd5 \nWindows Subsystem for Linux\u304c\u8868\u793a\u3055\u308c\u306a\u3044", "start_char_idx": 0, "end_char_idx": 263, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "bbe0cfa1-9266-4314-8cb6-65172eead885": {"__data__": {"id_": "bbe0cfa1-9266-4314-8cb6-65172eead885", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4901c299-983a-4a81-b4dc-a4e3726c79de", "node_type": null, "metadata": {}, "hash": "0a8fa1726a6728138449f1b5f92dc175f052e38ffc65289440212cfca1542941"}}, "hash": "e91d2b2711f6254b31cb4a6e1407c0a9d1ca9d751978103daf8edd5e60f9c971", "text": "\u5b9f\u884c\u74b0\u5883 (Windows WSL\u3092\u4f7f\u3046\u5834\u5408)\n\nWindows WSL\u3092\u4f7f\u3063\u3066\u958b\u767a\u74b0\u5883\u3092\u7acb\u3061\u4e0a\u3052\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3059\u308b\u3002\nDocker\u3092\u4f7f\u3046\u65b9\u6cd5\u3068\u6bd4\u8f03\u3057\u3066\u3001Docker\u304c\u4e0d\u8981\u306a\u70b9\u3068\u3001Docker\u30b3\u30f3\u30c6\u30ca\u7d42\u4e86\u3067\u30c7\u30fc\u30bf\u304c\u5931\u308f\u308c\u308b\u3053\u3068\u3092\u6c17\u306b\u3059\u308b\u5fc5\u8981\u304c\u7121\u3044\u7279\u9577\u304c\u3042\u308b\u3002", "start_char_idx": 0, "end_char_idx": 128, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "69b8f82f-50a6-4828-8968-c1466952a5cd": {"__data__": {"id_": "69b8f82f-50a6-4828-8968-c1466952a5cd", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "59eb9170-d56e-4e4e-8bad-c988419e986b", "node_type": null, "metadata": {}, "hash": "6cd5956a8d42ec18af4e27085a6ed72ec96ce0645d352055ed3692dc9fad6415"}}, "hash": "1a71b16d44a105575f51837b07eb4d77ea7542a6ad04a0b9c090c0e36289efc0", "text": "Step 1. WSL\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\n- \u4ee5\u4e0b\u3092\u53c2\u7167\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\n - Windows10\u7528Windows Subsystem for Linux\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb \u30ac\u30a4\u30c9", "start_char_idx": 0, "end_char_idx": 91, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e831b8c2-60e6-405d-87f7-121747e5b6d7": {"__data__": {"id_": "e831b8c2-60e6-405d-87f7-121747e5b6d7", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6840ec80-7677-4911-a635-7a9f0ed9bc7e", "node_type": null, "metadata": {}, "hash": "e9aafba21a0bf8c547619f9ee4f3c6e52adfce4dde03ab6361af4b8bff690e82"}}, "hash": "06a076492234559fb9690284e9f3e7801584e107e8d88428beab300e52763f64", "text": "a. WSL1\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\uff08\u7c21\u5358\uff09\n\n- \u4e0a\u8a18URL\u306e\u300c\u624b\u52d5\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306e\u624b\u9806\u300d\u306e\u300c\u624b\u9806 1 - Linux \u7528 Windows \u30b5\u30d6\u30b7\u30b9\u30c6\u30e0\u3092\u6709\u52b9\u306b\u3059\u308b\u300d\u300c\u624b\u9806 6 - \u9078\u629e\u3057\u305f Linux \u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u300d\u306e\u307f\u5b9f\u65bd\u3059\u308c\u3070\u826f\u3044\u3002\n- \u300c\u624b\u9806 6 - \u9078\u629e\u3057\u305f Linux \u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u300d\u3067\u306f\u3001Ubuntu 18.04 LTS \u307e\u305f\u306f Ubuntu 20.04 LTS \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308c\u3070\u826f\u3044\u3002", "start_char_idx": 0, "end_char_idx": 230, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "de710693-42f0-4cef-acc8-412ce43ecf84": {"__data__": {"id_": "de710693-42f0-4cef-acc8-412ce43ecf84", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7375eebb-d5e6-4a0b-a8d0-4024d9f0118b", "node_type": null, "metadata": {}, "hash": "6c4f27aa26a936aa149543a418728edcaf41c394ec3a45d2c2c4c21f2aa896e6"}}, "hash": "533ea28c1b9e14af0becf2c0333a2c3a80a27934cda76efad2f2fd461e9b23b8", "text": "b. a\u4ee5\u5916\u306e\u5834\u5408\n\n- WSL1\u3067\u3082WSL2\u3067\u3082\u826f\u3044\u3002\n- Linux\u306e\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u306f\u3069\u308c\u3067\u3082\u826f\u3044\u306f\u305a\u3067\u3042\u308b\u304c\u3001Ubuntu 18.04 LTS \u307e\u305f\u306f\u3000Ubuntu 20.04 LTS \u3092\u304a\u3059\u3059\u3081\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 108, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "727acea5-6cc9-4807-b68d-cc9314661756": {"__data__": {"id_": "727acea5-6cc9-4807-b68d-cc9314661756", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "bbcc61c2-bb80-4a95-b584-56fca2fd0ddc", "node_type": null, "metadata": {}, "hash": "47b14bdeb884bfbb566cc80a59f56edd021afb9a379c52912c8ece10d46cef69"}}, "hash": "994b625fe99c68dfbffb6165d6ab2ee81acd1dde2605855a3292f5d0832dcf91", "text": "Step 2. X\u30b5\u30fc\u30d0\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\nGWSL\u307e\u305f\u306fVcXsrv\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 44, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ba7d40e1-7292-480b-818c-c15a39187f6c": {"__data__": {"id_": "ba7d40e1-7292-480b-818c-c15a39187f6c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "daa4a211-9852-48df-b162-dd67e4ba0d52", "node_type": null, "metadata": {}, "hash": "8baee7da534327a7509bba53913a5260837453edd8045efc9ba95b48ce649e57"}}, "hash": "bbc772222d3b489fdb25e2f7f8548f1198c9e4350d7f421e19c54f85e3298bc9", "text": "a. GWSL\u3092\u4f7f\u3046\u5834\u5408\uff08\u304a\u3059\u3059\u3081\uff09\n\n1. \u30b9\u30bf\u30fc\u30c8\u30e1\u30cb\u30e5\u30fc\u304b\u3089Microsoft Store\u30a2\u30d7\u30ea\u3092\u8d77\u52d5\u3059\u308b\u3002\n2. GWSL\u3092\u691c\u7d22\u3057\u3066\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 80, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f8c4152e-64b9-4488-b0f1-093f8bd58377": {"__data__": {"id_": "f8c4152e-64b9-4488-b0f1-093f8bd58377", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3b3a2d21-adb6-4092-a532-63108fb15859", "node_type": null, "metadata": {}, "hash": "a6c62e7dcc30e22a0112bda5171989e7d9162ed5d45cac639916888fce5cd421"}}, "hash": "b68d725964c8ceb32217e7635110fdd8b55fb8bbd2c09dab6d0f36e1ff7daca1", "text": "b. VcXsrv\u3092\u4f7f\u3046\u5834\u5408\n\n1. https://sourceforge.net/projects/vcxsrv/ \u304b\u3089VcXsrv\u3092\u5165\u624b\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 82, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d83b7aea-8abd-462f-b03b-f68ebd2bb9e4": {"__data__": {"id_": "d83b7aea-8abd-462f-b03b-f68ebd2bb9e4", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d3282272-5bd9-45cc-bc61-31ffea91d07f", "node_type": null, "metadata": {}, "hash": "e39ce59a931b48a6ba6a728b439164b23b7f3fd1604ca89c8037b4dc755f8b71"}}, "hash": "80a8d42a9a91a75d18a0a27cc73784d710336122993337a6aeafac42cfa7b2e4", "text": "Step 3. WSL\u306e\u8d77\u52d5", "start_char_idx": 0, "end_char_idx": 14, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9e0fad21-aa28-466a-9c2f-38571c907bbc": {"__data__": {"id_": "9e0fad21-aa28-466a-9c2f-38571c907bbc", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "16e5a958-c32d-4137-b409-d59e67c521b3", "node_type": null, "metadata": {}, "hash": "4f85715b1e81521dd8758c8855888ed518c66b5a7c1cbd0eca9e8804184c8c02"}}, "hash": "30cd5e72953564de5d57b0e65cd0dcc2cb3ae7183351c33c67825bd9009c6649", "text": "\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u958b\u304f\n\n- \u30b9\u30bf\u30fc\u30c8\u30e1\u30cb\u30e5\u30fc\u304b\u3089\u3001Step 1\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fLinux \u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3(Ubuntu 18.04 LTS \u306a\u3069)\u3092\u8d77\u52d5\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 82, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cdad89c0-2a46-4638-a097-bc4323c75f5d": {"__data__": {"id_": "cdad89c0-2a46-4638-a097-bc4323c75f5d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f8ee7337-e899-408b-b0e7-376c08a81c9f", "node_type": null, "metadata": {}, "hash": "73e573bf989ce33b8170255cea2dd9f5b2c72f1b8171c31e793fe7b32d62a043"}}, "hash": "572fd1f9c541efbc0f48ae9cae08caefb07817886a57f95325280a3fe50e6225", "text": "\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30fb\u8a2d\u5b9a\uff08\u521d\u56de\u306e\u307f\u5fc5\u8981\uff09\n- \u4e0b\u8a18\u3092\u5b9f\u884c\u3059\u308b\u3002\n\n```\nsudo apt-get install -y python3-pip\nsudo apt-get install -y python3-pyqt5\npip3 install --upgrade pip\npip3 install numpy\nsudo apt-get install -y git\n\necho 'export DISPLAY=localhost:0.0' >> ~/.bashrc\nsource ~/.bashrc\n```", "start_char_idx": 0, "end_char_idx": 261, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "667ae363-e738-48ce-80e9-03ee7261b5ce": {"__data__": {"id_": "667ae363-e738-48ce-80e9-03ee7261b5ce", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d22bfe9b-4a60-4262-8009-0f953b68f228", "node_type": null, "metadata": {}, "hash": "522faa52f3c2c5c7384334deeb728098e08d6c3fa36f2adfbf34535955903f43"}}, "hash": "e988d16fea2f492f1ee9a2e8f338634e8176dfa6c18954f0d5ec6202fca8027d", "text": "Step 4. X\u30b5\u30fc\u30d0\u306e\u8d77\u52d5\n\nStep 2 \u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fX\u30b5\u30fc\u30d0\u3092\u8d77\u52d5\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 43, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "b380cdaa-9db0-4777-8838-a810de25f3e3": {"__data__": {"id_": "b380cdaa-9db0-4777-8838-a810de25f3e3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "82c7c5f2-14c0-4ea1-96e0-55d24815a934", "node_type": null, "metadata": {}, "hash": "fa37d63b1ca55f0d16cf399d9b6fc1d15d83693d09379312d2e58011ebf60a3e"}}, "hash": "0b926c1ce06879262363c9af32679d89e1665700b0a9767658dfa633b02eb90e", "text": "a. GWSL\u3092\u4f7f\u3046\u5834\u5408\n\n1. \u30b9\u30bf\u30fc\u30c8\u30e1\u30cb\u30e5\u30fc\u304b\u3089GWSL\u3092\u8d77\u52d5\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 37, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7c86d2b4-553e-494a-9634-90ac4128bbb3": {"__data__": {"id_": "7c86d2b4-553e-494a-9634-90ac4128bbb3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c1b73fca-d756-461d-a74b-52bb8dcb596f", "node_type": null, "metadata": {}, "hash": "8da5d1ff8dba815062cf3149f9303064a0cf2a38bf742e6e2d0a6703773d81b6"}}, "hash": "f8dd9534c4ed192f5ebb16b17009bd7b75a872a53cea7ed06a829aae2c769b11", "text": "b. VcXsrv\u3092\u4f7f\u3046\u5834\u5408\n\n1. \u30b9\u30bf\u30fc\u30c8\u30e1\u30cb\u30e5\u30fc\u3084\u30c7\u30b9\u30af\u30c8\u30c3\u30d7\u306e\u30b7\u30e7\u30fc\u30c8\u30ab\u30c3\u30c8\u304b\u3089VcXsrv\u3092\u8d77\u52d5\u3059\u308b\u3002\n2. \u300c\u6b21\u3078\u300d\u30923\u56de\u62bc\u3057\u3066\u3001\u300c\u5b8c\u4e86\u300d\u3092\u62bc\u3059\u3068X\u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3059\u308b\u3002", "start_char_idx": 0, "end_char_idx": 89, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e4f631ce-d334-40a6-a3ca-be65bf27de24": {"__data__": {"id_": "e4f631ce-d334-40a6-a3ca-be65bf27de24", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "29cc3093-4a3e-47b6-b979-10f0b68c7536", "node_type": null, "metadata": {}, "hash": "bcdd7c74099d267463c8b6c1d4d2dbbd63e58f1444cb0910d3476db27bd2314f"}}, "hash": "74594b8a935849394f1b34c1ab86ed44696bb2e02a3d3df4c9343f8fdc168ec5", "text": "Step 5. tetris\u306e\u8d77\u52d5", "start_char_idx": 0, "end_char_idx": 17, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "81674c4f-60e0-4f23-86bb-333d830f42e3": {"__data__": {"id_": "81674c4f-60e0-4f23-86bb-333d830f42e3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cc0fc37b-8587-4386-9d6b-38919ef43c54", "node_type": null, "metadata": {}, "hash": "81d5cfbb884b0a84e0ec4e24d93585c756da11803416e9bcd7370b9c7ceaa308"}}, "hash": "688e499cc7f32d7887525f3014eb66bd16afb4b0f70d955d374260423646de38", "text": "\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3092\u53d6\u5f97\u3059\u308b\uff08\u521d\u56de\u306e\u307f\u5fc5\u8981\uff09\n\n```\ngit clone https://github.com/seigot/tetris\n```", "start_char_idx": 0, "end_char_idx": 71, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "180ad649-c7c7-4415-aee5-031c9a29b264": {"__data__": {"id_": "180ad649-c7c7-4415-aee5-031c9a29b264", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6b4993ef-0255-4ba9-a433-ff91419b52c6", "node_type": null, "metadata": {}, "hash": "c967ce45493587ad0df9356570e782c11c6a0cc4349a0e0fa5110b9e45ef1f6f"}}, "hash": "bb349e876cf3c3a4b001ab9e6279ab13a77759a8de4c444e14fc25904a7cafc0", "text": "tetris\u3092\u5b9f\u884c\u3059\u308b\n```\ncd ~/tetris\npython start.py\n```\n\n\u4e00\u5ea6\u5b9f\u884c\u74b0\u5883\u3092\u7acb\u3061\u4e0a\u3052\u305f\u5f8c\u306e2\u56de\u76ee\u304b\u3089\u306f\u3001Step 3\u4ee5\u964d\u3092\u5b9f\u884c\u3059\u308c\u3070\u826f\u3044\u3002", "start_char_idx": 0, "end_char_idx": 87, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9451cf88-0557-48bb-8a7c-dcc314a20424": {"__data__": {"id_": "9451cf88-0557-48bb-8a7c-dcc314a20424", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "94748ba8-d795-48ad-a384-2ab41d37d2c6", "node_type": null, "metadata": {}, "hash": "5d278beb44f92177de05dd8af0e48432d5299b01d0b31eed92939e3b05cc2e21"}}, "hash": "bf2fcdfcbdaa8ec2382572a65d0ca22e05f730aab22ba8a29337397df34163a5", "text": "\u53c2\u8003\u30b9\u30b3\u30a2\n\n\uff08\u6ce8\uff09\u4e71\u6570\u306e\u5f71\u97ff\u306b\u3088\u308a\u591a\u5c11\u306e\u3070\u3089\u3064\u304d\u3042\u308a\n\n| | level1 | level2 | level3 | \u81ea\u7531\u90e8\u9580 |\n| --- | --- | --- | --- | - |\n| random | -916 | -731 | -4948 | - |\n| sample(python start.py -m sample) | 6935 | 6259 | 3737 | - |\n| kyad\u3055\u3093\u4f5c\u306e\u7121\u9650\u30c6\u30c8\u30ea\u30b9 | - | - | - | 20592 |\n| \u7406\u8ad6\u5024 | 23400+\u843d\u4e0b\u30dc\u30fc\u30ca\u30b9 | - | - | - |\n\n\u7406\u8ad6\u5024\u306f\u300c\uff08\u5168\u3066I\u5b57\u306e\u68d2\u304c\u51fa\u308b\u306a\u3069\u3057\u3066\uff09\u5168\u30664\u30e9\u30a4\u30f3\u6d88\u3057\u3092\u3057\u305f\u5834\u5408\u300d\u3092\u60f3\u5b9a\u3002\n\u8a08\u7b97\u5f0f\uff1a(\u5236\u9650\u6642\u9593 180(s))\u00f7(\u30c6\u30c8\u30ea\u30b91\u56de\u306b\u639b\u304b\u308b\u6642\u9593 10(s))\u00d71300\u70b9 = 23400\u70b9", "start_char_idx": 0, "end_char_idx": 419, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "973a5c15-9c8a-42ae-85cb-6c8af18389c1": {"__data__": {"id_": "973a5c15-9c8a-42ae-85cb-6c8af18389c1", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "82205a0a-94e4-4add-85ad-eb1ad91dc841", "node_type": null, "metadata": {}, "hash": "2c202d296a7baf8e61ec252c62f2b05e12925993bf215363979f2d40579229bb"}}, "hash": "0c46bf7ab5106bf2dd5229a3750f6290445de592574ed44012fe2dcecca9173c", "text": "Tetris\n\n\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u5b66\u7fd2\u3092\u76ee\u7684\u3068\u3057\u305f\u3001\u30d6\u30ed\u30c3\u30af\u3092\u64cd\u4f5c\u3057\u3066\u30b9\u30b3\u30a2\u3092\u7af6\u3046\u30b2\u30fc\u30e0\u3067\u3059\u3002\nFAQ\u306f\u3053\u3061\u3089\u3002\ntutorial\u306f\u3053\u3061\u3089\u3002\ntetris_score_server\u306f\u3053\u3061\u3089 \ntetris_battle_server\u306f\u3053\u3061\u3089", "start_char_idx": 0, "end_char_idx": 119, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2ad0fdd8-af1f-4e11-92d8-d03b8aa427a5": {"__data__": {"id_": "2ad0fdd8-af1f-4e11-92d8-d03b8aa427a5", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e634f55c-48e7-49a7-b21f-c19bd5a37209", "node_type": null, "metadata": {}, "hash": "742b49df0e47b96c43cc7d40fc10747ac73d2d227fb5c5431538105a79ab441d"}}, "hash": "e5545bd17609d3a16c0353ae946f6bc483f1ec02d0248de35ecb257b1b50a28d", "text": "\u5b9f\u884c\u74b0\u5883\u6e96\u5099", "start_char_idx": 0, "end_char_idx": 6, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "696b6e1e-aca8-41e9-aab9-366de11b3628": {"__data__": {"id_": "696b6e1e-aca8-41e9-aab9-366de11b3628", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "665c9256-7794-422e-a4f5-58dd63d3b321", "node_type": null, "metadata": {}, "hash": "5dfbee350772f5b745c56ac069197bfb43162848ff624e7f22e79d6cde89de2f"}}, "hash": "80354b7ac9fbfc874e2d41a09f6727d3f7cd0b2583fb218c50e97a10b3320e2c", "text": "Mac\u74b0\u5883\n\nFinder\u2192Application\u2192Utility\u2192Terminal\u304b\u3089\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u8d77\u52d5\u3057\u3066\u4ee5\u4e0b\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3002 \ninstall\u6210\u529f\u5f8c\u3001\u5b9f\u884c\u65b9\u6cd5\u306b\u3088\u308a\u30b2\u30fc\u30e0\u958b\u59cb\u3059\u308c\u3070OK \n\n```", "start_char_idx": 0, "end_char_idx": 105, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9182231f-5675-4be9-af96-104c16d80c50": {"__data__": {"id_": "9182231f-5675-4be9-af96-104c16d80c50", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1dd849ba-339d-447c-b1c4-1f3ed80a1d26", "node_type": null, "metadata": {}, "hash": "46b2b348610ee5402d1152434b28b0ed43c426d2c560e0838db6fbe20b7cb10e"}}, "hash": "d3e33f86ff7ae9b75879beb46ef9697702eb4d7ae99420a3d616eb08fc772cee", "text": "install pyqt5 and NumPy\nbrew install python3\nbrew install pyqt5\nbrew install numpy", "start_char_idx": 0, "end_char_idx": 82, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "52478ae6-356e-4b5c-a343-763c91d2c220": {"__data__": {"id_": "52478ae6-356e-4b5c-a343-763c91d2c220", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e7dc43f6-ce13-4157-87b6-179846175fcd", "node_type": null, "metadata": {}, "hash": "1a23aef87f93ead4c0a4b3df59cfc5c985c062012ef82ed22a05ba8ad4354ddc"}}, "hash": "72827143a803f42f12ffb6a19be809c92d8dd73660752fa5444ab54b7fd0ef25", "text": "install other packages\nbrew install git\n```\n\ndoc/files/install_mac.md\u306b\u624b\u9806\u3092\u8a18\u8f09", "start_char_idx": 0, "end_char_idx": 75, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d3828701-0ae8-4d1e-970b-2b3f5bb2c1de": {"__data__": {"id_": "d3828701-0ae8-4d1e-970b-2b3f5bb2c1de", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ea87ba5e-ff95-44f9-83a5-8884bd886de4", "node_type": null, "metadata": {}, "hash": "c357f0fdaa56552d5d1954c7d8ef1fdcbc3e20524d37eac3b052b07e8e6e0d9e"}}, "hash": "572782118ae517b0d67bd3b237343dae09c205301509ad246b2af8b86597373e", "text": "windows\u74b0\u5883\n\nwindows\u306epowershell\u3092\u4f7f\u3063\u3066\u30c6\u30c8\u30ea\u30b9\u74b0\u5883\u3092\u69cb\u7bc9\u3059\u308b\u5834\u5408\u306e\u624b\u9806\nWSL(Windows Subsystem for Linux)\u3092\u4f7f\u3046\u5834\u5408\u306e\u624b\u9806\nDocker for Windows\u3092\u4f7f\u3046\u5834\u5408\u306e\u624b\u9806", "start_char_idx": 0, "end_char_idx": 117, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "432a84d5-ac00-45d3-8385-11447b8d7c56": {"__data__": {"id_": "432a84d5-ac00-45d3-8385-11447b8d7c56", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1d431df7-1378-4d96-81b1-74bb9c8a243c", "node_type": null, "metadata": {}, "hash": "fb9f4c97b618490fc52284e86e0742b4f71a418875e5b8edee3bb306c5e5905f"}}, "hash": "33d0a2dfb6544dbd9e01298975f7b39d3a621c09e19d3274c89359c50eb5f468", "text": "Ubuntu/JetsonNano\u74b0\u5883\n\ndoc/files/install_ubuntu.md\u306b\u624b\u9806\u3092\u8a18\u8f09", "start_char_idx": 0, "end_char_idx": 54, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "0937c300-83a4-4da1-a471-1bbfdbc90777": {"__data__": {"id_": "0937c300-83a4-4da1-a471-1bbfdbc90777", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f184122c-81e5-45a9-a612-d8b852c042b3", "node_type": null, "metadata": {}, "hash": "4126a897a947e6cd9ae8555f6e14004dd197f02449a7e41918f26870840f02a4"}}, "hash": "73abec11ae83c3d845e4789961513aa68b792ac68d4fcf72fbd4147d3a096eb1", "text": "docker\u74b0\u5883\n\ndocker/README.md\u306b\u624b\u9806\u3092\u8a18\u8f09", "start_char_idx": 0, "end_char_idx": 32, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "1806da72-5923-4cae-8a66-854195e37b92": {"__data__": {"id_": "1806da72-5923-4cae-8a66-854195e37b92", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7545faa8-19a4-4494-9d5d-e6240d0e9e3c", "node_type": null, "metadata": {}, "hash": "5fcc42c5e0be702504afec721edd6e98e3f87ca1000a0b01ee706aebd7f15243"}}, "hash": "ac2e46d0d7aebdd9a96f5dd75f2ad629b5b7325660b3f588f20b1c0830fe598f", "text": "\u5b9f\u884c\u65b9\u6cd5\n\n\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u53d6\u5f97\n\n```shell\ncd $HOME\ngit clone https://github.com/seigot/tetris\n```\n\n\u30b2\u30fc\u30e0\u958b\u59cb\u7528\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\n\n```shell\ncd tetris\npython start.py\n```\n\n!Screenshot", "start_char_idx": 0, "end_char_idx": 150, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "245e052e-d059-43e0-ac54-ff8eaec19421": {"__data__": {"id_": "245e052e-d059-43e0-ac54-ff8eaec19421", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9158184e-4261-4309-ba1d-143eee835163", "node_type": null, "metadata": {}, "hash": "9ecb62085a5698f224b17fb11870f585ad1885ea9b118b9f5d4d51ee35d048b3"}}, "hash": "15fe0ca08f63ada0ddb10341851b0ee511e4d5a2803f6b473e0f2562c6d4ba79", "text": "\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\n\n\u5b9f\u884c\u6642\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4e0e\u3048\u308b\u3053\u3068\u3067\u3001\u30b9\u30b3\u30a2\u30a2\u30bf\u30c3\u30af\u7528\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u304c\u53ef\u80fd\u3067\u3059\u3002\n\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u306e\u8a73\u7d30\u306f\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002\n\n```shell\npython start.py -m sample\n```", "start_char_idx": 0, "end_char_idx": 132, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "133900ec-e422-400e-8951-d617ba58f528": {"__data__": {"id_": "133900ec-e422-400e-8951-d617ba58f528", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c1142512-08a2-4305-8728-79e2a42d6d2d", "node_type": null, "metadata": {}, "hash": "3622fc3be6c37d9687218043fabb68508a51044b8f0d416424fa45fd97fcf55c"}}, "hash": "d3ea684cbcff3d3f44f2ad5f2e959808e6c2bb142598238b86808abd01506696", "text": "\u624b\u52d5\u64cd\u4f5c\n\n\u5b9f\u884c\u6642\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4e0e\u3048\u308b\u3053\u3068\u3067\u3001\u624b\u52d5\u64cd\u4f5c\u304c\u53ef\u80fd\u3067\u3059\u3002\n\u64cd\u4f5c\u65b9\u6cd5\u306f\u3001PC\u64cd\u4f5c\u6e96\u62e0\u3068\u30b2\u30fc\u30e0\u6a5f\u30b3\u30f3\u30c8\u30ed\u30fc\u30e9\u6e96\u62e0\u306e2\u7a2e\u985e\u3092\u9078\u629e\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u307e\u3059\u3002\n\n| \u624b\u52d5\u64cd\u4f5c | PC\u64cd\u4f5c\u6e96\u62e0 | \u30b2\u30fc\u30e0\u6a5f\u30b3\u30f3\u30c8\u30ed\u30fc\u30e9\u6e96\u62e0 |\n| ---- | ---- | ---- |\n| \u5b9f\u884c\u30b3\u30de\u30f3\u30c9 | python start.py -m keyboard | python start.py -m gamepad |\n| *up* key | \u56de\u8ee2 | \u843d\u4e0b |\n| *left* key | \u5de6\u306b\u79fb\u52d5 | \u5de6\u306b\u79fb\u52d5 |\n| *right* key | \u53f3\u306b\u79fb\u52d5 | \u53f3\u306b\u79fb\u52d5 |\n| *m* key | \u4e0b\u306b\u79fb\u52d5 | \u4e0b\u306b\u79fb\u52d5 |\n| *space* key | \u843d\u4e0b | \u56de\u8ee2 |\n| *P* key | Pause | Pause |\n| *c* key | hold | hold |\n\n`--nextShapeMode hate`\u30aa\u30d7\u30b7\u30e7\u30f3\u4ed8\u304d\u3067\u5b9f\u884c\u3059\u308b\u3068hate\u30e2\u30fc\u30c9(\u624b\u52d5\u64cd\u4f5c\u3067\u904a\u3076\u7528)\u306b\u306a\u308a\u307e\u3059\u3002\n\n```\npython3 start.py -m keyboard --nextShapeMode hate\n```", "start_char_idx": 0, "end_char_idx": 573, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "dc7fea2d-d282-49a9-91f4-c446fc482cbd": {"__data__": {"id_": "dc7fea2d-d282-49a9-91f4-c446fc482cbd", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "1bdfabe8-645c-44e1-bf10-382e2ce59a95", "node_type": null, "metadata": {}, "hash": "ebfbcf337ad74220b86f417cda9b0f9356263a561fe3ae42d7e882c80ca22b93"}}, "hash": "74b87a4bc6c3bfb8c97292605570c6d0a209fcce5bd92302931d7060193335ed", "text": "AI\u5b9f\u88c5\n\n\u5b9f\u884c\u65b9\u6cd5\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u7528\u610f\u3057\u3066\u3044\u307e\u3059 \n\u8a73\u7d30\u306fAI\u306b\u3064\u3044\u3066\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 45, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "33a80011-707c-4e4a-9953-f035ad1c2b39": {"__data__": {"id_": "33a80011-707c-4e4a-9953-f035ad1c2b39", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6bf39a3d-3528-4146-9655-90a9f95fd9df", "node_type": null, "metadata": {}, "hash": "138ac834c4ef2c728f0b211902c83d924731055afea07cde455e4f7b40c3fa43"}}, "hash": "e369cba4793f176c0182b2af6f8c17daee11f03d379738842d08b4ef3219a299", "text": "art\n\n\u5b9f\u884c\u65b9\u6cd5\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u7528\u610f\u3057\u3066\u3044\u307e\u3059 \n\u8a73\u7d30\u306fart\u306b\u3064\u3044\u3066\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 45, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "2bef8386-2742-4384-9cf6-e5745950cf70": {"__data__": {"id_": "2bef8386-2742-4384-9cf6-e5745950cf70", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cc4f60e5-a448-44f5-bbf7-192d6c8ac8ef", "node_type": null, "metadata": {}, "hash": "5a4018b32d1f9eb65d99ac472df3d5fd0a0b7af898ec898f68e198465b3b64fc"}}, "hash": "1eef8adc227abede23b78d017f6ddd491f13ecbe1512412bc73b5a8df53b095d", "text": "\u81ea\u52d5\u8a55\u4fa1\u30b5\u30fc\u30d0\n\n\u8868\u984c\u306e\u30b5\u30fc\u30d0\u3092\u6709\u5fd7\u3067\u7528\u610f\u3057\u3066\u3044\u307e\u3059 \nhttps://github.com/ChallengeClub/tetris_score_server", "start_char_idx": 0, "end_char_idx": 81, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "f1a4fa1b-c4ae-4569-b4c2-fa91c9bfd7f3": {"__data__": {"id_": "f1a4fa1b-c4ae-4569-b4c2-fa91c9bfd7f3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e777e248-adaa-40b9-990c-dc90e67fb8d8", "node_type": null, "metadata": {}, "hash": "d6ca574883449f20288b9b44e8d518f8b015d452ce6ce3f89d44af75dff8b450"}}, "hash": "02d1279f3621c1dbd254dbc34871780f4a240bd50cd58332e4bd3e8a32fa668f", "text": "Play rules\n\n\u5236\u9650\u6642\u9593\u5185\u306e\u7372\u5f97\u30b9\u30b3\u30a2\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 30, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "04c8b63d-9486-4ae2-b381-b813de511e5b": {"__data__": {"id_": "04c8b63d-9486-4ae2-b381-b813de511e5b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b723447b-5b47-449f-a42d-d20057e87d73", "node_type": null, "metadata": {}, "hash": "a4696646ce9bae8d157fdff91734ce76b06deda64357995f88e117a7bcb77ab2"}}, "hash": "dc52f58c03ff5bc8e446e8af8f061852db451f602bf80bb20b7fdca18e5f1dc7", "text": "Score\n\n\u52a0\u70b9\n\n| \u9805\u76ee | \u5f97\u70b9 | \u5099\u8003 |\n| ---- | ---- | ---- |\n| 1\u30e9\u30a4\u30f3\u6d88\u3057 | + 100\u70b9 | - |\n| 2\u30e9\u30a4\u30f3\u6d88\u3057 | + 300\u70b9 | - |\n| 3\u30e9\u30a4\u30f3\u6d88\u3057 | + 700\u70b9 | - |\n| 4\u30e9\u30a4\u30f3\u6d88\u3057 | + 1300\u70b9 | - |\n| \u843d\u4e0b\u30dc\u30fc\u30ca\u30b9 | + \u843d\u4e0b\u3057\u305f\u30d6\u30ed\u30c3\u30af\u6570\u3092\u5f97\u70b9\u306b\u52a0\u7b97 | - |\n| \u5168\u6d88\u3057\u30dc\u30fc\u30ca\u30b9 | + \u5168\u6d88\u3057\u6642\u306b\u5f97\u70b9 | - |\n\n\u6e1b\u70b9\n\n| \u9805\u76ee | \u5f97\u70b9 | \u5099\u8003 |\n| ---- | ---- | ---- |\n| gameover | - 500\u70b9 | \u30d6\u30ed\u30c3\u30af\u51fa\u73fe\u6642\u306b\u30d5\u30a3\u30fc\u30eb\u30c9\u304c\u57cb\u307e\u3063\u3066\u3044\u305f\u3089gameover", "start_char_idx": 0, "end_char_idx": 361, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "c7a0f770-07cf-40d3-8c24-c4ea170b3e3b": {"__data__": {"id_": "c7a0f770-07cf-40d3-8c24-c4ea170b3e3b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "563f86ff-c773-4318-b5de-4b005a00c15c", "node_type": null, "metadata": {}, "hash": "1f81865e2d10819aa6ec86cacfbb3112278aa0c8f2ca0d8f8ebd2b2d9725f925"}}, "hash": "3fa5679701cf8e55db4464d883798e2063ead8db27959f564330f18781935bce", "text": "game level\n\n\u5b9f\u884c\u6642\u3001\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4e0e\u3048\u308b\u3053\u3068\u3067\u3001\u96e3\u6613\u5ea6\uff08\u30ec\u30d9\u30eb\uff09\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059\u3002\n\n| | level1 | level2 | level3 | level4 | \n| --- | --- | --- | --- | --- | \n| \u5b9f\u884c\u65b9\u6cd5 | python start.py | python start.py -l2 | python start.py -l3 | python start.py -l4 | \n| \u5236\u9650\u6642\u9593 | 180\u79d2 | 180\u79d2 | 180\u79d2 | 180\u79d2 | \n| \u30d6\u30ed\u30c3\u30af\u306e\u9806\u756a | \u56fa\u5b9a(1-7\u307e\u3067\u9806\u756a\u306b\u7e70\u308a\u8fd4\u3057) | \u30e9\u30f3\u30c0\u30e0 | \u30e9\u30f3\u30c0\u30e0 | \u30e9\u30f3\u30c0\u30e0 |\n| \u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u521d\u671f\u30d6\u30ed\u30c3\u30af | \u306a\u3057 | \u306a\u3057 | \u3042\u308a | \u3042\u308a | \n| \u30d5\u30ec\u30fc\u30e0\u66f4\u65b0\u983b\u5ea6 | \u7d041\u79d2 | \u7d041\u79d2 | \u7d041\u79d2 | \u7d040.001\u79d2 | \n| \u5168\u6d88\u3057\u30dc\u30fc\u30ca\u30b9 | +0 | +0 | +4000 | +4000 | \n| \u5099\u8003 | - | - | - | - | \n\n\u5404\u30ec\u30d9\u30eb\u306e\u53c2\u8003\u30b9\u30b3\u30a2", "start_char_idx": 0, "end_char_idx": 534, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cf10fd93-8d52-401a-b4bc-f3008db7d7d7": {"__data__": {"id_": "cf10fd93-8d52-401a-b4bc-f3008db7d7d7", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "91f6257d-54c7-4248-a056-4007af9d01e8", "node_type": null, "metadata": {}, "hash": "65aa46410bf5fbaccee92fe649a7c30209ca8e2bf99122ff13dbb1e01cd0110d"}}, "hash": "ac8c1fe82ef4c4067e382821ab2286a61b5c724e4a0bcb5df0ecce546599a716", "text": "\u30d5\u30a1\u30a4\u30eb\u69cb\u6210", "start_char_idx": 0, "end_char_idx": 6, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8f2dd465-226d-4a17-ae7c-7aec5260cf67": {"__data__": {"id_": "8f2dd465-226d-4a17-ae7c-7aec5260cf67", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d08c0faa-fdf2-4c79-82cd-f51349b011f0", "node_type": null, "metadata": {}, "hash": "cbb9a6257df1e9a36f26e32661d3001b2ce67df5837a7964240e818719deb219"}}, "hash": "a8d5b00d6ed2733eca4e8f70424e8c32e3200dca6cb53c5a4b535e6fb9a75a11", "text": "\u30d5\u30a1\u30a4\u30eb\u4e00\u89a7\n\n* `game_manager/game_manager.py` : \u30b2\u30fc\u30e0\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\n* `game_manager/board_manager.py` : \u30dc\u30fc\u30c9\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\n* `game_manager/block_controller.py` : \u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\uff08\u30d6\u30ed\u30c3\u30af\u306e\u64cd\u4f5c\u306f\u3001\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u66f4\u65b0\u3057\u3066\u4e0b\u3055\u3044\u3002\uff09\n* `start.py` : \u30b2\u30fc\u30e0\u958b\u59cb\u7528\u30b3\u30de\u30f3\u30c9", "start_char_idx": 0, "end_char_idx": 206, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "37d9b31b-7d06-415c-b9f3-075dfc822f2d": {"__data__": {"id_": "37d9b31b-7d06-415c-b9f3-075dfc822f2d", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9ec3d41b-c7f8-4ef3-a6a6-1f5a5bbcacb9", "node_type": null, "metadata": {}, "hash": "81b5ba7f3960b120263cfafa83c748a06de7c37996562db8476e5a5a904aa4af"}}, "hash": "e25dd51b5ff81b2f5350a100e4915183af66a838d722a45b984794dbd6fa0201", "text": "\u8a73\u7d30\n\n\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u69cb\u6210\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\n\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u3001\u7ba1\u7406\u30d7\u30ed\u30b0\u30e9\u30e0\u304b\u3089\u5b9a\u671f\u7684\u306b\u547c\u3073\u51fa\u3055\u308c\u308b\u306e\u3067\u3001\u30dc\u30fc\u30c9\u60c5\u5831\u304b\u3089\u6b21\u306e\u52d5\u4f5c\u3092\u6c7a\u5b9a\u3057\u3066\u4e0b\u3055\u3044\u3002 \n\n```mermaid\n graph TB\n\n subgraph \u30b2\u30fc\u30e0\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\n B1[\"game_manager.py\"]\n C1[\"board_manager.py\"]\n D1[\"block_controller.py\u3053\u3053\u3067\u73fe\u5728\u306e\u30d6\u30ed\u30c3\u30af\u306e\u52d5\u4f5c\u3092\u6c7a\u5b9a\u3059\u308b\"]\n B1 --update--> C1\n B1 --getNextMove--> D1\n D1 --NextMove--> B1\n subgraph \u30dc\u30fc\u30c9\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\n C1\n end\n subgraph \u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\n D1\n end\n end\n\n\n subgraph \u30b2\u30fc\u30e0\u958b\u59cb\u7528\u30b3\u30de\u30f3\u30c9\n A1[start.py] --> B1\n end\nstyle \u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0 fill:#fef\n```\n\n\u8a73\u7d30\n- \u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066\u306e\u8aac\u660e \n- \u30dc\u30fc\u30c9\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066\u306e\u8aac\u660e \n- \u30b2\u30fc\u30e0\u7ba1\u7406\u7528\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3064\u3044\u3066\u306e\u8aac\u660e", "start_char_idx": 0, "end_char_idx": 552, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "af0dfa52-56da-4cda-91fa-0cb38fc91cb3": {"__data__": {"id_": "af0dfa52-56da-4cda-91fa-0cb38fc91cb3", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "61677468-6c24-4927-91b0-6cef5b80852e", "node_type": null, "metadata": {}, "hash": "df741be94531c7b2aec861b0725560bb5fd8e24b2063432e4f566d348cdd1cf7"}}, "hash": "4e22598bf4a7078ce980d66c570dfa27a8c5cb82903ee689f0a8a07ef0bbc2a4", "text": "\u30b3\u30fc\u30c9\u4f5c\u6210\u306e\u306f\u3058\u3081\u304b\u305f", "start_char_idx": 0, "end_char_idx": 11, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8a155880-671e-4bdf-b782-5e9767d5a009": {"__data__": {"id_": "8a155880-671e-4bdf-b782-5e9767d5a009", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "4937f679-5b1b-4d83-92ae-87c2b7e37adf", "node_type": null, "metadata": {}, "hash": "b2362bd0aab520e116359d399eb918aa3135e0142891bea7b5ff6373ca36af03"}}, "hash": "b47020a3649f23726d8649d27e8a8789702a159e3fc67ce7c1cef0e60cc17b5d", "text": "\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306efork\n\n\u307e\u305a\u3001Github\u30a2\u30ab\u30a6\u30f3\u30c8\u3092\u53d6\u5f97\u3057\u3066\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u81ea\u30ea\u30dd\u30b8\u30c8\u30ea\u306bfork\u3057\u3066\u4e0b\u3055\u3044\u3002\n\n> \u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30d5\u30a9\u30fc\u30af\u306e\u4f8b \n> \n> 0. GitHub\u30a2\u30ab\u30a6\u30f3\u30c8\u3092\u4f5c\u6210/\u30ed\u30b0\u30a4\u30f3\u3059\u308b\u3002 \n> 1. GitHub \u3067\u3001https://github.com/seigot/tetris\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u79fb\u52d5\u3057\u307e\u3059 \n> 2. \u30da\u30fc\u30b8\u306e\u53f3\u4e0a\u306b\u3042\u308b [Fork] \u3092\u30af\u30ea\u30c3\u30af\u3057\u307e\u3059\u3002 \n> \u53c2\u8003\uff1a\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u30d5\u30a9\u30fc\u30af\u3059\u308b \n\n\u305d\u306e\u5f8c\u3001\u81ea\u30ea\u30dd\u30b8\u30c8\u30ea\u306bfork\u3057\u305f`tetris`\u3092\u30ed\u30fc\u30ab\u30eb\u30de\u30b7\u30f3\u306b\u53d6\u5f97\u3057\u3066\u4e0b\u3055\u3044\u3002\n\n```\ncd ~\ngit clone https://github.com//tetris # \"\"\u3055\u3093\uff08yourname=\u81ea\u5206\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u540d\u306b\u8aad\u307f\u304b\u3048\u3066\u4e0b\u3055\u3044\uff09\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u53d6\u5f97\u3059\u308b\u5834\u5408\ngit clone https://github.com/seigot/tetris # \u3053\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u53d6\u5f97\u3059\u308b\u5834\u5408\n```\n\n\u65e2\u306b`tetris`\u304c\u5b58\u5728\u3057\u3066\u304a\u308a\u3001\u3053\u308c\u3092\u524a\u9664\u3057\u305f\u3044\u5834\u5408\u306f`rm -f`\u3092\u5b9f\u884c\u3057\u3066\u4e0b\u3055\u3044\u3002\n\n```", "start_char_idx": 0, "end_char_idx": 480, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "d06f932c-e25f-424e-8550-eb1a0384e2a4": {"__data__": {"id_": "d06f932c-e25f-424e-8550-eb1a0384e2a4", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c4f7b5ef-1a59-4619-b54e-cf41a374b635", "node_type": null, "metadata": {}, "hash": "aed89004c900c7d3cb2a09b35b4dfa5418a58b4bdb11ec989ab971df0687faf2"}}, "hash": "f72a2eb3ae3bd452f9fff99b6e4a7e2f647c22f357cd28368d434a0d2ae94fb7", "text": "ubuntu/mac\u7b49\u306e\u5834\u5408\nsudo rm -rf tetris", "start_char_idx": 0, "end_char_idx": 33, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "6080eb17-0342-412b-a707-27fcebbc620b": {"__data__": {"id_": "6080eb17-0342-412b-a707-27fcebbc620b", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7c144053-e6f9-4cad-bb77-c063d620e0b8", "node_type": null, "metadata": {}, "hash": "c34472d25635e043249f0fb2d9356e90196542b699a43f8ae897f9d95cb29ffa"}}, "hash": "d572759e22f1b607fa64d253eba25a73065af5d1a5104f4ef2d84af25e736f6f", "text": "windows powershell\u306e\u5834\u5408\nRemove-Item -Recurse -Force tetris\n```\n\n\u53d6\u5f97\u5f8c\u306f\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u5909\u66f4\u3001\u5909\u66f4\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u53cd\u6620\u3059\u308b\u7b49\u3057\u3066\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u3092\u9032\u3081\u3066\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 104, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "93673750-be8f-498d-8a61-7be02dcccabf": {"__data__": {"id_": "93673750-be8f-498d-8a61-7be02dcccabf", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e060e8ed-36f1-4c0e-aef7-6a91690af859", "node_type": null, "metadata": {}, "hash": "7b91bdf9832c343cdfc6f15696d527b20da25388334abbd26ea23c73dafa225b"}}, "hash": "0f6a9f7244dfa736dcda0f5243ce47dcee02e6100a47c3a01f5486ac1481e094", "text": "\u5b9f\u884c\n\n`\u5b9f\u884c\u65b9\u6cd5`\u3092\u53c2\u8003\u306b\u5b9f\u884c\u74b0\u5883\u306e\u69cb\u7bc9\u3092\u3057\u3066\u4e0b\u3055\u3044\u3002\n\u74b0\u5883\u69cb\u7bc9\u306e\u5b8c\u4e86\u5f8c\u3001\u30d6\u30ed\u30c3\u30af\u64cd\u4f5c\u7528\u30d7\u30ed\u30b0\u30e9\u30e0`block_controller.py`\u3092\u66f4\u65b0\u3057\u3066\u3044\u3063\u3066\u304f\u3060\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 84, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "37705651-ac14-4c53-b486-933c2536c261": {"__data__": {"id_": "37705651-ac14-4c53-b486-933c2536c261", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f9836538-74a0-4207-8c08-c6103728d6be", "node_type": null, "metadata": {}, "hash": "6558ae706b5384ac0f9c146bd8aae447876a6d73a50fd36d635fb38843055a83"}}, "hash": "c9abf08d487db6d61ceb279442ea9c93056c354366680e264eeb76793f0c562a", "text": "\u81ea\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30d0\u30a4\u30ca\u30ea\u3092\u516c\u5f0f\u30ea\u30ea\u30fc\u30b9\u3059\u308b\n\n\u63d0\u51fa\u6642\u3001\u81ea\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30d0\u30a4\u30ca\u30ea\u3092\u516c\u5f0f\u30ea\u30ea\u30fc\u30b9\u3059\u308b\u5834\u5408\u306f\u3001Github\u30ea\u30ea\u30fc\u30b9\u306e\u6a5f\u80fd\u3092\u4f7f\u3046\u3068\u7c21\u5358\u306a\u306e\u3067\u304a\u52e7\u3081\u3067\u3059\u3002\n\n> \u81ea\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30b3\u30fc\u30c9\u3092\u63d0\u51fa\uff08\u30d0\u30a4\u30ca\u30ea\u30ea\u30ea\u30fc\u30b9\uff09\u3059\u308b\u5834\u5408\u306e\u624b\u9806\u53c2\u8003 \n> \u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30ea\u30ea\u30fc\u30b9\u3092\u7ba1\u7406\u3059\u308b \n> 7.\u30aa\u30d7\u30b7\u30e7\u30f3\u3067\u3001\u30b3\u30f3\u30d1\u30a4\u30eb\u3055\u308c\u305f\u30d7\u30ed\u30b0\u30e9\u30e0\u306a\u3069\u306e\u30d0\u30a4\u30ca\u30ea\u30d5\u30a1\u30a4\u30eb\u3092\u30ea\u30ea\u30fc\u30b9\u306b\u542b\u3081\u308b\u306b\u306f\u3001\u30c9\u30e9\u30c3\u30b0\u30a2\u30f3\u30c9\u30c9\u30ed\u30c3\u30d7\u3059\u308b\u304b\u30d0\u30a4\u30ca\u30ea\u30dc\u30c3\u30af\u30b9\u3067\u624b\u52d5\u3067\u9078\u629e\u3057\u307e\u3059\u3002", "start_char_idx": 0, "end_char_idx": 214, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "cbdea7e8-6b23-4688-9c05-3c5c0eae95db": {"__data__": {"id_": "cbdea7e8-6b23-4688-9c05-3c5c0eae95db", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a8ff9a92-9b91-4767-9913-174a84af5115", "node_type": null, "metadata": {}, "hash": "e4999aa5225211d59ee355ec433c70d3c51cffb64e4c26400a6bd12aef838195"}}, "hash": "106223d09513286ec8def3e651a54899882a0f69e38d23e544e303042425311a", "text": "\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u53d6\u308a\u8fbc\u307f\n\n\u4eca\u5f8c\u3001\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3082\u30d0\u30fc\u30b8\u30e7\u30f3\u30a2\u30c3\u30d7\u3057\u3066\u3044\u304f\u4e88\u5b9a\u3067\u3059\u3002\n\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u30a2\u30c3\u30d7\u3092\u53d6\u308a\u8fbc\u3080\u5834\u5408\u306f\u3001fork\u3057\u305f\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u3066\u4ee5\u4e0b\u3092\u5b9f\u884c\u3057\u3066\u4e0b\u3055\u3044\u3002\n\n\u203b\u8ffd\u8a18 2021/5\u3088\u308a\u3001Github UI\u4e0a\u304b\u3089\u64cd\u4f5c\u53ef\u80fd\u306b\u306a\u3063\u305f\u3088\u3046\u3067\u3059\u3002\nGitHub\u65b0\u6a5f\u80fd\u300cFetch upstream\u300d\u4f7f\u3063\u3066\u307f\u305f\uff01\u30001\u30af\u30ea\u30c3\u30af\u3067\u89aa\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u8ffd\u5f93\uff08\u540c\u671f\uff09\n\n```\ngit checkout master # \u30ed\u30fc\u30ab\u30eb\u306emain\u30d6\u30e9\u30f3\u30c1\u306b\u79fb\u52d5\ngit remote add upstream https://github.com/seigot/tetris # fork\u5143\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092upstream \u3068\u3044\u3046\u540d\u524d\u3067\u30ea\u30e2\u30fc\u30c8\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u767b\u9332\uff08\u540d\u524d\u306f\u306a\u3093\u3067\u3082\u3044\u3044\u3002\u767b\u9332\u6e08\u307f\u306a\u3089\u30b9\u30ad\u30c3\u30d7\uff09\ngit fetch upstream # upstream \u304b\u3089\u6700\u65b0\u306e\u30b3\u30fc\u30c9\u3092fetch\ngit merge upstream/master # upstream/main \u3092 \u30ed\u30fc\u30ab\u30eb\u306emaster \u306bmerge\ngit push # \u5909\u66f4\u3092\u53cd\u6620\n```\n\n\u53c2\u8003\uff1agithub \u3067 fork \u3057\u305f\u30ea\u30dd\u30b8\u30c8\u30ea\u3067\u672c\u5bb6\u306b\u8ffd\u5f93\u3059\u308b", "start_char_idx": 0, "end_char_idx": 679, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "8644d86d-7af8-419e-8e32-3d2a92d15039": {"__data__": {"id_": "8644d86d-7af8-419e-8e32-3d2a92d15039", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ce3f47aa-6516-42c1-8cbe-7d9f9f3fe51c", "node_type": null, "metadata": {}, "hash": "799f26a3a5b37f437ec5981059f5fa9077e4bfd0ccd5eee7b7d9f6c29cf2fba3"}}, "hash": "9681963c6acfcc4d815e3c56c34f17c5e37e0d571c30bbfb2ab1293203210df7", "text": "Pull Request\u3092\u9001\u308b\uff08Optional\uff09\n\n\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3078\u4fee\u6b63\u30ea\u30af\u30a8\u30b9\u30c8\u3092\u9001\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002\u8a73\u3057\u304f\u306f\u53c2\u8003\u3092\u3054\u53c2\u7167\u4e0b\u3055\u3044\u3002\n\n\u203b\u8ffd\u8a18\u3000Pull Request\u7df4\u7fd2\u7528\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u307e\u3057\u305f\u3002\ntest_pull_request\n\n\u89e3\u8aac\u56f3:\n\n!Git Commentary\n\n\u53c2\u8003\uff1a\nGitHub-\u30d7\u30eb\u30ea\u30af\u30a8\u30b9\u30c8\u306e\u4f5c\u6210\u65b9\u6cd5\n[\u5b9f\u8df5] \u306f\u3058\u3081\u3066\u306ePull Request\u3092\u3084\u3063\u3066\u307f\u3088\u3046\n\u3010GitHub\u3011Pull Request\u306e\u624b\u9806", "start_char_idx": 0, "end_char_idx": 220, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "ff7e4c0c-eac9-4a0a-99d6-5121a3dddc82": {"__data__": {"id_": "ff7e4c0c-eac9-4a0a-99d6-5121a3dddc82", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "16639bfd-8648-44ca-a3a0-e2ba6a3a98c8", "node_type": null, "metadata": {}, "hash": "404dffb6e738c78b09e5c7c0fe81c0724dc8441cef22ce6768b1c50a16763a3a"}}, "hash": "a061a144cc810806b6331f13ae1b3b31be78773916faa529d962c2c74d620729", "text": "FAQ\n\ndoc/files/FAQ.md\u3092\u53c2\u7167\u4e0b\u3055\u3044\u3002", "start_char_idx": 0, "end_char_idx": 28, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "e7244407-bbd7-414f-9c02-b7d3c51f4dcd": {"__data__": {"id_": "e7244407-bbd7-414f-9c02-b7d3c51f4dcd", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "623b56b5-c42d-498e-879c-067728194106", "node_type": null, "metadata": {}, "hash": "bcc5ca85c973ca80abf44aaf25d5ffdb47c2ccbe1c1ca0e9b0c8dc6486e50675"}}, "hash": "666793a656f4c8185b3058dec79e0950f6cd864dbb2fdf48fb7d69cfa8a1b84e", "text": "\u53c2\u8003\n\nhttps://github.com/LoveDaisy/tetris_game \nhttps://github.com/seigot/tetris_game (2021.12\u6642\u70b9\u307e\u3067\u4f7f\u7528)\nhttp://zetcode.com/gui/pyqt5/tetris/\n\u30c6\u30c8\u30ea\u30b9\u306e\u6b74\u53f2\u3092\u300c\u30d6\u30ed\u30c3\u30af\u304c\u843d\u3061\u308b\u30eb\u30fc\u30eb\u300d\u306e\u9032\u5316\u304b\u3089\u5b66\u3076\n\u300e\u30c6\u30c8\u30ea\u30b9\u3067Python\u3092\u5b66\u307c\u3046v4\u300f\u512a\u52dd\u30b3\u30fc\u30c9\u89e3\u8aac", "start_char_idx": 0, "end_char_idx": 192, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "9c9867a3-6bc3-4544-8032-094305ccc606": {"__data__": {"id_": "9c9867a3-6bc3-4544-8032-094305ccc606", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ffce1b10-7adb-42a0-9334-4d3664ace063", "node_type": null, "metadata": {}, "hash": "20ce693b38d9e49b77cbddd8004582360afefd358ab6a138e7f4d1436ee4a689"}}, "hash": "6fe1bd880ad0b7f931b83094121722e8edc899a89c1c6edfc322793a26ae9e87", "text": "\u4eca\u5f8c\u306e\u8ab2\u984c", "start_char_idx": 0, "end_char_idx": 5, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "7a71c62a-768b-4849-b2ca-5a26fa8a2847": {"__data__": {"id_": "7a71c62a-768b-4849-b2ca-5a26fa8a2847", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "268e1e4b-d8da-42ba-90c7-ac6017dabb9e", "node_type": null, "metadata": {}, "hash": "87a990097b311f05cbe3e1cbceaefa2c30dc28ef95c13fd730cb588afd3f9456"}}, "hash": "6a21ce2c0c1aded57cf46c0a207c7698019322027e2cce09a237fbafac6e8e1c", "text": "\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u306e\u30e9\u30f3\u30c0\u30e0\u6027\n\n\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u306e\u30e9\u30f3\u30c0\u30e0\u6027\u306f\u3001\u73fe\u5728\u306frandom\u95a2\u6570\u306e\u51fa\u529b\u306b\u4f9d\u5b58\u3057\u3066\u3044\u307e\u3059\u3002\n\u3057\u304b\u3057\u3001\u3053\u3061\u3089\u306e\u8a18\u4e8b\u306b\u3088\u308b\u3068\u9078\u629e\u65b9\u5f0f\u304c\u8272\u3005\u3042\u308a\u305d\u3046\u3067\u3059\u3002\n\u6709\u8b58\u8005\u306e\u65b9\u304b\u3089\u30a2\u30c9\u30d0\u30a4\u30b9\u9802\u3051\u308b\u3068\u52a9\u304b\u308a\u307e\u3059\u3002\n\n* \u53c2\u8003\uff1a\u6b21\u306e\u30d6\u30ed\u30c3\u30af\u9078\u629e\u51e6\u7406 game_manager.py\n\n```\nnextShapeIndex = np_randomShape.random.randint(1, 8)\n```", "start_char_idx": 0, "end_char_idx": 198, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n"}, "__type__": "1"}, "83938eff-db4f-4cfe-b2e4-725387540be3": {"__data__": {"id_": 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"29cc3093-4a3e-47b6-b979-10f0b68c7536", "81674c4f-60e0-4f23-86bb-333d830f42e3": "cc0fc37b-8587-4386-9d6b-38919ef43c54", "180ad649-c7c7-4415-aee5-031c9a29b264": "6b4993ef-0255-4ba9-a433-ff91419b52c6", "9451cf88-0557-48bb-8a7c-dcc314a20424": "94748ba8-d795-48ad-a384-2ab41d37d2c6", "973a5c15-9c8a-42ae-85cb-6c8af18389c1": "82205a0a-94e4-4add-85ad-eb1ad91dc841", "2ad0fdd8-af1f-4e11-92d8-d03b8aa427a5": "e634f55c-48e7-49a7-b21f-c19bd5a37209", "696b6e1e-aca8-41e9-aab9-366de11b3628": "665c9256-7794-422e-a4f5-58dd63d3b321", "9182231f-5675-4be9-af96-104c16d80c50": "1dd849ba-339d-447c-b1c4-1f3ed80a1d26", "52478ae6-356e-4b5c-a343-763c91d2c220": "e7dc43f6-ce13-4157-87b6-179846175fcd", "d3828701-0ae8-4d1e-970b-2b3f5bb2c1de": "ea87ba5e-ff95-44f9-83a5-8884bd886de4", "432a84d5-ac00-45d3-8385-11447b8d7c56": "1d431df7-1378-4d96-81b1-74bb9c8a243c", "0937c300-83a4-4da1-a471-1bbfdbc90777": "f184122c-81e5-45a9-a612-d8b852c042b3", "1806da72-5923-4cae-8a66-854195e37b92": "7545faa8-19a4-4494-9d5d-e6240d0e9e3c", "245e052e-d059-43e0-ac54-ff8eaec19421": "9158184e-4261-4309-ba1d-143eee835163", "133900ec-e422-400e-8951-d617ba58f528": "c1142512-08a2-4305-8728-79e2a42d6d2d", "dc7fea2d-d282-49a9-91f4-c446fc482cbd": "1bdfabe8-645c-44e1-bf10-382e2ce59a95", "33a80011-707c-4e4a-9953-f035ad1c2b39": "6bf39a3d-3528-4146-9655-90a9f95fd9df", "2bef8386-2742-4384-9cf6-e5745950cf70": "cc4f60e5-a448-44f5-bbf7-192d6c8ac8ef", "f1a4fa1b-c4ae-4569-b4c2-fa91c9bfd7f3": "e777e248-adaa-40b9-990c-dc90e67fb8d8", "04c8b63d-9486-4ae2-b381-b813de511e5b": "b723447b-5b47-449f-a42d-d20057e87d73", "c7a0f770-07cf-40d3-8c24-c4ea170b3e3b": "563f86ff-c773-4318-b5de-4b005a00c15c", "cf10fd93-8d52-401a-b4bc-f3008db7d7d7": "91f6257d-54c7-4248-a056-4007af9d01e8", "8f2dd465-226d-4a17-ae7c-7aec5260cf67": "d08c0faa-fdf2-4c79-82cd-f51349b011f0", "37d9b31b-7d06-415c-b9f3-075dfc822f2d": "9ec3d41b-c7f8-4ef3-a6a6-1f5a5bbcacb9", "af0dfa52-56da-4cda-91fa-0cb38fc91cb3": "61677468-6c24-4927-91b0-6cef5b80852e", "8a155880-671e-4bdf-b782-5e9767d5a009": "4937f679-5b1b-4d83-92ae-87c2b7e37adf", "d06f932c-e25f-424e-8550-eb1a0384e2a4": "c4f7b5ef-1a59-4619-b54e-cf41a374b635", "6080eb17-0342-412b-a707-27fcebbc620b": "7c144053-e6f9-4cad-bb77-c063d620e0b8", "93673750-be8f-498d-8a61-7be02dcccabf": "e060e8ed-36f1-4c0e-aef7-6a91690af859", "37705651-ac14-4c53-b486-933c2536c261": "f9836538-74a0-4207-8c08-c6103728d6be", "cbdea7e8-6b23-4688-9c05-3c5c0eae95db": "a8ff9a92-9b91-4767-9913-174a84af5115", "8644d86d-7af8-419e-8e32-3d2a92d15039": "ce3f47aa-6516-42c1-8cbe-7d9f9f3fe51c", "ff7e4c0c-eac9-4a0a-99d6-5121a3dddc82": "16639bfd-8648-44ca-a3a0-e2ba6a3a98c8", "e7244407-bbd7-414f-9c02-b7d3c51f4dcd": "623b56b5-c42d-498e-879c-067728194106", "9c9867a3-6bc3-4544-8032-094305ccc606": "ffce1b10-7adb-42a0-9334-4d3664ace063", "7a71c62a-768b-4849-b2ca-5a26fa8a2847": "268e1e4b-d8da-42ba-90c7-ac6017dabb9e", "83938eff-db4f-4cfe-b2e4-725387540be3": "f00cf63f-75fe-4ccd-aeef-91783ebd153b", "ad6bdffa-b1d6-4451-b6d6-f7c43bace4fe": "bfd1115a-3599-4761-a2df-20da99be5902", "052bf4f8-4827-464c-bc7d-c8d8e4697670": "2ce78b70-9965-4bef-afed-8005e9808f4a", "e6aed9e4-13ac-46ae-b02e-d6fd2d2cb428": "9b9a4941-3530-464c-bc90-233826c569de"}} \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/tools/config.py b/cloud/sam/scripts/chat_function/tools/config.py new file mode 100644 index 00000000..8414c5ad --- /dev/null +++ b/cloud/sam/scripts/chat_function/tools/config.py @@ -0,0 +1,5 @@ +import os + +DYNAMODB_TABLE_NAME = 'tetrisChat' +OPENAI_MODEL_NAME = "gpt-3.5-turbo-0613"#gpt-3.5-turbo-0613" +DYNAMODB_INDEX_NAME = 'user_id-char_name-index' \ No newline at end of file diff --git a/cloud/sam/scripts/chat_function/tools/response.py b/cloud/sam/scripts/chat_function/tools/response.py new file mode 100644 index 00000000..e6029e1b --- /dev/null +++ b/cloud/sam/scripts/chat_function/tools/response.py @@ -0,0 +1,28 @@ +import json + +class HttpResponse: + + @staticmethod + def generate_response(status_code, message): + return { + "statusCode": status_code, + "body": json.dumps({ + "message": message, + }), + } + + @classmethod + def success(cls, message): + return cls.generate_response(200, message) + + @classmethod + def redirect(cls, message): + return cls.generate_response(300, message) + + @classmethod + def client_error(cls, message): + return cls.generate_response(400, message) + + @classmethod + def server_error(cls, message): + return cls.generate_response(500, message) diff --git a/cloud/sam/scripts/chat_function/tools/utils.py b/cloud/sam/scripts/chat_function/tools/utils.py new file mode 100644 index 00000000..c71cfa12 --- /dev/null +++ b/cloud/sam/scripts/chat_function/tools/utils.py @@ -0,0 +1,137 @@ +import boto3 +import openai +import json +from tools.config import DYNAMODB_TABLE_NAME, DYNAMODB_INDEX_NAME, OPENAI_MODEL_NAME +from botocore.exceptions import ClientError +from boto3.dynamodb.conditions import Key +from role.role_tetris import TetrisIndexSearch + +# ドメイン駆動設計におけるリポジトリ部分 +class DynamoDBManager: + def __init__(self, table_name=DYNAMODB_TABLE_NAME, index_name=DYNAMODB_INDEX_NAME): + self.table_name = table_name + self.index_name = index_name + self.client = boto3.client('dynamodb') + self.resource = boto3.resource('dynamodb') + self.table = self.resource.Table(self.table_name) + + def get_max_conversation_id(self, user_id, char_name): + response = self.table.query( + IndexName=self.index_name, + KeyConditionExpression=Key('user_id').eq(user_id) & Key('char_name').eq(char_name), + ScanIndexForward=False + ) + items = response.get('Items', []) + max_order_id = max(item['order_id'] for item in items) if items else -1 + return max_order_id, items + + def delete_items_with_secondary_index(self, user_id, char_name): + dynamodb = boto3.resource('dynamodb') + table = dynamodb.Table(self.table_name) + + response = table.query( + IndexName=self.index_name, + KeyConditionExpression='user_id = :uid and char_name = :cname', + ExpressionAttributeValues={ + ':uid': user_id, + ':cname': char_name, + }, + ) + + primary_keys = [{'user_id': item['user_id'], 'order_id': item['order_id']} for item in response['Items']] + + for key in primary_keys: + print('delete_item:', key) + table.delete_item(Key=key) + + print(f'Deleted {len(primary_keys)} item(s) from {self.table_name}.') + + def store_conversation(self, user_id, char_name, max_order_id, role, content, name=None, function_call=None): + item = { + 'user_id': user_id, + 'order_id': (max_order_id + 1), + 'char_name': char_name, + 'role': role, + 'content': content + } + if name: + item['name'] = name + if function_call: + item['function_call'] = function_call + self.table.put_item(Item=item) + +class OpenAIManager: + def __init__(self, model_name=OPENAI_MODEL_NAME): + self.model_name = model_name + self.tetris_assistant = TetrisIndexSearch() + + @staticmethod + def get_secret(): + secret_name = "openai" + region_name = "ap-northeast-1" + session = boto3.session.Session() + client = session.client( + service_name='secretsmanager', + region_name=region_name + ) + try: + get_secret_value_response = client.get_secret_value( + SecretId=secret_name + ) + except ClientError as e: + raise e + + secret = get_secret_value_response['SecretString'] + key_value = json.loads(secret) + openai.api_key = key_value['openai'] + + def get_chat_response(self, messages): + return openai.ChatCompletion.create( + model=self.model_name, + messages=messages + ) + + def get_chat_response_func(self, messages, functions): + return openai.ChatCompletion.create( + model=self.model_name, + messages=messages, + functions=functions, + function_call="auto" + ) + + @staticmethod + def create_function_args(func_name, args): + function_call = { + "name": func_name, + "arguments": json.dumps(args) + } + return function_call + + def execute_function(self, func_name, **args): + # 定義された関数を実行 + if func_name == "search_tetris_index": + return self.tetris_assistant.search_tetris_index(**args) + else: + func = globals().get(func_name) + if func: + return func(**args) + else: + raise Exception(f"No function named {func_name} found.") + + + def execute_function_call(self, response_data): + # gptが定義した関数名や引数を取得する + call_data = response_data["message"]["function_call"] + func_name = call_data["name"] + args = eval(call_data["arguments"]) + print(f"func_name: {func_name}, args: {args},\n") + + function_response = self.execute_function(func_name, **args) + print(f"function_response: {function_response}") + + # 2回目のAPI実行のための関数の引数を作成 + function_args = self.create_function_args(func_name, args) + #print(f"function_args: {function_args}") + + return func_name, args, function_response, function_args + diff --git a/cloud/sam/template.yaml b/cloud/sam/template.yaml index cd71c7d5..f7d5d47a 100644 --- a/cloud/sam/template.yaml +++ b/cloud/sam/template.yaml @@ -240,7 +240,48 @@ Resources: Path: /trainings/{section} Method: get RestApiId: !Ref TetrisAPI - + + ChatFunction: + Type: AWS::Serverless::Function + Properties: + PackageType: Image + ImageUri: 132308390451.dkr.ecr.ap-northeast-1.amazonaws.com/chat + Timeout: 120 + MemorySize: 512 + Policies: + - AmazonDynamoDBFullAccess + - SecretsManagerReadWrite + Events: + ChatAPP: + Type: Api + Properties: + Path: /chat + Method: get + RestApiId: !Ref TetrisAPI + ChatAPPPost: + Type: Api + Properties: + Path: /chat + Method: post + RestApiId: !Ref TetrisAPI + ChatAPPPut: + Type: Api + Properties: + Path: /chat + Method: put + RestApiId: !Ref TetrisAPI + ChatAPPDelete: + Type: Api + Properties: + Path: /chat + Method: delete + RestApiId: !Ref TetrisAPI + Metadata: + Dockerfile: Dockerfile + DockerContext: ./scripts/chat_function + DockerTag: python3.9-v1 + + Layer: Type: AWS::Serverless::LayerVersion Properties: From 7db1729e215d89c95c3f087236a5856992ab5845 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Mon, 28 Aug 2023 23:03:11 +0900 Subject: [PATCH 02/10] =?UTF-8?q?DynamoDB=E3=81=AE=E3=83=86=E3=83=BC?= =?UTF-8?q?=E3=83=96=E3=83=AB=E5=90=8D=E3=82=92=E5=A4=89=E6=9B=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/scripts/chat_function/tools/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud/sam/scripts/chat_function/tools/config.py b/cloud/sam/scripts/chat_function/tools/config.py index 8414c5ad..df341b5d 100644 --- a/cloud/sam/scripts/chat_function/tools/config.py +++ b/cloud/sam/scripts/chat_function/tools/config.py @@ -1,5 +1,5 @@ import os -DYNAMODB_TABLE_NAME = 'tetrisChat' +DYNAMODB_TABLE_NAME = 'tetris_chat_log' OPENAI_MODEL_NAME = "gpt-3.5-turbo-0613"#gpt-3.5-turbo-0613" DYNAMODB_INDEX_NAME = 'user_id-char_name-index' \ No newline at end of file From 7019a6fed67801ebdc734a55d00cb282387ba9ba Mon Sep 17 00:00:00 2001 From: sota1111 Date: Mon, 28 Aug 2023 23:03:47 +0900 Subject: [PATCH 03/10] =?UTF-8?q?Secrets=20Manager=E3=81=AB=E7=99=BB?= =?UTF-8?q?=E9=8C=B2=E3=81=99=E3=82=8Bopenai=E3=81=AEkey=E3=81=AE=E5=90=8D?= =?UTF-8?q?=E5=89=8D=E3=82=92=E5=A4=89=E6=9B=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/scripts/chat_function/tools/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cloud/sam/scripts/chat_function/tools/utils.py b/cloud/sam/scripts/chat_function/tools/utils.py index c71cfa12..4ee719ba 100644 --- a/cloud/sam/scripts/chat_function/tools/utils.py +++ b/cloud/sam/scripts/chat_function/tools/utils.py @@ -67,7 +67,7 @@ def __init__(self, model_name=OPENAI_MODEL_NAME): @staticmethod def get_secret(): - secret_name = "openai" + secret_name = "openai-key" region_name = "ap-northeast-1" session = boto3.session.Session() client = session.client( @@ -83,7 +83,7 @@ def get_secret(): secret = get_secret_value_response['SecretString'] key_value = json.loads(secret) - openai.api_key = key_value['openai'] + openai.api_key = key_value['openai-key'] def get_chat_response(self, messages): return openai.ChatCompletion.create( From bfed94a2bc6f289f0b85702b8596879dbd6103f2 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Mon, 28 Aug 2023 23:25:25 +0900 Subject: [PATCH 04/10] =?UTF-8?q?=E8=BE=9E=E6=9B=B8=E3=83=87=E3=83=BC?= =?UTF-8?q?=E3=82=BF=E3=81=AE=E5=9F=BA=E3=81=AB=E3=81=AA=E3=82=8B=E3=83=87?= =?UTF-8?q?=E3=83=BC=E3=82=BF=E3=81=AE=E5=89=8A=E9=99=A4=20=E8=BE=9E?= =?UTF-8?q?=E6=9B=B8=E3=83=87=E3=83=BC=E3=82=BF=E3=81=AE=E4=BD=9C=E6=88=90?= =?UTF-8?q?=E6=96=B9=E6=B3=95=E3=82=92local.py=E3=81=AB=E3=82=B3=E3=83=A1?= =?UTF-8?q?=E3=83=B3=E3=83=88=E8=BF=BD=E8=A8=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/scripts/chat_function/data/FAQ.md | 170 --------- .../scripts/chat_function/data/GameStatus.md | 218 ----------- cloud/sam/scripts/chat_function/data/ai.md | 207 ----------- cloud/sam/scripts/chat_function/data/art.md | 228 ------------ .../chat_function/data/block_controller.md | 143 ------- .../data/block_controller_sample.md | 78 ---- .../chat_function/data/board_manager.md | 250 ------------- .../sam/scripts/chat_function/data/docker.md | 75 ---- .../chat_function/data/game_manager.md | 131 ------- .../scripts/chat_function/data/install_mac.md | 39 -- .../chat_function/data/install_ubuntu.md | 25 -- .../chat_function/data/install_windows.md | 39 -- .../data/install_windows_powershell.md | 205 ---------- .../chat_function/data/install_windows_wsl.md | 81 ---- .../chat_function/data/reference_score.md | 13 - .../sam/scripts/chat_function/data/tetris.md | 350 ------------------ cloud/sam/scripts/chat_function/local.py | 4 + 17 files changed, 4 insertions(+), 2252 deletions(-) delete mode 100644 cloud/sam/scripts/chat_function/data/FAQ.md delete mode 100644 cloud/sam/scripts/chat_function/data/GameStatus.md delete mode 100644 cloud/sam/scripts/chat_function/data/ai.md delete mode 100644 cloud/sam/scripts/chat_function/data/art.md delete mode 100644 cloud/sam/scripts/chat_function/data/block_controller.md delete mode 100644 cloud/sam/scripts/chat_function/data/block_controller_sample.md delete mode 100644 cloud/sam/scripts/chat_function/data/board_manager.md delete mode 100644 cloud/sam/scripts/chat_function/data/docker.md delete mode 100644 cloud/sam/scripts/chat_function/data/game_manager.md delete mode 100644 cloud/sam/scripts/chat_function/data/install_mac.md delete mode 100644 cloud/sam/scripts/chat_function/data/install_ubuntu.md delete mode 100644 cloud/sam/scripts/chat_function/data/install_windows.md delete mode 100644 cloud/sam/scripts/chat_function/data/install_windows_powershell.md delete mode 100644 cloud/sam/scripts/chat_function/data/install_windows_wsl.md delete mode 100644 cloud/sam/scripts/chat_function/data/reference_score.md delete mode 100644 cloud/sam/scripts/chat_function/data/tetris.md diff --git a/cloud/sam/scripts/chat_function/data/FAQ.md b/cloud/sam/scripts/chat_function/data/FAQ.md deleted file mode 100644 index 3877223a..00000000 --- a/cloud/sam/scripts/chat_function/data/FAQ.md +++ /dev/null @@ -1,170 +0,0 @@ -# FAQ - -## level1ではブロックの順番は固定とのことだが、具体的にはどんな順番になっているのか -`level1（ブロックの順番固定）`の場合は、[ブロックのindex値](https://github.com/seigot/tetris/blob/master/doc/files/block_controller.md#ブロック情報)が`1→2→3→4→5→6→7→1→...`の順番に出現します。 - -## python実行環境について - -| 環境 | 環境構築手順 | -| ---- | ---- | -| ubuntu18.04,20.04 | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_ubuntu.md) | -| Mac | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_mac.md) | -| Windows+powershell | [こちら](https://github.com/seigot/tetris/blob/master/doc/files/install_windows_powershell.md) | -| Windows+Docker | [こちら](https://github.com/seigot/tetris/blob/master/docker/README.md) | -| JetsonNano | （動作未確認だがおそらく動くはず） | -| RaspberryPi | （動作未確認だがおそらく動くはず） | -| Windows+GoogleChrome+ubuntu-free-online-linux | [chrome webstore URL](https://chrome.google.com/webstore/detail/ubuntu-free-online-linux/pmaonbjcobmgkemldgcedmpbmmncpbgi?hl=ja) | -| AWS | EC2 4CPU 8GBメモリ 20GBストレージ、GPU環境で動作確認済（課金に注意） | - -## `Windows+GoogleChrome+ubuntu-free-online-linux`の環境構築について - ->google chrome上でubuntu serverを実行する
-・[google chrome用のubuntu online server拡張プラグイン](https://chrome.google.com/webstore/detail/ubuntu-free-online-linux/pmaonbjcobmgkemldgcedmpbmmncpbgi)をインストール
-・選択肢のうち、"Xubuntu"を選択（ubuntu18.04かつ軽量なものが良さそう）
-・serverにログインして以下を実施
-　・Desktop上で右クリックし"Open Terminal Here"を選択
-　・terminal上で以下コマンドを実行
-``` -sudo apt install −y git -git clone http://github.com/seigot/tetris -cd tetris game -bash doc/files/install_ubuntu.sh -``` - ---> installが成功すればOK - -``` -python start.py -``` - ---> テトリスが表示されればOK - -## `sudo apt install`時に`E: ロック /var/lib/dpkg/lock-frontend が取得できませんでした - open (11: リソースが一時的に利用できません)`のエラーが出る - -[こちらのサイト](https://marginalia.hatenablog.com/entry/2019/07/03/133854)参照
-以下で解決するはず - -``` -$ sudo rm /var/lib/apt/lists/lock -$ sudo rm /var/lib/dpkg/lock -$ sudo rm /var/lib/dpkg/lock-frontend -``` - -## サンプルプログラム（`python start.py -s y`で動くやつ）の中身はどうなってるのか -[こちら](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)で解説 - -## スコアアタック時の動作PC環境について - -2021/7時点で以下を想定しています。
-PC: [ZBOX Magnux en52060](https://www.zotac.com/jp/product/mini_pcs/magnus-en52060v#spec)
- -``` -- OS : ubuntu18.04 -- CPU: Intel Core i5 -- Memory: 16GB -- NVIDIA GeForce RTX 2060 -``` - -``` -- pythonバージョンは以下 -$ python3 --version -Python 3.6.9 -$ python3 -c 'import torch; print(torch.__version__) ' -1.4.0 -``` - -ソフト環境は以下スクリプトで構築しています。(散らかっててすみません)
-[auto_setup_for_Linux.sh](https://github.com/seigot/tetris/blob/master/docker/auto_setup_for_Linux.sh)
- -## Windows Docker install時のトラブルシューティング - -``` -①BIOSのCPU関連設定 -　Docker for Windowsをインストールして起動すると、 -　「An error occurred Hardware assisted virtualization and data execution protection -　 must be enabled in the BIOS.」と出てくる。 -　⇒ 以下サイトの「1. BIOS設定の確認」をすることでエラー解消 -　　 https://qiita.com/LemonmanNo39/items/b1b104e7fb609464727b - -②WSLのインストール -　Dockerを起動したところ、①は解消されたが以下のメッセージが表示される。 -　「WSL2 installation is incomplete」 -　⇒ 以下を参考にしてWSL2をインストールする。 -　　 https://docs.microsoft.com/ja-jp/windows/wsl/install-win10 -　　 ※ Docker for Windowsをインストールするときに一緒にWSLも入れられたっぽい - -③ Dockerイメージの取得途中で死ぬ -自宅のネットワーク回線が貧弱なのか、下記コマンドでイメージを持ってくる途中で止まる。 -docker run -p 6080:80 --shm-size=512m seigott/tetris_docker - -⇒ Docker for Windowsを起動して、ウィンドウ上部の"歯車(設定)ボタン"、"Docker Engine"をクリック。 -　 configファイルに、「"max-concurrent-download":1」を追記。 -　※これでデフォルトの3並列ダウンロードが直列になる - -④ Dockerイメージの取得途中で、「docker: unauthorized authentication required」と出て死ぬ -⇒ windows power shellを管理者権限で実行してコマンドを叩くといけたっぽい。 -``` - -## pytorch v1.4 インストール方法 - -ubuntu18.04環境では、以下のようにしてインストールできることを確認済
-[pytorch v1.4 インストール済のDocker環境（お試し版）](https://github.com/seigot/tetris/blob/master/docker/README.md)を作成しました。追加で必要なものがあればDockerfileを更新して下さい。 - -``` -function install_torch(){ - ### pytorch from pip image (v1.4) - sudo apt-get install -y libopenblas-base libopenmpi-dev - sudo apt-get -y install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev - - #python -m pip install https://download.pytorch.org/whl/cu101/torch-1.4.0-cp27-cp27mu-linux_x86_64.whl - python -m pip install torchvision==0.2.2 - pip3 install torch==1.4.0 torchvision==0.2.2 - pip install 'pillow<7' -} -``` -## Dockerはコンテナ終了の度にデータが消えて手間が掛かるので何とかしたい -[WSL(Windows Subsystem for Linux)を使う場合](https://github.com/seigot/tetris/blob/master/doc/files/install_windows_wsl.md)の手順を用意しました。
-（kyadさんありがとうございます）
-
-追記：cygwin環境構築手順
-[isshy-youさんによる`Cygwin Install for tetris`構築手順](https://github.com/isshy-you/tetris_game/wiki/Cygwin-Install-for-tetris_game)
-（isshy-youさんありがとうございます） - -## pythonコマンドが見つからない旨のエラーが出る場合 - -ubuntu20.04等では、`python3`があるにも関わらず`python`コマンドがない - -``` -$ python start.py -CompletedProcess(args='python --version', returncode=127, stderr='/bin/sh: 1: python: not found\n') -/bin/sh: 1: python: not found -error: subprocess failed. -``` - -``` -$ python --version - -Command 'python' not found, did you mean: - - command 'python3' from deb python3 - command 'python' from deb python-is-python3 -``` - -ログ記載の通り`python-is-python3`をインストールすればOK - -``` -sudo apt install -y python-is-python3 -``` - -## スコア評価用サーバについて - -以下を作成してみました。 - -[https://github.com/seigot/tetris_score_server](https://github.com/seigot/tetris_score_server) - -## `python start.py`実行時に`TypeError: arguments did not match any overloaded call:`エラーが出る - -以下issueで対策中
-> [`python start.py`実行時に`TypeError: arguments did not match any overloaded call:`エラーが出る](https://github.com/seigot/tetris/issues/5) - -## 以下、順次追記 diff --git a/cloud/sam/scripts/chat_function/data/GameStatus.md b/cloud/sam/scripts/chat_function/data/GameStatus.md deleted file mode 100644 index 5023a8cf..00000000 --- a/cloud/sam/scripts/chat_function/data/GameStatus.md +++ /dev/null @@ -1,218 +0,0 @@ -## `GameStatus`データ構造 - -[`block_controller.py`](https://github.com/seigot/tetris/blob/master/block_controller.py)内の`def GetNextMove(self, nextMove, GameStatus)`で扱う`GameStatus`は辞書型データです。
-ブロックの動きを決定するための参考データとして以下を格納しています。 - -``` -* field_info : フィールド情報 -* block_info : ブロック情報 -* judge_info : 審判情報 -* debug_info : デバッグ情報 -``` - -以下、各情報の詳細について記載します。 - -## field_info - -以下を格納しています。 - -``` -* 'backboard': フィールドのデータ -* 'height': フィールドの高さ -* 'width' : フィールドの幅 -* 'withblock' : フィールド+ブロックの位置を合わせたデータ -``` - -具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 - -``` -'field_info': {'backboard': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'height': 22, - 'width': 10, - 'withblock': [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, -``` - -## block_info - -以下を格納しています。 - -``` -'currentDirection': 現在のブロックが初期形状から何回回転した形状か -'currentShape': { - 'class': 現在のブロックを管理しているクラスのアドレス - 'direction_range': 現在のブロックが回転できる回数 - 'index': 現在のブロックのindex値 -}, -'currentX': 現在のブロック操作の基準点(x座標) -'currentY': 現在のブロック操作の基準点(y座標) -'nextShape': { - 'class': 次のブロックを管理しているクラスのアドレス - 'direction_range': 次のブロックが回転できる回数 - 'index' : 次のブロックのindex値 -} -``` - -具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 - -``` -'block_info': {'currentDirection': 0, - 'currentShape': {'class': , - 'direction_range': (0, 1), - 'index': 1}, - 'currentX': 5, - 'currentY': 1, - 'nextShape': {'class': , - 'direction_range': (0, 1, 2, - 3), - 'index': 2}}, -``` - -## debug_info - -以下を格納しています。
-デバック用に準備している情報であり、必ずしも使う必要はないかもしれないです。 - -``` -'dropdownscore': 落下により獲得したスコアの合計 -'line_score': { lineを消した時などに獲得できるスコア - 'line1': 1line消した時の獲得スコア - 'line2': 2line消した時の獲得スコア - 'line3': 3line消した時の獲得スコア - 'line4': 4line消した時の獲得スコア - 'gameover': game overになった時の獲得スコア -}, -'line_score_stat': 消したlineの統計データ -'linescore': lineを消して獲得したスコアの合計 -'shape_info': { - 'shapeI': { - 'color': ShapeIの色 - 'index': ShapeIのindex - }, - 'shapeJ': { - 'color': ShapeJの色 - 'index': ShapeJのindex - }, - 'shapeL': { - 'color': ShapeLの色 - 'index': ShapeLのindex - }, - 'shapeNone': { - 'color': ShapeNone(ブロックがない状態)の色 - 'index': ShapeNone(ブロックがない状態)のindex - }, - 'shapeO': { - 'color': ShapeOの色 - 'index': ShapeOのindex - }, - 'shapeS': { - 'color': ShapeSの色 - 'index': ShapeSのindex - }, - 'shapeT': { - 'color': ShapeTの色 - 'index': ShapeTのindex - }, - 'shapeZ': { - 'color': ShapeZの色 - 'index': ShapeZのindex - } -}, -'shape_info_stat': ゲーム開始時から現時点までに出現したブロックの統計情報 -``` - -具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 - -``` - 'debug_info': {'dropdownscore': 0, - 'line_score': {'line1': 100, - 'line2': 300, - 'line3': 700, - 'line4': 1300, - 'gameover': -500}, - 'line_score_stat': [0, 0, 0, 0], - 'linescore': 0, - 'shape_info': {'shapeI': {'color': 'red', - 'index': 1}, - 'shapeJ': {'color': 'purple', - 'index': 3}, - 'shapeL': {'color': 'green', - 'index': 2}, - 'shapeNone': {'color': 'none', - 'index': 0}, - 'shapeO': {'color': 'pink', - 'index': 5}, - 'shapeS': {'color': 'blue', - 'index': 6}, - 'shapeT': {'color': 'gold', - 'index': 4}, - 'shapeZ': {'color': 'yellow', - 'index': 7}}, - 'shape_info_stat': [0, 1, 0, 0, 0, 0, 0, 0]}, -``` - -## judge_info - -以下を格納しています。 - -``` -'block_index': 現在のブロックがゲーム開始時から何番目に登場したブロックか -'elapsed_time': ゲーム開始時からの経過時間(s) -'game_time': 制限時間(s) -'gameover_count': ゲーム開始時から現在までにゲームオーバーになった回数 -'line': ゲーム開始時から現在までに消したラインの数 -'score': ゲーム開始時から現在までに獲得した合計スコア -``` - -具体的には以下のようなデータとなっています。実際に出力してみると分かり易いと思います。 - -``` - 'judge_info': {'block_index': 1, - 'elapsed_time': 1.083, - 'game_time': 180, - 'gameover_count': 0, - 'line': 0, - 'score': 0}} - ``` - diff --git a/cloud/sam/scripts/chat_function/data/ai.md b/cloud/sam/scripts/chat_function/data/ai.md deleted file mode 100644 index 4ceca12c..00000000 --- a/cloud/sam/scripts/chat_function/data/ai.md +++ /dev/null @@ -1,207 +0,0 @@ -# AIについて（準備中） - -# 1.環境準備 -- sample のコードでは [pytorch](https://pytorch.org/get-started/locally/) を使ってAIのニューラルネットワークを構築します。 -- pytorch　については[こちら](https://pytorch.org/get-started/locally/)を参考にし、環境に合わせてインストールしてください。 - -### 例) WindowsのCPU動作用をインストールする場合 -``` -pip3 install torch torchvision torchaudio -``` -### 例) MacのCPU実行用をインストールする場合 -``` -pip3 install torch torchvision torchaudio -``` - -# 2.学習と推論 - -* サンプルとして下記の２つのニューラルネットワークが -[こちら](../../game_manager/machine_learning/model/deepqnet.py) -のコードに定義されています。 - - * sample: DQN(Deep Q Network)を使った学習・推論 - * sample2: MLP (Multilayer perceptron)を使った学習・推論 - - -## 2.1学習の実行 -- DQN(Deep Q Network)を使う場合 - -``` -python start.py -m train_sample -d 1 -l 2 -t -1 -``` - -- MLP (Multilayer perceptron)を使う場合 -``` -python start.py -m train_sample2 -d 1 -l 2 -t -1 -``` - -## 2.2推論の実行 - -- DQN(Deep Q Network)を使う場合 -``` -python start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt -``` - -- MLP (Multilayer perceptron)を使う場合 -``` -python start.py -m predict_sample2 -l 2 --predict_weight weight/MLP/sample_weight.pt -``` - -## 2.3 再学習/追加学習（Fine Tuning) の実行 -- 学習済みの重みを初期状態として学習する場合は下記の手順で　configのyamlファイルを設定する。 -1. ft_weight に学習済み重みファイルのパスを設定 -2. finetune を　False → True に変更 -3. initial_epsilon を小さくする（0より大きい１以下の値） - -- initial_epsilon は ε-greedy法というアルゴリズムのパラメータになり、学習初期ににどの程度ランダムな行動を取るかを設定します。追加学習の場合は、小さい値を設定することで、学習済みのモデルを有効活用できます。 -- ε-greedy法については下記のサイトなどが参考になります。 - - [https://horomary.hatenablog.com/entry/2021/01/26/233351](https://horomary.hatenablog.com/entry/2021/01/26/233351) - -## 2.4 -m オプションについて - - 学習/推論の切り替えおよびサンプル/本番コードの切り替には　"-m"　オプションを使用します。 - -**※ sampleコードは基本的に変更せず、[本番用コード](../../game_manager/machine_learning/block_controller_train.py )で開発してください** - -- "-m" オプションに対して下記の引数を渡すことで書くモードを切り替えられる。 - -|オプション名|説明| -| ---- | ---- | -| train_sample| [サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py)を用いてDQNによる学習を行う| -| train_sample2| [サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py)を用いてMLPによる学習を行う| -| train| [本番用コード](../../game_manager/machine_learning/block_controller_train.py ) を用いて学習を行う| -| predict_sample| [サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py)を用いてDQNによる推論を行う。
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| -| predict_sample2| [サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py)を用いてMLPによる学習を行う　
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| -| predict| [本番用コード](../../game_manager/machine_learning/block_controller_train.py ) を用いて学習を行う　
実行時に--predict_weight オプションにより重みのパスを選択する必要があります。| - - -## 2.5 AI関連のその他オプションについて -- AI学習関連のオプションは下記 - -|オプション名|引数|説明| train or predict| -| ---- | ---- | ---- | ---- | -| *--train_yaml* | 学習に用いるyamlファイルのパス|学習に用いるyamlファイルを選択する。|train| -| *--predict_weight* | 推論に用いるweightファイルのパス|推論に用いるweightファイルを選択する|predict| - - -# 3. 強化学習について -[サンプルコード1](../../game_manager/machine_learning/block_controller_train_sample.py) 　および　[サンプルコード2](../../game_manager/machine_learning/block_controller_train_sample2.py) では強化学習と呼ばれる方法でテトリスをプレイするための最適なパラメータを学習します。 - -![Screenshot](../../doc/pics/rainforcement_learning.png) - - -# 4. パラメータについて -## 4.1 yamlファイルについて -- サンプルコードではyaml ファイルによってハイパーパラメータ（パラメータを学習するためのパラメータ）を設定する。 -- サンプル用yaml ファイルのフォーマットは下記 - -
共通 - -|パラメータ名|説明|型|train or predict|default| -| ---- | ---- | ---- | ---- | ---- | -|**common** |- |- |- |-| -|ft_weight|ファインチューニング(初期値として学習済み
の重みを用いる学習)に用いる重みファイルのパス|str|train|None| -|log_path| tensorboardのログ出力フォルダ名|str|共通|tensorboard| -
- -
model関連 - -|パラメータ名|説明|型|train or predict|default| -| ---- | ---- | ---- | ---- | ---- | -|**model** |- |- |- |-| -|name|使用するAIモデル名|str|共通|DQN| -|finetune|ファインチューニングするかどうかのフラグ|bool|train|False| -|**state** |- |- |- |-| -|dim|状態の次元数|int|共通|4| -
- -
学習 - -|パラメータ名|説明|型|train or predict|default| -| ---- | ---- | ---- | ---- | ---- | -|**train** |- |- |- |-| -|optimizer|最適化関数(サンプルではAdamまたはSGDが使用可能)|str|train|Adam| -|lr|学習率|float|train|1.0e-3| -|lr_gamma|更新率(optimizerがSGDの時のみ使用)|float|train|0.1| -|lr_momentum|モーメンタム(optimizerがSGDの時のみ使用)|float|train|0.99| -|lr_step_size|ステップサイズ(optimizerがSGDの時のみ使用)|int|train|1000| -|num_epoch|エポック数(学習するイテレーション数)|int|train|5000| -|num_decay_epochs|ε-greedyのεを最小にするエポック数
(ここからランダム性を下げる。)|int|train|3000| -|initial_epsilon|ε-greedyのεの初期値|float|train|1.0| -|final_epsilon|ε-greedyのεの最小値|float|train|1.0e-3| -|batch_size|バッチサイズ|int|train|512| -|gamma|割引率|float|train|0.99| -|max_penalty|ペナルティの最大値|float|train|-1| -|target_net|TargetNetworkの使用フラグ|bool|train|True| -|target_copy_intarval|TargetNetworkにコピーするインターバル|int|train|500| -|replay_memory_size|Experience replayのreplay bufferのサイズ|int|train|30000| -|double_dqn|Double DQNの使用フラグ|bool|train|True| -|reward_clipping|Reward clippingの使用フラグ|bool|train|True| -|prioritized_replay|Prioritized Experience Replayの使用フラグ|bool|train|True| -|multi_step_learning|Multi step learningの使用フラグ|bool|train|True| -|multi_step_num|Multi step learningのステップ数|int|train|0,100,300,700,1300,-1300
　左から　0列崩し,1列崩し,2列崩し,3列崩し,4列崩し,ゲームオーバー)| -|reward_list|点数に応じた報酬の設定値
（0列崩し,1列崩し,2列崩し,3列崩し,4列崩し,ゲームオーバーの順）|int|train|3| -|reward_weight|点数以外で与えられるペナルティの重み
（隣接する行との高さ差分,最大の高さ,穴の数 の順)
0以上の値が必須|float|train|0.01,0.0,0.01
（左から　隣接する行との高さ差分,最大の高さ,穴の数 ）| -
- -## 4.2 パラメータ調整例（得たい効果とパラメータの関係例） -- 下記にパラメータ調整の方針例を記載する -- それぞれの効果が常に得られるとは限らないので実験を繰り返しながら、最適な値を探してみてください。 -1. ブロックを積み上げる - - reward_listの３列崩し、４列崩しの値を大きくする - - reward_weightの max_heightを小さくする - -2. ゲームオーバの発生を防ぐ（ブロックを積み上げ量を減らす） - - reward_listの３列崩し、４列崩しの値を小さくする - - reward_weightの max_heightを大きくする - -3. 過去の盤面からの影響を小さくする - - gamma(割引率)を大きくする。 - -4. 学習を安定させる - - reward_clipping を Trueにする。 - - target_net を Trueにする - - double_DQN を Trueにする。 - -5. 学習を早くする。（学習するモデルのパラメータを減らす） - - モデルのレイヤ数、カーネルサイズを減らす - - MLPを使用する - -6. 学習を早くする。（効果の高いデータを効率的に学習に使用する） - - prioritized_replay を Trueにする。 - - lr（学習率）を大きくする。（大きすぎると収束しなくなるので注意） - - batch_size　を大きくする （学習に使用するメモリ量が大きくなるので注意） - - replay_memory_size　（学習に使用するメモリ量が大きくなるので注意） - - -# 5.チュートリアルの実行 -- サンプル用のコードを試すための[チュートリアル用Jupyter-notebook]()../../game_manager/machine_learning/) -を用意しているので、コード理解のためにお使いください。 - -- Jupyter-notebookか下記の手順で使用できます. -- 1. Jupyter-notebookのインストール -``` -pip install jupyter -``` - -- 2. Jupyter-notebookの起動 -``` -jupyter notebook -``` -下記のような画面が、ブラウザで開かれるので -game_manager > machine_learning > model > tutorial -の準備進み、***.ipynb ファイルを開いてください。 -![Screenshot](../../doc/pics/jupyter-sample.png) - -- Jupyter-notebook のセルは　Shit＋Enter key で実行できます - -- Jupyter-notebook の使い方は下記のサイトなどが参考になります。 -[https://qiita.com/takuyanin/items/8bf396e7b6b051670147](https://qiita.com/takuyanin/items/8bf396e7b6b051670147) - -- venv などの仮想環境を用いている場合は下記のサイトなどが参考になります。 -　　 -[https://qiita.com/smiler5617/items/e0d9b3034d79457cc253](https://qiita.com/smiler5617/items/e0d9b3034d79457cc253) - -# 6. さらなる強化に向けて -- 第3回での cookie4869 の強化資料を下記で共有いたします。 - -![AI強化資料](../pics/20220920_Tetris_AI.pdf) diff --git a/cloud/sam/scripts/chat_function/data/art.md b/cloud/sam/scripts/chat_function/data/art.md deleted file mode 100644 index 7b5004ca..00000000 --- a/cloud/sam/scripts/chat_function/data/art.md +++ /dev/null @@ -1,228 +0,0 @@ -# artについて - -本ドキュメントではテトリスアートの取り組みについて記載する - -# 概要 - -通常、テトリスは上から落ちてくるブロック（テトリミノ）を操作して、横列を埋めて消していくようにして遊ぶ。 -一方、テトリスアートはブロックでフィールドに模様を描くことを目的とする。（ブロックを消すことを必ずしも目的としない） -詳しくは"テトリスアート"でgoogle検索をしてみて下さい。 - -# 取り組み方 - -1.まず、ブロックでフィールドに描きたい模様をイメージする。 -2.次に、模様がテトリス上で実現可能かどうかを検討する。（作図などがお勧めです） -3.実現可能であればブロックを操作して模様を作成する。 - -3.によりブロックを操作する場合、後述するconfigファイルを使い各ブロックの色や出現順序(index)などを調整できるようにしている。 - -# サンプルコード - -`python start.py`実行時に以下オプションを指定するとサンプルコードが実行される -[youtube-link: tetris art sample](https://www.youtube.com/watch?v=Seh6g9_nL6o) - -`1:onigiri` - -``` -python start.py -l1 -m art --art_config_filepath config/art/art_config_sample1.json -``` - -![Screenshot](../pics/art_sample_onigiri.png) - -`2:manji` - -``` -python start.py -l1 -m art --art_config_filepath config/art/art_config_sample2.json -``` - -![Screenshot](../pics/art_sample_manji.png) - -`3:cartoon charactor` - -``` -python start.py -l1 -m art --art_config_filepath config/art/art_config_sample3.json -``` - -![Screenshot](../pics/art_sample_cartoon.png) - -other sample - -| name | --art_config_filepath | note | -| ---- | ---- | ---- | -| 4:heart | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample4.json | - | -| 5:hamburger_shop | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample5.json | - | -| 6:parking | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample6.json | - | -| 7:team rocket | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample7.json | - | -| 8:happy_new_year_2023 | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample8.json | - | -| 9:taka | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample9.json | - | -| ... | - | - | - -contribution - -| name | --art_config_filepath | thanks | -| ---- | ---- | ---- | -| mario | python start.py -l1 -m art --art_config_filepath config/art/art_config_sample_tanaka_2.json | @tanaken | -| ... | - | - | - -# configファイルの説明 - -テトリスアートを作成し易くするために各ブロックの色や出現順序(index)などをconfigファイルで調整できるようにしている。 -以下はconfigファイルの説明である。 - -art用configファイルサンプル -[config/art/art_config_sample_default.json](https://github.com/seigot/tetris/blob/master/config/art/art_config_sample_default.json) - -``` -{ - // 各ブロックの色を調整する。色についてはカラーコードを参照下さい。 - "color": { - "shapeI": "0xCC6666", - "shapeL": "0x66CC66", - "shapeJ": "0x6666CC", - "shapeT": "0xCCCC66", - "shapeO": "0xCC66CC", - "shapeS": "0x66CCCC", - "shapeZ": "0xDAAA00" - }, - // 各ブロックの出現順序(index)/direction/x/yを調整する。 - // 行を追加することが可能であり、追加すると記載に応じたブロックが出現する。 - "block_order": [ - [1,0,0,1], - [2,0,0,1], - [3,0,0,1], - [4,0,0,1], - [5,0,0,1], - [6,0,0,1], - [7,0,0,1] - ] -} -``` - -ブロックの色については以下のカラーコードを参考にしてください。 - -> [色の名前とカラーコードが一目でわかるWEB色見本 原色大辞典 - HTMLカラーコード](https://www.colordic.org/) - -各ブロックの出現順序(index)/direction/x/yについては以下を参考にしてください。 - -> [ブロック操作用プログラムについて](./block_controller.md) - -# 実行方法 - -以下のように`-m art`,`--art_config_filepath`によりモード及びconfigファイルを指定する。 - -``` -# "art_config_sample.json"を指定して実行 -python start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json - -# 落下速度を早くする場合は"-d500"を加えて実行 -python start.py -l1 -m art --art_config_filepath config/art/art_config_sample.json -d500 -``` - -自作したart用configファイル(`xxx.json`)を作成する場合は以下のようにファイルコピーして使用してください。 - -``` -# 事前にサンプルをコピーしておく(art_config_sample.json --> xxx.json) -cp config/art/art_config_sample.json config/art/xxx.json -# 実行 -python start.py -l1 -m art --art_config_filepath config/art/xxx.json -``` - - -# 作成アルゴリズムの例 -#### ◆2ドットプリンター方式 -横10マスのフィールドにテトリスミノを置いた後に残るブロックの個数は 4n-10m 個です。 -そのためテトリスアートを 4n-10m 個のドットの組み合わせに分解することで楽に作ることができます。 -汎用性が高いのは2ドット残し（32ブロック：n=8, m=3 や 52ブロック：n=13, m=5 など）。 -アートには1行あたり最大9個のドットを置けます。 -そのため基本的に各行を 2\*4 + 1ドットに分解し、余った1同士で2ドットを構成することを目指します。 -``` -2ドットを同じ行に残す場合、その組み合わせは恐らく2205通り（x座標10*9÷2、shape7*7）。 -2ドットを違う行に残す場合、その組み合わせは恐らく4900通り（x座標10*10、shape7*7）。 -アートを構成する2ドットを残せる出現順序 [index,direction,x,y] をあらかじめ用意すれば、config作成の自動化が可能になります。 -``` - - -**Step1：任意の2ドットを残す出現順序をまとめる** -`art_lib.py`： -> [https://github.com/mattshamrock/tetris/blob/art/art_lib.py](https://github.com/mattshamrock/tetris/blob/art/art_lib.py) -xs_0000 ← 同じ行に残す場合。例えば 'xs_5294'なら、x=5にshapeindex=2（Lミノ）、x=9にshapeindex=4（Tミノ）が残る置き方。 -xs_00_00 ← 違う行に残す場合。前半が1行目のx座標とshapeindex、後半が2行目のx座標とshapeindex。 -未発見の場合は False と記載 - -``` -art_lib.pyの現状： -【2023/3/E時点】 - ・'xs_0000'のうち32ブロック使用で実現可能なもの：全探索済み; 後述 - ・'xs_0000'のうち52ブロック以上使用で実現可能なもの：手作業。不完全 - ・'xs_00_00'のうち見つけたもの：手作業。とても不完全 -``` - -`art_lib.py`用の探索アルゴリズム： -> [https://github.com/mattshamrock/tetris/blob/art/artlib_search_32.py](https://github.com/mattshamrock/tetris/blob/art/artlib_search_32.py) -各xs_0000について、32ドット（8ミノ）の出現順序をシミュレートし、指定の2ドットのみが残るかを判定します。 -枝切りをすることで探索時間を減らそうとしています。手元PCでは36時間で半分の約1200通りが出力できます。 -後述の「反転」と組み合わせると約36時間で完了します。 - -`art_lib.py`用の反転アルゴリズム： -> [https://github.com/mattshamrock/tetris/blob/art/artlib_reverse.py](https://github.com/mattshamrock/tetris/blob/art/artlib_reverse.py) -横10マスのテトリスはすべての配置を左右反転させても成り立ちます。 -探索でart_lib.pyを埋めていく際、結果が出た出現順序を反転させれば探索のみで埋める約半分の時間で完了できるはずです。 -注意…基準点ズレの調整が必要な組み合わせが存在します。（例：shape:Sの形状:1 の反転とshape:Zの形状:1 は基準点が1マス違う） - - - - -**Step2：ドットアートを描く** -現状は手作業です -　　 - - -**Step3：ドットアートを数値に変換する** -ブロックのindex値に変換します。 -Step4の入力に合わせて文字数=10のstrに変換しています。 -空白=0です。 -現状はexcelで作業しています -　　 - -**Step4：art_lib.pyにある実現可能な2ドットの組み合わせを探索する** -`art_lib.py`の組み合わせ探索アルゴリズム： -> [https://github.com/mattshamrock/tetris/blob/art/create_art.py](https://github.com/mattshamrock/tetris/blob/art/create_art.py) -10個のindex値を2ドットの組み合わせに分解します。 -art_lib.pyでは'xs_0000'の出現順序が未発見の場合にFalseを返すことを利用して、実現可能な2ドットの組み合わせを探索できます。 -1行に奇数個のドット（空白を除く）がある場合、余ったドットのx座標とindex値も返します。これが'xs_00_00'の前半か後半のいずれかになります。 - -　 - -**Step5：Step4の結果を調整する** -・'xs_00_00' は余りとして返された情報を基に自力で発見し、`art_lib.py`に追加する必要があります。 -・余りとして返るドットのみで'xs_00_00'が構成されることを前提にしています。そのため各行のドット数（空白を除く）の偶奇によっては成立しません。 -・'xs_0000' において、すぐ下のマスが空白の場合に、そこにハマる形で意図しない結果になる場合があります。左右のマスにブロックがあれば引っ掛かるため回避可能な可能性が高く、その場合は順番を変更することで解決できます。 -　　 - -**Step6：2ドットの組み合わせをconfigファイルの出現順序に変換する** -Step4-5で得られた 'xs_0000' と 'xs_00_00' の羅列を`art_lib.py`の`artdict`のキーとして利用し、出力します。 -現状は余計な二重括弧　[[]] が入ってきてしまいます。置換等で取り除きます。 -各出力の最後のカンマ , が不足しています。二重括弧と合わせて置換で対応します。 -　　 - -**Step7：configファイルに転記する** -転記すればOK。 -色の調整は手作業です。 -　　 - -**今後に向けて：** -・`art_lib.py`の抜けている部分を埋める。52ブロック（13手）以上を効率的に探索する。 -・`art_lib.py`の抜けている部分を埋める。2行に分かれるブロックを効率的に探索する。32マスを8つに分割した結果がテトリスミノの形になるか判定する、という探し方が良い気がする。 -・Step5の調整を自動化する。 -・Step2を自動化する（画像生成AI的な） - - - - - - - -# 参考 -[「テトリス」の斬新すぎる遊び方が話題に。積み上げたブロックでマリオやルイージを再現!?](https://nlab.itmedia.co.jp/nl/articles/1109/13/news025.html) -[色の名前とカラーコードが一目でわかるWEB色見本 原色大辞典 - HTMLカラーコード](https://www.colordic.org/) -[youtube-link: tetris art sample](https://www.youtube.com/watch?v=Seh6g9_nL6o) diff --git a/cloud/sam/scripts/chat_function/data/block_controller.md b/cloud/sam/scripts/chat_function/data/block_controller.md deleted file mode 100644 index e815bf0f..00000000 --- a/cloud/sam/scripts/chat_function/data/block_controller.md +++ /dev/null @@ -1,143 +0,0 @@ ->本ページでは、[ブロック操作用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)について説明を追記頂ける方を募集しています。
->説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
- -# ブロック操作用プログラムについて - -[ブロック操作用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)では、ボード情報からブロックの次の動作を決定します。
-このファイルの`def GetNextMove`関数にて、ボード情報`GameStatus`を入力として受け取り、次の動作決定用のデータ`nextMove`を返す事でブロックが動く仕様になっています。
-
-以下は、ボード情報`GameStatus`、次の動作決定用のデータ`nextMove`について記載します。
- -# ボード情報 -ボード情報には以下が含まれます。
-詳細は、[`GameStatus`データ構造](https://github.com/seigot/tetris/blob/master/doc/files/GameStatus.md)や、[サンプルコード](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)のようにGameStatusをログ出力することで確認可能です。 - -``` -* field_info : フィールド情報 -* block_info : ブロック情報 -* judge_info : 審判情報 -* debug_info : デバッグ情報 -``` - -# フィールド情報 - -## フィールドの定義 -フィールドは横幅10×高さ22ブロック分の大きさです。
-座標系は、左上を原点(0,0)として、x軸は右側、y軸は下側を正の方向として定義しています。
- -![Screenshot](../pics/coordinate_difinition.PNG) - -フィールドのデータは、一次元配列として定義しています。
-座標(x, y)のデータは、一次元配列の[x + y * 横幅]番目にアクセスすることで取得可能です。
-
-| | フィールド | 一次元配列 | -| --- | --- | --- | -| データ | ![Screenshot](../pics/field_data.png) | ![Screenshot](../pics/matrix_data.png) | - -## フィールド情報に格納されているデータ - -上記の一次元配列の各座標にブロックが有るか無いかを管理しています。 - -``` -* `0以外` : ブロック有り -* `0` : ブロック無し -``` - -ブロックが有る場合は、各ブロックのIndex値を格納しています。
-Index値はブロック情報を参照下さい。 - -# ブロック情報 - -## ブロックの定義 -ブロックは７種類あり、Index値と初期形状を以下の通り定義しています。
-横移動と回転が可能です。
-回転は時計回りに回転します。ブロックによって１周の回転に必要な回数が異なります。
- -| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | -| --- | --- | --- | --- | --- | --- | --- | --- | -| index値 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | -| 初期形状 | ![Screenshot](../pics/ShapeI.png) | ![Screenshot](../pics/ShapeL.png) | ![Screenshot](../pics/ShapeJ.png) | ![Screenshot](../pics/ShapeT.png) | ![Screenshot](../pics/ShapeO.png) | ![Screenshot](../pics/ShapeS.png) | ![Screenshot](../pics/ShapeZ.png) | -| １周の回転に必要な回数 | 2 | 4 | 4 | 4 | 1 | 2 | 2 | - -## ブロック操作の基準点 -各ブロックには操作の基準となる点（以下、ブロック操作の基準点）があります。
-また、ブロック操作の基準点以外の座標も取得可能です。基準点(x,y)とその周囲の座標は以下のように表します。
- -![Screenshot](../pics/block_coordinate_difinition2.PNG) - -ブロック毎に操作の基準点(x,y)は異なります。各ブロックの形状毎の基準点とそれ以外の座標は以下の通り。
- -| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | -| --- | --- | --- | --- | --- | --- | --- | --- | -| 形状0における操作の基準点(x,y) | ![Screenshot](../pics/I11.PNG) | ![Screenshot](../pics/L11.PNG) | ![Screenshot](../pics/J11.PNG) | ![Screenshot](../pics/T11.PNG) | ![Screenshot](../pics/O11.PNG) | ![Screenshot](../pics/S11.PNG) | ![Screenshot](../pics/Z11.PNG) | -| 基準点とそれ以外の座標 | (x,y-1),(x,y),(x,y+1),(x,y+2) | (x,y-1),(x,y),(x,y+1),(x+1,y+1) | (x,y-1),(x,y),(x,y+1),(x-1,y+1) | (x,y-1),(x,y),(x+1,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x+1,y) | (x,y-1),(x+1,y-1),(x-1,y),(x,y) | (x-1,y-1),(x,y-1),(x,y),(x+1,y) | -| 形状1における操作の基準点(x,y) | ![Screenshot](../pics/I22.PNG) | ![Screenshot](../pics/L22.PNG) | ![Screenshot](../pics/J22.PNG) | ![Screenshot](../pics/T22.PNG) | --- | ![Screenshot](../pics/S22.PNG) | ![Screenshot](../pics/Z22.PNG) | -| 基準点とそれ以外の座標 | (x-2,y),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x-1,y+1) | (x-1,y-1),(x-1,y),(x,y),(x+1,y) | (x,y+1),(x-1,y),(x,y),(x+1,y) | --- | (x,y-1),(x,y),(x+1,y),(x+1,y+1) | (x+1,y-1),(x,y),(x+1,y),(x,y+1) | -| 形状2における操作の基準点(x,y) | --- | ![Screenshot](../pics/L33.PNG) | ![Screenshot](../pics/J33.PNG) | ![Screenshot](../pics/T33.PNG) | --- | --- | --- | -| 基準点とそれ以外の座標 | --- | (x-1,y-1),(x,y-1),(x,y),(x,y+1) | (x,y-1),(x+1,y-1),(x,y),(x,y+1) | (x,y-1),(x-1,y),(x,y),(x,y+1) | --- | --- | --- | -| 形状3における操作の基準点(x,y) | --- | ![Screenshot](../pics/L44.PNG) | ![Screenshot](../pics/J44.PNG) | ![Screenshot](../pics/T44.PNG) | --- | --- | --- | -| 基準点とそれ以外の座標 | --- | (x+1,y-1),(x-1,y),(x,y),(x+1,y) | (x-1,y),(x,y),(x+1,y),(x+1,y+1) | (x,y-1),(x-1,y),(x,y),(x+1,y) | --- | --- | --- | - -尚、基準点とそれ以外の座標については現在のブロック情報（メソッド`Shape_class.getCoords()`）から取得可能です。
-また、各形状におけるx,y方向の最大長は、現在のブロック情報（メソッド`Shape_class.getBoundingOffsets()`)から取得可能です。
-取得の具体的な実装方法は、[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)を参照ください。 - -## ブロック出現時の初期位置 -ブロックは、ブロック操作の基準点が座標(5,1)になるように出現します。 - -![Screenshot](../pics/init_block_location3.png) - -尚、ブロックの出現時、既に基準点(x, y)にブロックが存在していると`game over`となりフィールドがリセットされます。 - -## ブロック情報に格納されているデータ - -主に以下を格納しています。 - -* `現在のブロック操作の基準点(x,y)` -* `現在のブロックのIndex値` -* `次のブロックのIndex値` - -# 審判情報 - -以下を格納しています。 - -* `経過時間[s]` : ゲーム開始時からの経過時間 -* `スコア[点]` : ゲーム開始時から現在までに獲得した合計スコア -* `ライン数` : ゲーム開始時から現在までに消したラインの数 -* `ゲームオーバー回数` : ゲーム開始時から現在までにゲームオーバーになった回数 - -# デバッグ情報 - -以下等を格納しています。 - -* `スコア内訳` : 合計スコア（消したライン、落下ボーナス）の内訳 - -その他、詳細はコード上のGameStatusを参照下さい。 - -# 次の動作決定用のデータ - -以下のデータを、次の動作決定用のデータ`nextMove`に与えることで次の動作を決定します。
-`nextMove`は辞書型のデータであり、`nextMove["strategy"]`以下の各値に応じてブロックが動きます。
- -* `direction` : 現在の形状から時計回りに何回回転するか（範囲：0〜(1周の回転に必要な回数-1) ） -* `x` : x座標のどの位置に移動するか（範囲：0-9、x座標の絶対値を指定する） -* `y_operation` : y座標方向の操作（1:落下、0:移動） -* `y_moveblocknum` : y座標方向に移動する場合、y座標に何ブロック分移動するか（範囲：1-21） - -![Screenshot](../pics/nextMove.PNG) -
-（今後ルール変更がある場合は、ルールに応じて`nextMove`もアップデートしていく予定です。） - -# HOLD機能 - -HOLD機能とは、ブロックを保持する機能です。この機能を使うと現在のブロックがHOLD扱いになります。
-その後、もし保持しているブロックがない場合は次のブロックが落ちてきます。
-もし保持しているブロックが既にある場合はそのブロックが再び落ちてきます。
- -プログラムから使用する場合は`nextMove["strategy"]`の以下の値を調整してください。
-* `use_hold_function` : HOLD機能を使うかどうか（"y":使う、"n":使わない）
- -![Screenshot](../pics/holdfunction.PNG) - -# 使用例 -[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)を参照ください。 diff --git a/cloud/sam/scripts/chat_function/data/block_controller_sample.md b/cloud/sam/scripts/chat_function/data/block_controller_sample.md deleted file mode 100644 index f0af3569..00000000 --- a/cloud/sam/scripts/chat_function/data/block_controller_sample.md +++ /dev/null @@ -1,78 +0,0 @@ ->本ページでは、[ブロック操作用サンプルプログラム(block_controller_sample.py)](https://github.com/seigot/tetris/blob/master/game_manager/block_controller_sample.py)について説明を追記頂ける方を募集しています。
->説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
- -# ブロック操作用サンプルプログラム - -実行方法 - -``` -python start.py -m sample -``` - -一般的に、テトリスは他ゲーム同様に組合せ最適化問題と捉える事が可能であり、過去に様々なアルゴリズムが提案されている。（例えば下記）
-- [ニューラルネットワークと遺伝的アルゴリズムを用いた テトリスコントローラの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjACegQIAxAD&url=https%3A%2F%2Fipsj.ixsq.nii.ac.jp%2Fej%2F%3Faction%3Drepository_action_common_download%26item_id%3D109968%26item_no%3D1%26attribute_id%3D1%26file_no%3D1&usg=AOvVaw0ic6uDC29wGYWl8KKIL8P3) -- [テトリスにおけるAIの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjAEegQIDhAD&url=https%3A%2F%2Fwww.info.kindai.ac.jp%2F~takasi-i%2Fthesis%2F2016_13-1-037-0113_S_Kawahara.ppt&usg=AOvVaw1tik6miknSKem4wE5bmpFP) -- [Tetris is Hard, Even to Approximate](https://arxiv.org/pdf/cs/0210020.pdf) -- ... - -本リポジトリはプログラミング練習と共に、探索アルゴリズムの仮説検証にも利用できるようにしている。
-サンプルプログラムでは、一般的な探索アルゴリズムでの基本部分となる、現在ブロックの探索方法、盤面評価を簡潔に実装している。
- -## step1.探索方法 - -[ブロック操作用サンプルプログラム](block_controller_sample.py)の`def GetNextMove(self, nextMove, GameStatus):`関数の、
-`for direction0 in CurrentShapeDirectionRange:` -`for x0 in range(x0Min, x0Max):`部分が該当する。
-ここでは現在のブロックの情報（以下）を使って全探索を行っている。
-- 現在のブロックが回転できる回数
-- 現在のブロックが移動できるx軸方向の移動量
- -![Screenshot](../pics/search_method_sample.PNG) - -一般的に、考慮すべき組み合わせが多くなると[ゲーム木](https://ja.wikipedia.org/wiki/ゲーム木)を利用した探索範囲の効率化が行われる。
-今回も１秒（１フレーム更新頻度）以内に次の手を決める必要があり、状況によってはゲーム木の採用が必要と思われる。
-ただ、サンプルコードでは簡潔に理解できることを優先し、現在のブロックを用いた全探索を採用している。
- -## step2.盤面評価 - -[ブロック操作用サンプルプログラム](block_controller_sample.py)の`def GetNextMove(self, nextMove, GameStatus):`関数の、
-` EvalValue = self.calcEvaluationValueSample(board)`部分が該当する。
-
-ここでは以下を考えた盤面評価を行っている。
-- 仮に現在のブロックを探索候補箇所に置いた時、消せるブロックが多いほど良い。
-- 同様に、穴ぼこや孤立したブロックが少ないほど良い。
-- 同様に、高さにばらつきが少ないほど良い。
- -![Screenshot](../pics/board_evaluate_method_sample2.PNG) - - -`EvalValue = self.calcEvaluationValueSample(board)`内では以下のような変数を扱っている。
-[block_controller_sample.py#L140](https://github.com/seigot/tetris/blob/c7f1c540f7d0cd1aa6daeb1e5917d18be35895f5/game_manager/block_controller_sample.py#L140) - -``` -BlockMaxY：あるX座標のブロックの高さが入る -holeCandidates：あるX座標の穴候補の数 -holeConfirm：あるX座標の確定した穴の数（下から"穴","穴",”ブロック”,"穴","穴",”ブロック”,"なし","なし",”なし”・・・の場合は４） -nIsolatedBlocks：下がブロックなしのブロックの総数 -fullLines：あるY座標の横一列全部にブロックがあったら１が加算される（最大値は４） -nHoles += abs(x)：各X座標の穴の数の合計値 -absDy += abd(x)：でこぼこ具合を表現している。となりの列との高さの差分を合計して、値が大きい＝ブロックの高さが凸凹してる -``` - -しかし、サンプルプログラムの方法では盤面に穴が残る事がまだまだ多い。
- -# サンプルプログラムの課題 - -上記の通りサンプルプログラムは最小限の機能しかなく、いくつか課題が存在する。 - -- 数手先読み探索 - - サンプルプログラムでは探索時に現在のブロックしか考慮できていない。次のブロックや以降を考慮する方が良いはず。 -- 盤面評価の改良 - - サンプルプログラムでは目先の１ブロック消す事を優先している。４ブロック消すための手を選択できていない。 - - サンプルプログラムの手法では盤面に穴が残る事がまだまだ多い。 -- 最適解を人が考える必要がある。 - - AIのように自ら学習する方が楽に最適解を得られる可能性がある。 - -上記を解決するとより高いスコアが獲得できると思われる。
-以下の先行文献によるとサンプルプログラムで採用しているパラメータ以外も利用しており、改良の余地がある事は間違いない。
-[ニューラルネットワークと遺伝的アルゴリズムを用いた テトリスコントローラの開発](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKn83IqIPxAhWSK5QKHUWVC0cQFjACegQIAxAD&url=https%3A%2F%2Fipsj.ixsq.nii.ac.jp%2Fej%2F%3Faction%3Drepository_action_common_download%26item_id%3D109968%26item_no%3D1%26attribute_id%3D1%26file_no%3D1&usg=AOvVaw0ic6uDC29wGYWl8KKIL8P3) diff --git a/cloud/sam/scripts/chat_function/data/board_manager.md b/cloud/sam/scripts/chat_function/data/board_manager.md deleted file mode 100644 index 45db3a04..00000000 --- a/cloud/sam/scripts/chat_function/data/board_manager.md +++ /dev/null @@ -1,250 +0,0 @@ ->本ページでは、[ボード管理用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/board_manager.py)について説明を追記頂ける方を募集しています。
->説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
- -# ボード管理用プログラムについて - -ここでは [board_manager.py](../../game_manager/board_manager.py) の解説を行う。 - -このファイルにはテトリミノの 形状、回転方向を担う Shape Class と、画面ボードの各種処理を行う BoardData Class がある。 -block_controller.py 側からもよく使うメソッドが多いので解説を書いておく。 - -## Shape Class - -ここでは、[board_manager.py](../../game_manager/board_manager.py) の Shape Class について解説する。 -Shape Class は下記テトリミノの形状について保持しているクラスである。 - -| | ShapeI | ShapeL | ShapeJ | ShapeT | ShapeO | ShapeS | ShapeZ | -| --- | --- | --- | --- | --- | --- | --- | --- | -| index値 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | -| 初期形状 | ![Screenshot](../pics/ShapeI.png) | ![Screenshot](../pics/ShapeL.png) | ![Screenshot](../pics/ShapeJ.png) | ![Screenshot](../pics/ShapeT.png) | ![Screenshot](../pics/ShapeO.png) | ![Screenshot](../pics/ShapeS.png) | ![Screenshot](../pics/ShapeZ.png) | -| １周の回転に必要な回数 | 2 | 4 | 4 | 4 | 1 | 2 | 2 | - -詳細は、[ブロック情報](block_controller.md#ブロック情報) に詳しく記載がある。 - -### getRotatedOffsets method - -getRotatedOffsets メソッドは テトリミノ形状を回転した基準座標からの相対座標を返します。 -引数として direction テトリミノ回転方向を指定する。 - -[ブロック操作の基準点](block_controller.md#ブロック操作の基準点)の形状番号を direction で指定すると、ここで書かれている相対座標が返される。 - -### getCoords method - -getCoords メソッドは さらに x,y を引数に持ち、x,y を基準点に置いたテトリミノの絶対座標を返す。 - -### getBoundingOffsets method - -getBoundingOffsets メソッドはテトリミノの基準点からの最大 x, y 最小x, y を取得する。 -direction で回転方向を指定し、minX, maxX, minY, maxY が返ってくる。 - - - - - - -## BoardData Class - -ここでは、[board_manager.py](../../game_manager/board_manager.py) の BoardData Class について解説する。 -BoardData Class は画面ボードに配置されているテトリミノ情報が格納されており、画面ボードの処理はこの Class において行う。 - -### 画面ボード情報取得系メソッド - -#### getData method - -現状の画面ボードデータを返す。一次元配列である。 - - - -### テトリミノ情報取得関係メソッド - -#### getDataWithCurrentBlock method - -画面ボードデータコピーし動いているテトリミノデータを付加し、その画面ボードデータを返す。 - - -#### getValue method - -引数として x,y を指定し画面ボード上のその座標のテトリミノの有無(およびその種類)を返す。 -返り値は Shape Class の index値。何もないなら 0。 - - - -#### getCurrentShapeCoord method - -direction (回転状態)のテトリミノ座標配列を取得し、それをx,yに配置した場合の座標配列を返す - - - -#### getShapeListLength method - -予告テトリミノ配列の長さを返す - - - -#### getShapeDataFromShapeClass method - -テトリミノクラスデータ, テトリミノ座標配列、テトリミノ回転種類を返す - -引数 - - ShapeClass ... Shape のオブジェクト - -返値 - - ShapeClass ... Shape のオブジェクト - ShapeIdx ... Shape Class の index値 - ShapeRange ... Shape Class の回転方向配列。Oミノなら (0,) Lミノなら (0,1,2,3)。 - - - -#### getShapeData method - -テトリミノの index 番号から shape オブジェクトを返す - -引数 - - ShapeNumber ... テトリミノの index - -返値 - - ShapeClass ... Shape のオブジェクト - - - - - -### HOLD 関係 - -#### getholdShapeData method - -ホールドしている Shape オブジェクトを返す - - - - - -### ART 関係 - -#### getcolorTable method - -colorTableを返す - - - -#### getnextShapeIndexListDXY method - -index を指定して、nextShapeIndexList の d, x, y を返す - -引数 - - index - -返値 - - d - x - y - - - - - - -### ゲームターン進行関係メソッド - - -#### mergePiece method - -画面ボードに固着したテトリミノを書き込む - - -#### getNewShapeIndex method - -次のテトリミノの index 取得 - -#### createNewPiece method - -画面ボード上に新しいテトリミノの配置。配置できなかったら False を返す。 - - -#### tryMoveNext method - -direction 方向で x,y へ2回配置して動かせなかったら Reset - - -#### exchangeholdShape method - -HOLD 入れ替え - -#### removeFullLines method - -画面ボードの消去できるラインを探して消去し、画面ボードを更新、そして消した Line を返す - - - - - - - - -### テトリミノ配置確認関係メソッド - -#### tryMoveCurrent method - -テトリミノを direction 方向で x,y に動かせるかどうか確認する -動かせない場合 tryMove メソッドを使い False を返す - - -#### tryMove method - -direction (回転状態)のテトリミノ座標配列を取得し、それをx,yに配置可能か判定する -配置できない場合 False を返す - -#### moveDown method - -テノリミノを1つ落とし消去ラインとテトリミノ落下数を返す - -#### dropDown method - -テトリミノを一番下まで落とし消去ラインとテトリミノ落下数を返す - -#### moveLeft method - -左へテトリミノを1つ動かす -失敗したら False を返す - -#### moveRight method - -右へテトリミノを1つ動かす -失敗したら False を返す - -#### rotateRight method - -右回転させる -失敗したら False を返す - - -#### rotateLeft method - -左回転させる -失敗したら False を返す - - - - - - - -### 初期配置関係メソッド - - -#### clear method - -画面ボードと現テトリミノ情報をクリア - -#### addobstacle method - -初期障害物配置。Level3 Level4 用。 - - - diff --git a/cloud/sam/scripts/chat_function/data/docker.md b/cloud/sam/scripts/chat_function/data/docker.md deleted file mode 100644 index 8e30c67e..00000000 --- a/cloud/sam/scripts/chat_function/data/docker.md +++ /dev/null @@ -1,75 +0,0 @@ -# Tetris Game docker - -## step0. docker をインストールする - -[Install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu)
-[Install Docker Desktop on Mac](https://docs.docker.com/docker-for-mac/install/)
-[Install Docker Desktop on Windows](https://docs.docker.com/docker-for-windows/install/)
- -## step1. docker コンテナを起動する - -tetris ディレクトリで以下を実行する。 - -``` -docker-compose up -``` - -`pytorch`インストール済コンテナを使いたい場合、上記の代わりに以下を実行する。
- -``` -docker-compose -f docker-compose.pytorch.yaml up -``` - -## step2. docker コンテナにアクセスする - -コンテナ内に入ってターミナルを起動 - -``` -docker exec -it tetris-container bash -``` - -## step3. 起動ホスト OS 側で GUI 起動のための準備 - -docker コンテナ内でテトリスプログラムを実行する場合、GUI を表示させるためにはホスト OS 側で[x サーバ](https://qiita.com/kakkie/items/c6ccce13ce0beaefaad1)を起動する必要があります。 -x サーバの起動方法は以下を参照してください。 - -- Windows→[こちら](./README.xserver.md#Windows-の場合) -- Linux→ - -## step3'. GUI なしでテトリスプログラムを実行 - -GUI の起動が必要ない場合には先程立ち上げたコンソールかたコンテナ内の環境変数`QT_QPA_PLATFORM`を`offscreen`に設定します。 - -``` -export QT_QPA_PLATFORM=offscreen -``` - -## step4. テトリスプログラムの実行 - -`/tetris`にいることを確認し、以下を実行 - -``` -python start.py -``` - -テトリスの画面が表示されれば無事完了です。 - -## コンテナの停止 - -ホスト OS 側の tetris ディレクトリから以下を実行する。 - -``` -docker-compose stop -``` - -この場合には、コンテナは削除されずに残ります。 - -## コンテナの停止、削除 - -ホスト OS 側の tetris ディレクトリから以下を実行する。 - -``` -docker-compose down -``` - -この場合には、コンテナは削除されます。 diff --git a/cloud/sam/scripts/chat_function/data/game_manager.md b/cloud/sam/scripts/chat_function/data/game_manager.md deleted file mode 100644 index ecd6ba99..00000000 --- a/cloud/sam/scripts/chat_function/data/game_manager.md +++ /dev/null @@ -1,131 +0,0 @@ ->本ページでは、[ゲーム管理用プログラム](https://github.com/seigot/tetris/blob/master/game_manager/game_manager.py)について説明を追記頂ける方を募集しています。
->説明の追記方法は、[`Pull Requestを送る`](https://github.com/seigot/tetris#pull-requestを送るoptional)を参照下さい。
- -# ゲーム管理用プログラムについて - -## [start.py](../../start.py) の Option 設定 - -ここでは [start.py](../../start.py) で指定する option について解説する。 - -#### -m --mode モード - default : ルールベース - train : AI学習 - predict : AI 推論（予測実行) - predict_sample : AI MLP 推論（予測実行) - predict_sample2 : AI DQN 推論（予測実行) - keyboard : キーボード操作 - gamepad : ゲームパッド操作 -#### -l --game_levelレベル - 1 : Level 1 固定テトリミノ - 2 : Level 2 ランダムテトリミノ - 3 : Level 3 初期ブロックありテトリミノ - 4 : Level 4 初期ブロックありテトリミノ 0.001秒更新 -#### -d --drop_interval 更新間隔 - default => 1000 (=1秒) - ms 指定 - ※ テトリミノを DROP すれば この時間待てば次のテトリミノが出るが、降下だけさせた場合は次の操作ができるまでこの時間分停止する。 - ※ Level 4指定した場合は無効 -#### -t --game_time ゲーム時間 - default => 180 - 秒指定 - -1 で制限なし 学習時は -1 指定推奨。 -#### -r --random_seed 乱数のタネ - 整数指定。特別に必要なければ指定しない。 -#### -f --resultlogjson - default => result.json - 結果の json ファイル指定 -#### --train_yaml - default => config/default.yaml - AI 学習推論時 (train, predict)の設定ファイル指定 -#### --predict_weight - default => outputs/latest/best_weight.pt - AI 推論時 (predict) の学習結果 weight ファイル指定 - -## Game_Manager Class - -ここでは [game_manager.py](../../game_manager/game_manager.py) の Game_Manager Class を解説する。 - -### 概要 - -最初に start.py から呼び出される。このゲームの基本的な動作はこのクラスから各クラス、メソッドを実行することによって行われる。 - -### 初期化 - -\_\_init\_\_ にて Option や初期設定値を各インスタンス変数に格納する。 -\_\_init\_\_ にある initUI メソッドにて、Window 生成、位置設定表示が行われる。ここで SidePanel Class, Board Class のインスタンスも生成される。 -initUI の中の start メソッドで、スコア、画面ボード、予告テトリミノ、ステータスバーがクリアされる。また、この中で timerEvent も生成される。timerEvent の間隔は上記の drop_interval となる。これにより次の操作が可能となる。 - -### timerEvent method - -上記の初期化により timerEvent が drop_interval オプションにて指定された間隔で実行される。 -この Event によりゲームが進行していく。 -なお、timerEvent メソッド実行中に drop_interval 指定の時間を超えても、次の timerEvent は発生しない。 -よって、drop_interval が非常に短い (1ms など = Level 4) の場合は、timerEvent のロスが多発する。 -そういったルールの場合はいかに早く timerEvent を終了させるかが肝となる。 - -timerEvent において各 mode に対応する [block_controller.py](block_controller.md) (or block_controller_train*.py) の GetNextMove が呼び出される。 -ここで、[GameStatus](GameStatus.md) が GetNextMove に引き渡され、各自のプログラムで指示した操作結果が nextMove 変数に格納される。 - -この結果をもって、テトリミノのホールド、横移動、回転、落下、降下処理を行う。 - -ホールドはホールド画面のテトリミノとの入れ替えを行う。ただし、ホールド画面に何もない場合はホールド画面に現テトリミノを入れて timerEvent を終了する。 - -横移動は画面ボードの端やテトリミノにぶつかると停止する。 - -回転は**右回転** (rotateRight)で行い、画面ボードの端やテトリミノにぶつかるとエラーとなりtimerEvent無効となるので要注意である。 - -降下 (move down) 処理の場合は指定数分降下させる。 - -落下 (drop down) 処理が行われると、ただちにテトリミノは一番低いところまで落とされ、テトリミノは固定される。 - -いずれにしてもテトリミノの下側が画面ボード下限かテトリミノに当たっている場合は、消去ライン数計算(moveDown)とスコア計算(UpdateScore)が実施され、次の timerEvent では次のテトリミノが出る。 -なお、ゲームオーバーになればゲームオーバー分のスコアを減らされれ、画面ボードをクリアして次の timerEvent となる。 - - -### keyPressEvent method - -下記キー操作により Event が発生しテトリミノを操作する。 -なお、GetNextMove で指定する場合とことなり**左回転** (rotateLeft) である点に注意が必要である。 - -| 手動操作 | PC操作準拠 | ゲーム機コントローラ準拠 | -| ---- | ---- | ---- | -| 実行コマンド | python start.py -m keyboard | python start.py -m gamepad | -| *up* key | **左回転** | 落下 | -| *left* key | 左に移動 | 左に移動 | -| *right* key | 右に移動 | 右に移動 | -| *m* key | 降下（下に移動） | 降下（下に移動） | -| *space* key | 落下 | **左回転** | -| *P* key | Pause | Pause | -| *c* key | hold | hold | - -## SidePanel Class - -ここでは [game_manager.py](../../game_manager/game_manager.py) の SidePanel Class を解説すｒ。 -横の予告テトリミノ、およびホールドテトリミノ描画画面 Class である。 - - - -### paintEvent method - -ここでは上記のように 4つの予告テトリミノを描画する。 -また、下にホールド画面がありそこでホールドしたテトリミノも表示される。 -timerEvent や keyEvent などにより呼び出され描画される。 - -## Board Class - -ここでは [game_manager.py](../../game_manager/game_manager.py) の Board Class を解説する。 -ゲームの画面ボードおよびステータスバーの Class である。 - - - -### paintEvent method - -ここでは縦22,横10の画面ボードを描画する。 -固定されたテトリミノおよび動作中のテトリミノを描画する。 - -### updateData method - -ステータスバーの文字の更新を行う。 -また、 game_time 経過、もしくはブロック数上限に達したらプログラムを終了し、Result を表示する。 -学習モードで game_time を -1 にしていないと、ここで終了してしまう。 - diff --git a/cloud/sam/scripts/chat_function/data/install_mac.md b/cloud/sam/scripts/chat_function/data/install_mac.md deleted file mode 100644 index 5466ff12..00000000 --- a/cloud/sam/scripts/chat_function/data/install_mac.md +++ /dev/null @@ -1,39 +0,0 @@ -# 実行環境(Macの場合) - -Finder→Application→Utility→Terminalから、ターミナル画面を起動して以下コマンドを実行する。
- -``` -# install pyqt5 and NumPy -brew install python3 -brew install pyqt5 -brew install numpy -#pip3 install pyqt5 -#pip3 install numpy -# install other packages -brew install git -``` - -Mac(M1)環境等では`pip3 install pyqt5`でエラーになることがある。`brew`を使うことで解決することがある。 -[Mac(M1)にpip3 install pyqt5でpyqt5をインストールしようしようとするとエラーになる（Preparing metadata (pyproject.toml) ... error）](https://qiita.com/seigot/items/c779d187982268cf8b12) - -``` -brew install pyqt5 -``` - -`pytorch`を使う場合は以下のようにすればOK -[MacBook Pro (Apple Silicon, M1 PRO, 2021) でPython開発環境を整える #49](https://github.com/seigot/tetris/issues/49) -[AIについて]([https://github.com/seigot/tetris/blob/master/doc/files/install_mac.md](https://github.com/seigot/tetris/blob/master/doc/files/ai.md) - -``` -# install -pip3 install -U pip -pip3 install torch -pip3 install tensorboardX -python3 -c "import torch" -# 実行例 (詳細は"AIについて"リンク参照) -python3 start.py -m train_sample -d 1 -l 2 -t -1 -python3 start.py -m predict_sample -l 2 --predict_weight weight/DQN/sample_weight.pt -``` - -もし他にも足りないことがあれば`pull request`頂けると助かります - diff --git a/cloud/sam/scripts/chat_function/data/install_ubuntu.md b/cloud/sam/scripts/chat_function/data/install_ubuntu.md deleted file mode 100644 index dcd9a70a..00000000 --- a/cloud/sam/scripts/chat_function/data/install_ubuntu.md +++ /dev/null @@ -1,25 +0,0 @@ -# 実行環境(Ubuntu/JetsonNanoの場合) - -Need python3, PyQt5 and NumPy to be installed. - -``` -# install pyqt5 and NumPy -sudo apt-get install -y python3-pip -pip3 install --upgrade pip -pip3 install numpy -pip3 install pyqt5 -``` - -その他、パッケージのインストール - -``` -sudo apt-get install -y git -``` - -ubuntu20.04LTS以降の場合は`python`コマンドを使うために以下を実行する - -``` -sudo apt install python-is-python3 -``` - -その後は、実行方法を参照 diff --git a/cloud/sam/scripts/chat_function/data/install_windows.md b/cloud/sam/scripts/chat_function/data/install_windows.md deleted file mode 100644 index e9bc26ba..00000000 --- a/cloud/sam/scripts/chat_function/data/install_windows.md +++ /dev/null @@ -1,39 +0,0 @@ -# 実行環境(windowsの場合) -docker for windowsを使います。
- -### step0. DockerDesktopのインストール - -以下を参照する。
-[DockerDeskop](https://docs.docker.com/docker-for-windows/install)
-[Windows10用Windows Subsystem for Linuxのインストール ガイド](https://docs.microsoft.com/ja-jp/windows/wsl/install-win10) - -以下にインストール時のトラブルシューティングをまとめました。
-[FAQ/Windows Docker install時のトラブルシューティング](https://github.com/seigot/tetris/blob/master/doc/files/FAQ.md#windows-docker-install%E6%99%82%E3%81%AE%E3%83%88%E3%83%A9%E3%83%96%E3%83%AB%E3%82%B7%E3%83%A5%E3%83%BC%E3%83%86%E3%82%A3%E3%83%B3%E3%82%B0) - -### step1. パワーシェル上でコンテナを起動する
- -`Windowsボタン`から`Windows PowerShell`を起動してterminalから以下実行する。
-コンテナのダウンロード〜起動が完了するまで少し待つ。
- -``` -docker run -p 6080:80 --shm-size=512m seigott/tetris_docker -``` - -### step2. ブラウザからdockerコンテナにアクセスする - -``` -localhost:6080 -``` - -アクセスできたら以下により動作検証する - -左下アイコン --> system tools --> terminator - -Terminalを立ち上げて以下を実行 - -``` -cd ~/tetris -python start.py -``` - -注意）パワーシェルを閉じたり電源OFFするとコンテナも終了します。作成中データが失われないよう注意してください。 diff --git a/cloud/sam/scripts/chat_function/data/install_windows_powershell.md b/cloud/sam/scripts/chat_function/data/install_windows_powershell.md deleted file mode 100644 index 82679465..00000000 --- a/cloud/sam/scripts/chat_function/data/install_windows_powershell.md +++ /dev/null @@ -1,205 +0,0 @@ -# windowsのpowershellを使ってテトリス環境を構築する - -### powershellとは - -windowsで利用できる、コマンドラインインターフェース。 -「Windows」＋「S」で検索ボックス"powershell"と入力すると見つかるはず。 -> [Windows10 - PowerShell(パワーシェル)の開き方](https://www.curict.com/item/f0/f0f6ab0.html) - -## 1. git のインストール - -### Windows Package Manager(winget)がある場合 - -[Windows Package Manager(winget)](https://www.microsoft.com/ja-jp/p/app-installer/9nblggh4nns1?activetab=pivot:overviewtab)があることを確認する。 -powershellを起動して以下を実行すれば確認できる。 - -``` -winget --version -## v1.1.13405 等と出力されれば、Windows Package Manager(winget)がある -``` - -gitをインストールする。 - -``` -winget install Git.Git -## もし y/n と出る場合は、y と入力する。y=yes という意味である。 -``` - -新しくpowershellを起動し、以下を実行して結果が表示されればOK - -``` -git --version -## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） -``` - -### 上記でインストールができない場合 - -上記でインストールができない場合は、公式サイトのインストーラーを使う。以下をダウンロードする。 - -> [git Downloading Git](https://git-scm.com/download/win) -> 64-bit Git for Windows Setup - -以下の「Gitのインストール」部分記載の通り、インストールを進める。以下の記載部分は選択が必要。 - -> Windows 10 と Powershell（WSL含む） で git を利用する -> https://qiita.com/kerobot/items/78372640127771f92ee0#git-%E3%81%AE%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB -> > コマンドラインやPowerShellなどからGitのコマンドを利用するため「Git from the command line and also from 3rd-party software」を選択します。 - -powershell上で以下を実行して結果が表示されればOK - -``` -git --version -## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） -``` - -## 2. python のインストール - -### microsoft storeからインストールする - -powershell上で以下を実行、もしくは`microsoft store`上で`python`と検索し、インストールページからインストールする。 - -``` -python -``` - -![Screenshot](../pics/python3.10.msstore.png) - -新しくpowershellを起動し、以下を実行して結果が表示されればOK - -``` -python --version -## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） -``` - -### 古いバージョンのpythonが表示される場合 - -過去に古いバージョンのpythonをインストールしている場合にそちらが優先される可能性がある。 -「Windows」＋「python」で検索ボックスから古いpythonバージョンをアンインストールして再度新しいpythonをインストールすると解決することがある。 - -### 上記でインストールができない場合 - -上記でインストールができない場合は、公式サイトのインストーラーを使う。 -以下記事を参照し、パッケージのダウンロード～PowerShellの環境設定までを実施する - -> [Windows版Pythonのインストール](https://www.python.jp/install/windows/install.html) - -2023.1時点ではpythonのバージョン3.10.9が利用し易そうである。 -この場合は、以下のダウンロードリンクからインストーラ(`.exe`)を取得するとよい。 - -> [非公式Pythonダウンロードリンク](https://pythonlinks.python.jp/ja/index.html) - -特に以下の点を忘れずに実施する。 - -> > "Add Python 3.x to PATH" をチェックするのを忘れないようにしてください。 -> > もし忘れたら、あわてず落ち着いてもう一度インストールしましょう。何度でも繰り返して大丈夫です。 -> -> > PowerShellの環境設定¶ -> > PowerShellでスクリプトの実行を許可しておきます。 -> > スタートメニューで Windows PowerShell | Windows PowerShell を起動し、次のコマンドを実行します。 -> Set-ExecutionPolicy RemoteSigned -Scope CurrentUser -Force - -powershell上で以下を実行して結果が表示されればOK - -``` -python --version -## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） -``` - -## 3. powershellからtetrisを実行する - -powershellを新たに起動する -(「Windows」＋「S」で検索ボックス"powershell"と入力すると見つかるはず。見つかったらクリックして起動する。) - - -バージョンを確認する -ここでバージョンが何も表示されなければインストールが成功していないため、インストールからやり直す - -``` -git --version -## ex.) git version 2.34.1.windows.1 等と出力されればOK（最新バージョンであればOK） -python --version -## ex.) Python 3.10.9 等と出力されればOK（最新バージョンであればOK） -``` - -必要なパッケージをインストールする - -``` -pip install pyqt5 -pip install numpy -``` - -以下の通り実行し、tetrisが表示されればOK - -``` -cd ~ -mkdir work -cd work -git clone https://github.com/seigot/tetris -cd tetris -python start.py # ここでtetrisが表示されればOK -``` - -プログラム内部の説明については以下など参照ください - -[ブロック操作用サンプルプログラムについて](./block_controller_sample.md) -[AIについて](./ai.md) -[tetris artについて](./art.md) -[README.md](https://github.com/seigot/tetris#%E5%AE%9F%E8%A1%8C%E6%96%B9%E6%B3%95) - -### option. エディタのインストール - -最近流行りのエディタである`visual studio code`をインストールしておくと後のコード編集に便利である -powershell上でwingetが使える場合は以下を実行、 - -``` -# powershell上でwingetが使える場合 -winget install vscode -``` - -もしくは`microsoft store`上で`visual studio code`と検索し、インストールページからインストールする。 - -![Screenshot](../pics/vscode.msstore.png) - -powershell上で以下を実行して`vscode`と対象のコードが表示されればOK - -``` -# cd ~/work/tetris 等で事前にcloneした場所に移動しておく -code game_manager/block_controller.py -``` - -### `python start.py`実行後にtetrisが表示されない場合 - -実行環境の違いにより`python start.py`実行後にtetrisが表示されないことがたまにある。 -この場合はwarningやエラーログが出力されるはずなので、以下の実行成功時のログと比較すると解決に近づくと思われる。 - -``` -# （例）実行成功時のログ -$ python start.py -game_level: 1 -game_time: 180 -RANDOM_SEED: 0 -IS_MODE :default -OBSTACLE_HEIGHT: 0 -OBSTACLE_PROBABILITY: 0 -USER_NAME: window_sample -SHAPE_LIST_MAX: 6 -BLOCK_NUM_MAX: -1 -RESULT_LOG_JSON: result.json -TRAIN_YAML: config/default.yaml -PREDICT_WEIGHT: outputs/latest/best_weight.pt -Python 3.7.1 -CompletedProcess(args='python --version', returncode=0, stderr='') -=================================================> -...(以降、tetrisデバッグ情報が表示される) -``` - -## 参考 -[git Downloading Git](https://git-scm.com/download/win) -[Windows 10 と Powershell（WSL含む） で git を利用する](https://qiita.com/kerobot/items/78372640127771f92ee0#git-%E3%81%AE%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB) -[Windows版Pythonのインストール](https://www.python.jp/install/windows/install.html) -[非公式Pythonダウンロードリンク](https://pythonlinks.python.jp/ja/index.html) -[WindowsPowerShellでPythonを利用する](https://bluebirdofoz.hatenablog.com/entry/2019/01/19/141007) -[GNU Emacs for Windows再入門](https://emacs-jp.github.io/tips/emacs-for-windows) -[visual studio codeをinstall](https://azure.microsoft.com/ja-jp/products/visual-studio-code/) -[WindowsにBashをインストールする方法](https://lab.sonicmoov.com/development/windows-bash/) -[Windows Subsystem for Linuxが表示されない](https://qiita.com/taraka/items/0b5919ac8ee02d81f7ff) diff --git a/cloud/sam/scripts/chat_function/data/install_windows_wsl.md b/cloud/sam/scripts/chat_function/data/install_windows_wsl.md deleted file mode 100644 index c8f803f9..00000000 --- a/cloud/sam/scripts/chat_function/data/install_windows_wsl.md +++ /dev/null @@ -1,81 +0,0 @@ -# 実行環境 (Windows WSLを使う場合) - -Windows WSLを使って開発環境を立ち上げる方法を説明する。 -Dockerを使う方法と比較して、Dockerが不要な点と、Dockerコンテナ終了でデータが失われることを気にする必要が無い特長がある。 - -## Step 1. WSLのインストール - -- 以下を参照してインストールする。 - - [Windows10用Windows Subsystem for Linuxのインストール ガイド](https://docs.microsoft.com/ja-jp/windows/wsl/install-win10) - -### a. WSL1でインストールする場合（簡単） - -- 上記URLの「手動インストールの手順」の「手順 1 - Linux 用 Windows サブシステムを有効にする」「手順 6 - 選択した Linux ディストリビューションをインストールする」のみ実施すれば良い。 -- 「手順 6 - 選択した Linux ディストリビューションをインストールする」では、Ubuntu 18.04 LTS または Ubuntu 20.04 LTS をインストールすれば良い。 - -### b. a以外の場合 - -- WSL1でもWSL2でも良い。 -- Linuxのディストリビューションはどれでも良いはずであるが、Ubuntu 18.04 LTS または　Ubuntu 20.04 LTS をおすすめする。 - -## Step 2. Xサーバのインストール - -GWSLまたはVcXsrvをインストールする。 - -### a. GWSLを使う場合（おすすめ） - -1. スタートメニューからMicrosoft Storeアプリを起動する。 -2. GWSLを検索して、インストールする。 - -### b. VcXsrvを使う場合 - -1. https://sourceforge.net/projects/vcxsrv/ からVcXsrvを入手してインストールする。 - -## Step 3. WSLの起動 - -### ターミナルを開く - -- スタートメニューから、Step 1でインストールしたLinux ディストリビューション(Ubuntu 18.04 LTS など)を起動する。 - -### 必要なパッケージのインストール・設定（初回のみ必要） -- 下記を実行する。 - -``` -sudo apt-get install -y python3-pip -sudo apt-get install -y python3-pyqt5 -pip3 install --upgrade pip -pip3 install numpy -sudo apt-get install -y git - -echo 'export DISPLAY=localhost:0.0' >> ~/.bashrc -source ~/.bashrc -``` - -## Step 4. Xサーバの起動 - -Step 2 でインストールしたXサーバを起動する。 - -### a. GWSLを使う場合 - -1. スタートメニューからGWSLを起動する。 - -### b. VcXsrvを使う場合 - -1. スタートメニューやデスクトップのショートカットからVcXsrvを起動する。 -2. 「次へ」を3回押して、「完了」を押すとXサーバが起動する。 - -## Step 5. tetrisの起動 - -### ソースコードを取得する（初回のみ必要） - -``` -git clone https://github.com/seigot/tetris -``` - -### tetrisを実行する -``` -cd ~/tetris -python start.py -``` - -一度実行環境を立ち上げた後の2回目からは、Step 3以降を実行すれば良い。 diff --git a/cloud/sam/scripts/chat_function/data/reference_score.md b/cloud/sam/scripts/chat_function/data/reference_score.md deleted file mode 100644 index 75a01bd4..00000000 --- a/cloud/sam/scripts/chat_function/data/reference_score.md +++ /dev/null @@ -1,13 +0,0 @@ -# 参考スコア - -（注）乱数の影響により多少のばらつきあり
- -| | level1 | level2 | level3 | 自由部門 | -| --- | --- | --- | --- | - | -| random | -916 | -731 | -4948 | - | -| sample(python start.py -m sample) | 6935 | 6259 | 3737 | - | -| [kyadさん作の無限テトリス](https://github.com/kyad/tetris/blob/forever-branch/forever.md) | - | - | - | 20592 | -| 理論値 | 23400+落下ボーナス | - | - | - | - -理論値は「（全てI字の棒が出るなどして）全て4ライン消しをした場合」を想定。
-計算式：(制限時間 180(s))÷(テトリス1回に掛かる時間 10(s))×1300点 = 23400点
diff --git a/cloud/sam/scripts/chat_function/data/tetris.md b/cloud/sam/scripts/chat_function/data/tetris.md deleted file mode 100644 index b6dbc73c..00000000 --- a/cloud/sam/scripts/chat_function/data/tetris.md +++ /dev/null @@ -1,350 +0,0 @@ - - -**Table of Contents** - -- [Tetris](#tetris) - - [実行環境準備](#%E5%AE%9F%E8%A1%8C%E7%92%B0%E5%A2%83%E6%BA%96%E5%82%99) - - [Mac環境](#mac%E7%92%B0%E5%A2%83) - - [windows環境](#windows%E7%92%B0%E5%A2%83) - - [Ubuntu/JetsonNano環境](#ubuntujetsonnano%E7%92%B0%E5%A2%83) - - [docker環境](#docker%E7%92%B0%E5%A2%83) - - [実行方法](#%E5%AE%9F%E8%A1%8C%E6%96%B9%E6%B3%95) - - [サンプルコード](#%E3%82%B5%E3%83%B3%E3%83%97%E3%83%AB%E3%82%B3%E3%83%BC%E3%83%89) - - [手動操作](#%E6%89%8B%E5%8B%95%E6%93%8D%E4%BD%9C) - - [AI実装](#ai%E5%AE%9F%E8%A3%85) - - [art](#art) - - [自動評価サーバ](#%E8%87%AA%E5%8B%95%E8%A9%95%E4%BE%A1%E3%82%B5%E3%83%BC%E3%83%90) - - [Play rules](#play-rules) - - [Score](#score) - - [game level](#game-level) - - [ファイル構成](#%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB%E6%A7%8B%E6%88%90) - - [ファイル一覧](#%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB%E4%B8%80%E8%A6%A7) - - [詳細](#%E8%A9%B3%E7%B4%B0) - - [コード作成のはじめかた](#%E3%82%B3%E3%83%BC%E3%83%89%E4%BD%9C%E6%88%90%E3%81%AE%E3%81%AF%E3%81%98%E3%82%81%E3%81%8B%E3%81%9F) - - [本リポジトリのfork](#%E6%9C%AC%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AEfork) - - [実行](#%E5%AE%9F%E8%A1%8C) - - [自リポジトリのバイナリを公式リリースする](#%E8%87%AA%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AE%E3%83%90%E3%82%A4%E3%83%8A%E3%83%AA%E3%82%92%E5%85%AC%E5%BC%8F%E3%83%AA%E3%83%AA%E3%83%BC%E3%82%B9%E3%81%99%E3%82%8B) - - [本リポジトリの最新バージョン取り込み](#%E6%9C%AC%E3%83%AA%E3%83%9D%E3%82%B8%E3%83%88%E3%83%AA%E3%81%AE%E6%9C%80%E6%96%B0%E3%83%90%E3%83%BC%E3%82%B8%E3%83%A7%E3%83%B3%E5%8F%96%E3%82%8A%E8%BE%BC%E3%81%BF) - - [Pull Requestを送る（Optional）](#pull-request%E3%82%92%E9%80%81%E3%82%8Boptional) - - [FAQ](#faq) - - [参考](#%E5%8F%82%E8%80%83) - - [今後の課題](#%E4%BB%8A%E5%BE%8C%E3%81%AE%E8%AA%B2%E9%A1%8C) - - [次のブロックのランダム性](#%E6%AC%A1%E3%81%AE%E3%83%96%E3%83%AD%E3%83%83%E3%82%AF%E3%81%AE%E3%83%A9%E3%83%B3%E3%83%80%E3%83%A0%E6%80%A7) - - [競争要素の追加](#%E7%AB%B6%E4%BA%89%E8%A6%81%E7%B4%A0%E3%81%AE%E8%BF%BD%E5%8A%A0) - - [学習要素の追加](#%E5%AD%A6%E7%BF%92%E8%A6%81%E7%B4%A0%E3%81%AE%E8%BF%BD%E5%8A%A0) - - [LICENSE](#license) - - [Finnaly](#finnaly) - - - -# Tetris - -プログラミング学習を目的とした、ブロックを操作してスコアを競うゲームです。
-[FAQはこちら。](https://github.com/seigot/tetris/blob/master/doc/files/FAQ.md)
-[tutorialはこちら。](https://github.com/seigot/tetris_game_tutorial)
-[tetris_score_serverはこちら](https://github.com/seigot/tetris_score_server) -[tetris_battle_serverはこちら](https://github.com/seigot/tetris_battle_server) - -## 実行環境準備 - -#### Mac環境 - -Finder→Application→Utility→Terminalから、ターミナルを起動して以下コマンドを実行する。 -install成功後、[実行方法](https://github.com/seigot/tetris/tree/master#実行方法)によりゲーム開始すればOK - -``` -# install pyqt5 and NumPy -brew install python3 -brew install pyqt5 -brew install numpy -# install other packages -brew install git -``` - -[doc/files/install_mac.md](./doc/files/install_mac.md)に手順を記載 - -#### windows環境 - -[windowsのpowershellを使ってテトリス環境を構築する場合](./doc/files/install_windows_powershell.md)の手順
-[WSL(Windows Subsystem for Linux)を使う場合](./doc/files/install_windows_wsl.md)の手順
-[Docker for Windowsを使う場合](./doc/files/install_windows.md)の手順
- -#### Ubuntu/JetsonNano環境 - -[doc/files/install_ubuntu.md](./doc/files/install_ubuntu.md)に手順を記載 - -#### docker環境 - -[docker/README.md](docker/README.md)に手順を記載 - -## 実行方法 - -本リポジトリを取得 - -```shell -cd $HOME -git clone https://github.com/seigot/tetris -``` - -ゲーム開始用スクリプトを実行 - -```shell -cd tetris -python start.py -``` - -![Screenshot](doc/pics/screenshot_02.png) - -#### サンプルコード - -実行時、以下のようにオプションを与えることで、スコアアタック用サンプルコードの実行が可能です。
-サンプルコードの詳細は、[ブロック操作用サンプルプログラム](https://github.com/seigot/tetris/blob/master/doc/files/block_controller_sample.md)を参照下さい。
- -```shell -python start.py -m sample -``` - -#### 手動操作 - -実行時、以下のようにオプションを与えることで、手動操作が可能です。 -操作方法は、PC操作準拠とゲーム機コントローラ準拠の2種類を選択できるようにしています。 - -| 手動操作 | PC操作準拠 | ゲーム機コントローラ準拠 | -| ---- | ---- | ---- | -| 実行コマンド | python start.py -m keyboard | python start.py -m gamepad | -| *up* key | 回転 | 落下 | -| *left* key | 左に移動 | 左に移動 | -| *right* key | 右に移動 | 右に移動 | -| *m* key | 下に移動 | 下に移動 | -| *space* key | 落下 | 回転 | -| *P* key | Pause | Pause | -| *c* key | hold | hold | - -`--nextShapeMode hate`オプション付きで実行すると[hateモード(手動操作で遊ぶ用)](https://manabitimes.jp/math/795)になります。 - -``` -python3 start.py -m keyboard --nextShapeMode hate -``` - -#### AI実装 - -実行方法、サンプルコードを用意しています -詳細は[AIについて](doc/files/ai.md)を参照下さい。 - -#### art - -実行方法、サンプルコードを用意しています -詳細は[artについて](doc/files/art.md)を参照下さい。 - -#### 自動評価サーバ - -表題のサーバを有志で用意しています -https://github.com/ChallengeClub/tetris_score_server - -## Play rules - -制限時間内の獲得スコアを評価します。 - -### Score - -加点 - -| 項目 | 得点 | 備考 | -| ---- | ---- | ---- | -| 1ライン消し | + 100点 | - | -| 2ライン消し | + 300点 | - | -| 3ライン消し | + 700点 | - | -| 4ライン消し | + 1300点 | - | -| 落下ボーナス | + 落下したブロック数を得点に加算 | - | -| 全消しボーナス | + 全消し時に得点 | - | - -減点 - -| 項目 | 得点 | 備考 | -| ---- | ---- | ---- | -| gameover | - 500点 | ブロック出現時にフィールドが埋まっていたらgameover - -### game level - -実行時、オプションを与えることで、難易度（レベル）を指定できます。
- -| | level1 | level2 | level3 | level4 | -| --- | --- | --- | --- | --- | -| 実行方法 | python start.py | python start.py -l2 | python start.py -l3 | python start.py -l4 | -| 制限時間 | 180秒 | 180秒 | 180秒 | 180秒 | -| ブロックの順番 | 固定(1-7まで順番に繰り返し) | ランダム | ランダム | ランダム | -| フィールドの初期ブロック | なし | なし | あり | あり | -| フレーム更新頻度 | 約1秒 | 約1秒 | 約1秒 | 約0.001秒 | -| 全消しボーナス | +0 | +0 | +4000 | +4000 | -| 備考 | - | - | - | - | - -[各レベルの参考スコア](doc/files/reference_score.md) - -## ファイル構成 - -#### ファイル一覧 - -* `game_manager/game_manager.py` : ゲーム管理用プログラム -* `game_manager/board_manager.py` : ボード管理用プログラム -* `game_manager/block_controller.py` : ブロック操作用プログラム（ブロックの操作は、このファイルを更新して下さい。） -* `start.py` : ゲーム開始用コマンド - -#### 詳細 - -以下のような構成になっています。
-ブロック操作用プログラムは、管理プログラムから定期的に呼び出されるので、ボード情報から次の動作を決定して下さい。
- -```mermaid - graph TB - - subgraph ゲーム管理用プログラム - B1["game_manager.py"] - C1["board_manager.py"] - D1["block_controller.py
ここで現在のブロックの動作を決定する"] - B1 --update--> C1 - B1 --getNextMove--> D1 - D1 --NextMove--> B1 - subgraph ボード管理用プログラム - C1 - end - subgraph ブロック操作用プログラム - D1 - end - end - - - subgraph ゲーム開始用コマンド - A1[start.py] --> B1 - end -style ブロック操作用プログラム fill:#fef -``` - -詳細 -- [ブロック操作用プログラムについての説明](doc/files/block_controller.md)
-- [ボード管理用プログラムについての説明](doc/files/board_manager.md)
-- [ゲーム管理用プログラムについての説明](doc/files/game_manager.md)
- -## コード作成のはじめかた - -### 本リポジトリのfork - -まず、Githubアカウントを取得して本リポジトリを自リポジトリにforkして下さい。 - -> リポジトリのフォークの例
-> -> 0. GitHubアカウントを作成/ログインする。
-> 1. GitHub で、[https://github.com/seigot/tetris](https://github.com/seigot/tetris)リポジトリに移動します
-> 2. ページの右上にある [Fork] をクリックします。
-> 参考：[リポジトリをフォークする](https://docs.github.com/ja/get-started/quickstart/fork-a-repo#forking-a-repository)
- -その後、自リポジトリにforkした`tetris`をローカルマシンに取得して下さい。 - -``` -cd ~ -git clone https://github.com//tetris # ""さん（yourname=自分のアカウント名に読みかえて下さい）のリポジトリを取得する場合 -git clone https://github.com/seigot/tetris # このリポジトリを取得する場合 -``` - -既に`tetris`が存在しており、これを削除したい場合は`rm -f`を実行して下さい。 - -``` -# ubuntu/mac等の場合 -sudo rm -rf tetris - -# windows powershellの場合 -Remove-Item -Recurse -Force tetris -``` - -取得後はソースコード変更、変更リポジトリに反映する等してアップデートを進めて下さい。 - -### 実行 - -`実行方法`を参考に実行環境の構築をして下さい。
-環境構築の完了後、ブロック操作用プログラム[`block_controller.py`](https://github.com/seigot/tetris/blob/master/game_manager/block_controller.py)を更新していってください。
- -### 自リポジトリのバイナリを公式リリースする - -提出時、自リポジトリのバイナリを公式リリースする場合は、Githubリリースの機能を使うと簡単なのでお勧めです。 - -> 自リポジトリのコードを提出（バイナリリリース）する場合の手順参考
-> [リポジトリのリリースを管理する](https://docs.github.com/ja/free-pro-team@latest/github/administering-a-repository/managing-releases-in-a-repository)
-> 7.オプションで、コンパイルされたプログラムなどのバイナリファイルをリリースに含めるには、ドラッグアンドドロップするかバイナリボックスで手動で選択します。
- -### 本リポジトリの最新バージョン取り込み - -今後、本リポジトリもバージョンアップしていく予定です。
-本リポジトリのバージョンアップを取り込む場合は、forkしたリポジトリにて以下を実行して下さい。
- -※追記 2021/5より、Github UI上から操作可能になったようです。
-[GitHub新機能「Fetch upstream」使ってみた！　1クリックで親リポジトリに追従（同期）](https://note.com/llminatoll/n/n423296287697)
- -``` -git checkout master # ローカルのmainブランチに移動 -git remote add upstream https://github.com/seigot/tetris # fork元のリポジトリをupstream という名前でリモートリポジトリに登録（名前はなんでもいい。登録済みならスキップ） -git fetch upstream # upstream から最新のコードをfetch -git merge upstream/master # upstream/main を ローカルのmaster にmerge -git push # 変更を反映 -``` - -参考：[github で fork したリポジトリで本家に追従する](https://please-sleep.cou929.nu/track-original-at-forked-repo.html) - -### Pull Requestを送る（Optional） - -本リポジトリへ修正リクエストを送ることが可能です。詳しくは参考をご参照下さい。
-
-※追記　Pull Request練習用リポジトリを作成しました。
-[test_pull_request](https://github.com/seigot/test_pull_request)
-
-解説図:
- -![Git Commentary](doc/pics/20230115_Git_Commentary.png) - -参考：
-[GitHub-プルリクエストの作成方法](https://docs.github.com/ja/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)
-[[実践] はじめてのPull Requestをやってみよう](https://qiita.com/wataryooou/items/8dce6b6d5f54ab2cef04)
-[【GitHub】Pull Requestの手順](https://qiita.com/aipacommander/items/d61d21988a36a4d0e58b)
- -### FAQ - -[doc/files/FAQ.md](doc/files/FAQ.md)を参照下さい。 - -## 参考 - -[https://github.com/LoveDaisy/tetris_game](https://github.com/LoveDaisy/tetris_game)
-[https://github.com/seigot/tetris_game (2021.12時点まで使用)](https://github.com/seigot/tetris_game)
-[http://zetcode.com/gui/pyqt5/tetris/](http://zetcode.com/gui/pyqt5/tetris/)
-[テトリスの歴史を「ブロックが落ちるルール」の進化から学ぶ](https://gigazine.net/news/20191116-tetris-algorithm/)
-[『テトリスでPythonを学ぼうv4』優勝コード解説](https://qiita.com/mozziemagnet/private/b6839c9b7b438cbdb1b1)
- - -## 今後の課題 - -### 次のブロックのランダム性 - -次のブロックのランダム性は、現在はrandom関数の出力に依存しています。
-しかし、[こちらの記事](https://gigazine.net/news/20191116-tetris-algorithm/)によると選択方式が色々ありそうです。
-有識者の方からアドバイス頂けると助かります。
- -* 参考：次のブロック選択処理 [game_manager.py](game_manager/game_manager.py) - -``` -nextShapeIndex = np_randomShape.random.randint(1, 8) -``` - -### 競争要素の追加 - -例えば、AI学習を目的としたルールのアップデート、火力ボーナス、などが該当する -[issues](https://github.com/seigot/tetris/issues/67) - -### 学習要素の追加 - -例えば、文法、アルゴリズム、AI要素、などが該当する -[issues#120](https://github.com/seigot/tetris/issues/120) - -## LICENSE - -[MIT LICENSE](LICENSE) - -## Finnaly - -~ HAVE FUN ~ diff --git a/cloud/sam/scripts/chat_function/local.py b/cloud/sam/scripts/chat_function/local.py index 2249533a..ce0874bd 100644 --- a/cloud/sam/scripts/chat_function/local.py +++ b/cloud/sam/scripts/chat_function/local.py @@ -6,6 +6,9 @@ from llama_index import StorageContext, load_index_from_storage # ローカルで辞書を作成し、storageに保存する +# .envにopenaiのAPIkeyを記述する +# ./dataに.txtや.md等のテキストファイルを格納し、ローカル環境で実行する +# ./storageに辞書データを出力し、そのデータを用いて辞書検索を行う def main(): # APIkeyの設定 load_dotenv() @@ -17,6 +20,7 @@ def main(): # モデルの読み込み if(1): + # 辞書データを作成する documents = SimpleDirectoryReader(input_dir="./data").load_data() print("documents: ", documents) index = VectorStoreIndex.from_documents(documents) From fe45ecc4e29682a6fc3a467ef85ae39fb694df54 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Mon, 28 Aug 2023 23:49:05 +0900 Subject: [PATCH 05/10] =?UTF-8?q?=E5=80=8B=E4=BA=BA=E3=82=A2=E3=82=AB?= =?UTF-8?q?=E3=82=A6=E3=83=B3=E3=83=88ID=E3=81=8B=E3=82=89IAM=E3=82=A2?= =?UTF-8?q?=E3=82=AB=E3=82=A6=E3=83=B3=E3=83=88=E3=81=AEID=E3=81=AB?= =?UTF-8?q?=E5=A4=89=E6=9B=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/template.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud/sam/template.yaml b/cloud/sam/template.yaml index f7d5d47a..03b2746d 100644 --- a/cloud/sam/template.yaml +++ b/cloud/sam/template.yaml @@ -245,7 +245,7 @@ Resources: Type: AWS::Serverless::Function Properties: PackageType: Image - ImageUri: 132308390451.dkr.ecr.ap-northeast-1.amazonaws.com/chat + ImageUri: 868265869441.dkr.ecr.ap-northeast-1.amazonaws.com/chat Timeout: 120 MemorySize: 512 Policies: From 2e30a84156d7f63c3d9f59d85dc58b40ad2c8a01 Mon Sep 17 00:00:00 2001 From: EndoNrak Date: Tue, 29 Aug 2023 23:18:24 +0900 Subject: [PATCH 06/10] add resolve image repos in sam deploy command --- cloud/sam/Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/cloud/sam/Makefile b/cloud/sam/Makefile index c80ae3ec..3042be8e 100644 --- a/cloud/sam/Makefile +++ b/cloud/sam/Makefile @@ -3,11 +3,14 @@ check: --config-env $(ENV)\ --no-execute-changeset\ --no-fail-on-empty-changeset\ - --parameter-overrides FrontendOrigin=$(TETRIS_FRONT_ORIGIN) SQSUrl=$(SQS_URL) DynamoDBNewsTableName=tetris_news_table DynamoDBTrainingTableName=tetris_training_table TetrisTrainingBucketName=$(TETRIS_TRAINING_BUCKET_NAME) + --resolve-image-repos\ + --parameter-overrides FrontendOrigin=$(TETRIS_FRONT_ORIGIN) SQSUrl=$(SQS_URL) DynamoDBNewsTableName=tetris_news_table DynamoDBTrainingTableName=tetris_training_table TetrisTrainingBucketName=$(TETRIS_TRAINING_BUCKET_NAME)\ + deploy: sam deploy\ --config-env $(ENV)\ --no-confirm-changeset\ --no-fail-on-empty-changeset\ + --resolve-image-repos\ --parameter-overrides FrontendOrigin=$(TETRIS_FRONT_ORIGIN) SQSUrl=$(SQS_URL) DynamoDBNewsTableName=tetris_news_table DynamoDBTrainingTableName=tetris_training_table TetrisTrainingBucketName=$(TETRIS_TRAINING_BUCKET_NAME) From c42b0de8f3c1f0dc71e4ff3460f4c36a0a4b21e4 Mon Sep 17 00:00:00 2001 From: EndoNrak Date: Wed, 30 Aug 2023 21:47:47 +0900 Subject: [PATCH 07/10] fix sam template yaml --- cloud/sam/Makefile | 3 +-- cloud/sam/template.yaml | 12 +++++++++++- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/cloud/sam/Makefile b/cloud/sam/Makefile index 3042be8e..e1ada8ee 100644 --- a/cloud/sam/Makefile +++ b/cloud/sam/Makefile @@ -4,8 +4,7 @@ check: --no-execute-changeset\ --no-fail-on-empty-changeset\ --resolve-image-repos\ - --parameter-overrides FrontendOrigin=$(TETRIS_FRONT_ORIGIN) SQSUrl=$(SQS_URL) DynamoDBNewsTableName=tetris_news_table DynamoDBTrainingTableName=tetris_training_table TetrisTrainingBucketName=$(TETRIS_TRAINING_BUCKET_NAME)\ - + --parameter-overrides FrontendOrigin=$(TETRIS_FRONT_ORIGIN) SQSUrl=$(SQS_URL) DynamoDBNewsTableName=tetris_news_table DynamoDBTrainingTableName=tetris_training_table TetrisTrainingBucketName=$(TETRIS_TRAINING_BUCKET_NAME) deploy: sam deploy\ diff --git a/cloud/sam/template.yaml b/cloud/sam/template.yaml index 03b2746d..955a19d3 100644 --- a/cloud/sam/template.yaml +++ b/cloud/sam/template.yaml @@ -28,7 +28,6 @@ Globals: Function: Timeout: 3 MemorySize: 128 - Runtime: python3.9 Architectures: - x86_64 Tracing: Active @@ -60,6 +59,7 @@ Resources: Properties: CodeUri: ./scripts/get_entries_from_dynamodb Handler: get_entries_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBCompetitionTableName @@ -76,6 +76,7 @@ Resources: Properties: CodeUri: ./scripts/get_news_from_dynamodb Handler: get_news_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBNewsTableName @@ -92,6 +93,7 @@ Resources: Properties: CodeUri: ./scripts/get_news_detail_from_dynamodb Handler: get_news_detail_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBNewsTableName @@ -109,6 +111,7 @@ Resources: Properties: CodeUri: ./scripts/get_results_from_dynamodb Handler: get_results_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBTableName @@ -125,6 +128,7 @@ Resources: Properties: CodeUri: ./scripts/get_result_detail_from_dynamodb Handler: get_result_detail_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBTableName @@ -141,6 +145,7 @@ Resources: Properties: CodeUri: ./scripts/post_competition_entry Handler: post_competition_entry.lambda_handler + Runtime: python3.9 Layers: - Ref: Layer Policies: @@ -159,6 +164,7 @@ Resources: Properties: CodeUri: ./scripts/post_evaluation_request Handler: post_evaluation_request.lambda_handler + Runtime: python3.9 Layers: - Ref: Layer Policies: @@ -179,6 +185,7 @@ Resources: Properties: CodeUri: ./scripts/stop_evaluation Handler: stop_evaluation.lambda_handler + Runtime: python3.9 Layers: - Ref: Layer Policies: @@ -197,6 +204,7 @@ Resources: Properties: CodeUri: ./scripts/post_tetris_training_code Handler: post_tetris_training_code.lambda_handler + Runtime: python3.9 Timeout: 60 Policies: - S3ReadPolicy: @@ -214,6 +222,7 @@ Resources: Properties: CodeUri: ./scripts/get_training_detail_from_dynamodb Handler: get_training_detail_from_dynamodb.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBTrainingTableName @@ -230,6 +239,7 @@ Resources: Properties: CodeUri: ./scripts/get_trainings_in_section Handler: get_trainings_in_section.lambda_handler + Runtime: python3.9 Policies: - DynamoDBReadPolicy: TableName: !Ref DynamoDBTrainingTableName From 0a1e701dbdc1ea5f863a0ba1bdcb9e269813684e Mon Sep 17 00:00:00 2001 From: sota1111 Date: Thu, 31 Aug 2023 19:26:09 +0900 Subject: [PATCH 08/10] =?UTF-8?q?CORS=E8=A8=AD=E5=AE=9A=E3=82=92=E8=BF=BD?= =?UTF-8?q?=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/scripts/chat_function/tools/response.py | 7 +++++++ cloud/sam/template.yaml | 3 ++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/cloud/sam/scripts/chat_function/tools/response.py b/cloud/sam/scripts/chat_function/tools/response.py index e6029e1b..0fa27b62 100644 --- a/cloud/sam/scripts/chat_function/tools/response.py +++ b/cloud/sam/scripts/chat_function/tools/response.py @@ -2,10 +2,17 @@ class HttpResponse: + DEFAULT_HEADERS = { + 'Access-Control-Allow-Origin': 'frontend_origin', + 'Access-Control-Allow-Methods': 'GET,POST,PUT,DELETE,OPTIONS' + } + @staticmethod def generate_response(status_code, message): + headers = HttpResponse.DEFAULT_HEADERS return { "statusCode": status_code, + "headers": headers, "body": json.dumps({ "message": message, }), diff --git a/cloud/sam/template.yaml b/cloud/sam/template.yaml index 955a19d3..6493492b 100644 --- a/cloud/sam/template.yaml +++ b/cloud/sam/template.yaml @@ -51,7 +51,8 @@ Resources: Name: tetris_api StageName: tetris_api_stage Cors: - AllowMethods: "'POST, GET, PUT, OPTIONS'" + AllowMethods: "'POST, GET, PUT, DELETE, OPTIONS'" + AllowHeaders: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token,X-Amz-User-Agent'" AllowOrigin: !Sub "'${FrontendOrigin}'" GetEntriesFunction: From bfdcd9018633cd5ea96bc8817c343782ed2b3804 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Wed, 27 Sep 2023 20:42:38 +0900 Subject: [PATCH 09/10] =?UTF-8?q?=E5=BF=9C=E7=AD=94=E3=83=A1=E3=83=83?= =?UTF-8?q?=E3=82=BB=E3=83=BC=E3=82=B8=E3=81=AECORS=E5=AF=BE=E5=BF=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/scripts/chat_function/tools/response.py | 1 + 1 file changed, 1 insertion(+) diff --git a/cloud/sam/scripts/chat_function/tools/response.py b/cloud/sam/scripts/chat_function/tools/response.py index 0fa27b62..8983a27b 100644 --- a/cloud/sam/scripts/chat_function/tools/response.py +++ b/cloud/sam/scripts/chat_function/tools/response.py @@ -4,6 +4,7 @@ class HttpResponse: DEFAULT_HEADERS = { 'Access-Control-Allow-Origin': 'frontend_origin', + 'Access-Control-Allow-Headers': 'Content-Type, Authorization, X-Amz-Date, X-Api-Key, X-Amz-Security-Token, ,X-Amz-User-Agent,Origin, Accept, token, id, HEAD,X-CSRF-TOKEN', 'Access-Control-Allow-Methods': 'GET,POST,PUT,DELETE,OPTIONS' } From aab87727ad73a83558e1e356c5d2d5d7fe8938d8 Mon Sep 17 00:00:00 2001 From: sota1111 Date: Wed, 27 Sep 2023 21:01:59 +0900 Subject: [PATCH 10/10] =?UTF-8?q?CORS=E8=A8=AD=E5=AE=9A=E3=83=86=E3=82=B9?= =?UTF-8?q?=E3=83=88?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cloud/sam/template.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/cloud/sam/template.yaml b/cloud/sam/template.yaml index 6493492b..ffe45a4e 100644 --- a/cloud/sam/template.yaml +++ b/cloud/sam/template.yaml @@ -51,9 +51,10 @@ Resources: Name: tetris_api StageName: tetris_api_stage Cors: - AllowMethods: "'POST, GET, PUT, DELETE, OPTIONS'" - AllowHeaders: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token,X-Amz-User-Agent'" + AllowMethods: "'GET,POST,PUT,DELETE,OPTIONS,PATCH,HEAD'" + AllowHeaders: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token,X-Amz-User-Agent,Origin,Accept,X-Custom-Header'" AllowOrigin: !Sub "'${FrontendOrigin}'" + MaxAge: "'-1'" GetEntriesFunction: Type: AWS::Serverless::Function