| Name | growKNN4fish JSON |
| Version |
3.1.1
JSON |
| download |
| home_page | None |
| Summary | growable fish classifier, KNN |
| upload_time | 2024-09-08 13:21:56 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# fishKNN
물고기의 길이와 무게를 입력하면 해당 물고기가 도미인지 빙어인지 예측하는 프로그램입니다. 예측 후에는 해당 예측이 맞았는지 확인하며, 해당 데이터를 csv로 저장하고 다시 학습을 진행하는 진화형 KNN 프로그램입니다.
### Versions
- `0.5.x` : project init publish. predict & get-pkl.
- `1.2.x` : help msg added.
- `2.0.x` : scatter plot added.
- `3.0.x` : common.py added (remove duplicated code)
### Installation
```
$ pip install growKNN4fish
```
### Usage
```bash
$ fish-help # 사용가능한 CLI Command를 출력합니다.
╭──────────────────────── 사용할 수 있는 CLI Command ───────────────────────╮
│ │
│ $ fish-predict # 길이와 무게를 입력받아 물고기의 종류를 예측합니다. │
│ $ show-data # 저장된 csv파일을 DataFrame형식으로 출력합니다. │
│ $ get-pkl # 저장된 pkl파일을 원하는 위치로 복사합니다. │
│ $ draw-plot # 저장된 csv파일을 scatter plot으로 출력합니다. │
│ │
╰───────────────────────────────────────────────────────────────────────────╯
$ fish-predict # 길이와 무게를 입력하면 물고기의 종류를 예측합니다.
🆕 물고기의 길이를 입력하세요(cm) :
🆕 물고기의 무게를 입력하세요(kg) :
🆕 도미가 맞나요? (y/n)
⛔ y 또는 n으로 답해주세요. # y, n(대소문자 구분X) 외의 값을 입력할 경우 발생. 올바른 값을 입력할 때까지 반복.
🆕 훈련을 시작합니다.
#### scatter plot 출력 (>=v2.0.0)
┌─────────────────────────────────────────────────────┐
2.02┤ *│
│ │
│ │
1.56┤ │
│ │
│ │
│ * │
1.10┤ * │
│ │
│ │
0.63┤ │
│ │
│ │
0.17┤ │
│ * │
│ │
│ │
-0.29┤ │
│ │
│ │
-0.75┤* * │
└┬────────────┬────────────┬────────────┬────────────┬┘
-0.74 -0.10 0.54 1.18 1.82
Weight Length
⛔ 충분한 데이터가 없습니다. # csv에 저장된 데이터가 1개인 경우
🆕 훈련을 종료합니다. (훈련시간 : 0초)
$ get-pkl # model.pkl 파일을 원하는 위치에 저장합니다. 추후 테스트를 위해 pkl파일을 가져오기 위한 프로그램입니다.
🆕 pkl파일을 저장할 경로를 입력해주세요(현재 경로기준 상대경로)
>>> /home/root2/hw/fishKNN/
⛔ 훈련된 pkl파일이 없습니다. # 저장된 pkl파일이 없는 경우 발생.
⛔ 모델 훈련 후 다시 확인해주세요.
$ show-data # 지금까지 저장된 csv를 DataFrame형태로 출력
Length Weight Label
0 35.0 700.0 도미
1 31.5 500.0 도미
⛔ 저장된 데이터가 없습니다. # 저장된 csv가 없는 경우
$ draw-plot # 지금까지 저장된 csv를 scatter plot으로 출력
┌─────────────────────────────────────────────────────┐
2.02┤ *│
│ │
│ │
1.56┤ │
│ │
│ │
│ * │
1.10┤ * │
│ │
│ │
0.63┤ │
│ │
│ │
0.17┤ │
│ * │
│ │
│ │
-0.29┤ │
│ │
│ │
-0.75┤* * │
└┬────────────┬────────────┬────────────┬────────────┬┘
-0.74 -0.10 0.54 1.18 1.82
Weight Length
```
### Dependency
### License
- MIT
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"description": "# fishKNN\n\n\ubb3c\uace0\uae30\uc758 \uae38\uc774\uc640 \ubb34\uac8c\ub97c \uc785\ub825\ud558\uba74 \ud574\ub2f9 \ubb3c\uace0\uae30\uac00 \ub3c4\ubbf8\uc778\uc9c0 \ube59\uc5b4\uc778\uc9c0 \uc608\uce21\ud558\ub294 \ud504\ub85c\uadf8\ub7a8\uc785\ub2c8\ub2e4. \uc608\uce21 \ud6c4\uc5d0\ub294 \ud574\ub2f9 \uc608\uce21\uc774 \ub9de\uc558\ub294\uc9c0 \ud655\uc778\ud558\uba70, \ud574\ub2f9 \ub370\uc774\ud130\ub97c csv\ub85c \uc800\uc7a5\ud558\uace0 \ub2e4\uc2dc \ud559\uc2b5\uc744 \uc9c4\ud589\ud558\ub294 \uc9c4\ud654\ud615 KNN \ud504\ub85c\uadf8\ub7a8\uc785\ub2c8\ub2e4.\n\n### Versions\n- `0.5.x` : project init publish. predict & get-pkl.\n- `1.2.x` : help msg added.\n- `2.0.x` : scatter plot added.\n- `3.0.x` : common.py added (remove duplicated code) \n\n### Installation\n```\n$ pip install growKNN4fish\n```\n\n### Usage\n```bash\n$ fish-help # \uc0ac\uc6a9\uac00\ub2a5\ud55c CLI Command\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4.\n \u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 CLI Command \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\n \u2502 \u2502\n \u2502 $ fish-predict # \uae38\uc774\uc640 \ubb34\uac8c\ub97c \uc785\ub825\ubc1b\uc544 \ubb3c\uace0\uae30\uc758 \uc885\ub958\ub97c \uc608\uce21\ud569\ub2c8\ub2e4. \u2502\n \u2502 $ show-data # \uc800\uc7a5\ub41c csv\ud30c\uc77c\uc744 DataFrame\ud615\uc2dd\uc73c\ub85c \ucd9c\ub825\ud569\ub2c8\ub2e4. \u2502\n \u2502 $ get-pkl # \uc800\uc7a5\ub41c pkl\ud30c\uc77c\uc744 \uc6d0\ud558\ub294 \uc704\uce58\ub85c \ubcf5\uc0ac\ud569\ub2c8\ub2e4. \u2502\n \u2502 $ draw-plot # \uc800\uc7a5\ub41c csv\ud30c\uc77c\uc744 scatter plot\uc73c\ub85c \ucd9c\ub825\ud569\ub2c8\ub2e4. \u2502\n \u2502 \u2502\n \u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\n\n$ fish-predict # \uae38\uc774\uc640 \ubb34\uac8c\ub97c \uc785\ub825\ud558\uba74 \ubb3c\uace0\uae30\uc758 \uc885\ub958\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.\n\ud83c\udd95 \ubb3c\uace0\uae30\uc758 \uae38\uc774\ub97c \uc785\ub825\ud558\uc138\uc694(cm) :\n\ud83c\udd95 \ubb3c\uace0\uae30\uc758 \ubb34\uac8c\ub97c \uc785\ub825\ud558\uc138\uc694(kg) :\n\ud83c\udd95 \ub3c4\ubbf8\uac00 \ub9de\ub098\uc694? (y/n)\n\u26d4 y \ub610\ub294 n\uc73c\ub85c \ub2f5\ud574\uc8fc\uc138\uc694. # y, n(\ub300\uc18c\ubb38\uc790 \uad6c\ubd84X) \uc678\uc758 \uac12\uc744 \uc785\ub825\ud560 \uacbd\uc6b0 \ubc1c\uc0dd. \uc62c\ubc14\ub978 \uac12\uc744 \uc785\ub825\ud560 \ub54c\uae4c\uc9c0 \ubc18\ubcf5.\n\n\ud83c\udd95 \ud6c8\ub828\uc744 \uc2dc\uc791\ud569\ub2c8\ub2e4.\n#### scatter plot \ucd9c\ub825 (>=v2.0.0)\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n 2.02\u2524 *\u2502\n \u2502 \u2502\n \u2502 \u2502\n 1.56\u2524 \u2502\n \u2502 \u2502\n \u2502 \u2502\n \u2502 * \u2502\n 1.10\u2524 * \u2502\n \u2502 \u2502\n \u2502 \u2502\n 0.63\u2524 \u2502\n \u2502 \u2502\n \u2502 \u2502\n 0.17\u2524 \u2502\n \u2502 * \u2502\n \u2502 \u2502\n \u2502 \u2502\n-0.29\u2524 \u2502\n \u2502 \u2502\n \u2502 \u2502\n-0.75\u2524* * \u2502\n \u2514\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2518\n -0.74 -0.10 0.54 1.18 1.82\nWeight Length\n\u26d4 \ucda9\ubd84\ud55c \ub370\uc774\ud130\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. # csv\uc5d0 \uc800\uc7a5\ub41c \ub370\uc774\ud130\uac00 1\uac1c\uc778 \uacbd\uc6b0\n\ud83c\udd95 \ud6c8\ub828\uc744 \uc885\ub8cc\ud569\ub2c8\ub2e4. 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