| Name | knn-j25ng JSON |
| Version |
2.0.3
JSON |
| download |
| home_page | None |
| Summary | Implements KNN model using KNeighborsClassifier |
| upload_time | 2024-09-09 11:53:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Weekly program no.1 - 자가 학습 KNN
## 프로그램 동작 방식
- [ ] 학습된 모델이 없는 상태에서 출발
- [ ] 사용자는 길이, 무게 데이터를 입력하고 프로그램 예측 값을 출력 (이때 프로그램은 학습 데이터가 없어 임의의 값을 예측)
- [ ] 예측값에 대한 정답 유무 입력
- [ ] 프로그램에 입력된 데이터(훈련데이터)와 정답(타겟) 데이터를 저장
- [ ] 위 과정이 반복 되면서 모델을 진화 시켜 나간다.
## usage
```bash
# install
## use git url
$ pip install git+https://github.com/j25ng/knn_j25ng.git
## use pypi
$ pip install knn-j25ng
# data training
$ fish
🐟 물고기의 길이를 입력하세요 (cm): 10.8
🐟 물고기의 무게를 입력하세요 (g): 8.7
🐟 이 물고기는 빙어입니다.
🐟 예측이 맞습니까? (y/n): y
🐟 예측 성공🥳
```
```bash
# data chart(use matplotlib)
$ chart
### display the figure window ###
```
### chart window
<img src="https://github.com/user-attachments/assets/f585310b-d655-4d6e-a411-8648da14eecc" width=600 />
## data
```bash
$ cd ~/code/data
$ tree
.
├── data.json
└── target.json
0 directories, 2 files
```
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"description": "# Weekly program no.1 - \uc790\uac00 \ud559\uc2b5 KNN\n\n## \ud504\ub85c\uadf8\ub7a8 \ub3d9\uc791 \ubc29\uc2dd\n- [ ] \ud559\uc2b5\ub41c \ubaa8\ub378\uc774 \uc5c6\ub294 \uc0c1\ud0dc\uc5d0\uc11c \ucd9c\ubc1c\n- [ ] \uc0ac\uc6a9\uc790\ub294 \uae38\uc774, \ubb34\uac8c \ub370\uc774\ud130\ub97c \uc785\ub825\ud558\uace0 \ud504\ub85c\uadf8\ub7a8 \uc608\uce21 \uac12\uc744 \ucd9c\ub825 (\uc774\ub54c \ud504\ub85c\uadf8\ub7a8\uc740 \ud559\uc2b5 \ub370\uc774\ud130\uac00 \uc5c6\uc5b4 \uc784\uc758\uc758 \uac12\uc744 \uc608\uce21)\n- [ ] \uc608\uce21\uac12\uc5d0 \ub300\ud55c \uc815\ub2f5 \uc720\ubb34 \uc785\ub825\n- [ ] \ud504\ub85c\uadf8\ub7a8\uc5d0 \uc785\ub825\ub41c \ub370\uc774\ud130(\ud6c8\ub828\ub370\uc774\ud130)\uc640 \uc815\ub2f5(\ud0c0\uac9f) \ub370\uc774\ud130\ub97c \uc800\uc7a5\n- [ ] \uc704 \uacfc\uc815\uc774 \ubc18\ubcf5 \ub418\uba74\uc11c \ubaa8\ub378\uc744 \uc9c4\ud654 \uc2dc\ucf1c \ub098\uac04\ub2e4.\n\n\n## usage\n```bash\n# install\n## use git url\n$ pip install git+https://github.com/j25ng/knn_j25ng.git\n## use pypi\n$ pip install knn-j25ng\n\n# data training\n$ fish\n\ud83d\udc1f \ubb3c\uace0\uae30\uc758 \uae38\uc774\ub97c \uc785\ub825\ud558\uc138\uc694 (cm): 10.8\n\ud83d\udc1f \ubb3c\uace0\uae30\uc758 \ubb34\uac8c\ub97c \uc785\ub825\ud558\uc138\uc694 (g): 8.7\n\ud83d\udc1f \uc774 \ubb3c\uace0\uae30\ub294 \ube59\uc5b4\uc785\ub2c8\ub2e4.\n\ud83d\udc1f \uc608\uce21\uc774 \ub9de\uc2b5\ub2c8\uae4c? (y/n): y\n\ud83d\udc1f \uc608\uce21 \uc131\uacf5\ud83e\udd73\n```\n\n```bash\n# data chart(use matplotlib)\n$ chart\n### display the figure window ###\n```\n### chart window\n<img src=\"https://github.com/user-attachments/assets/f585310b-d655-4d6e-a411-8648da14eecc\" width=600 />\n\n## data\n```bash\n$ cd ~/code/data\n$ tree\n.\n\u251c\u2500\u2500 data.json\n\u2514\u2500\u2500 target.json\n\n0 directories, 2 files\n```\n",
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