semvec/pypi-packages huggingface dataset:
fishml_guess 0.2.0
Default template for PDM package
License: MIT
Requires Python: ==3.11.*

# weekly_hw1 - 물고기 판별기
***

## How this program works
1. 학습된 모델이 없는 상태
2. 사용자는 길이, 무게 데이터를 입력
3. 사용자가 입력하는 처음 5번째까지의 데이터를 바탕으로 예측값 출력(도미, 빙어)
4. 사용자는 예측값의 정답을 다시 입력
5. 위 과정을 반복
6. 모델은 쌓인 데이터를 바탕으로 학습을 반복하고 진화함

## Install
```bash
$ pip install fishml_guess 
```

## Usage
```bash
$ guess-fish
길이를 입력하세요(cm): # 길이 입력
무게를 입력하세요(g): # 무게 입력
예측 결과 도미입니다.🐠 
예측한 결과가 맞나요?(🐠/🐟): # 도미/빙어 중 정답을 입력
```

## Result
![image](https://github.com/user-attachments/assets/e1910049-cc4a-4643-97f2-cd18dd023500)

## Issue

데이터가 5개 이상일 때부터 물고기 판별기가 작동하도록 설정
5개 이상을 입력하더라도 데이터 입력만을 요구(예측값을 출력하지 않음)
guess-fish를 exit해야만 pickle 파일이 생성되고 모델 실행

![image](https://github.com/user-attachments/assets/50ba4e2f-2f3e-4269-b21c-ebf55df6954a)

## Refence
https://github.com/hahahellooo/hw_fishml.git
fishml_me 0.1.1
Default template for PDM package
License: MIT
Requires Python: ==3.11.*

# weekly_hw1 - 물고기 판별기
***

## How this program works
1. 학습된 모델이 없는 상태
2. 사용자는 길이, 무게 데이터를 입력
3. 사용자가 입력하는 처음 5번째까지의 데이터를 바탕으로 예측값 출력(도미, 빙어)
4. 사용자는 예측값의 정답을 다시 입력
5. 위 과정을 반복
6. 모델은 쌓인 데이터를 바탕으로 학습을 반복하고 진화함

## Install
```bash
$ pip install 
```

## Usage
```bash
$ guess-fish
길이를 입력하세요(cm): # 길이 입력
무게를 입력하세요(g): # 무게 입력
예측 결과 도미입니다.🐠 
예측한 결과가 맞나요?(🐠/🐟): # 도미/빙어 중 정답을 입력
```

## Result
![image](https://github.com/user-attachments/assets/e1910049-cc4a-4643-97f2-cd18dd023500)

## Issue

데이터가 5개 이상일 때부터 물고기 판별기가 작동하도록 설정
5개 이상을 입력하더라도 데이터 입력만을 요구(예측값을 출력하지 않음)
guess-fish를 exit해야만 pickle 파일이 생성되고 모델 실행

![image](https://github.com/user-attachments/assets/50ba4e2f-2f3e-4269-b21c-ebf55df6954a)

## Refence
https://github.com/hahahellooo/hw_fishml.git
datatransformtoolbox 0.1.5
Python library to data transformation
License: MIT
Requires Python: <4.0,>=3.10

# Data Transformation Library

The Data Transformation Library is a Python library that provides a set of functions for common data transformation tasks. It contains simple function to make their every day coding easier. 

## Features

1. **Transpose a Matrix (2D Tensor)**
   - Function: `transpose2d`
   - Transposes the axes of a 2D list.

2. **Time Series Windowing**
   - Function: `window1d`
   - Creates sliding windows over a 1D list or NumPy array.

3. **2D Convolution**
   - Function: `convolution2d`
   - Applies 2D cross-correlation to an input matrix using a kernel.

## Installation

You can install the package using pip:

```sh
pip install datatransformtoolbox
 
or

poetry add datatransformtoolbox
```
# Usage
Here is a simple example of how to use transpose library:

```sh

import numpy as np
from datatransformtoolbox.transpose import transpose2d


matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

transposed_matrix = transpose2d(matrix)
print(transposed_matrix)
# Output: [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

```

Another simple example of how to use Time series Windowing library:

```
import numpy as np
from datatransformtoolbox.window import window1

input_array = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
size = 2
shift = 1
stride = 1

# Call the function
windows = window1d(input_array, size, shift, stride)

# Print the results
print("Generated windows:")
for i, window in enumerate(windows):
    print(f"Window {i+1}: {window}")

# Example output:
#Generated windows:
#Window 1: [1 2]
#Window 2: [2 3]
#Window 3: [3 4]
#Window 4: [4 5]
#Window 5: [5 6]
#Window 6: [6 7]
#Window 7: [7 8]
#Window 8: [8 9]
#Window 9: [ 9 10]
```
Another simple example of how to use Cross-Correlation library:

```
import numpy as np
from datatransformtoolbox.crosscorrelation import convolution2d

# Define the input matrix and kernel
input_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
kernel = np.array([[1, 0], [0, -1]])

# Perform the 2D convolution with a stride of 1
result = convolution2d(input_matrix, kernel, stride=1)

# Print the result
print(result)

#Output
#[[-4. -4.]
# [-4. -4.]]
```
# License
This project is licensed under the MIT License. See the LICENSE file for more details.

# Authors
Johnny Lazo johnny.lazoq@gmail.com
noisocr 0.2
Tools to simulate post-OCR noisy texts.
License: MIT

# NoisOCR

Tools to simulate post-OCR noisy texts.

**Features:**
- Sliding window;
- Sliding window with hyphenation;
- Simulate text errors;
- Simulate text annotations.


### Install
```sh
pip install noisocr
```

#### Sliding window:
```py
import noisocr

text = "Lorem Ipsum is simply dummy...type specimen book."
max_window_size = 50

windows = noisocr.sliding_window(text, max_window_size)

# Output:
# [
#   'Lorem Ipsum is simply dummy text of the printing', 
#   ...
#   'type and scrambled it to make a type specimen', 
#   'book.'
# ]
```

#### Sliding window with hyphenation:
* See the package https://pypi.org/project/PyHyphen to see all supported languages.

```py
import noisocr

text = "Lorem Ipsum is simply dummy...type specimen book."
max_window_size = 50

windows = noisocr.sliding_window(text, max_window_size)

# Output:
# [
#   'Lorem Ipsum is simply dummy text of the printing ',        
#   'typesetting industry. Lorem Ipsum has been the in-', 
#   ...
#   'scrambled it to make a type specimen book.'
# ]
```

#### Simulate text errors:
* See the package https://pypi.org/project/typo to see all possible error types.

```py
import noisocr

text = "Hello world."
text_with_errors = noisocr.simulate_errors(text, interactions=1)
# Output: Hello, wotrld!
text_with_errors = noisocr.simulate_errors(text, 2)
# Output: Hsllo,wlorld!
text_with_errors = noisocr.simulate_errors(text, 5)
# Output: fllo,w0rlr!
```

#### Simulate text annotations:
* By default, the annotations found in the [BRESSAY](https://icdar2024.ecomp.poli.br/dataset) dataset were used. But you can define which types of annotations you want to simulate. For annotations with internal text, use the pattern `##--text--##`.

```py
import noisocr

text = "Hello world."
text_with_annotation = noisocr.simulate_annotation(text, probability=0.5)
# Output: Hello, $$--xxx--$$
text_with_annotation = noisocr.simulate_annotation(text, probability=0.5)
# Output: Hello, ##--world!--##
text_with_annotation = noisocr.simulate_annotation(text, 0.01)
# Output: Hello world.
```