semvec/pypi-packages huggingface dataset:
MotionMySql 0.0.1
Dynamically constructing add/delete/reference statements using python
Requires Python: >=3.6

# python-MySQL-
Use query statements to dynamically fetch database column names and generate insert statements
Enter the MySQL database address username and password.
Select the database and table names.
## Insert Functions
First, use a query to retrieve the database columns.
Create a dictionary and generate an insert statement.
## Delete Function
First use the query function to check whether the input column and the keyword of the column exist or not.
Delete the entire row by entering the column name and the keyword of the column to be deleted.
## Query Function
Input the column and the keyword of the column to form a query statement.
## Modify
Input the column name, keyword and content to be changed.
Use the column name and keyword to determine what to change.
Finally, modify the table with the changes.
irt-test 0.1.2
Package for calc difficult of test tasks and ability of test subjects by Rasch model (IRT 0).
Requires Python: <4.0,>=3.11

# irt_test
Package for calc difficult of test tasks and ability of test subjects by Rasch model (IRT 0).

## Installation
You can install irt_test using pip:

```bash
pip install irt-test
```

## Example Usage

### ℹ️ Important

**🔥 Note:** The Rasch model works only with binary data, also no column or row should contain a single value, for example, a row of zeros or ones will be excluded from the analysis.

### Input DataFrame example:
|       |   Task 1 |   Task 2 |   Task 3 |   Task 4 |   Task 5 |
|:------|---------:|---------:|---------:|---------:|---------:|
| Julia |        1 |        0 |        1 |        1 |        1 |
| Ivan  |        1 |        1 |        1 |        0 |        1 |
| Anna  |        1 |        0 |        0 |        0 |        1 |
| Peter |        0 |        0 |        0 |        1 |        1 |

### For get logits of tasks and subjects use 'irt' function.

This function calculates scores for the subjects' abilities and tasks' difficulty in the form of logits using the IRT model.

**Parameters:**
- `df` (DataFrame): A matrix with only zeros and ones values.
- `steps` (int): Number of learning steps (if 0, the model will run until the error > accept).
- `accept` (float): Acceptable error value (ignored when steps > 0).

**Returns:**
- `result` (IrtResult): An object containing logit vectors, rejected subjects and tasks, and model error.

```python
from irt_test.irt import irt

# Get logits and additional info from IRT0 model
irt_result = irt(df)
```
irt_result object contains logits of tasks and subjects from the IRT0 model, along with error and rejected units.

**Attributes:**
- `abilities` (pandas.Series): Logits of subjects' abilities (subjects are the index of the Series).
- `difficult` (pandas.Series): Logits of task difficulty (tasks are the index of the Series).
- `err` (float): Metric of the difference between real test results and estimated results by logits.
- `rejected_tasks` (list): Names of tasks that cannot be used to calculate difficulty logits.
- `rejected_subjects` (list): Names of subjects that cannot be used to calculate ability logits.