semvec/pypi-packages huggingface dataset:
teg-regression 0.1.3
Linear regression, with F-tests, AIC, and linear constraints.
Requires Python: >=3.6

Linear regression, with p-values and AIC for the full model, p-values per coefficient, and tests of linear constraints on regression coefficients. The formulas are based on Bingham & Fry.

The module uses only the modules numpy and math (built-in).

## Basic usage

By default, do not add an explicit intercept to the predictors. The intercept will be appended as an additional predictor, unless the argument explicit_intercept is given and set to True (for if you want to define constraints involving the intercept).

Example, using simulated data:

	import numpy as np

	import teg_regression

	nObs = 300

	nPred = 5

	fix_coeffs = {0: 1, 3: 2} # Set to empty dict to simulate the null model.

	X, y = teg_regression.sim_data(nObs, nPred, fix_coeffs=fix_coeffs, fix_intercept=20)

	O = teg_regression.run_regression(X, y)

The function returns a dictionary O with statistical values and prints out (unless report=False is set as an argument):

Test of the model:

	F(5,294) = 0.849, p = 0.516

	AIC constrained - free = -1.28e+03 (negative supports constraints).

Tests per coefficient.

	Predictor 0: b = 0.00522, F(1,294) = 0.00842, p = 0.927

	Predictor 1: b = 0.00324, F(1,294) = 0.00373, p = 0.951

	Predictor 2: b = -0.0759, F(1,294) = 1.88, p = 0.172

	Predictor 3: b = -0.093, F(1,294) = 2.69, p = 0.102

	Predictor 4: b = 0.0182, F(1,294) = 0.115, p = 0.735

	Intercept: b = 20.6, F(1,294) = 1.04e+05, p = 1.11e-16
	
### Linear constraints

Linear constraints specify hypotheses in terms of weighted sums of the coefficients, to be tested against the free model. E.g., assuming we have four predictors, the matrix equation

[1 0 0 0]           [0]

[0 1 0 0] * betas = [0]

[0 0 1 0]           [0]

would constrain the first three predictors to be zero.

Similarly,

[1 -1 0 0] * betas = 0

Would constrain the first and second predictor to be equal.

The Constraints argument, specifying the desired hypothesis, is a dictionary with the "coefficients" matrix, as a 2D array, and the "constants" vector, also as an array. The example below shows the Constraints setup to set two specific predictor-coefficients to 0.

	pred_to_test = [1, 2]

	Constraints = {}

	Constraints['coefficients'] = np.array([[0 for a in range(X.shape[1])] for newrow in range(2)]).reshape(2, X.shape[1])

	Constraints['coefficients'][0][pred_to_test[0]] = 1

	Constraints['coefficients'][1][pred_to_test[1]] = 1

	Constraints['constants'] = np.array([0, 0])

	O = teg_regression.run_regression(X, y, Constraints)
portf 0.1.1
A pyhton packages for creating portfolio site within minutes!
Requires Python: >=3.6

# Portfolio Builder

>  **🔧Create Your Portfolio Site Within minutes**

![site image](https://lh3.googleusercontent.com/ltHaeziGbC3kvBIZKAhmuS9FVfBb7u1vFnvMPLd5PL1vH-yK7BEA-laSoVT4gdY2V6E=w2400)

 *📃follow given Documentation To create Your Site👇*

---
*To install the library 👇*

```python
pip install portf
```
---
```python
from portf import builder

# create object from Data Class
dt = builder.Data("Your Name")

# Edit Attributes of Object 
dt.image = "Image Url"
dt.about = "Enter details about you"

# To The the app
builder.RunApp(port=5000,dt)

```

> *References for Data Class*

| Attribute | Description | Type 
| -----   | ---- | ---- 
| name | Your Name | str 
| resume | Resume download url | str 
| occupation  | your job | str 
|image | image url | str
| insta | Instagram Url | str
| git | github Url | str
| twitter | twitter Url | str
| linkedin | linkedin Ur | str
| about | details about You | str
| age | 0 | int
email | example@gmail.com | email
|phone | +910000000000 | number
address | Your Address | str
language | languages you know | str
Programming | your Skills | str
tools | your Skills | str
internet | Your Skills | str
other | other Skills | str

> Other Attributes

```python 
work = [{"time":"JUNE 2021 - PRESENT",
         "company":"Company Name",
         "post":"Job Post",
         "info":"Info about job"}] # list fo object

projects = [{"time":"JUNE 2021 - PRESENT",
             "name":"Name of Project",
             "info":"Info about project"}] # list fo object

education = [{"time":"2018 - 2020",
             "name":"Name of degree",
             "course":"Course Name",
             "collage":"Collage Name",
             "info":"Info about Course",
             "projects":["Project info",'achievements']}] # list fo object
```
---
` this module uses flask for website generation, feel free to contribute`

*More features and ==themes== will be coming soon✅*

> Created By @saigenix , All rights are reserved