semvec/pypi-packages huggingface dataset:
r-config-loader 0.2.2
Standard way that I am using to use config

# r_config_loader

Standard way that I am using to use config

## usage:

#### from str:

```python
from r_config_loader.r_config_loader import RConfig

r_config = RConfig()

str_yaml = """
    yek:
      ramin: 2
      mehran: 'salam'
    # comment to test
    dow:
      se:
        p1: 2.2
      f1: 'khodafez'
"""

r_config.update_from_str(str_yaml)

```

#### from file:

```python
from r_config_loader.r_config_loader import RConfig
from pathlib import Path

r_config = RConfig()

path = Path('/path/to/config.yaml')

r_config.update_from_file(path)

```
cima-comutations-factors-3 0.0.1
Provide comutations factors based on CIMA H mortability table
Requires Python: >=3.6

# cima h commutations factors
## This package aim to provide commutations factors computed based on CIMA H table for actuarials projects
## The current commutations factors list are:
* lx: Number of survivors at age x according to the mortability table 
* dx: Number of death at age x according to the mortability table
* W: The age where there is no survivors according the MT
* qx: Death probality betwwen age x and x+1
* Cx: Present value of survivor numbers
* Dx: 
* Mx:
* Rx:
* Sx:
* Nx:
* A_k:  Certain present value for a annuity of 1F payable in advance over k years
* A_xn: Probable present value at age x over n years
* nEx: Deferred capital at age x over n years
cima-comutations-factors-2 0.0.1
Provide comutations factors based on CIMA H mortability table
Requires Python: >=3.6

# cima h commutations factors
## This package aim to provide commutations factors computed based on CIMA H table for actuarials projects
## The current commutations factors list are:
* lx: Number of survivors at age x according to the mortability table 
* dx: Number of death at age x according to the mortability table
* W: The age where there is no survivors according the MT
* qx: Death probality betwwen age x and x+1
* Cx: Present value of survivor numbers
* Dx: 
* Mx:
* Rx:
* Sx:
* Nx:
* A_k:  Certain present value for a annuity of 1F payable in advance over k years
* A_xn: Probable present value at age x over n years
* nEx: Deferred capital at age x over n years
oneNeuron-pypi-alan-ai-learner 0.0.3
its an implimentation of Perceptron
Requires Python: >=3.7

# oneNeuron_pypi
oneNeuron_pypi



## How to use this

```python
from oneNeuron.perceptron import Perceptron

## get X and y and then use below commands
model = Perceptron(eta=eta, epochs=epochs)
model.fit(X, y)
```

# Reference -
[official python docs](https://packaging.python.org/tutorials/packaging-projects/)

[github docs for github actions](https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)